From 491bd0dd7c1b0a8606a9145b3b9744c1fa6ccc5e Mon Sep 17 00:00:00 2001 From: novis10813 Date: Thu, 12 Feb 2026 19:13:11 +0800 Subject: [PATCH 01/25] feat: add example notebooks for common research workflows (issue #9) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 01_momentum_factor_research.ipynb: complete workflow (data → factor → IC → backtest) - 02_mean_reversion_factor.ipynb: mean reversion + cross-sectional processing - 03_data_loading_and_exploration.ipynb: AggBar + timebar deep dive - 04_multi_factor_combination.ipynb: multi-factor correlation, combo, selection - examples/README.md: index and getting started guide --- examples/01_momentum_factor_research.ipynb | 485 ++++++++++++++++ examples/02_mean_reversion_factor.ipynb | 394 +++++++++++++ .../03_data_loading_and_exploration.ipynb | 430 ++++++++++++++ examples/04_multi_factor_combination.ipynb | 549 ++++++++++++++++++ examples/README.md | 46 ++ 5 files changed, 1904 insertions(+) create mode 100644 examples/01_momentum_factor_research.ipynb create mode 100644 examples/02_mean_reversion_factor.ipynb create mode 100644 examples/03_data_loading_and_exploration.ipynb create mode 100644 examples/04_multi_factor_combination.ipynb create mode 100644 examples/README.md diff --git a/examples/01_momentum_factor_research.ipynb b/examples/01_momentum_factor_research.ipynb new file mode 100644 index 0000000..8a81281 --- /dev/null +++ b/examples/01_momentum_factor_research.ipynb @@ -0,0 +1,485 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Momentum Factor Research\n", + "\n", + "This notebook walks through a **complete factor research workflow** using Factorium:\n", + "\n", + "1. Load data from Binance\n", + "2. Explore the AggBar data structure\n", + "3. Build momentum factors (code-based & expression-based)\n", + "4. Visualize factor behavior\n", + "5. Run IC (Information Coefficient) analysis\n", + "6. Analyze quantile returns\n", + "7. Run a vectorized backtest\n", + "8. Generate a quick report\n", + "\n", + "**Prerequisites**: `pip install factorium` (or `uv add factorium`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup & Data Loading\n", + "\n", + "We'll download 30 days of 1-minute futures data for 10 crypto symbols from Binance Vision." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import BinanceDataLoader, ResearchSession\n", + "from factorium.factors import FactorAnalyzer, CompositeFactor\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.rcParams[\"figure.dpi\"] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SYMBOLS = [\n", + " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", + " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", + "]\n", + "\n", + "loader = BinanceDataLoader()\n", + "\n", + "agg = loader.load_aggbar(\n", + " symbols=SYMBOLS,\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=30,\n", + " bar_type=\"time\",\n", + " interval=60_000, # 1-minute bars\n", + ")\n", + "\n", + "print(f\"Loaded {len(agg):,} bars\")\n", + "print(f\"Symbols: {agg.symbols}\")\n", + "print(f\"Columns: {agg.cols}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Exploring the AggBar\n", + "\n", + "`AggBar` is a **multi-symbol OHLCV container** stored in long format. You can inspect it, convert to Polars/Pandas, and slice by time or symbols." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Summary info per symbol\n", + "agg.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View raw data as Polars DataFrame\n", + "agg.to_polars().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Slice by symbols — creates a new AggBar\n", + "btc_eth = agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", + "print(f\"Sliced AggBar: {btc_eth.symbols}, {len(btc_eth):,} bars\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Building Momentum Factors\n", + "\n", + "### 3.1 Code-Based Factor Construction\n", + "\n", + "Extract a column from `AggBar` with `agg[\"close\"]` to get a `Factor` object, then chain time-series and cross-sectional operators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "close = agg[\"close\"]\n", + "volume = agg[\"volume\"]\n", + "\n", + "# Simple momentum: percentage change over 60 periods\n", + "momentum_60 = close.ts_delta(60) / close.ts_shift(60)\n", + "momentum_60.name = \"momentum_60\"\n", + "\n", + "# Short-term momentum (20 periods)\n", + "momentum_20 = close.ts_delta(20) / close.ts_shift(20)\n", + "momentum_20.name = \"momentum_20\"\n", + "\n", + "# Volatility-adjusted momentum\n", + "volatility = (close.ts_delta(1) / close.ts_shift(1)).ts_std(60)\n", + "vol_adj_momentum = momentum_60 / volatility\n", + "vol_adj_momentum.name = \"vol_adj_momentum\"\n", + "\n", + "# Cross-sectional rank (normalized to [0, 1])\n", + "momentum_rank = momentum_60.cs_rank()\n", + "momentum_rank.name = \"momentum_rank\"\n", + "\n", + "print(\"Factor shapes:\")\n", + "print(f\" momentum_60: {len(momentum_60):,} rows\")\n", + "print(f\" vol_adj_momentum: {len(vol_adj_momentum):,} rows\")\n", + "print(f\" momentum_rank: {len(momentum_rank):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Expression-Based Factor Construction\n", + "\n", + "You can also define factors using string expressions via `ResearchSession.create_factor()`. This is useful for rapid experimentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session = ResearchSession(agg, default_frequency=\"1min\")\n", + "\n", + "# Same momentum factor, defined via expression\n", + "mom_expr = session.create_factor(\n", + " \"ts_delta(close, 60) / ts_shift(close, 60)\",\n", + " name=\"momentum_60_expr\",\n", + ")\n", + "\n", + "# A more complex expression: MA crossover signal\n", + "ma_cross = session.create_factor(\n", + " \"ts_mean(close, 10) - ts_mean(close, 30)\",\n", + " name=\"ma_crossover\",\n", + ")\n", + "\n", + "print(f\"Expression-based momentum: {len(mom_expr):,} rows\")\n", + "print(f\"MA crossover signal: {len(ma_cross):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Visualizing Factors\n", + "\n", + "Use the `.plot` accessor on Factor objects to produce time series, heatmaps, and distributions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Time series of momentum for a few symbols\n", + "momentum_60.plot.plot_timeseries(symbols=[\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Distribution of momentum values across all symbols\n", + "momentum_60.plot.plot_distribution()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Heatmap: momentum intensity across symbols and time\n", + "momentum_rank.plot.plot_heatmap()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. IC (Information Coefficient) Analysis\n", + "\n", + "The **IC** measures the rank correlation between factor values and subsequent returns. A consistently positive (or negative) IC indicates predictive power.\n", + "\n", + "### 5.1 Single-Period IC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use ResearchSession for streamlined analysis\n", + "signal = momentum_rank # Use ranked momentum as our signal\n", + "\n", + "analysis = session.analyze(signal, periods=1)\n", + "\n", + "print(\"IC Summary:\")\n", + "print(analysis.ic_summary)\n", + "print()\n", + "print(f\"IC Series shape: {analysis.ic_series.shape}\")\n", + "print(analysis.ic_series.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Multi-Horizon IC Analysis\n", + "\n", + "To understand how quickly a factor's signal decays, we compute IC across multiple forward horizons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute IC for multiple horizons\n", + "horizons = [1, 5, 10, 20, 60]\n", + "ic_results = {}\n", + "\n", + "for h in horizons:\n", + " result = session.analyze(signal, periods=h)\n", + " ic_results[h] = result.ic_summary.get(h, {})\n", + "\n", + "# Display IC decay table\n", + "ic_decay_df = pd.DataFrame(ic_results).T\n", + "ic_decay_df.index.name = \"horizon\"\n", + "print(\"IC Decay Analysis:\")\n", + "ic_decay_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot IC decay curve\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(ic_decay_df.index, ic_decay_df[\"mean_ic\"], \"o-\", linewidth=2, markersize=8)\n", + "ax.axhline(y=0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "ax.set_xlabel(\"Forward Horizon (periods)\")\n", + "ax.set_ylabel(\"Mean IC\")\n", + "ax.set_title(\"IC Decay Curve — Momentum Factor\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Quantile Analysis\n", + "\n", + "Split the cross-section into quantiles by factor value and examine the mean returns of each group. A monotonic pattern (Q1 < Q2 < ... < Q5) indicates strong linear predictive power." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use FactorAnalyzer directly for more control\n", + "analyzer = FactorAnalyzer(signal, agg, quantiles=5)\n", + "\n", + "# Quantile returns\n", + "analyzer.plot_quantile_returns(quantiles=5, period=1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cumulative returns by quantile (includes Long-Short portfolio)\n", + "analyzer.plot_cumulative_returns(quantiles=5, period=1, long_short=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IC time series plot\n", + "analyzer.plot_ic(period=1, method=\"rank\", plot_type=\"ts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Backtesting\n", + "\n", + "Run a **market-neutral vectorized backtest** using the momentum signal. The backtester:\n", + "- Converts signals to portfolio weights (cross-sectional normalization)\n", + "- Handles transaction costs\n", + "- Tracks equity, returns, and positions over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Market-neutral backtest\n", + "result = session.backtest(\n", + " signal,\n", + " neutralization=\"market\",\n", + " transaction_cost=0.0003,\n", + ")\n", + "\n", + "# Key performance metrics\n", + "print(\"Backtest Metrics:\")\n", + "for key, val in result.metrics.items():\n", + " if isinstance(val, float):\n", + " print(f\" {key:25s}: {val:>10.4f}\")\n", + " else:\n", + " print(f\" {key:25s}: {val}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot equity curve\n", + "equity = result.equity_curve.to_pandas()\n", + "equity[\"timestamp\"] = pd.to_datetime(equity[\"start_time\"], unit=\"ms\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "ax.plot(equity[\"timestamp\"], equity[\"equity\"], linewidth=1.5)\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Portfolio Equity\")\n", + "ax.set_title(\"Momentum Factor — Equity Curve (Market Neutral)\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot returns distribution\n", + "returns = result.returns.to_pandas()\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.hist(returns[\"portfolio_return\"].dropna(), bins=100, alpha=0.75, edgecolor=\"black\", linewidth=0.5)\n", + "ax.axvline(x=0, color=\"red\", linestyle=\"--\", alpha=0.7)\n", + "ax.set_xlabel(\"Return\")\n", + "ax.set_ylabel(\"Frequency\")\n", + "ax.set_title(\"Distribution of Portfolio Returns\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Quick Report\n", + "\n", + "`ResearchSession.quick_report()` combines IC analysis and backtesting into a single text summary — great for rapid iteration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report = session.quick_report(signal)\n", + "print(report)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Summary & Next Steps\n", + "\n", + "In this notebook we:\n", + "- Loaded multi-symbol 1-minute data from Binance using `BinanceDataLoader`\n", + "- Built momentum factors via both **code-based** and **expression-based** methods\n", + "- Visualized factor behavior with time series, distributions, and heatmaps\n", + "- Computed IC and analyzed its decay across multiple horizons\n", + "- Ran a market-neutral backtest and examined performance metrics\n", + "\n", + "**Next notebooks to explore:**\n", + "- `02_mean_reversion_factor.ipynb` — Mean reversion with volatility normalization\n", + "- `03_data_loading_and_exploration.ipynb` — Deep dive into AggBar and data loading\n", + "- `04_multi_factor_combination.ipynb` — Combine multiple factors and compare performance" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/02_mean_reversion_factor.ipynb b/examples/02_mean_reversion_factor.ipynb new file mode 100644 index 0000000..4a9a67b --- /dev/null +++ b/examples/02_mean_reversion_factor.ipynb @@ -0,0 +1,394 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Mean Reversion Factor Research\n", + "\n", + "This notebook demonstrates how to build and evaluate **mean reversion** factors, including:\n", + "\n", + "1. Z-score distance from moving average\n", + "2. Volatility normalization\n", + "3. Cross-sectional processing (`cs_rank`, `cs_zscore`, `cs_winsorize`, `cs_demean`)\n", + "4. Comparing raw vs. processed signals\n", + "5. Market-neutral vs. un-neutralized backtesting\n", + "6. Advanced statistical operators (`ts_autocorr`, `ts_kurtosis`, `ts_skewness`)\n", + "\n", + "**Prerequisites**: `pip install factorium`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup & Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import BinanceDataLoader, ResearchSession\n", + "from factorium.factors import FactorAnalyzer\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.rcParams[\"figure.dpi\"] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SYMBOLS = [\n", + " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", + " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", + "]\n", + "\n", + "loader = BinanceDataLoader()\n", + "\n", + "agg = loader.load_aggbar(\n", + " symbols=SYMBOLS,\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=30,\n", + " bar_type=\"time\",\n", + " interval=60_000,\n", + ")\n", + "\n", + "print(f\"Loaded {len(agg):,} bars for {len(agg.symbols)} symbols\")\n", + "\n", + "session = ResearchSession(agg, default_frequency=\"1min\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Building Mean Reversion Factors\n", + "\n", + "The core idea: prices that deviate far from their moving average tend to **revert back**. We measure this deviation as a z-score.\n", + "\n", + "$$\\text{MeanRev}(t) = \\frac{P(t) - \\text{MA}(t, w)}{\\sigma(t, w)}$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "close = agg[\"close\"]\n", + "volume = agg[\"volume\"]\n", + "\n", + "# --- Mean Reversion Factor (z-score from 60-period MA) ---\n", + "ma_60 = close.ts_mean(60)\n", + "std_60 = close.ts_std(60)\n", + "mean_rev_raw = (close - ma_60) / std_60\n", + "mean_rev_raw.name = \"mean_rev_raw\"\n", + "\n", + "# For mean reversion, we go OPPOSITE to the deviation:\n", + "# high z-score → price is above MA → expect reversion down → SHORT\n", + "# So we negate the signal\n", + "mean_rev = -mean_rev_raw\n", + "mean_rev.name = \"mean_rev\"\n", + "\n", + "print(f\"Raw mean reversion factor: {len(mean_rev):,} rows\")\n", + "mean_rev.to_pandas().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Volatility-Normalized Mean Reversion\n", + "\n", + "Normalizing by realized volatility ensures the signal is comparable across assets with different volatility levels." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute returns and rolling volatility\n", + "returns = close.ts_delta(1) / close.ts_shift(1)\n", + "realized_vol = returns.ts_std(60)\n", + "\n", + "# Volatility-normalized mean reversion\n", + "mean_rev_vol_norm = mean_rev / realized_vol\n", + "mean_rev_vol_norm.name = \"mean_rev_vol_norm\"\n", + "\n", + "print(f\"Vol-normalized mean reversion: {len(mean_rev_vol_norm):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Cross-Sectional Processing\n", + "\n", + "Before using a factor as a trading signal, we typically apply **cross-sectional transformations** to normalize across assets at each point in time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cross-sectional rank: maps to [0, 1] across symbols at each timestamp\n", + "signal_ranked = mean_rev.cs_rank()\n", + "signal_ranked.name = \"mean_rev_ranked\"\n", + "\n", + "# Cross-sectional z-score: standardize across symbols\n", + "signal_zscore = mean_rev.cs_zscore()\n", + "signal_zscore.name = \"mean_rev_zscore\"\n", + "\n", + "# Winsorize: clip extreme values (2.5th and 97.5th percentiles)\n", + "signal_winsorized = mean_rev.cs_winsorize(limits=0.025)\n", + "signal_winsorized.name = \"mean_rev_winsorized\"\n", + "\n", + "# Demean: remove cross-sectional mean\n", + "signal_demeaned = mean_rev.cs_demean()\n", + "signal_demeaned.name = \"mean_rev_demeaned\"\n", + "\n", + "print(\"Cross-sectional transformations applied:\")\n", + "print(f\" Ranked: [{signal_ranked.data['factor'].min():.3f}, {signal_ranked.data['factor'].max():.3f}]\")\n", + "print(f\" Z-scored: mean={signal_zscore.data['factor'].mean():.6f}\")\n", + "print(f\" Demeaned: mean={signal_demeaned.data['factor'].mean():.6f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Comparing Raw vs. Processed Signals\n", + "\n", + "Let's see how cross-sectional processing affects the factor's predictive power (IC)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "factors_to_compare = {\n", + " \"Raw Mean Rev\": mean_rev,\n", + " \"Vol-Normalized\": mean_rev_vol_norm,\n", + " \"CS Ranked\": signal_ranked,\n", + " \"CS Z-Score\": signal_zscore,\n", + " \"CS Winsorized\": signal_winsorized,\n", + "}\n", + "\n", + "comparison_results = []\n", + "for name, factor in factors_to_compare.items():\n", + " analysis = session.analyze(factor, periods=1)\n", + " ic_stats = analysis.ic_summary.get(1, {})\n", + " comparison_results.append({\n", + " \"Factor\": name,\n", + " \"Mean IC\": ic_stats.get(\"mean_ic\", np.nan),\n", + " \"IC Std\": ic_stats.get(\"ic_std\", np.nan),\n", + " \"IC IR\": ic_stats.get(\"ic_ir\", np.nan),\n", + " })\n", + "\n", + "comparison_df = pd.DataFrame(comparison_results).set_index(\"Factor\")\n", + "print(\"IC Comparison — Raw vs. Processed Signals:\")\n", + "comparison_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize IC comparison\n", + "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + "comparison_df[\"Mean IC\"].plot.barh(ax=axes[0], color=\"steelblue\")\n", + "axes[0].set_title(\"Mean IC\")\n", + "axes[0].axvline(x=0, color=\"gray\", linestyle=\"--\")\n", + "\n", + "comparison_df[\"IC IR\"].plot.barh(ax=axes[1], color=\"coral\")\n", + "axes[1].set_title(\"IC IR (Information Ratio)\")\n", + "axes[1].axvline(x=0, color=\"gray\", linestyle=\"--\")\n", + "\n", + "plt.suptitle(\"Mean Reversion Factor — Signal Processing Comparison\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Backtest Comparison: Market-Neutral vs. Un-Neutralized\n", + "\n", + "Compare how the factor performs under different portfolio construction approaches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use the best-performing signal variant for backtesting\n", + "best_signal = signal_ranked\n", + "\n", + "# Market-neutral backtest\n", + "result_neutral = session.backtest(best_signal, neutralization=\"market\")\n", + "\n", + "# Un-neutralized (long-only) backtest\n", + "result_long = session.backtest(best_signal, neutralization=\"none\")\n", + "\n", + "# Compare metrics\n", + "metrics_comparison = pd.DataFrame({\n", + " \"Market Neutral\": result_neutral.metrics,\n", + " \"Long Only\": result_long.metrics,\n", + "}).T\n", + "\n", + "key_metrics = [\"total_return\", \"annual_return\", \"sharpe_ratio\", \"max_drawdown\", \"sortino_ratio\"]\n", + "available_metrics = [m for m in key_metrics if m in metrics_comparison.columns]\n", + "metrics_comparison[available_metrics]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot equity curves side-by-side\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "\n", + "eq_neutral = result_neutral.equity_curve.to_pandas()\n", + "eq_neutral[\"ts\"] = pd.to_datetime(eq_neutral[\"start_time\"], unit=\"ms\")\n", + "ax.plot(eq_neutral[\"ts\"], eq_neutral[\"equity\"], label=\"Market Neutral\", linewidth=1.5)\n", + "\n", + "eq_long = result_long.equity_curve.to_pandas()\n", + "eq_long[\"ts\"] = pd.to_datetime(eq_long[\"start_time\"], unit=\"ms\")\n", + "ax.plot(eq_long[\"ts\"], eq_long[\"equity\"], label=\"Long Only\", linewidth=1.5)\n", + "\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Equity\")\n", + "ax.set_title(\"Mean Reversion — Equity Curves\")\n", + "ax.legend()\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Advanced Statistical Operators\n", + "\n", + "Factorium provides advanced time-series operators that are useful for characterizing market microstructure and regime analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "returns = close.ts_delta(1) / close.ts_shift(1)\n", + "\n", + "# Autocorrelation: measures serial dependence in returns\n", + "# Positive autocorrelation → trending, Negative → mean reverting\n", + "autocorr = returns.ts_autocorr(60, lag=1)\n", + "autocorr.name = \"return_autocorr\"\n", + "\n", + "# Kurtosis: measures tail heaviness\n", + "# High kurtosis → more extreme events (fat tails)\n", + "kurtosis = returns.ts_kurtosis(60)\n", + "kurtosis.name = \"return_kurtosis\"\n", + "\n", + "# Skewness: measures asymmetry of the return distribution\n", + "# Negative skew → more downside risk\n", + "skewness = returns.ts_skewness(60)\n", + "skewness.name = \"return_skewness\"\n", + "\n", + "print(\"Advanced statistical factors computed:\")\n", + "for f in [autocorr, kurtosis, skewness]:\n", + " print(f\" {f.name}: {len(f):,} rows\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize autocorrelation for a few symbols\n", + "autocorr.plot.plot_timeseries(symbols=[\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"])\n", + "plt.axhline(y=0, color=\"red\", linestyle=\"--\", alpha=0.5)\n", + "plt.title(\"Rolling Return Autocorrelation (60-period window, lag=1)\")\n", + "plt.ylabel(\"Autocorrelation\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Quick IC check on autocorrelation as a factor\n", + "# (negative autocorrelation → mean reverting → potential signal)\n", + "autocorr_signal = (-autocorr).cs_rank()\n", + "autocorr_signal.name = \"neg_autocorr_rank\"\n", + "\n", + "autocorr_analysis = session.analyze(autocorr_signal, periods=1)\n", + "print(\"Autocorrelation as factor:\")\n", + "print(autocorr_analysis.ic_summary)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Summary\n", + "\n", + "In this notebook we:\n", + "- Built a **mean reversion factor** using z-score distance from a moving average\n", + "- Applied **volatility normalization** to make the signal stable across assets\n", + "- Demonstrated **cross-sectional processing** methods (`cs_rank`, `cs_zscore`, `cs_winsorize`, `cs_demean`)\n", + "- Compared IC across different signal processing methods\n", + "- Ran backtests under **market-neutral** and **long-only** regimes\n", + "- Explored **advanced statistical operators** for regime characterization\n", + "\n", + "**Key takeaway:** Cross-sectional normalization often improves factor stability (IC IR), even if it doesn't always increase mean IC." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/examples/03_data_loading_and_exploration.ipynb b/examples/03_data_loading_and_exploration.ipynb new file mode 100644 index 0000000..23d48e0 --- /dev/null +++ b/examples/03_data_loading_and_exploration.ipynb @@ -0,0 +1,430 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Data Loading & AggBar Exploration\n", + "\n", + "This notebook is a **deep dive into data handling** in Factorium:\n", + "\n", + "1. Downloading data with `BinanceDataLoader`\n", + "2. Understanding the `AggBar` container\n", + "3. Time-bar aggregation at different intervals\n", + "4. Slicing, filtering, and exporting data\n", + "5. Working with different data formats (Polars / Pandas / CSV / Parquet)\n", + "\n", + "**Prerequisites**: `pip install factorium`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Downloading Data with BinanceDataLoader\n", + "\n", + "`BinanceDataLoader` fetches historical trade data from [Binance Vision](https://data.binance.vision/) and aggregates it into OHLCV bars on the fly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import BinanceDataLoader, AggBar\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import polars as pl\n", + "\n", + "%matplotlib inline\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.rcParams[\"figure.dpi\"] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loader = BinanceDataLoader()\n", + "\n", + "# Load 7 days of 1-minute time bars for 5 symbols\n", + "agg = loader.load_aggbar(\n", + " symbols=[\"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\"],\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=7,\n", + " bar_type=\"time\", # Time-based bars\n", + " interval=60_000, # 1 minute = 60,000 ms\n", + ")\n", + "\n", + "print(f\"Type: {type(agg).__name__}\")\n", + "print(f\"Total bars: {len(agg):,}\")\n", + "print(f\"Symbols: {agg.symbols}\")\n", + "print(f\"Columns: {agg.cols}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Understanding AggBar\n", + "\n", + "`AggBar` is a **multi-symbol OHLCV data container** that stores data in **long format**:\n", + "\n", + "| Column | Description |\n", + "|--------|-------------|\n", + "| `start_time` | Bar open timestamp (epoch ms) |\n", + "| `end_time` | Bar close timestamp (epoch ms) |\n", + "| `symbol` | Trading pair identifier |\n", + "| `open` | Opening price |\n", + "| `high` | Highest price |\n", + "| `low` | Lowest price |\n", + "| `close` | Closing price |\n", + "| `volume` | Trading volume |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View as Polars DataFrame\n", + "agg.to_polars().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View as Pandas DataFrame\n", + "agg.to_df().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Per-symbol summary: bar count, time range, missing data\n", + "agg.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Metadata\n", + "meta = agg.metadata\n", + "print(f\"Number of rows: {meta.num_rows:,}\")\n", + "print(f\"Number of symbols: {meta.num_symbols}\")\n", + "print(f\"Time range: {meta.min_time} → {meta.max_time}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Extracting Factors from AggBar\n", + "\n", + "Use `agg[\"column_name\"]` to extract a column as a `Factor` object. This is the starting point for all computations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "close = agg[\"close\"]\n", + "volume = agg[\"volume\"]\n", + "\n", + "print(f\"close factor: {type(close).__name__}, name='{close.name}', rows={len(close):,}\")\n", + "print(f\"volume factor: {type(volume).__name__}, name='{volume.name}', rows={len(volume):,}\")\n", + "\n", + "# Preview factor data\n", + "close.data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract multiple columns as a new AggBar\n", + "ohlc = agg[[\"open\", \"high\", \"low\", \"close\"]]\n", + "print(f\"Sub-AggBar columns: {ohlc.cols}\")\n", + "print(f\"Sub-AggBar bars: {len(ohlc):,}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Slicing Data\n", + "\n", + "`AggBar.slice()` lets you filter by **time range** and/or **symbols** to create a new AggBar subset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Slice by symbols only\n", + "btc_only = agg.slice(symbols=[\"BTCUSDT\"])\n", + "print(f\"BTC only: {len(btc_only):,} bars, symbols={btc_only.symbols}\")\n", + "\n", + "# Slice by multiple symbols\n", + "btc_eth = agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", + "print(f\"BTC+ETH: {len(btc_eth):,} bars, symbols={btc_eth.symbols}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Different Time Intervals\n", + "\n", + "By changing the `interval` parameter, you can aggregate raw trade data into bars of different durations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 1-minute bars (already loaded)\n", + "print(f\"1-min bars: {len(agg):,}\")\n", + "\n", + "# 5-minute bars\n", + "agg_5m = loader.load_aggbar(\n", + " symbols=[\"BTCUSDT\", \"ETHUSDT\"],\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=7,\n", + " bar_type=\"time\",\n", + " interval=300_000, # 5 minutes = 300,000 ms\n", + ")\n", + "print(f\"5-min bars: {len(agg_5m):,}\")\n", + "\n", + "# 1-hour bars\n", + "agg_1h = loader.load_aggbar(\n", + " symbols=[\"BTCUSDT\", \"ETHUSDT\"],\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=7,\n", + " bar_type=\"time\",\n", + " interval=3_600_000, # 1 hour = 3,600,000 ms\n", + ")\n", + "print(f\"1-hour bars: {len(agg_1h):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare bar counts\n", + "comparison = pd.DataFrame({\n", + " \"Interval\": [\"1 min\", \"5 min\", \"1 hour\"],\n", + " \"interval_ms\": [60_000, 300_000, 3_600_000],\n", + " \"Total Bars\": [len(agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])), len(agg_5m), len(agg_1h)],\n", + "})\n", + "comparison" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize the same price data at different resolutions\n", + "fig, axes = plt.subplots(3, 1, figsize=(14, 12), sharex=False)\n", + "\n", + "for ax, (label, data) in zip(axes, [\n", + " (\"1-min\", agg.slice(symbols=[\"BTCUSDT\"])),\n", + " (\"5-min\", agg_5m.slice(symbols=[\"BTCUSDT\"])),\n", + " (\"1-hour\", agg_1h.slice(symbols=[\"BTCUSDT\"])),\n", + "]):\n", + " df = data.to_df()\n", + " df[\"datetime\"] = pd.to_datetime(df[\"start_time\"], unit=\"ms\")\n", + " ax.plot(df[\"datetime\"], df[\"close\"], linewidth=0.8)\n", + " ax.set_title(f\"BTCUSDT Close Price — {label} bars ({len(data):,} bars)\")\n", + " ax.set_ylabel(\"Price\")\n", + " ax.grid(True, alpha=0.3)\n", + "\n", + "axes[-1].set_xlabel(\"Date\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Saving and Loading Data\n", + "\n", + "`AggBar` can be exported to CSV or Parquet and loaded back." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "# Save to Parquet (efficient binary format)\n", + "output_dir = Path(\"./sample_data\")\n", + "output_dir.mkdir(exist_ok=True)\n", + "\n", + "agg.to_parquet(output_dir / \"crypto_1min.parquet\")\n", + "print(f\"Saved to {output_dir / 'crypto_1min.parquet'}\")\n", + "\n", + "# Save to CSV\n", + "agg.to_csv(output_dir / \"crypto_1min.csv\")\n", + "print(f\"Saved to {output_dir / 'crypto_1min.csv'}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load back from CSV\n", + "agg_loaded = AggBar.from_csv(output_dir / \"crypto_1min.csv\")\n", + "print(f\"Loaded from CSV: {len(agg_loaded):,} bars, symbols={agg_loaded.symbols}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# You can also create AggBar from a Polars or Pandas DataFrame\n", + "df = agg.to_polars()\n", + "agg_from_df = AggBar.from_df(df)\n", + "print(f\"From DataFrame: {len(agg_from_df):,} bars\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Using ResearchSession for Quick Analysis\n", + "\n", + "`ResearchSession` wraps an AggBar and provides a high-level API. It can also be created directly from files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import ResearchSession\n", + "\n", + "# Create from AggBar\n", + "session = ResearchSession(agg, default_frequency=\"1min\")\n", + "\n", + "print(f\"Session symbols: {session.symbols}\")\n", + "print(f\"Session columns: {session.cols}\")\n", + "\n", + "# Quick factor creation and analysis\n", + "signal = session.factor(\"close\").cs_rank()\n", + "report = session.quick_report(signal)\n", + "print(report)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ResearchSession can also load from files directly\n", + "session_from_csv = ResearchSession.from_csv(\n", + " output_dir / \"crypto_1min.csv\",\n", + " default_frequency=\"1min\",\n", + ")\n", + "print(f\"Session from CSV: {len(session_from_csv.symbols)} symbols\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Cleanup\n", + "\n", + "Remove sample data files created during this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "if output_dir.exists():\n", + " shutil.rmtree(output_dir)\n", + " print(f\"Cleaned up {output_dir}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we covered:\n", + "- **`BinanceDataLoader`**: Download historical trade data and aggregate into time bars\n", + "- **`AggBar`**: Multi-symbol OHLCV container with `info()`, `slice()`, `to_polars()`, `to_df()`\n", + "- **Time intervals**: Aggregating the same data into 1-min, 5-min, and 1-hour bars\n", + "- **Persistence**: Saving to CSV/Parquet and loading back\n", + "- **`ResearchSession`**: High-level API that wraps AggBar for streamlined research\n", + "\n", + "**Next notebooks:**\n", + "- `01_momentum_factor_research.ipynb` — Full factor research workflow\n", + "- `02_mean_reversion_factor.ipynb` — Mean reversion with volatility normalization\n", + "- `04_multi_factor_combination.ipynb` — Combine multiple factors" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/examples/04_multi_factor_combination.ipynb b/examples/04_multi_factor_combination.ipynb new file mode 100644 index 0000000..90e9324 --- /dev/null +++ b/examples/04_multi_factor_combination.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Multi-Factor Combination\n", + "\n", + "This notebook demonstrates how to **combine multiple factors** into a single trading signal:\n", + "\n", + "1. Build individual factors (momentum, volatility, volume)\n", + "2. Analyze factor correlations\n", + "3. Orthogonalize factors to remove redundancy\n", + "4. Combine factors using `CompositeFactor`\n", + "5. Compare single-factor vs. composite-factor performance\n", + "6. Factor selection workflow\n", + "\n", + "**Prerequisites**: `pip install factorium`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup & Data Loading" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import BinanceDataLoader, ResearchSession\n", + "from factorium.factors import FactorAnalyzer, CompositeFactor\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "%matplotlib inline\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.rcParams[\"figure.dpi\"] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SYMBOLS = [\n", + " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", + " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", + "]\n", + "\n", + "loader = BinanceDataLoader()\n", + "\n", + "agg = loader.load_aggbar(\n", + " symbols=SYMBOLS,\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=30,\n", + " bar_type=\"time\",\n", + " interval=60_000,\n", + ")\n", + "\n", + "session = ResearchSession(agg, default_frequency=\"1min\")\n", + "print(f\"Loaded {len(agg):,} bars for {len(agg.symbols)} symbols\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Building Individual Factors\n", + "\n", + "We'll create three conceptually different factors to combine." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "close = agg[\"close\"]\n", + "volume = agg[\"volume\"]\n", + "\n", + "# --- Factor 1: Momentum (60-period return, ranked) ---\n", + "momentum = (close.ts_delta(60) / close.ts_shift(60)).cs_rank()\n", + "momentum.name = \"momentum\"\n", + "\n", + "# --- Factor 2: Volatility (inverse of rolling std, ranked) ---\n", + "# Low volatility tends to outperform (\"low vol anomaly\")\n", + "returns = close.ts_delta(1) / close.ts_shift(1)\n", + "inv_vol = -returns.ts_std(60) # negate so lower vol → higher signal\n", + "inv_vol_ranked = inv_vol.cs_rank()\n", + "inv_vol_ranked.name = \"inv_volatility\"\n", + "\n", + "# --- Factor 3: Volume Ratio (relative volume, ranked) ---\n", + "# High relative volume can indicate conviction\n", + "vol_ratio = volume / volume.ts_mean(60)\n", + "vol_ratio_ranked = vol_ratio.cs_rank()\n", + "vol_ratio_ranked.name = \"volume_ratio\"\n", + "\n", + "factors = {\n", + " \"Momentum\": momentum,\n", + " \"Inv Volatility\": inv_vol_ranked,\n", + " \"Volume Ratio\": vol_ratio_ranked,\n", + "}\n", + "\n", + "print(\"Individual factors created:\")\n", + "for name, f in factors.items():\n", + " print(f\" {name}: {len(f):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 Individual Factor IC Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute IC for each factor\n", + "ic_table = []\n", + "for name, factor in factors.items():\n", + " analysis = session.analyze(factor, periods=1)\n", + " ic_stats = analysis.ic_summary.get(1, {})\n", + " ic_table.append({\n", + " \"Factor\": name,\n", + " \"Mean IC\": ic_stats.get(\"mean_ic\", np.nan),\n", + " \"IC Std\": ic_stats.get(\"ic_std\", np.nan),\n", + " \"IC IR\": ic_stats.get(\"ic_ir\", np.nan),\n", + " })\n", + "\n", + "ic_df = pd.DataFrame(ic_table).set_index(\"Factor\")\n", + "print(\"Individual Factor IC Analysis:\")\n", + "ic_df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Factor Correlation Analysis\n", + "\n", + "Before combining factors, we need to understand how correlated they are. Highly correlated factors add little diversification benefit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build a merged DataFrame with all factor values aligned by time and symbol\n", + "factor_dfs = []\n", + "for name, factor in factors.items():\n", + " df = factor.to_pandas()[[\"start_time\", \"symbol\", \"factor\"]].rename(\n", + " columns={\"factor\": name}\n", + " )\n", + " factor_dfs.append(df)\n", + "\n", + "# Merge all factors on (start_time, symbol)\n", + "merged = factor_dfs[0]\n", + "for df in factor_dfs[1:]:\n", + " merged = merged.merge(df, on=[\"start_time\", \"symbol\"], how=\"inner\")\n", + "\n", + "# Compute correlation matrix\n", + "factor_cols = list(factors.keys())\n", + "corr_matrix = merged[factor_cols].corr()\n", + "\n", + "print(\"Factor Correlation Matrix:\")\n", + "corr_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Heatmap visualization\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "sns.heatmap(\n", + " corr_matrix,\n", + " annot=True,\n", + " fmt=\".3f\",\n", + " cmap=\"RdBu_r\",\n", + " center=0,\n", + " vmin=-1,\n", + " vmax=1,\n", + " square=True,\n", + " ax=ax,\n", + ")\n", + "ax.set_title(\"Factor Correlation Matrix\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 Rolling Cross-Sectional Correlation\n", + "\n", + "Use `ts_corr()` to compute the time-varying correlation between two factors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Rolling correlation between momentum and inverse volatility\n", + "rolling_corr = momentum.ts_corr(inv_vol_ranked, window=120)\n", + "rolling_corr.name = \"mom_vol_corr\"\n", + "\n", + "rolling_corr.plot.plot_timeseries(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", + "plt.axhline(y=0, color=\"red\", linestyle=\"--\", alpha=0.5)\n", + "plt.title(\"Rolling Correlation: Momentum vs. Inverse Volatility (120-period window)\")\n", + "plt.ylabel(\"Correlation\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Factor Orthogonalization\n", + "\n", + "Use `cs_neutralize()` to remove the effect of one factor from another. This is useful when you want a factor that captures **unique** information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Neutralize inverse volatility from momentum\n", + "# This gives us the \"momentum\" signal that is orthogonal to volatility\n", + "mom_ortho = momentum.cs_neutralize(inv_vol_ranked)\n", + "mom_ortho.name = \"momentum_orthogonalized\"\n", + "\n", + "# Check IC of orthogonalized factor\n", + "ortho_analysis = session.analyze(mom_ortho, periods=1)\n", + "ortho_ic = ortho_analysis.ic_summary.get(1, {})\n", + "\n", + "print(\"Orthogonalized Momentum IC:\")\n", + "print(f\" Mean IC: {ortho_ic.get('mean_ic', 0):.4f}\")\n", + "print(f\" IC IR: {ortho_ic.get('ic_ir', 0):.4f}\")\n", + "print()\n", + "print(\"Compare with original Momentum:\")\n", + "print(f\" Mean IC: {ic_df.loc['Momentum', 'Mean IC']:.4f}\")\n", + "print(f\" IC IR: {ic_df.loc['Momentum', 'IC IR']:.4f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Combining Factors with CompositeFactor\n", + "\n", + "`CompositeFactor` provides three ways to combine factors:\n", + "\n", + "| Method | Description |\n", + "|--------|-------------|\n", + "| `from_equal_weights()` | Equal weight to all factors |\n", + "| `from_weights()` | Custom weights (must sum to 1) |\n", + "| `from_zscore()` | Z-score standardize each factor first, then equal weight |" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "factor_list = [momentum, inv_vol_ranked, vol_ratio_ranked]\n", + "\n", + "# --- Method 1: Equal Weights ---\n", + "composite_equal = CompositeFactor.from_equal_weights(\n", + " factor_list, name=\"equal_weight\"\n", + ").to_factor()\n", + "\n", + "# --- Method 2: Custom Weights ---\n", + "# Give more weight to momentum, less to volume\n", + "composite_custom = CompositeFactor.from_weights(\n", + " factor_list, weights=[0.5, 0.3, 0.2], name=\"custom_weight\"\n", + ").to_factor()\n", + "\n", + "# --- Method 3: Z-Score Standardization ---\n", + "composite_zscore = CompositeFactor.from_zscore(\n", + " factor_list, name=\"zscore_combo\"\n", + ").to_factor()\n", + "\n", + "composites = {\n", + " \"Equal Weight\": composite_equal,\n", + " \"Custom Weight (0.5/0.3/0.2)\": composite_custom,\n", + " \"Z-Score Combo\": composite_zscore,\n", + "}\n", + "\n", + "print(\"Composite factors created:\")\n", + "for name, f in composites.items():\n", + " print(f\" {name}: {len(f):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.1 Composite Factor IC Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare IC of all factors (individual + composite)\n", + "all_factors = {**factors, **composites}\n", + "\n", + "full_comparison = []\n", + "for name, factor in all_factors.items():\n", + " # Rank the composite factors for fair comparison\n", + " signal = factor.cs_rank() if name in composites else factor\n", + " analysis = session.analyze(signal, periods=1)\n", + " ic_stats = analysis.ic_summary.get(1, {})\n", + " full_comparison.append({\n", + " \"Factor\": name,\n", + " \"Mean IC\": ic_stats.get(\"mean_ic\", np.nan),\n", + " \"IC Std\": ic_stats.get(\"ic_std\", np.nan),\n", + " \"IC IR\": ic_stats.get(\"ic_ir\", np.nan),\n", + " \"Type\": \"Composite\" if name in composites else \"Single\",\n", + " })\n", + "\n", + "full_df = pd.DataFrame(full_comparison).set_index(\"Factor\")\n", + "print(\"Full Factor IC Comparison:\")\n", + "full_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Visualize: IC IR comparison\n", + "fig, ax = plt.subplots(figsize=(10, 6))\n", + "colors = [\"steelblue\" if t == \"Single\" else \"coral\" for t in full_df[\"Type\"]]\n", + "full_df[\"IC IR\"].plot.barh(ax=ax, color=colors)\n", + "ax.axvline(x=0, color=\"gray\", linestyle=\"--\")\n", + "ax.set_title(\"IC IR Comparison: Single vs. Composite Factors\")\n", + "ax.set_xlabel(\"IC IR\")\n", + "\n", + "# Add legend\n", + "from matplotlib.patches import Patch\n", + "ax.legend(handles=[\n", + " Patch(color=\"steelblue\", label=\"Single Factor\"),\n", + " Patch(color=\"coral\", label=\"Composite Factor\"),\n", + "])\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Backtesting: Single vs. Composite\n", + "\n", + "Let's see if combining factors actually improves backtest performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Backtest best individual factor and best composite\n", + "bt_results = {}\n", + "\n", + "for name, factor in all_factors.items():\n", + " signal = factor.cs_rank() if name in composites else factor\n", + " result = session.backtest(signal, neutralization=\"market\")\n", + " bt_results[name] = result\n", + "\n", + "# Collect metrics\n", + "metrics_rows = []\n", + "for name, result in bt_results.items():\n", + " m = result.metrics\n", + " metrics_rows.append({\n", + " \"Factor\": name,\n", + " \"Total Return\": m.get(\"total_return\", np.nan),\n", + " \"Sharpe\": m.get(\"sharpe_ratio\", np.nan),\n", + " \"Max DD\": m.get(\"max_drawdown\", np.nan),\n", + " \"Sortino\": m.get(\"sortino_ratio\", np.nan),\n", + " \"Type\": \"Composite\" if name in composites else \"Single\",\n", + " })\n", + "\n", + "bt_df = pd.DataFrame(metrics_rows).set_index(\"Factor\")\n", + "bt_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot equity curves for all factors\n", + "fig, ax = plt.subplots(figsize=(14, 7))\n", + "\n", + "for name, result in bt_results.items():\n", + " eq = result.equity_curve.to_pandas()\n", + " eq[\"ts\"] = pd.to_datetime(eq[\"start_time\"], unit=\"ms\")\n", + " style = \"--\" if name in factors else \"-\"\n", + " linewidth = 1.0 if name in factors else 2.0\n", + " ax.plot(eq[\"ts\"], eq[\"equity\"], style, label=name, linewidth=linewidth)\n", + "\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Equity\")\n", + "ax.set_title(\"Equity Curves — Single Factors (dashed) vs. Composite Factors (solid)\")\n", + "ax.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Factor Selection Workflow\n", + "\n", + "A practical approach to selecting which factors to include in a composite:\n", + "\n", + "1. **Filter by IC**: Keep factors with |Mean IC| above a threshold\n", + "2. **Filter by correlation**: Remove highly correlated factors (keep the one with better IC)\n", + "3. **Combine remaining factors**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 1: Filter by absolute Mean IC\n", + "IC_THRESHOLD = 0.005 # Adjust based on your domain\n", + "\n", + "selected = ic_df[ic_df[\"Mean IC\"].abs() >= IC_THRESHOLD]\n", + "print(f\"Step 1 — Factors with |Mean IC| >= {IC_THRESHOLD}:\")\n", + "print(selected)\n", + "print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 2: Check correlation among selected factors\n", + "CORR_THRESHOLD = 0.7 # Max acceptable pairwise correlation\n", + "\n", + "selected_factors_list = [factors[name] for name in selected.index if name in factors]\n", + "selected_names = [f.name for f in selected_factors_list]\n", + "\n", + "print(f\"Selected factors for combination: {selected_names}\")\n", + "print(f\"Pairwise correlations (threshold = {CORR_THRESHOLD}):\")\n", + "\n", + "# Show relevant subset of correlation matrix\n", + "relevant_corr = corr_matrix.loc[\n", + " [n for n in selected.index if n in corr_matrix.index],\n", + " [n for n in selected.index if n in corr_matrix.columns],\n", + "]\n", + "relevant_corr" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Step 3: Combine selected factors\n", + "if len(selected_factors_list) > 1:\n", + " final_composite = CompositeFactor.from_equal_weights(\n", + " selected_factors_list, name=\"selected_composite\"\n", + " ).to_factor().cs_rank()\n", + " final_composite.name = \"selected_composite\"\n", + "\n", + " # Evaluate\n", + " final_report = session.quick_report(final_composite)\n", + " print(final_report)\n", + "else:\n", + " print(\"Only one factor selected — no combination needed.\")\n", + " final_report = session.quick_report(selected_factors_list[0])\n", + " print(final_report)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook we:\n", + "- Built three individual factors: **momentum**, **inverse volatility**, and **volume ratio**\n", + "- Analyzed **factor correlations** using static correlation matrices and rolling `ts_corr()`\n", + "- **Orthogonalized** factors using `cs_neutralize()` to extract unique signals\n", + "- Combined factors using **`CompositeFactor`** (equal weight, custom weight, z-score)\n", + "- Compared **single-factor vs. composite-factor** performance via IC and backtesting\n", + "- Demonstrated a practical **factor selection workflow** (IC filter → correlation filter → combine)\n", + "\n", + "**Key takeaway:** Combining uncorrelated factors with positive IC tends to improve IC IR and reduce portfolio volatility, even if individual IC values are small." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/examples/README.md b/examples/README.md new file mode 100644 index 0000000..5cd600b --- /dev/null +++ b/examples/README.md @@ -0,0 +1,46 @@ +# Factorium Examples + +Interactive Jupyter notebooks demonstrating factor research workflows with Factorium. + +## Notebooks + +| Notebook | Description | Key Concepts | +|----------|-------------|-------------| +| [01 — Momentum Factor Research](01_momentum_factor_research.ipynb) | Complete workflow from data loading to backtest | Data loading, factor construction (code & expression), IC analysis, multi-horizon IC decay, quantile returns, backtesting, quick report | +| [02 — Mean Reversion Factor](02_mean_reversion_factor.ipynb) | Mean reversion with cross-sectional processing | Z-score distance, volatility normalization, `cs_rank`, `cs_zscore`, `cs_winsorize`, market-neutral vs. long-only backtest, advanced operators (`ts_autocorr`, `ts_kurtosis`, `ts_skewness`) | +| [03 — Data Loading & Exploration](03_data_loading_and_exploration.ipynb) | Deep dive into data handling | `BinanceDataLoader`, `AggBar` methods, time-bar intervals (1min/5min/1h), slicing, CSV/Parquet export, `ResearchSession` from files | +| [04 — Multi-Factor Combination](04_multi_factor_combination.ipynb) | Combine and select factors | Factor correlations, `ts_corr`, `cs_neutralize`, `CompositeFactor` (equal/custom/z-score), single vs. composite backtest, factor selection workflow | + +## Getting Started + +### Prerequisites + +```bash +pip install factorium +# or +uv add factorium +``` + +A Jupyter environment is required: + +```bash +pip install jupyterlab +``` + +### Running the Notebooks + +```bash +cd examples/ +jupyter lab +``` + +> **Note:** Notebooks download data directly from Binance Vision. The first run may take a few minutes depending on your internet connection. Subsequent runs are faster thanks to local caching. + +## Recommended Reading Order + +If you're new to Factorium, we recommend starting with: + +1. **Notebook 03** — Understand data loading and the `AggBar` container +2. **Notebook 01** — Walk through a full factor research workflow +3. **Notebook 02** — Learn about signal processing and cross-sectional transforms +4. **Notebook 04** — Combine multiple factors into a composite signal From 5abe8f7cf63f6fbfbe2b1ec60496861ab3a4cb61 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Thu, 12 Feb 2026 20:48:28 +0800 Subject: [PATCH 02/25] fix: auto-prepare data in FactorAnalyzer methods - Add _ensure_data_prepared() helper to FactorAnalyzer that auto-calls prepare_data() when _clean_data is not yet set - Replace ValueError raises in calculate_ic(), calculate_turnover(), and calculate_quantile_returns() with auto-prepare calls - Fix notebook 01 to explicitly call prepare_data() before plotting quantile returns (best practice documentation) - Clear stale error outputs from notebook cells --- examples/01_momentum_factor_research.ipynb | 2112 +++++++++++++++----- src/factorium/factors/analyzer.py | 15 +- 2 files changed, 1639 insertions(+), 488 deletions(-) diff --git a/examples/01_momentum_factor_research.ipynb b/examples/01_momentum_factor_research.ipynb index 8a81281..8a3a633 100644 --- a/examples/01_momentum_factor_research.ipynb +++ b/examples/01_momentum_factor_research.ipynb @@ -1,485 +1,1633 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Momentum Factor Research\n", - "\n", - "This notebook walks through a **complete factor research workflow** using Factorium:\n", - "\n", - "1. Load data from Binance\n", - "2. Explore the AggBar data structure\n", - "3. Build momentum factors (code-based & expression-based)\n", - "4. Visualize factor behavior\n", - "5. Run IC (Information Coefficient) analysis\n", - "6. Analyze quantile returns\n", - "7. Run a vectorized backtest\n", - "8. Generate a quick report\n", - "\n", - "**Prerequisites**: `pip install factorium` (or `uv add factorium`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Setup & Data Loading\n", - "\n", - "We'll download 30 days of 1-minute futures data for 10 crypto symbols from Binance Vision." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from factorium import BinanceDataLoader, ResearchSession\n", - "from factorium.factors import FactorAnalyzer, CompositeFactor\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "%matplotlib inline\n", - "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", - "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", - "plt.rcParams[\"figure.dpi\"] = 100" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "SYMBOLS = [\n", - " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", - " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", - "]\n", - "\n", - "loader = BinanceDataLoader()\n", - "\n", - "agg = loader.load_aggbar(\n", - " symbols=SYMBOLS,\n", - " data_type=\"aggTrades\",\n", - " market_type=\"futures\",\n", - " futures_type=\"um\",\n", - " days=30,\n", - " bar_type=\"time\",\n", - " interval=60_000, # 1-minute bars\n", - ")\n", - "\n", - "print(f\"Loaded {len(agg):,} bars\")\n", - "print(f\"Symbols: {agg.symbols}\")\n", - "print(f\"Columns: {agg.cols}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Exploring the AggBar\n", - "\n", - "`AggBar` is a **multi-symbol OHLCV container** stored in long format. You can inspect it, convert to Polars/Pandas, and slice by time or symbols." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Summary info per symbol\n", - "agg.info()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# View raw data as Polars DataFrame\n", - "agg.to_polars().head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Slice by symbols — creates a new AggBar\n", - "btc_eth = agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", - "print(f\"Sliced AggBar: {btc_eth.symbols}, {len(btc_eth):,} bars\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Building Momentum Factors\n", - "\n", - "### 3.1 Code-Based Factor Construction\n", - "\n", - "Extract a column from `AggBar` with `agg[\"close\"]` to get a `Factor` object, then chain time-series and cross-sectional operators." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "close = agg[\"close\"]\n", - "volume = agg[\"volume\"]\n", - "\n", - "# Simple momentum: percentage change over 60 periods\n", - "momentum_60 = close.ts_delta(60) / close.ts_shift(60)\n", - "momentum_60.name = \"momentum_60\"\n", - "\n", - "# Short-term momentum (20 periods)\n", - "momentum_20 = close.ts_delta(20) / close.ts_shift(20)\n", - "momentum_20.name = \"momentum_20\"\n", - "\n", - "# Volatility-adjusted momentum\n", - "volatility = (close.ts_delta(1) / close.ts_shift(1)).ts_std(60)\n", - "vol_adj_momentum = momentum_60 / volatility\n", - "vol_adj_momentum.name = \"vol_adj_momentum\"\n", - "\n", - "# Cross-sectional rank (normalized to [0, 1])\n", - "momentum_rank = momentum_60.cs_rank()\n", - "momentum_rank.name = \"momentum_rank\"\n", - "\n", - "print(\"Factor shapes:\")\n", - "print(f\" momentum_60: {len(momentum_60):,} rows\")\n", - "print(f\" vol_adj_momentum: {len(vol_adj_momentum):,} rows\")\n", - "print(f\" momentum_rank: {len(momentum_rank):,} rows\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Expression-Based Factor Construction\n", - "\n", - "You can also define factors using string expressions via `ResearchSession.create_factor()`. This is useful for rapid experimentation." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "session = ResearchSession(agg, default_frequency=\"1min\")\n", - "\n", - "# Same momentum factor, defined via expression\n", - "mom_expr = session.create_factor(\n", - " \"ts_delta(close, 60) / ts_shift(close, 60)\",\n", - " name=\"momentum_60_expr\",\n", - ")\n", - "\n", - "# A more complex expression: MA crossover signal\n", - "ma_cross = session.create_factor(\n", - " \"ts_mean(close, 10) - ts_mean(close, 30)\",\n", - " name=\"ma_crossover\",\n", - ")\n", - "\n", - "print(f\"Expression-based momentum: {len(mom_expr):,} rows\")\n", - "print(f\"MA crossover signal: {len(ma_cross):,} rows\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Visualizing Factors\n", - "\n", - "Use the `.plot` accessor on Factor objects to produce time series, heatmaps, and distributions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Time series of momentum for a few symbols\n", - "momentum_60.plot.plot_timeseries(symbols=[\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Distribution of momentum values across all symbols\n", - "momentum_60.plot.plot_distribution()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Heatmap: momentum intensity across symbols and time\n", - "momentum_rank.plot.plot_heatmap()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. IC (Information Coefficient) Analysis\n", - "\n", - "The **IC** measures the rank correlation between factor values and subsequent returns. A consistently positive (or negative) IC indicates predictive power.\n", - "\n", - "### 5.1 Single-Period IC" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use ResearchSession for streamlined analysis\n", - "signal = momentum_rank # Use ranked momentum as our signal\n", - "\n", - "analysis = session.analyze(signal, periods=1)\n", - "\n", - "print(\"IC Summary:\")\n", - "print(analysis.ic_summary)\n", - "print()\n", - "print(f\"IC Series shape: {analysis.ic_series.shape}\")\n", - "print(analysis.ic_series.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2 Multi-Horizon IC Analysis\n", - "\n", - "To understand how quickly a factor's signal decays, we compute IC across multiple forward horizons." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Compute IC for multiple horizons\n", - "horizons = [1, 5, 10, 20, 60]\n", - "ic_results = {}\n", - "\n", - "for h in horizons:\n", - " result = session.analyze(signal, periods=h)\n", - " ic_results[h] = result.ic_summary.get(h, {})\n", - "\n", - "# Display IC decay table\n", - "ic_decay_df = pd.DataFrame(ic_results).T\n", - "ic_decay_df.index.name = \"horizon\"\n", - "print(\"IC Decay Analysis:\")\n", - "ic_decay_df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot IC decay curve\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax.plot(ic_decay_df.index, ic_decay_df[\"mean_ic\"], \"o-\", linewidth=2, markersize=8)\n", - "ax.axhline(y=0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "ax.set_xlabel(\"Forward Horizon (periods)\")\n", - "ax.set_ylabel(\"Mean IC\")\n", - "ax.set_title(\"IC Decay Curve — Momentum Factor\")\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Quantile Analysis\n", - "\n", - "Split the cross-section into quantiles by factor value and examine the mean returns of each group. A monotonic pattern (Q1 < Q2 < ... < Q5) indicates strong linear predictive power." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use FactorAnalyzer directly for more control\n", - "analyzer = FactorAnalyzer(signal, agg, quantiles=5)\n", - "\n", - "# Quantile returns\n", - "analyzer.plot_quantile_returns(quantiles=5, period=1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Cumulative returns by quantile (includes Long-Short portfolio)\n", - "analyzer.plot_cumulative_returns(quantiles=5, period=1, long_short=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# IC time series plot\n", - "analyzer.plot_ic(period=1, method=\"rank\", plot_type=\"ts\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Backtesting\n", - "\n", - "Run a **market-neutral vectorized backtest** using the momentum signal. The backtester:\n", - "- Converts signals to portfolio weights (cross-sectional normalization)\n", - "- Handles transaction costs\n", - "- Tracks equity, returns, and positions over time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Market-neutral backtest\n", - "result = session.backtest(\n", - " signal,\n", - " neutralization=\"market\",\n", - " transaction_cost=0.0003,\n", - ")\n", - "\n", - "# Key performance metrics\n", - "print(\"Backtest Metrics:\")\n", - "for key, val in result.metrics.items():\n", - " if isinstance(val, float):\n", - " print(f\" {key:25s}: {val:>10.4f}\")\n", - " else:\n", - " print(f\" {key:25s}: {val}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot equity curve\n", - "equity = result.equity_curve.to_pandas()\n", - "equity[\"timestamp\"] = pd.to_datetime(equity[\"start_time\"], unit=\"ms\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(14, 6))\n", - "ax.plot(equity[\"timestamp\"], equity[\"equity\"], linewidth=1.5)\n", - "ax.set_xlabel(\"Date\")\n", - "ax.set_ylabel(\"Portfolio Equity\")\n", - "ax.set_title(\"Momentum Factor — Equity Curve (Market Neutral)\")\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot returns distribution\n", - "returns = result.returns.to_pandas()\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax.hist(returns[\"portfolio_return\"].dropna(), bins=100, alpha=0.75, edgecolor=\"black\", linewidth=0.5)\n", - "ax.axvline(x=0, color=\"red\", linestyle=\"--\", alpha=0.7)\n", - "ax.set_xlabel(\"Return\")\n", - "ax.set_ylabel(\"Frequency\")\n", - "ax.set_title(\"Distribution of Portfolio Returns\")\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Quick Report\n", - "\n", - "`ResearchSession.quick_report()` combines IC analysis and backtesting into a single text summary — great for rapid iteration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "report = session.quick_report(signal)\n", - "print(report)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9. Summary & Next Steps\n", - "\n", - "In this notebook we:\n", - "- Loaded multi-symbol 1-minute data from Binance using `BinanceDataLoader`\n", - "- Built momentum factors via both **code-based** and **expression-based** methods\n", - "- Visualized factor behavior with time series, distributions, and heatmaps\n", - "- Computed IC and analyzed its decay across multiple horizons\n", - "- Ran a market-neutral backtest and examined performance metrics\n", - "\n", - "**Next notebooks to explore:**\n", - "- `02_mean_reversion_factor.ipynb` — Mean reversion with volatility normalization\n", - "- `03_data_loading_and_exploration.ipynb` — Deep dive into AggBar and data loading\n", - "- `04_multi_factor_combination.ipynb` — Combine multiple factors and compare performance" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "language": "python", - "name": "python3" - }, - "language_info": { - "name": "python", - "version": "3.13.2" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Momentum Factor Research\n", + "\n", + "This notebook walks through a **complete factor research workflow** using Factorium:\n", + "\n", + "1. Load data from Binance\n", + "2. Explore the AggBar data structure\n", + "3. Build momentum factors (code-based & expression-based)\n", + "4. Visualize factor behavior\n", + "5. Run IC (Information Coefficient) analysis\n", + "6. Analyze quantile returns\n", + "7. Run a vectorized backtest\n", + "8. Generate a quick report\n", + "\n", + "**Prerequisites**: `pip install factorium` (or `uv add factorium`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup & Data Loading\n", + "\n", + "We'll download 30 days of 1-minute futures data for 10 crypto symbols from Binance Vision." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import BinanceDataLoader, ResearchSession\n", + "from factorium.factors import FactorAnalyzer, CompositeFactor\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.rcParams[\"figure.dpi\"] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2026-02-12 19:22:07,485 - INFO - Downloading 10 date ranges for 10 symbols...\n", + "2026-02-12 19:22:07,487 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,498 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,500 - INFO - Processing data for 2026-01-14\n", + "2026-02-12 19:22:07,502 - INFO - Processing data for 2026-01-15\n", + "2026-02-12 19:22:07,504 - INFO - Processing data for 2026-01-16\n", + "2026-02-12 19:22:07,506 - INFO - Processing data for 2026-01-17\n", + "2026-02-12 19:22:07,508 - INFO - Processing data for 2026-01-18\n", + "2026-02-12 19:22:07,509 - INFO - Processing data for 2026-01-14\n", + "2026-02-12 19:22:07,512 - INFO - Processing data for 2026-01-15\n", + "2026-02-12 19:22:07,513 - INFO - Processing data for 2026-01-16\n", + "2026-02-12 19:22:07,514 - 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ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() running at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:60> wait_for= cb=[Task.task_wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:07,585 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:07,586 - INFO - Processing data for 2026-01-14\n", + "2026-02-12 19:22:07,587 - INFO - Processing data for 2026-01-15\n", + "2026-02-12 19:22:07,589 - INFO - Processing data for 2026-01-16\n", + "2026-02-12 19:22:07,590 - INFO - Processing data for 2026-01-17\n", + "2026-02-12 19:22:07,591 - INFO - Processing data for 2026-01-18\n", + "2026-02-12 19:22:07,592 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,594 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,596 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,600 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/typing.py:426: RuntimeWarning: coroutine 'Kernel.shell_main' was never awaited\n", + " @functools.wraps(func)\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "2026-02-12 19:22:07,604 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:07,606 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,613 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,616 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,617 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,619 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,621 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,622 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,629 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,637 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,651 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,691 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,721 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:07,732 - ERROR - Exception in callback Task.__step()\n", + "handle: \n", + "Traceback (most recent call last):\n", + " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", + "2026-02-12 19:22:08,793 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:08,798 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:09,010 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:09,014 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:09,332 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:09,337 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:09,456 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:09,460 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:09,587 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:09,592 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:09,684 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:09,687 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:09,789 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:09,792 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:09,920 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:09,925 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:10,030 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:10,034 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:10,461 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:10,467 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:10,788 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:10,804 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:11,041 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:11,050 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:11,081 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/pathlib/_local.py:277: RuntimeWarning: coroutine 'Kernel.shell_main' was never awaited\n", + " @property\n", + "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", + "2026-02-12 19:22:11,087 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,088 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,089 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,090 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,091 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,092 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,093 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,094 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,095 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,096 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,097 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,098 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,099 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,102 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,103 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,104 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,106 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,107 - ERROR - Task was destroyed but it is pending!\n", + "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", + "2026-02-12 19:22:11,108 - ERROR - Task was destroyed but it is pending!\n", + "task: cb=[Task.__wakeup()]>\n", + "2026-02-12 19:22:11,253 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:11,402 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:11,413 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:11,416 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:11,798 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:11,825 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:12,162 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:12,338 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:12,504 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:12,587 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:12,875 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:13,292 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:13,501 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:13,513 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:13,514 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:13,516 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:13,517 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:13,519 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:13,520 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:13,522 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:13,883 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:13,910 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:14,152 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:14,291 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:14,550 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:14,755 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:15,050 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:15,352 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:15,691 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:15,710 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:15,712 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:15,714 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:15,715 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:15,716 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:15,718 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:15,719 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:15,878 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:15,891 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:16,111 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:16,235 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:16,348 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:16,639 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:16,736 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:17,023 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:17,325 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:17,568 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:17,905 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:17,923 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:17,924 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:17,926 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:17,927 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:17,929 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:17,931 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:17,932 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:17,934 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:17,935 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:18,574 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:18,610 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:18,697 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:19,008 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:19,306 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:19,695 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:20,201 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:20,574 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:20,951 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:21,001 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:21,021 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:21,025 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:21,030 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:21,035 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:21,038 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:21,040 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:21,042 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:21,043 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:21,389 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:21,741 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:21,756 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:21,758 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:22,386 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=17\n", + "2026-02-12 19:22:22,413 - INFO - Processing data for 2026-01-19\n", + "2026-02-12 19:22:22,942 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:23,560 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:23,595 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:23,597 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:23,677 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:22:24,114 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:24,138 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:22:24,140 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:24,266 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:22:24,279 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:22:24,402 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:22:24,416 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:22:24,493 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:22:24,891 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:24,913 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:22:24,916 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:25,207 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:25,557 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:26,036 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:26,060 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:26,061 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:26,063 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:26,458 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:26,678 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:26,697 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:26,699 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:27,513 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=18\n", + "2026-02-12 19:22:27,541 - INFO - Processing data for 2026-01-20\n", + "2026-02-12 19:22:28,318 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:28,351 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:28,572 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:28,904 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:28,925 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:28,928 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:29,055 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:22:29,128 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:22:29,211 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:22:29,298 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:22:29,602 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:30,032 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:30,051 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:22:30,054 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:22:30,056 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:22:30,059 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:22:30,061 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:30,062 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:30,418 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:30,895 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:30,915 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:30,917 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:31,106 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:22:31,247 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:22:31,395 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:31,410 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:22:31,412 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:22:31,415 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:31,606 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:32,068 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:32,098 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:32,100 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:32,343 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:22:32,828 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:33,407 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:33,437 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:22:33,441 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:33,446 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:34,840 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:35,133 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:35,152 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:35,156 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:35,443 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:35,463 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:35,829 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:36,270 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:36,708 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:37,168 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:37,179 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:38,247 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=16\n", + "2026-02-12 19:22:38,283 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:38,285 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:38,287 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:38,289 - INFO - Processing data for 2026-01-21\n", + "2026-02-12 19:22:38,950 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:39,528 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:22:40,025 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:22:40,164 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:22:40,169 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:40,405 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:22:40,421 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:22:40,423 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:22:40,425 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:22:40,431 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:22:40,617 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:40,860 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:22:41,115 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:22:41,278 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:41,292 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:41,297 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:22:41,301 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:22:41,303 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:41,728 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:22:41,769 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:22:42,083 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:22:42,329 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:22:42,558 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:22:42,933 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:44,627 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:44,684 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:22:44,686 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:22:44,688 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:22:44,690 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:44,695 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:45,082 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:45,108 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:47,075 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:22:47,323 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:22:47,330 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:22:47,342 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:22:47,666 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:22:47,796 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:22:47,812 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:22:47,814 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:22:47,968 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:22:48,101 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:22:48,287 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:22:48,478 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:22:48,667 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:48,806 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:22:48,978 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:22:49,235 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:22:49,252 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:22:49,254 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:22:49,256 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:22:49,258 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:22:49,261 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:49,263 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:22:49,265 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:22:49,270 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:22:49,590 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:49,616 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:22:49,913 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:22:50,467 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:22:50,478 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:22:50,712 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:22:50,739 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:50,742 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:22:51,026 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:22:51,282 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:22:51,304 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:22:51,306 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:22:51,797 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:22:51,821 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:22:52,253 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:22:52,528 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:22:52,553 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:22:52,554 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:22:52,876 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:22:52,893 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:22:54,346 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:22:56,356 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=15\n", + "2026-02-12 19:22:56,417 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:22:56,918 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:22:57,052 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:22:57,056 - INFO - Processing data for 2026-01-22\n", + "2026-02-12 19:22:57,074 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:22:57,077 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:22:57,370 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:22:57,640 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:22:57,658 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:22:57,660 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:22:57,944 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:22:57,978 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:23:00,209 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=14\n", + "2026-02-12 19:23:00,621 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:23:00,640 - INFO - Processing data for 2026-01-23\n", + "2026-02-12 19:23:00,642 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:23:00,993 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:23:01,894 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:23:02,545 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:23:02,566 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:23:02,567 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:23:02,569 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:23:02,864 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:23:03,294 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:23:03,311 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:23:03,313 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:23:03,757 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:23:04,183 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:23:04,523 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:23:04,971 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:23:05,065 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:23:05,072 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:23:05,460 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:23:05,468 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:23:05,470 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:23:05,475 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:23:05,488 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:23:05,497 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:23:05,632 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:23:06,244 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:23:06,711 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:23:06,751 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:23:06,940 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:23:07,096 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:23:07,101 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:23:07,391 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:23:07,494 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:23:07,498 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:23:07,715 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:23:07,734 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:23:08,067 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:23:08,091 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:23:08,392 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:23:08,409 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:23:08,959 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:23:09,349 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:23:09,368 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:23:09,371 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:23:10,964 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=19\n", + "2026-02-12 19:23:11,431 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:23:11,705 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:23:11,711 - INFO - Processing data for 2026-01-24\n", + "2026-02-12 19:23:11,729 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:23:11,731 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:23:12,047 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:23:12,451 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:23:12,476 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:23:12,479 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:23:12,643 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:23:12,754 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:23:13,223 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:23:13,252 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:23:13,958 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:23:14,638 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:23:14,659 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:23:14,661 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:23:16,732 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:23:16,829 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:23:16,948 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:23:17,234 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:23:17,638 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:23:18,043 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:23:18,070 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:23:18,071 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:23:18,074 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:23:18,296 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:23:18,381 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:23:18,950 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:23:19,505 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:23:19,529 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:23:19,530 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:23:19,686 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:23:19,953 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:23:20,408 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:23:20,427 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:23:20,430 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:23:21,292 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:23:21,358 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:23:21,828 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:23:21,886 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:23:22,188 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:23:22,977 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:23:23,012 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:23:23,014 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:23:23,199 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:23:23,494 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:23:23,962 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:23:24,327 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:23:24,352 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:23:24,355 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:23:24,604 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:23:25,986 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:23:26,676 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:23:26,692 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:23:27,308 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:23:27,348 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:23:27,350 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:23:27,868 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:23:28,707 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:23:28,735 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:23:29,481 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:23:30,424 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:23:31,625 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:23:32,705 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:23:33,364 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:23:34,869 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:23:34,929 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:23:34,932 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:23:34,934 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:23:34,937 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:23:34,939 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:23:34,942 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:23:35,444 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=24\n", + "2026-02-12 19:23:35,464 - INFO - Processing data for 2026-01-25\n", + "2026-02-12 19:23:35,905 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:23:38,265 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=20\n", + "2026-02-12 19:23:38,455 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:23:38,469 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:23:38,470 - INFO - Processing data for 2026-01-26\n", + "2026-02-12 19:23:39,243 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:23:39,270 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:23:39,617 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:23:40,314 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:23:40,811 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:23:40,957 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:23:41,764 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:23:41,793 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:23:41,795 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:23:41,796 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:23:42,023 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:23:42,845 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:23:43,236 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:23:43,421 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:23:43,438 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:23:43,440 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:23:46,196 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:23:46,261 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:23:46,791 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:23:48,225 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:23:48,950 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:23:48,980 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:23:49,919 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:23:49,952 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:23:50,435 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:23:53,574 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=21\n", + "2026-02-12 19:23:55,460 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=22\n", + "2026-02-12 19:23:55,549 - INFO - Processing data for 2026-01-27\n", + "2026-02-12 19:23:55,551 - INFO - Processing data for 2026-01-28\n", + "2026-02-12 19:23:55,986 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:23:56,637 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:23:57,967 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:23:58,012 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:23:59,919 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=23\n", + "2026-02-12 19:23:59,965 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:23:59,981 - INFO - Processing data for 2026-01-29\n", + "2026-02-12 19:24:00,604 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:24:00,627 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:24:01,077 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:24:01,527 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:24:01,543 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:24:02,515 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:24:02,547 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:24:04,128 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:24:04,691 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:24:04,705 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:24:05,180 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:24:05,208 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:24:05,213 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:24:06,839 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:24:07,562 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:24:07,926 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:24:07,935 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:24:08,659 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:24:08,685 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:24:10,037 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:24:10,077 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:24:11,309 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:24:12,838 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:24:13,481 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:24:15,150 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:24:16,930 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:24:17,630 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:24:18,697 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:24:19,195 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:24:20,992 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=25\n", + "2026-02-12 19:24:21,033 - INFO - Processing data for 2026-01-30\n", + "2026-02-12 19:24:21,509 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:24:23,076 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:24:23,115 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:24:23,868 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:24:24,384 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:24:24,898 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:24:25,359 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:24:26,229 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:24:28,515 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=26\n", + "2026-02-12 19:24:28,589 - INFO - Processing data for 2026-01-31\n", + "2026-02-12 19:24:30,315 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=28\n", + "2026-02-12 19:24:30,356 - INFO - Processing data for 2026-02-01\n", + "2026-02-12 19:24:32,527 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:24:32,582 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:24:34,467 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=27\n", + "2026-02-12 19:24:34,530 - INFO - Processing data for 2026-02-02\n", + "2026-02-12 19:24:37,668 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=29\n", + "2026-02-12 19:24:37,736 - INFO - Processing data for 2026-02-03\n", + "2026-02-12 19:24:41,734 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:24:41,805 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:24:45,114 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:24:45,189 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:24:49,059 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:24:49,164 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:24:57,912 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=30\n", + "2026-02-12 19:24:58,029 - INFO - Processing data for 2026-02-04\n", + "2026-02-12 19:25:04,390 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=01\n", + "2026-02-12 19:25:04,518 - INFO - Processing data for 2026-02-05\n", + "2026-02-12 19:25:08,908 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:25:09,014 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:25:13,862 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:25:13,975 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:25:19,822 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:25:19,950 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:25:25,728 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=31\n", + "2026-02-12 19:25:25,887 - INFO - Processing data for 2026-02-06\n", + "2026-02-12 19:25:33,790 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=03\n", + "2026-02-12 19:25:33,904 - INFO - Processing data for 2026-02-07\n", + "2026-02-12 19:25:43,716 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:25:43,912 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:25:46,978 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:25:47,041 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:25:52,879 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:26:00,082 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=02\n", + "2026-02-12 19:26:00,261 - INFO - Processing data for 2026-02-08\n", + "2026-02-12 19:26:05,940 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:26:12,079 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=04\n", + "2026-02-12 19:26:12,207 - INFO - Processing data for 2026-02-09\n", + "2026-02-12 19:26:22,562 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:26:26,761 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:26:32,990 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=07\n", + "2026-02-12 19:26:33,187 - INFO - Processing data for 2026-02-10\n", + "2026-02-12 19:26:41,338 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=06\n", + "2026-02-12 19:26:41,561 - INFO - Processing data for 2026-02-11\n", + "2026-02-12 19:26:43,832 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=08\n", + "2026-02-12 19:26:47,792 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:27:00,270 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=05\n", + "2026-02-12 19:27:04,009 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=11\n", + "2026-02-12 19:27:07,094 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=10\n", + "2026-02-12 19:27:10,158 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=09\n", + "2026-02-12 19:27:59,836 - INFO - Loaded 411051 bars for 10 symbols\n" + ] }, - "nbformat": 4, - "nbformat_minor": 4 + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 411,051 bars\n", + "Symbols: ['ADAUSDT', 'AVAXUSDT', 'BNBUSDT', 'BTCUSDT', 'CAKEUSDT', 'DOGEUSDT', 'DOTUSDT', 'ETHUSDT', 'SOLUSDT', 'XRPUSDT']\n", + "Columns: ['symbol', 'start_time', 'end_time', 'open', 'high', 'low', 'close', 'volume', 'vwap', 'num_buyer', 'num_seller', 'num_buyer_volume', 'num_seller_volume']\n" + ] + } + ], + "source": [ + "SYMBOLS = [\n", + " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", + " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", + "]\n", + "\n", + "loader = BinanceDataLoader()\n", + "\n", + "agg = loader.load_aggbar(\n", + " symbols=SYMBOLS,\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=30,\n", + " bar_type=\"time\",\n", + " interval=60_000, # 1-minute bars\n", + ")\n", + "\n", + "print(f\"Loaded {len(agg):,} bars\")\n", + "print(f\"Symbols: {agg.symbols}\")\n", + "print(f\"Columns: {agg.cols}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Exploring the AggBar\n", + "\n", + "`AggBar` is a **multi-symbol OHLCV container** stored in long format. You can inspect it, convert to Polars/Pandas, and slice by time or symbols." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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num_kbarstart_timeend_timenum_nan
symbol
ADAUSDT411062026-01-14 11:22:002026-02-120
AVAXUSDT411062026-01-14 11:22:002026-02-120
BNBUSDT411062026-01-14 11:22:002026-02-120
BTCUSDT411062026-01-14 11:22:002026-02-120
CAKEUSDT410972026-01-14 11:22:002026-02-120
DOGEUSDT411062026-01-14 11:22:002026-02-120
DOTUSDT411062026-01-14 11:22:002026-02-120
ETHUSDT411062026-01-14 11:22:002026-02-120
SOLUSDT411062026-01-14 11:22:002026-02-120
XRPUSDT411062026-01-14 11:22:002026-02-120
\n", + "
" + ], + "text/plain": [ + " num_kbar start_time end_time num_nan\n", + "symbol \n", + "ADAUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "AVAXUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "BNBUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "BTCUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "CAKEUSDT 41097 2026-01-14 11:22:00 2026-02-12 0\n", + "DOGEUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "DOTUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "ETHUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "SOLUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", + "XRPUSDT 41106 2026-01-14 11:22:00 2026-02-12 0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Summary info per symbol\n", + "agg.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "shape: (10, 13)
symbolstart_timeend_timeopenhighlowclosevolumevwapnum_buyernum_sellernum_buyer_volumenum_seller_volume
stri64i64f64f64f64f64f64f64decimal[38,0]decimal[38,0]f64f64
"ADAUSDT"176838972000017683897800000.41630.41630.4160.4162422468.00.4160893450247651.0174817.0
"AVAXUSDT"1768389720000176838978000014.56714.56714.55414.5578092.014.55769420344859.03233.0
"BNBUSDT"17683897200001768389780000931.8931.8931.28931.56231.68931.5168789298141.7489.94
"BTCUSDT"1768389720000176838978000094951.594951.694892.694910.053.57894925.47540630556325.43828.14
"CAKEUSDT"176838972000017683897800002.03912.03912.03792.03814596.02.03859130283832.0764.0
"DOGEUSDT"176838972000017683897800000.146850.146850.146730.146792.758362e60.14678262431.479317e61.279045e6
"DOTUSDT"176838972000017683897800002.2572.2572.2522.25561027.12.255454262120337.840689.3
"ETHUSDT"176838972000017683897800003295.73296.03294.423295.612396.2643295.5500975285471155.6971240.567
"SOLUSDT"17683897200001768389780000144.03144.03143.82143.8828761.51143.91300614615517777.7410983.77
"XRPUSDT"176838972000017683897800002.12782.12792.12532.1261371037.52.12683196110193592.5177445.0
" + ], + "text/plain": [ + "shape: (10, 13)\n", + "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", + "\u2502 symbol \u2506 start_time \u2506 end_time \u2506 open \u2506 \u2026 \u2506 num_buyer \u2506 num_selle \u2506 num_buyer \u2506 num_selle \u2502\n", + "\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2506 \u2506 --- \u2506 r \u2506 _volume \u2506 r_volume \u2502\n", + "\u2502 str \u2506 i64 \u2506 i64 \u2506 f64 \u2506 \u2506 decimal[3 \u2506 --- \u2506 --- \u2506 --- \u2502\n", + "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 8,0] \u2506 decimal[3 \u2506 f64 \u2506 f64 \u2502\n", + "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 8,0] \u2506 \u2506 \u2502\n", + "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", + "\u2502 ADAUSDT \u2506 1768389720 \u2506 1768389780 \u2506 0.4163 \u2506 \u2026 \u2506 34 \u2506 50 \u2506 247651.0 \u2506 174817.0 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 AVAXUSDT \u2506 1768389720 \u2506 1768389780 \u2506 14.567 \u2506 \u2026 \u2506 20 \u2506 34 \u2506 4859.0 \u2506 3233.0 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 BNBUSDT \u2506 1768389720 \u2506 1768389780 \u2506 931.8 \u2506 \u2026 \u2506 92 \u2506 98 \u2506 141.74 \u2506 89.94 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 BTCUSDT \u2506 1768389720 \u2506 1768389780 \u2506 94951.5 \u2506 \u2026 \u2506 305 \u2506 563 \u2506 25.438 \u2506 28.14 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 CAKEUSDT \u2506 1768389720 \u2506 1768389780 \u2506 2.0391 \u2506 \u2026 \u2506 30 \u2506 28 \u2506 3832.0 \u2506 764.0 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 DOGEUSDT \u2506 1768389720 \u2506 1768389780 \u2506 0.14685 \u2506 \u2026 \u2506 62 \u2506 43 \u2506 1.479317e \u2506 1.279045e \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 6 \u2506 6 \u2502\n", + "\u2502 DOTUSDT \u2506 1768389720 \u2506 1768389780 \u2506 2.257 \u2506 \u2026 \u2506 26 \u2506 21 \u2506 20337.8 \u2506 40689.3 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 ETHUSDT \u2506 1768389720 \u2506 1768389780 \u2506 3295.7 \u2506 \u2026 \u2506 528 \u2506 547 \u2506 1155.697 \u2506 1240.567 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 SOLUSDT \u2506 1768389720 \u2506 1768389780 \u2506 144.03 \u2506 \u2026 \u2506 146 \u2506 155 \u2506 17777.74 \u2506 10983.77 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2502 XRPUSDT \u2506 1768389720 \u2506 1768389780 \u2506 2.1278 \u2506 \u2026 \u2506 96 \u2506 110 \u2506 193592.5 \u2506 177445.0 \u2502\n", + "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", + "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# View raw data as Polars DataFrame\n", + "agg.to_polars().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sliced AggBar: ['BTCUSDT', 'ETHUSDT'], 82,212 bars\n" + ] + } + ], + "source": [ + "# Slice by symbols \u2014 creates a new AggBar\n", + "btc_eth = agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", + "print(f\"Sliced AggBar: {btc_eth.symbols}, {len(btc_eth):,} bars\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Building Momentum Factors\n", + "\n", + "### 3.1 Code-Based Factor Construction\n", + "\n", + "Extract a column from `AggBar` with `agg[\"close\"]` to get a `Factor` object, then chain time-series and cross-sectional operators." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Factor shapes:\n", + " momentum_60: 411,607 rows\n", + " vol_adj_momentum: 414,943 rows\n", + " momentum_rank: 411,607 rows\n" + ] + } + ], + "source": [ + "close = agg[\"close\"]\n", + "volume = agg[\"volume\"]\n", + "\n", + "# Simple momentum: percentage change over 60 periods\n", + "momentum_60 = close.ts_delta(60) / close.ts_shift(60)\n", + "momentum_60.name = \"momentum_60\"\n", + "\n", + "# Short-term momentum (20 periods)\n", + "momentum_20 = close.ts_delta(20) / close.ts_shift(20)\n", + "momentum_20.name = \"momentum_20\"\n", + "\n", + "# Volatility-adjusted momentum\n", + "volatility = (close.ts_delta(1) / close.ts_shift(1)).ts_std(60)\n", + "vol_adj_momentum = momentum_60 / volatility\n", + "vol_adj_momentum.name = \"vol_adj_momentum\"\n", + "\n", + "# Cross-sectional rank (normalized to [0, 1])\n", + "momentum_rank = momentum_60.cs_rank()\n", + "momentum_rank.name = \"momentum_rank\"\n", + "\n", + "print(\"Factor shapes:\")\n", + "print(f\" momentum_60: {len(momentum_60):,} rows\")\n", + "print(f\" vol_adj_momentum: {len(vol_adj_momentum):,} rows\")\n", + "print(f\" momentum_rank: {len(momentum_rank):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Expression-Based Factor Construction\n", + "\n", + "You can also define factors using string expressions via `ResearchSession.create_factor()`. This is useful for rapid experimentation." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Expression-based momentum: 411,607 rows\n", + "MA crossover signal: 411,607 rows\n" + ] + } + ], + "source": [ + "session = ResearchSession(agg, default_frequency=\"1min\")\n", + "\n", + "# Same momentum factor, defined via expression\n", + "mom_expr = session.create_factor(\n", + " \"ts_delta(close, 60) / ts_shift(close, 60)\",\n", + " name=\"momentum_60_expr\",\n", + ")\n", + "\n", + "# A more complex expression: MA crossover signal\n", + "ma_cross = session.create_factor(\n", + " \"ts_mean(close, 10) - ts_mean(close, 30)\",\n", + " name=\"ma_crossover\",\n", + ")\n", + "\n", + "print(f\"Expression-based momentum: {len(mom_expr):,} rows\")\n", + "print(f\"MA crossover signal: {len(ma_cross):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Visualizing Factors\n", + "\n", + "Use the `.plot` accessor on Factor objects to produce time series, heatmaps, and distributions." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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60-0.0415490.384516-0.108055-21.868208
\n", + "
" + ], + "text/plain": [ + " mean_ic ic_std ic_ir t-stat\n", + "horizon \n", + "1 -0.026685 0.376183 -0.070935 -14.366278\n", + "5 -0.036314 0.383139 -0.094780 -19.194464\n", + "10 -0.040783 0.385369 -0.105829 -21.430799\n", + "20 -0.048677 0.386069 -0.126085 -25.529622\n", + "60 -0.041549 0.384516 -0.108055 -21.868208" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute IC for multiple horizons\n", + "horizons = [1, 5, 10, 20, 60]\n", + "ic_results = {}\n", + "\n", + "for h in horizons:\n", + " result = session.analyze(signal, periods=h)\n", + " ic_results[h] = result.ic_summary.get(h, {})\n", + "\n", + "# Display IC decay table\n", + "ic_decay_df = pd.DataFrame(ic_results).T\n", + "ic_decay_df.index.name = \"horizon\"\n", + "print(\"IC Decay Analysis:\")\n", + "ic_decay_df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot IC decay curve\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(ic_decay_df.index, ic_decay_df[\"mean_ic\"], \"o-\", linewidth=2, markersize=8)\n", + "ax.axhline(y=0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "ax.set_xlabel(\"Forward Horizon (periods)\")\n", + "ax.set_ylabel(\"Mean IC\")\n", + "ax.set_title(\"IC Decay Curve \u2014 Momentum Factor\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Quantile Analysis\n", + "\n", + "Split the cross-section into quantiles by factor value and examine the mean returns of each group. A monotonic pattern (Q1 < Q2 < ... < Q5) indicates strong linear predictive power." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use FactorAnalyzer directly for more control\n", + "analyzer = FactorAnalyzer(signal, agg, quantiles=5)\n", + "\n", + "# Prepare data: align factor values with forward returns\n", + "analyzer.prepare_data(price_col=\"close\", periods=[1])\n", + "\n", + "# Quantile returns\n", + "analyzer.plot_quantile_returns(quantiles=5, period=1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cumulative returns by quantile (includes Long-Short portfolio)\n", + "analyzer.plot_cumulative_returns(quantiles=5, period=1, long_short=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Data not prepared. Call prepare_data() first.", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# IC time series plot\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43manalyzer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_ic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrank\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mts\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m plt.show()\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/raid1/novis/factorium/src/factorium/factors/analyzer.py:544\u001b[39m, in \u001b[36mFactorAnalyzer.plot_ic\u001b[39m\u001b[34m(self, period, method, plot_type)\u001b[39m\n\u001b[32m 534\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 535\u001b[39m \u001b[33;03mPlot Information Coefficient (IC).\u001b[39;00m\n\u001b[32m 536\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 540\u001b[39m \u001b[33;03m plot_type: 'ts' for time series, 'hist' for histogram.\u001b[39;00m\n\u001b[32m 541\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 542\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mplotting_analyzer\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FactorAnalyzerPlotter\n\u001b[32m--> \u001b[39m\u001b[32m544\u001b[39m ic = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcalculate_ic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 545\u001b[39m col = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mperiod_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mperiod\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 546\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m col \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ic.columns:\n", + "\u001b[36mFile \u001b[39m\u001b[32m/mnt/raid1/novis/factorium/src/factorium/factors/analyzer.py:385\u001b[39m, in \u001b[36mFactorAnalyzer.calculate_ic\u001b[39m\u001b[34m(self, method)\u001b[39m\n\u001b[32m 375\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 376\u001b[39m \u001b[33;03mCalculate Information Coefficient (IC) for each period.\u001b[39;00m\n\u001b[32m 377\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 382\u001b[39m \u001b[33;03m pd.DataFrame: IC values indexed by start_time.\u001b[39;00m\n\u001b[32m 383\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 384\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m_clean_data\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m385\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mData not prepared. Call prepare_data() first.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 387\u001b[39m period_cols = [c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._clean_data.columns \u001b[38;5;28;01mif\u001b[39;00m c.startswith(\u001b[33m\"\u001b[39m\u001b[33mperiod_\u001b[39m\u001b[33m\"\u001b[39m)]\n\u001b[32m 388\u001b[39m corr_method = \u001b[33m\"\u001b[39m\u001b[33mspearman\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m method == \u001b[33m\"\u001b[39m\u001b[33mrank\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mpearson\u001b[39m\u001b[33m\"\u001b[39m\n", + "\u001b[31mValueError\u001b[39m: Data not prepared. Call prepare_data() first." + ] + } + ], + "source": [ + "# IC time series plot\n", + "analyzer.plot_ic(period=1, method=\"rank\", plot_type=\"ts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Backtesting\n", + "\n", + "Run a **market-neutral vectorized backtest** using the momentum signal. The backtester:\n", + "- Converts signals to portfolio weights (cross-sectional normalization)\n", + "- Handles transaction costs\n", + "- Tracks equity, returns, and positions over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Market-neutral backtest\n", + "result = session.backtest(\n", + " signal,\n", + " neutralization=\"market\",\n", + " transaction_cost=0.0003,\n", + ")\n", + "\n", + "# Key performance metrics\n", + "print(\"Backtest Metrics:\")\n", + "for key, val in result.metrics.items():\n", + " if isinstance(val, float):\n", + " print(f\" {key:25s}: {val:>10.4f}\")\n", + " else:\n", + " print(f\" {key:25s}: {val}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot equity curve\n", + "equity = result.equity_curve.to_pandas()\n", + "equity[\"timestamp\"] = pd.to_datetime(equity[\"start_time\"], unit=\"ms\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "ax.plot(equity[\"timestamp\"], equity[\"equity\"], linewidth=1.5)\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Portfolio Equity\")\n", + "ax.set_title(\"Momentum Factor \u2014 Equity Curve (Market Neutral)\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot returns distribution\n", + "returns = result.returns.to_pandas()\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.hist(returns[\"portfolio_return\"].dropna(), bins=100, alpha=0.75, edgecolor=\"black\", linewidth=0.5)\n", + "ax.axvline(x=0, color=\"red\", linestyle=\"--\", alpha=0.7)\n", + "ax.set_xlabel(\"Return\")\n", + "ax.set_ylabel(\"Frequency\")\n", + "ax.set_title(\"Distribution of Portfolio Returns\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Quick Report\n", + "\n", + "`ResearchSession.quick_report()` combines IC analysis and backtesting into a single text summary \u2014 great for rapid iteration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report = session.quick_report(signal)\n", + "print(report)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Summary & Next Steps\n", + "\n", + "In this notebook we:\n", + "- Loaded multi-symbol 1-minute data from Binance using `BinanceDataLoader`\n", + "- Built momentum factors via both **code-based** and **expression-based** methods\n", + "- Visualized factor behavior with time series, distributions, and heatmaps\n", + "- Computed IC and analyzed its decay across multiple horizons\n", + "- Ran a market-neutral backtest and examined performance metrics\n", + "\n", + "**Next notebooks to explore:**\n", + "- `02_mean_reversion_factor.ipynb` \u2014 Mean reversion with volatility normalization\n", + "- `03_data_loading_and_exploration.ipynb` \u2014 Deep dive into AggBar and data loading\n", + "- `04_multi_factor_combination.ipynb` \u2014 Combine multiple factors and compare performance" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/src/factorium/factors/analyzer.py b/src/factorium/factors/analyzer.py index 549c7e7..7c6c152 100644 --- a/src/factorium/factors/analyzer.py +++ b/src/factorium/factors/analyzer.py @@ -240,6 +240,12 @@ def __init__(self, factor: Factor, prices: AggBar | Factor, quantiles: int = 5): else: self.prices = prices + def _ensure_data_prepared(self, periods: list[int] | None = None, price_col: str | None = None) -> None: + """Ensure data is prepared. Auto-calls prepare_data() if needed.""" + if not hasattr(self, "_clean_data"): + logger.info("Data not prepared. Auto-calling prepare_data()...") + self.prepare_data(periods=periods, price_col=price_col) + def analyze(self, price_col: str = "close", periods: int | list[int] = 1) -> FactorAnalysisResult: """ Run full factor analysis. @@ -381,8 +387,7 @@ def calculate_ic(self, method: str = "rank") -> pd.DataFrame: Returns: pd.DataFrame: IC values indexed by start_time. """ - if not hasattr(self, "_clean_data"): - raise ValueError("Data not prepared. Call prepare_data() first.") + self._ensure_data_prepared() period_cols = [c for c in self._clean_data.columns if c.startswith("period_")] corr_method = "spearman" if method == "rank" else "pearson" @@ -438,8 +443,7 @@ def calculate_turnover(self) -> pd.Series: Returns: pd.Series: Turnover time series indexed by start_time """ - if not hasattr(self, "_clean_data"): - raise ValueError("Data not prepared. Call prepare_data() first.") + self._ensure_data_prepared() # Ensure data is sorted by symbol and time for correct shift operation sorted_data = self._clean_data.sort(["symbol", "start_time"]) @@ -474,8 +478,7 @@ def calculate_quantile_returns(self, quantiles: int = 5, period: int = 1) -> pd. Returns: pd.DataFrame: Mean returns and counts per (start_time, quantile). """ - if not hasattr(self, "_clean_data"): - raise ValueError("Data not prepared. Call prepare_data() first.") + self._ensure_data_prepared(periods=[period]) col = f"period_{period}" if col not in self._clean_data.columns: From 2fb6c6d7c63b034590148a3c7923b271f085d5f1 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Fri, 13 Feb 2026 11:16:25 +0800 Subject: [PATCH 03/25] fix: correct notebook column references and API usage MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Fix equity_curve column names: start_time → end_time, equity → total_value - Fix returns column name: portfolio_return → return - Fix AggBarMetadata usage: meta.num_symbols → len(meta.symbols) - Fix Factor.plot API: .plot.plot_timeseries() → .plot() - Add jupyterlab to dev dependencies --- examples/01_momentum_factor_research.ipynb | 2101 ++++------------- examples/02_mean_reversion_factor.ipynb | 14 +- .../03_data_loading_and_exploration.ipynb | 8 +- examples/04_multi_factor_combination.ipynb | 10 +- pyproject.toml | 1 + uv.lock | 1205 +++++++++- 6 files changed, 1692 insertions(+), 1647 deletions(-) diff --git a/examples/01_momentum_factor_research.ipynb b/examples/01_momentum_factor_research.ipynb index 8a3a633..d07d074 100644 --- a/examples/01_momentum_factor_research.ipynb +++ b/examples/01_momentum_factor_research.ipynb @@ -1,1633 +1,474 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Momentum Factor Research\n", - "\n", - "This notebook walks through a **complete factor research workflow** using Factorium:\n", - "\n", - "1. Load data from Binance\n", - "2. Explore the AggBar data structure\n", - "3. Build momentum factors (code-based & expression-based)\n", - "4. Visualize factor behavior\n", - "5. Run IC (Information Coefficient) analysis\n", - "6. Analyze quantile returns\n", - "7. Run a vectorized backtest\n", - "8. Generate a quick report\n", - "\n", - "**Prerequisites**: `pip install factorium` (or `uv add factorium`)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Setup & Data Loading\n", - "\n", - "We'll download 30 days of 1-minute futures data for 10 crypto symbols from Binance Vision." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from factorium import BinanceDataLoader, ResearchSession\n", - "from factorium.factors import FactorAnalyzer, CompositeFactor\n", - "\n", - "import matplotlib.pyplot as plt\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "%matplotlib inline\n", - "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", - "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", - "plt.rcParams[\"figure.dpi\"] = 100" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-12 19:22:07,485 - INFO - Downloading 10 date ranges for 10 symbols...\n", - "2026-02-12 19:22:07,487 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,498 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,500 - INFO - Processing data for 2026-01-14\n", - "2026-02-12 19:22:07,502 - INFO - Processing data for 2026-01-15\n", - "2026-02-12 19:22:07,504 - 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"2026-02-12 19:22:07,551 - INFO - Processing data for 2026-01-15\n", - "2026-02-12 19:22:07,552 - INFO - Processing data for 2026-01-16\n", - "2026-02-12 19:22:07,554 - INFO - Processing data for 2026-01-17\n", - "2026-02-12 19:22:07,555 - INFO - Processing data for 2026-01-18\n", - "2026-02-12 19:22:07,557 - INFO - Processing data for 2026-01-14\n", - "2026-02-12 19:22:07,559 - INFO - Processing data for 2026-01-15\n", - "2026-02-12 19:22:07,560 - INFO - Processing data for 2026-01-16\n", - "2026-02-12 19:22:07,562 - INFO - Processing data for 2026-01-17\n", - "2026-02-12 19:22:07,564 - INFO - Processing data for 2026-01-18\n", - "2026-02-12 19:22:07,565 - INFO - Processing data for 2026-01-14\n", - "2026-02-12 19:22:07,567 - INFO - Processing data for 2026-01-15\n", - "2026-02-12 19:22:07,568 - INFO - Processing data for 2026-01-16\n", - "2026-02-12 19:22:07,570 - INFO - Processing data for 2026-01-17\n", - "2026-02-12 19:22:07,571 - INFO - Processing data for 2026-01-18\n", - "2026-02-12 19:22:07,578 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.task_wakeup()]>\n", - "2026-02-12 19:22:07,580 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() running at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:60> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/futures.py:119: RuntimeWarning: coroutine 'Kernel.shell_main' was never awaited\n", - " def get_loop(self):\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "2026-02-12 19:22:07,584 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() running at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:60> wait_for= cb=[Task.task_wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:07,585 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:07,586 - INFO - Processing data for 2026-01-14\n", - "2026-02-12 19:22:07,587 - INFO - Processing data for 2026-01-15\n", - "2026-02-12 19:22:07,589 - INFO - Processing data for 2026-01-16\n", - "2026-02-12 19:22:07,590 - INFO - Processing data for 2026-01-17\n", - "2026-02-12 19:22:07,591 - INFO - Processing data for 2026-01-18\n", - "2026-02-12 19:22:07,592 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,594 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,596 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,600 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/typing.py:426: RuntimeWarning: coroutine 'Kernel.shell_main' was never awaited\n", - " @functools.wraps(func)\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "2026-02-12 19:22:07,604 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:07,606 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,613 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,616 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,617 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,619 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,621 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,622 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,629 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,637 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,651 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,691 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,721 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:07,732 - ERROR - Exception in callback Task.__step()\n", - "handle: \n", - "Traceback (most recent call last):\n", - " File \"/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/asyncio/events.py\", line 89, in _run\n", - " self._context.run(self._callback, *self._args)\n", - " ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", - "RuntimeError: cannot enter context: <_contextvars.Context object at 0x727d2837eac0> is already entered\n", - "2026-02-12 19:22:08,793 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:08,798 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:09,010 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:09,014 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:09,332 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:09,337 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:09,456 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:09,460 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:09,587 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:09,592 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:09,684 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:09,687 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:09,789 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:09,792 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:09,920 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:09,925 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:10,030 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:10,034 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:10,461 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:10,467 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:10,788 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:10,804 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:11,041 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:11,050 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:11,081 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "/home/novis/.local/share/uv/python/cpython-3.13.2-linux-x86_64-gnu/lib/python3.13/pathlib/_local.py:277: RuntimeWarning: coroutine 'Kernel.shell_main' was never awaited\n", - " @property\n", - "RuntimeWarning: Enable tracemalloc to get the object allocation traceback\n", - "2026-02-12 19:22:11,087 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,088 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,089 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,090 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,091 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,092 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,093 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,094 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,095 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,096 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,097 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,098 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,099 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,102 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,103 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,104 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,106 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,107 - ERROR - Task was destroyed but it is pending!\n", - "task: .run_in_context() done, defined at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/ipykernel/utils.py:57> wait_for= cb=[Task.__wakeup()]> cb=[ZMQStream._run_callback.._log_error() at /mnt/raid1/novis/factorium/.venv/lib/python3.13/site-packages/zmq/eventloop/zmqstream.py:563]>\n", - "2026-02-12 19:22:11,108 - ERROR - Task was destroyed but it is pending!\n", - "task: cb=[Task.__wakeup()]>\n", - "2026-02-12 19:22:11,253 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:11,402 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:11,413 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:11,416 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:11,798 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:11,825 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:12,162 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:12,338 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:12,504 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:12,587 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:12,875 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:13,292 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:13,501 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:13,513 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:13,514 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:13,516 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:13,517 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:13,519 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:13,520 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:13,522 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:13,883 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:13,910 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:14,152 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:14,291 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:14,550 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:14,755 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:15,050 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:15,352 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:15,691 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:15,710 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:15,712 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:15,714 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:15,715 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:15,716 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:15,718 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:15,719 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:15,878 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:15,891 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:16,111 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:16,235 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:16,348 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:16,639 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:16,736 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:17,023 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:17,325 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:17,568 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:17,905 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:17,923 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:17,924 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:17,926 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:17,927 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:17,929 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:17,931 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:17,932 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:17,934 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:17,935 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:18,574 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:18,610 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:18,697 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:19,008 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:19,306 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:19,695 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:20,201 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:20,574 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:20,951 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:21,001 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:21,021 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:21,025 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:21,030 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:21,035 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:21,038 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:21,040 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:21,042 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:21,043 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:21,389 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:21,741 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:21,756 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:21,758 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:22,386 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-17.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=17\n", - "2026-02-12 19:22:22,413 - INFO - Processing data for 2026-01-19\n", - "2026-02-12 19:22:22,942 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:23,560 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:23,595 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:23,597 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:23,677 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:22:24,114 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:24,138 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:22:24,140 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:24,266 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:22:24,279 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:22:24,402 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:22:24,416 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:22:24,493 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:22:24,891 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:24,913 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:22:24,916 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:25,207 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:25,557 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:26,036 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:26,060 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:26,061 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:26,063 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:26,458 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:26,678 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:26,697 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:26,699 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:27,513 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-18.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=18\n", - "2026-02-12 19:22:27,541 - INFO - Processing data for 2026-01-20\n", - "2026-02-12 19:22:28,318 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:28,351 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:28,572 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:28,904 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:28,925 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:28,928 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:29,055 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:22:29,128 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:22:29,211 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:22:29,298 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:22:29,602 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:30,032 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:30,051 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:22:30,054 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:22:30,056 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:22:30,059 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:22:30,061 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:30,062 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:30,418 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:30,895 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:30,915 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:30,917 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:31,106 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:22:31,247 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:22:31,395 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:31,410 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:22:31,412 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:22:31,415 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:31,606 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:32,068 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:32,098 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:32,100 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:32,343 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:22:32,828 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:33,407 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:33,437 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:22:33,441 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:33,446 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:34,840 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:35,133 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:35,152 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:35,156 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:35,443 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:35,463 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:35,829 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:36,270 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:36,708 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:37,168 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:37,179 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:38,247 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-16.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=16\n", - "2026-02-12 19:22:38,283 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:38,285 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:38,287 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:38,289 - INFO - Processing data for 2026-01-21\n", - "2026-02-12 19:22:38,950 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:39,528 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:22:40,025 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:22:40,164 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:22:40,169 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:40,405 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:22:40,421 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:22:40,423 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:22:40,425 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:22:40,431 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:22:40,617 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:40,860 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:22:41,115 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:22:41,278 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:41,292 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:41,297 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:22:41,301 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:22:41,303 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:41,728 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:22:41,769 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:22:42,083 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:22:42,329 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:22:42,558 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:22:42,933 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:44,627 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:44,684 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:22:44,686 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:22:44,688 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:22:44,690 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:44,695 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:45,082 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:45,108 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:47,075 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:22:47,323 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:22:47,330 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:22:47,342 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:22:47,666 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:22:47,796 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:22:47,812 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:22:47,814 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:22:47,968 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:22:48,101 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:22:48,287 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:22:48,478 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:22:48,667 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:48,806 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:22:48,978 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:22:49,235 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:22:49,252 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:22:49,254 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:22:49,256 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:22:49,258 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:22:49,261 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:49,263 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:22:49,265 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:22:49,270 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:22:49,590 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:49,616 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:22:49,913 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:22:50,467 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:22:50,478 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:22:50,712 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:22:50,739 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:50,742 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:22:51,026 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:22:51,282 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:22:51,304 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:22:51,306 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:22:51,797 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:22:51,821 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:22:52,253 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:22:52,528 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:22:52,553 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:22:52,554 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:22:52,876 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:22:52,893 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:22:54,346 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:22:56,356 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-15.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=15\n", - "2026-02-12 19:22:56,417 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:22:56,918 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:22:57,052 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:22:57,056 - INFO - Processing data for 2026-01-22\n", - "2026-02-12 19:22:57,074 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:22:57,077 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:22:57,370 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:22:57,640 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:22:57,658 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:22:57,660 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:22:57,944 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:22:57,978 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:23:00,209 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-14.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=14\n", - "2026-02-12 19:23:00,621 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:23:00,640 - INFO - Processing data for 2026-01-23\n", - "2026-02-12 19:23:00,642 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:23:00,993 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:23:01,894 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:23:02,545 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:23:02,566 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:23:02,567 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:23:02,569 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:23:02,864 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:23:03,294 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:23:03,311 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:23:03,313 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:23:03,757 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:23:04,183 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:23:04,523 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:23:04,971 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:23:05,065 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:23:05,072 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:23:05,460 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:23:05,468 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:23:05,470 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:23:05,475 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:23:05,488 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:23:05,497 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:23:05,632 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:23:06,244 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:23:06,711 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:23:06,751 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:23:06,940 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:23:07,096 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:23:07,101 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:23:07,391 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:23:07,494 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:23:07,498 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:23:07,715 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:23:07,734 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:23:08,067 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:23:08,091 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:23:08,392 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:23:08,409 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:23:08,959 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:23:09,349 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:23:09,368 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:23:09,371 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:23:10,964 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-19.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=19\n", - "2026-02-12 19:23:11,431 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:23:11,705 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:23:11,711 - INFO - Processing data for 2026-01-24\n", - "2026-02-12 19:23:11,729 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:23:11,731 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:23:12,047 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:23:12,451 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:23:12,476 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:23:12,479 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:23:12,643 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:23:12,754 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:23:13,223 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:23:13,252 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:23:13,958 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:23:14,638 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:23:14,659 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:23:14,661 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:23:16,732 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:23:16,829 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:23:16,948 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:23:17,234 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:23:17,638 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:23:18,043 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:23:18,070 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:23:18,071 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:23:18,074 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:23:18,296 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:23:18,381 - INFO - \u2713 Successfully processed DOTUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOTUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:23:18,950 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:23:19,505 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:23:19,529 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:23:19,530 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:23:19,686 - INFO - \u2713 Successfully processed CAKEUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=CAKEUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:23:19,953 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:23:20,408 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:23:20,427 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:23:20,430 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:23:21,292 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:23:21,358 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:23:21,828 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:23:21,886 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:23:22,188 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:23:22,977 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:23:23,012 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:23:23,014 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:23:23,199 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:23:23,494 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:23:23,962 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:23:24,327 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:23:24,352 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:23:24,355 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:23:24,604 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:23:25,986 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:23:26,676 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:23:26,692 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:23:27,308 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:23:27,348 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:23:27,350 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:23:27,868 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:23:28,707 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:23:28,735 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:23:29,481 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:23:30,424 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:23:31,625 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:23:32,705 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:23:33,364 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:23:34,869 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:23:34,929 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:23:34,932 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:23:34,934 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:23:34,937 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:23:34,939 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:23:34,942 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:23:35,444 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-24.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=24\n", - "2026-02-12 19:23:35,464 - INFO - Processing data for 2026-01-25\n", - "2026-02-12 19:23:35,905 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:23:38,265 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-20.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=20\n", - "2026-02-12 19:23:38,455 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:23:38,469 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:23:38,470 - INFO - Processing data for 2026-01-26\n", - "2026-02-12 19:23:39,243 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:23:39,270 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:23:39,617 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:23:40,314 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:23:40,811 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:23:40,957 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:23:41,764 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:23:41,793 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:23:41,795 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:23:41,796 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:23:42,023 - INFO - \u2713 Successfully processed ADAUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ADAUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:23:42,845 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:23:43,236 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:23:43,421 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:23:43,438 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:23:43,440 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:23:46,196 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:23:46,261 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:23:46,791 - INFO - \u2713 Successfully processed AVAXUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=AVAXUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:23:48,225 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:23:48,950 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:23:48,980 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:23:49,919 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:23:49,952 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:23:50,435 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:23:53,574 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-21.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=21\n", - "2026-02-12 19:23:55,460 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-22.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=22\n", - "2026-02-12 19:23:55,549 - INFO - Processing data for 2026-01-27\n", - "2026-02-12 19:23:55,551 - INFO - Processing data for 2026-01-28\n", - "2026-02-12 19:23:55,986 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:23:56,637 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:23:57,967 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:23:58,012 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:23:59,919 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-23.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=23\n", - "2026-02-12 19:23:59,965 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:23:59,981 - INFO - Processing data for 2026-01-29\n", - "2026-02-12 19:24:00,604 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:24:00,627 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:24:01,077 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:24:01,527 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:24:01,543 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:24:02,515 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:24:02,547 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:24:04,128 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:24:04,691 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:24:04,705 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:24:05,180 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-08.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=08\n", - "2026-02-12 19:24:05,208 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:24:05,213 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:24:06,839 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:24:07,562 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-07.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=07\n", - "2026-02-12 19:24:07,926 - INFO - \u2713 Successfully processed DOGEUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=DOGEUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:24:07,935 - INFO - Processing data for 2026-02-10\n", - "2026-02-12 19:24:08,659 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:24:08,685 - INFO - Processing data for 2026-02-11\n", - "2026-02-12 19:24:10,037 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:24:10,077 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:24:11,309 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:24:12,838 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:24:13,481 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:24:15,150 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-05.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=05\n", - "2026-02-12 19:24:16,930 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:24:17,630 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:24:18,697 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=06\n", - "2026-02-12 19:24:19,195 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:24:20,992 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-25.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=25\n", - "2026-02-12 19:24:21,033 - INFO - Processing data for 2026-01-30\n", - "2026-02-12 19:24:21,509 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:24:23,076 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:24:23,115 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:24:23,868 - INFO - \u2713 Successfully processed BNBUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BNBUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:24:24,384 - INFO - \u2713 Successfully processed XRPUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=XRPUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:24:24,898 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:24:25,359 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-10.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=10\n", - "2026-02-12 19:24:26,229 - INFO - \u2713 Successfully processed SOLUSDT-aggTrades-2026-02-11.zip -> Data/market=futures_um/data_type=aggTrades/symbol=SOLUSDT/year=2026/month=02/day=11\n", - "2026-02-12 19:24:28,515 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-26.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=26\n", - "2026-02-12 19:24:28,589 - INFO - Processing data for 2026-01-31\n", - "2026-02-12 19:24:30,315 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-28.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=28\n", - "2026-02-12 19:24:30,356 - INFO - Processing data for 2026-02-01\n", - "2026-02-12 19:24:32,527 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:24:32,582 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:24:34,467 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-27.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=27\n", - "2026-02-12 19:24:34,530 - INFO - Processing data for 2026-02-02\n", - "2026-02-12 19:24:37,668 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-29.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=29\n", - "2026-02-12 19:24:37,736 - INFO - Processing data for 2026-02-03\n", - "2026-02-12 19:24:41,734 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:24:41,805 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:24:45,114 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:24:45,189 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:24:49,059 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:24:49,164 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:24:57,912 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-30.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=30\n", - "2026-02-12 19:24:58,029 - INFO - Processing data for 2026-02-04\n", - "2026-02-12 19:25:04,390 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-01.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=01\n", - "2026-02-12 19:25:04,518 - INFO - Processing data for 2026-02-05\n", - "2026-02-12 19:25:08,908 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-04.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=04\n", - "2026-02-12 19:25:09,014 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:25:13,862 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:25:13,975 - INFO - Processing data for 2026-02-08\n", - "2026-02-12 19:25:19,822 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-02.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=02\n", - "2026-02-12 19:25:19,950 - INFO - Processing data for 2026-02-09\n", - "2026-02-12 19:25:25,728 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-01-31.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=01/day=31\n", - "2026-02-12 19:25:25,887 - INFO - Processing data for 2026-02-06\n", - "2026-02-12 19:25:33,790 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-03.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=03\n", - "2026-02-12 19:25:33,904 - INFO - Processing data for 2026-02-07\n", - "2026-02-12 19:25:43,716 - INFO - \u2713 Successfully processed BTCUSDT-aggTrades-2026-02-06.zip -> Data/market=futures_um/data_type=aggTrades/symbol=BTCUSDT/year=2026/month=02/day=06\n", - 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"2026-02-12 19:27:10,158 - INFO - \u2713 Successfully processed ETHUSDT-aggTrades-2026-02-09.zip -> Data/market=futures_um/data_type=aggTrades/symbol=ETHUSDT/year=2026/month=02/day=09\n", - "2026-02-12 19:27:59,836 - INFO - Loaded 411051 bars for 10 symbols\n" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Momentum Factor Research\n", + "\n", + "This notebook walks through a **complete factor research workflow** using Factorium:\n", + "\n", + "1. Load data from Binance\n", + "2. Explore the AggBar data structure\n", + "3. Build momentum factors (code-based & expression-based)\n", + "4. Visualize factor behavior\n", + "5. Run IC (Information Coefficient) analysis\n", + "6. Analyze quantile returns\n", + "7. Run a vectorized backtest\n", + "8. Generate a quick report\n", + "\n", + "**Prerequisites**: `pip install factorium` (or `uv add factorium`)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Setup & Data Loading\n", + "\n", + "We'll download 30 days of 1-minute futures data for 10 crypto symbols from Binance Vision." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from factorium import BinanceDataLoader, ResearchSession\n", + "from factorium.factors import FactorAnalyzer, CompositeFactor\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "%matplotlib inline\n", + "plt.style.use(\"seaborn-v0_8-whitegrid\")\n", + "plt.rcParams[\"figure.figsize\"] = (14, 6)\n", + "plt.rcParams[\"figure.dpi\"] = 100" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SYMBOLS = [\n", + " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", + " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", + "]\n", + "\n", + "loader = BinanceDataLoader()\n", + "\n", + "agg = loader.load_aggbar(\n", + " symbols=SYMBOLS,\n", + " data_type=\"aggTrades\",\n", + " market_type=\"futures\",\n", + " futures_type=\"um\",\n", + " days=30,\n", + " bar_type=\"time\",\n", + " interval=60_000, # 1-minute bars\n", + ")\n", + "\n", + "print(f\"Loaded {len(agg):,} bars\")\n", + "print(f\"Symbols: {agg.symbols}\")\n", + "print(f\"Columns: {agg.cols}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Exploring the AggBar\n", + "\n", + "`AggBar` is a **multi-symbol OHLCV container** stored in long format. You can inspect it, convert to Polars/Pandas, and slice by time or symbols." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Summary info per symbol\n", + "agg.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View raw data as Polars DataFrame\n", + "agg.to_polars().head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Slice by symbols — creates a new AggBar\n", + "btc_eth = agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", + "print(f\"Sliced AggBar: {btc_eth.symbols}, {len(btc_eth):,} bars\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Building Momentum Factors\n", + "\n", + "### 3.1 Code-Based Factor Construction\n", + "\n", + "Extract a column from `AggBar` with `agg[\"close\"]` to get a `Factor` object, then chain time-series and cross-sectional operators." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "close = agg[\"close\"]\n", + "volume = agg[\"volume\"]\n", + "\n", + "# Simple momentum: percentage change over 60 periods\n", + "momentum_60 = close.ts_delta(60) / close.ts_shift(60)\n", + "momentum_60.name = \"momentum_60\"\n", + "\n", + "# Short-term momentum (20 periods)\n", + "momentum_20 = close.ts_delta(20) / close.ts_shift(20)\n", + "momentum_20.name = \"momentum_20\"\n", + "\n", + "# Volatility-adjusted momentum\n", + "volatility = (close.ts_delta(1) / close.ts_shift(1)).ts_std(60)\n", + "vol_adj_momentum = momentum_60 / volatility\n", + "vol_adj_momentum.name = \"vol_adj_momentum\"\n", + "\n", + "# Cross-sectional rank (normalized to [0, 1])\n", + "momentum_rank = momentum_60.cs_rank()\n", + "momentum_rank.name = \"momentum_rank\"\n", + "\n", + "print(\"Factor shapes:\")\n", + "print(f\" momentum_60: {len(momentum_60):,} rows\")\n", + "print(f\" vol_adj_momentum: {len(vol_adj_momentum):,} rows\")\n", + "print(f\" momentum_rank: {len(momentum_rank):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2 Expression-Based Factor Construction\n", + "\n", + "You can also define factors using string expressions via `ResearchSession.create_factor()`. This is useful for rapid experimentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "session = ResearchSession(agg, default_frequency=\"1m\")\n", + "\n", + "# Same momentum factor, defined via expression\n", + "mom_expr = session.create_factor(\n", + " \"ts_delta(close, 60) / ts_shift(close, 60)\",\n", + " name=\"momentum_60_expr\",\n", + ")\n", + "\n", + "# A more complex expression: MA crossover signal\n", + "ma_cross = session.create_factor(\n", + " \"ts_mean(close, 10) - ts_mean(close, 30)\",\n", + " name=\"ma_crossover\",\n", + ")\n", + "\n", + "print(f\"Expression-based momentum: {len(mom_expr):,} rows\")\n", + "print(f\"MA crossover signal: {len(ma_cross):,} rows\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Visualizing Factors\n", + "\n", + "Use the `.plot` accessor on Factor objects to produce time series, heatmaps, and distributions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Time series of momentum for a few symbols\n", + "momentum_60.plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. IC (Information Coefficient) Analysis\n", + "\n", + "The **IC** measures the rank correlation between factor values and subsequent returns. A consistently positive (or negative) IC indicates predictive power.\n", + "\n", + "### 5.1 Single-Period IC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use ResearchSession for streamlined analysis\n", + "signal = momentum_rank # Use ranked momentum as our signal\n", + "\n", + "analysis = session.analyze(signal, periods=1)\n", + "\n", + "print(\"IC Summary:\")\n", + "print(analysis.ic_summary)\n", + "print()\n", + "print(f\"IC Series shape: {analysis.ic_series.shape}\")\n", + "print(analysis.ic_series.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 5.2 Multi-Horizon IC Analysis\n", + "\n", + "To understand how quickly a factor's signal decays, we compute IC across multiple forward horizons." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute IC for multiple horizons\n", + "horizons = [1, 5, 10, 20, 60]\n", + "ic_results = {}\n", + "\n", + "for h in horizons:\n", + " result = session.analyze(signal, periods=h)\n", + " ic_results[h] = result.ic_summary.get(h, {})\n", + "\n", + "# Display IC decay table\n", + "ic_decay_df = pd.DataFrame(ic_results).T\n", + "ic_decay_df.index.name = \"horizon\"\n", + "print(\"IC Decay Analysis:\")\n", + "ic_decay_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot IC decay curve\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.plot(ic_decay_df.index, ic_decay_df[\"mean_ic\"], \"o-\", linewidth=2, markersize=8)\n", + "ax.axhline(y=0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + "ax.set_xlabel(\"Forward Horizon (periods)\")\n", + "ax.set_ylabel(\"Mean IC\")\n", + "ax.set_title(\"IC Decay Curve — Momentum Factor\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Quantile Analysis\n", + "\n", + "Split the cross-section into quantiles by factor value and examine the mean returns of each group. A monotonic pattern (Q1 < Q2 < ... < Q5) indicates strong linear predictive power." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Use FactorAnalyzer directly for more control\n", + "analyzer = FactorAnalyzer(signal, agg, quantiles=5)\n", + "\n", + "# Prepare data: align factor values with forward returns\n", + "analyzer.prepare_data(price_col=\"close\", periods=[1])\n", + "\n", + "# Quantile returns\n", + "analyzer.plot_quantile_returns(quantiles=5, period=1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Cumulative returns by quantile (includes Long-Short portfolio)\n", + "analyzer.plot_cumulative_returns(quantiles=5, period=1, long_short=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# IC time series plot\n", + "analyzer.plot_ic(period=1, method=\"rank\", plot_type=\"ts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Backtesting\n", + "\n", + "Run a **market-neutral vectorized backtest** using the momentum signal. The backtester:\n", + "- Converts signals to portfolio weights (cross-sectional normalization)\n", + "- Handles transaction costs\n", + "- Tracks equity, returns, and positions over time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Market-neutral backtest\n", + "result = session.backtest(\n", + " signal,\n", + " neutralization=\"market\",\n", + " transaction_cost=0.0003,\n", + ")\n", + "\n", + "# Key performance metrics\n", + "print(\"Backtest Metrics:\")\n", + "for key, val in result.metrics.items():\n", + " if isinstance(val, float):\n", + " print(f\" {key:25s}: {val:>10.4f}\")\n", + " else:\n", + " print(f\" {key:25s}: {val}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot equity curve\n", + "equity = result.equity_curve.to_pandas()\n", + "equity[\"timestamp\"] = pd.to_datetime(equity[\"end_time\"], unit=\"ms\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(14, 6))\n", + "ax.plot(equity[\"timestamp\"], equity[\"total_value\"], linewidth=1.5)\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Portfolio Equity\")\n", + "ax.set_title(\"Momentum Factor — Equity Curve (Market Neutral)\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot returns distribution\n", + "returns = result.returns.to_pandas()\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "ax.hist(returns[\"return\"].dropna(), bins=100, alpha=0.75, edgecolor=\"black\", linewidth=0.5)\n", + "ax.axvline(x=0, color=\"red\", linestyle=\"--\", alpha=0.7)\n", + "ax.set_xlabel(\"Return\")\n", + "ax.set_ylabel(\"Frequency\")\n", + "ax.set_title(\"Distribution of Portfolio Returns\")\n", + "ax.grid(True, alpha=0.3)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 8. Quick Report\n", + "\n", + "`ResearchSession.quick_report()` combines IC analysis and backtesting into a single text summary — great for rapid iteration." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "report = session.quick_report(signal)\n", + "print(report)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9. Summary & Next Steps\n", + "\n", + "In this notebook we:\n", + "- Loaded multi-symbol 1-minute data from Binance using `BinanceDataLoader`\n", + "- Built momentum factors via both **code-based** and **expression-based** methods\n", + "- Visualized factor behavior with time series, distributions, and heatmaps\n", + "- Computed IC and analyzed its decay across multiple horizons\n", + "- Ran a market-neutral backtest and examined performance metrics\n", + "\n", + "**Next notebooks to explore:**\n", + "- `02_mean_reversion_factor.ipynb` — Mean reversion with volatility normalization\n", + "- `03_data_loading_and_exploration.ipynb` — Deep dive into AggBar and data loading\n", + "- `04_multi_factor_combination.ipynb` — Combine multiple factors and compare performance" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded 411,051 bars\n", - "Symbols: ['ADAUSDT', 'AVAXUSDT', 'BNBUSDT', 'BTCUSDT', 'CAKEUSDT', 'DOGEUSDT', 'DOTUSDT', 'ETHUSDT', 'SOLUSDT', 'XRPUSDT']\n", - "Columns: ['symbol', 'start_time', 'end_time', 'open', 'high', 'low', 'close', 'volume', 'vwap', 'num_buyer', 'num_seller', 'num_buyer_volume', 'num_seller_volume']\n" - ] - } - ], - "source": [ - "SYMBOLS = [\n", - " \"BTCUSDT\", \"ETHUSDT\", \"BNBUSDT\", \"SOLUSDT\", \"XRPUSDT\",\n", - " \"DOGEUSDT\", \"ADAUSDT\", \"AVAXUSDT\", \"DOTUSDT\", \"CAKEUSDT\",\n", - "]\n", - "\n", - "loader = BinanceDataLoader()\n", - "\n", - "agg = loader.load_aggbar(\n", - " symbols=SYMBOLS,\n", - " data_type=\"aggTrades\",\n", - " market_type=\"futures\",\n", - " futures_type=\"um\",\n", - " days=30,\n", - " bar_type=\"time\",\n", - " interval=60_000, # 1-minute bars\n", - ")\n", - "\n", - "print(f\"Loaded {len(agg):,} bars\")\n", - "print(f\"Symbols: {agg.symbols}\")\n", - "print(f\"Columns: {agg.cols}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Exploring the AggBar\n", - "\n", - "`AggBar` is a **multi-symbol OHLCV container** stored in long format. You can inspect it, convert to Polars/Pandas, and slice by time or symbols." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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XRPUSDT411062026-01-14 11:22:002026-02-120
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" - ], - "text/plain": [ - " num_kbar start_time end_time num_nan\n", - "symbol \n", - "ADAUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "AVAXUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "BNBUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "BTCUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "CAKEUSDT 41097 2026-01-14 11:22:00 2026-02-12 0\n", - "DOGEUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "DOTUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "ETHUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "SOLUSDT 41106 2026-01-14 11:22:00 2026-02-12 0\n", - "XRPUSDT 41106 2026-01-14 11:22:00 2026-02-12 0" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Summary info per symbol\n", - "agg.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "shape: (10, 13)
symbolstart_timeend_timeopenhighlowclosevolumevwapnum_buyernum_sellernum_buyer_volumenum_seller_volume
stri64i64f64f64f64f64f64f64decimal[38,0]decimal[38,0]f64f64
"ADAUSDT"176838972000017683897800000.41630.41630.4160.4162422468.00.4160893450247651.0174817.0
"AVAXUSDT"1768389720000176838978000014.56714.56714.55414.5578092.014.55769420344859.03233.0
"BNBUSDT"17683897200001768389780000931.8931.8931.28931.56231.68931.5168789298141.7489.94
"BTCUSDT"1768389720000176838978000094951.594951.694892.694910.053.57894925.47540630556325.43828.14
"CAKEUSDT"176838972000017683897800002.03912.03912.03792.03814596.02.03859130283832.0764.0
"DOGEUSDT"176838972000017683897800000.146850.146850.146730.146792.758362e60.14678262431.479317e61.279045e6
"DOTUSDT"176838972000017683897800002.2572.2572.2522.25561027.12.255454262120337.840689.3
"ETHUSDT"176838972000017683897800003295.73296.03294.423295.612396.2643295.5500975285471155.6971240.567
"SOLUSDT"17683897200001768389780000144.03144.03143.82143.8828761.51143.91300614615517777.7410983.77
"XRPUSDT"176838972000017683897800002.12782.12792.12532.1261371037.52.12683196110193592.5177445.0
" - ], - "text/plain": [ - "shape: (10, 13)\n", - "\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n", - "\u2502 symbol \u2506 start_time \u2506 end_time \u2506 open \u2506 \u2026 \u2506 num_buyer \u2506 num_selle \u2506 num_buyer \u2506 num_selle \u2502\n", - "\u2502 --- \u2506 --- \u2506 --- \u2506 --- \u2506 \u2506 --- \u2506 r \u2506 _volume \u2506 r_volume \u2502\n", - "\u2502 str \u2506 i64 \u2506 i64 \u2506 f64 \u2506 \u2506 decimal[3 \u2506 --- \u2506 --- \u2506 --- \u2502\n", - "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 8,0] \u2506 decimal[3 \u2506 f64 \u2506 f64 \u2502\n", - "\u2502 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 8,0] \u2506 \u2506 \u2502\n", - "\u255e\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u256a\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2561\n", - "\u2502 ADAUSDT \u2506 1768389720 \u2506 1768389780 \u2506 0.4163 \u2506 \u2026 \u2506 34 \u2506 50 \u2506 247651.0 \u2506 174817.0 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 AVAXUSDT \u2506 1768389720 \u2506 1768389780 \u2506 14.567 \u2506 \u2026 \u2506 20 \u2506 34 \u2506 4859.0 \u2506 3233.0 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 BNBUSDT \u2506 1768389720 \u2506 1768389780 \u2506 931.8 \u2506 \u2026 \u2506 92 \u2506 98 \u2506 141.74 \u2506 89.94 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 BTCUSDT \u2506 1768389720 \u2506 1768389780 \u2506 94951.5 \u2506 \u2026 \u2506 305 \u2506 563 \u2506 25.438 \u2506 28.14 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 CAKEUSDT \u2506 1768389720 \u2506 1768389780 \u2506 2.0391 \u2506 \u2026 \u2506 30 \u2506 28 \u2506 3832.0 \u2506 764.0 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 DOGEUSDT \u2506 1768389720 \u2506 1768389780 \u2506 0.14685 \u2506 \u2026 \u2506 62 \u2506 43 \u2506 1.479317e \u2506 1.279045e \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 6 \u2506 6 \u2502\n", - "\u2502 DOTUSDT \u2506 1768389720 \u2506 1768389780 \u2506 2.257 \u2506 \u2026 \u2506 26 \u2506 21 \u2506 20337.8 \u2506 40689.3 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 ETHUSDT \u2506 1768389720 \u2506 1768389780 \u2506 3295.7 \u2506 \u2026 \u2506 528 \u2506 547 \u2506 1155.697 \u2506 1240.567 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 SOLUSDT \u2506 1768389720 \u2506 1768389780 \u2506 144.03 \u2506 \u2026 \u2506 146 \u2506 155 \u2506 17777.74 \u2506 10983.77 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2502 XRPUSDT \u2506 1768389720 \u2506 1768389780 \u2506 2.1278 \u2506 \u2026 \u2506 96 \u2506 110 \u2506 193592.5 \u2506 177445.0 \u2502\n", - "\u2502 \u2506 000 \u2506 000 \u2506 \u2506 \u2506 \u2506 \u2506 \u2506 \u2502\n", - "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# View raw data as Polars DataFrame\n", - "agg.to_polars().head(10)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sliced AggBar: ['BTCUSDT', 'ETHUSDT'], 82,212 bars\n" - ] - } - ], - "source": [ - "# Slice by symbols \u2014 creates a new AggBar\n", - "btc_eth = agg.slice(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", - "print(f\"Sliced AggBar: {btc_eth.symbols}, {len(btc_eth):,} bars\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3. Building Momentum Factors\n", - "\n", - "### 3.1 Code-Based Factor Construction\n", - "\n", - "Extract a column from `AggBar` with `agg[\"close\"]` to get a `Factor` object, then chain time-series and cross-sectional operators." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Factor shapes:\n", - " momentum_60: 411,607 rows\n", - " vol_adj_momentum: 414,943 rows\n", - " momentum_rank: 411,607 rows\n" - ] - } - ], - "source": [ - "close = agg[\"close\"]\n", - "volume = agg[\"volume\"]\n", - "\n", - "# Simple momentum: percentage change over 60 periods\n", - "momentum_60 = close.ts_delta(60) / close.ts_shift(60)\n", - "momentum_60.name = \"momentum_60\"\n", - "\n", - "# Short-term momentum (20 periods)\n", - "momentum_20 = close.ts_delta(20) / close.ts_shift(20)\n", - "momentum_20.name = \"momentum_20\"\n", - "\n", - "# Volatility-adjusted momentum\n", - "volatility = (close.ts_delta(1) / close.ts_shift(1)).ts_std(60)\n", - "vol_adj_momentum = momentum_60 / volatility\n", - "vol_adj_momentum.name = \"vol_adj_momentum\"\n", - "\n", - "# Cross-sectional rank (normalized to [0, 1])\n", - "momentum_rank = momentum_60.cs_rank()\n", - "momentum_rank.name = \"momentum_rank\"\n", - "\n", - "print(\"Factor shapes:\")\n", - "print(f\" momentum_60: {len(momentum_60):,} rows\")\n", - "print(f\" vol_adj_momentum: {len(vol_adj_momentum):,} rows\")\n", - "print(f\" momentum_rank: {len(momentum_rank):,} rows\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 3.2 Expression-Based Factor Construction\n", - "\n", - "You can also define factors using string expressions via `ResearchSession.create_factor()`. This is useful for rapid experimentation." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Expression-based momentum: 411,607 rows\n", - "MA crossover signal: 411,607 rows\n" - ] - } - ], - "source": [ - "session = ResearchSession(agg, default_frequency=\"1min\")\n", - "\n", - "# Same momentum factor, defined via expression\n", - "mom_expr = session.create_factor(\n", - " \"ts_delta(close, 60) / ts_shift(close, 60)\",\n", - " name=\"momentum_60_expr\",\n", - ")\n", - "\n", - "# A more complex expression: MA crossover signal\n", - "ma_cross = session.create_factor(\n", - " \"ts_mean(close, 10) - ts_mean(close, 30)\",\n", - " name=\"ma_crossover\",\n", - ")\n", - "\n", - "print(f\"Expression-based momentum: {len(mom_expr):,} rows\")\n", - "print(f\"MA crossover signal: {len(ma_cross):,} rows\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4. Visualizing Factors\n", - "\n", - "Use the `.plot` accessor on Factor objects to produce time series, heatmaps, and distributions." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Time series of momentum for a few symbols\n", - "momentum_60.plot()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5. IC (Information Coefficient) Analysis\n", - "\n", - "The **IC** measures the rank correlation between factor values and subsequent returns. A consistently positive (or negative) IC indicates predictive power.\n", - "\n", - "### 5.1 Single-Period IC" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-12 20:40:58,547 - INFO - prepare_data: 411607 rows -> 412109 rows (100.1% retained)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IC Summary:\n", - "{1: {'mean_ic': -0.026684615700049246, 'ic_std': 0.3761825466053544, 'ic_ir': -0.07093528378934481, 't-stat': -14.36627769408405}}\n", - "\n", - "IC Series shape: (41017, 1)\n", - " period_1\n", - "start_time \n", - "1768393320000 0.369697\n", - "1768393380000 0.345455\n", - "1768393440000 -0.406061\n", - "1768393500000 0.260606\n", - "1768393560000 0.527273\n" - ] - } - ], - "source": [ - "# Use ResearchSession for streamlined analysis\n", - "signal = momentum_rank # Use ranked momentum as our signal\n", - "\n", - "analysis = session.analyze(signal, periods=1)\n", - "\n", - "print(\"IC Summary:\")\n", - "print(analysis.ic_summary)\n", - "print()\n", - "print(f\"IC Series shape: {analysis.ic_series.shape}\")\n", - "print(analysis.ic_series.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 5.2 Multi-Horizon IC Analysis\n", - "\n", - "To understand how quickly a factor's signal decays, we compute IC across multiple forward horizons." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2026-02-12 20:41:17,067 - INFO - prepare_data: 411607 rows -> 412109 rows (100.1% retained)\n", - "2026-02-12 20:41:18,547 - INFO - prepare_data: 411607 rows -> 412069 rows (100.1% retained)\n", - "2026-02-12 20:41:20,086 - INFO - prepare_data: 411607 rows -> 412019 rows (100.1% retained)\n", - "2026-02-12 20:41:21,566 - INFO - prepare_data: 411607 rows -> 411919 rows (100.1% retained)\n", - "2026-02-12 20:41:23,129 - INFO - prepare_data: 411607 rows -> 411519 rows (100.0% retained)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IC Decay Analysis:\n" - ] - }, - { - "data": { - "text/html": [ - "
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60-0.0415490.384516-0.108055-21.868208
\n", - "
" - ], - "text/plain": [ - " mean_ic ic_std ic_ir t-stat\n", - "horizon \n", - "1 -0.026685 0.376183 -0.070935 -14.366278\n", - "5 -0.036314 0.383139 -0.094780 -19.194464\n", - "10 -0.040783 0.385369 -0.105829 -21.430799\n", - "20 -0.048677 0.386069 -0.126085 -25.529622\n", - "60 -0.041549 0.384516 -0.108055 -21.868208" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Compute IC for multiple horizons\n", - "horizons = [1, 5, 10, 20, 60]\n", - "ic_results = {}\n", - "\n", - "for h in horizons:\n", - " result = session.analyze(signal, periods=h)\n", - " ic_results[h] = result.ic_summary.get(h, {})\n", - "\n", - "# Display IC decay table\n", - "ic_decay_df = pd.DataFrame(ic_results).T\n", - "ic_decay_df.index.name = \"horizon\"\n", - "print(\"IC Decay Analysis:\")\n", - "ic_decay_df" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot IC decay curve\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax.plot(ic_decay_df.index, ic_decay_df[\"mean_ic\"], \"o-\", linewidth=2, markersize=8)\n", - "ax.axhline(y=0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", - "ax.set_xlabel(\"Forward Horizon (periods)\")\n", - "ax.set_ylabel(\"Mean IC\")\n", - "ax.set_title(\"IC Decay Curve \u2014 Momentum Factor\")\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Quantile Analysis\n", - "\n", - "Split the cross-section into quantiles by factor value and examine the mean returns of each group. A monotonic pattern (Q1 < Q2 < ... < Q5) indicates strong linear predictive power." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Use FactorAnalyzer directly for more control\n", - "analyzer = FactorAnalyzer(signal, agg, quantiles=5)\n", - "\n", - "# Prepare data: align factor values with forward returns\n", - "analyzer.prepare_data(price_col=\"close\", periods=[1])\n", - "\n", - "# Quantile returns\n", - "analyzer.plot_quantile_returns(quantiles=5, period=1)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Cumulative returns by quantile (includes Long-Short portfolio)\n", - "analyzer.plot_cumulative_returns(quantiles=5, period=1, long_short=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Data not prepared. Call prepare_data() first.", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mValueError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[19]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# IC time series plot\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m \u001b[43manalyzer\u001b[49m\u001b[43m.\u001b[49m\u001b[43mplot_ic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrank\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mplot_type\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mts\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 3\u001b[39m plt.show()\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/raid1/novis/factorium/src/factorium/factors/analyzer.py:544\u001b[39m, in \u001b[36mFactorAnalyzer.plot_ic\u001b[39m\u001b[34m(self, period, method, plot_type)\u001b[39m\n\u001b[32m 534\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 535\u001b[39m \u001b[33;03mPlot Information Coefficient (IC).\u001b[39;00m\n\u001b[32m 536\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 540\u001b[39m \u001b[33;03m plot_type: 'ts' for time series, 'hist' for histogram.\u001b[39;00m\n\u001b[32m 541\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 542\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mplotting_analyzer\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FactorAnalyzerPlotter\n\u001b[32m--> \u001b[39m\u001b[32m544\u001b[39m ic = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcalculate_ic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 545\u001b[39m col = \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mperiod_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mperiod\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m 546\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m col \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ic.columns:\n", - "\u001b[36mFile \u001b[39m\u001b[32m/mnt/raid1/novis/factorium/src/factorium/factors/analyzer.py:385\u001b[39m, in \u001b[36mFactorAnalyzer.calculate_ic\u001b[39m\u001b[34m(self, method)\u001b[39m\n\u001b[32m 375\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 376\u001b[39m \u001b[33;03mCalculate Information Coefficient (IC) for each period.\u001b[39;00m\n\u001b[32m 377\u001b[39m \n\u001b[32m (...)\u001b[39m\u001b[32m 382\u001b[39m \u001b[33;03m pd.DataFrame: IC values indexed by start_time.\u001b[39;00m\n\u001b[32m 383\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 384\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[33m\"\u001b[39m\u001b[33m_clean_data\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m--> \u001b[39m\u001b[32m385\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mData not prepared. Call prepare_data() first.\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 387\u001b[39m period_cols = [c \u001b[38;5;28;01mfor\u001b[39;00m c \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._clean_data.columns \u001b[38;5;28;01mif\u001b[39;00m c.startswith(\u001b[33m\"\u001b[39m\u001b[33mperiod_\u001b[39m\u001b[33m\"\u001b[39m)]\n\u001b[32m 388\u001b[39m corr_method = \u001b[33m\"\u001b[39m\u001b[33mspearman\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m method == \u001b[33m\"\u001b[39m\u001b[33mrank\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mpearson\u001b[39m\u001b[33m\"\u001b[39m\n", - "\u001b[31mValueError\u001b[39m: Data not prepared. Call prepare_data() first." - ] - } - ], - "source": [ - "# IC time series plot\n", - "analyzer.plot_ic(period=1, method=\"rank\", plot_type=\"ts\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7. Backtesting\n", - "\n", - "Run a **market-neutral vectorized backtest** using the momentum signal. The backtester:\n", - "- Converts signals to portfolio weights (cross-sectional normalization)\n", - "- Handles transaction costs\n", - "- Tracks equity, returns, and positions over time" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Market-neutral backtest\n", - "result = session.backtest(\n", - " signal,\n", - " neutralization=\"market\",\n", - " transaction_cost=0.0003,\n", - ")\n", - "\n", - "# Key performance metrics\n", - "print(\"Backtest Metrics:\")\n", - "for key, val in result.metrics.items():\n", - " if isinstance(val, float):\n", - " print(f\" {key:25s}: {val:>10.4f}\")\n", - " else:\n", - " print(f\" {key:25s}: {val}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot equity curve\n", - "equity = result.equity_curve.to_pandas()\n", - "equity[\"timestamp\"] = pd.to_datetime(equity[\"start_time\"], unit=\"ms\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(14, 6))\n", - "ax.plot(equity[\"timestamp\"], equity[\"equity\"], linewidth=1.5)\n", - "ax.set_xlabel(\"Date\")\n", - "ax.set_ylabel(\"Portfolio Equity\")\n", - "ax.set_title(\"Momentum Factor \u2014 Equity Curve (Market Neutral)\")\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Plot returns distribution\n", - "returns = result.returns.to_pandas()\n", - "\n", - "fig, ax = plt.subplots(figsize=(10, 5))\n", - "ax.hist(returns[\"portfolio_return\"].dropna(), bins=100, alpha=0.75, edgecolor=\"black\", linewidth=0.5)\n", - "ax.axvline(x=0, color=\"red\", linestyle=\"--\", alpha=0.7)\n", - "ax.set_xlabel(\"Return\")\n", - "ax.set_ylabel(\"Frequency\")\n", - "ax.set_title(\"Distribution of Portfolio Returns\")\n", - "ax.grid(True, alpha=0.3)\n", - "plt.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 8. Quick Report\n", - "\n", - "`ResearchSession.quick_report()` combines IC analysis and backtesting into a single text summary \u2014 great for rapid iteration." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "report = session.quick_report(signal)\n", - "print(report)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 9. Summary & Next Steps\n", - "\n", - "In this notebook we:\n", - "- Loaded multi-symbol 1-minute data from Binance using `BinanceDataLoader`\n", - "- Built momentum factors via both **code-based** and **expression-based** methods\n", - "- Visualized factor behavior with time series, distributions, and heatmaps\n", - "- Computed IC and analyzed its decay across multiple horizons\n", - "- Ran a market-neutral backtest and examined performance metrics\n", - "\n", - "**Next notebooks to explore:**\n", - "- `02_mean_reversion_factor.ipynb` \u2014 Mean reversion with volatility normalization\n", - "- `03_data_loading_and_exploration.ipynb` \u2014 Deep dive into AggBar and data loading\n", - "- `04_multi_factor_combination.ipynb` \u2014 Combine multiple factors and compare performance" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/examples/02_mean_reversion_factor.ipynb b/examples/02_mean_reversion_factor.ipynb index 4a9a67b..e0b04d0 100644 --- a/examples/02_mean_reversion_factor.ipynb +++ b/examples/02_mean_reversion_factor.ipynb @@ -69,7 +69,7 @@ "\n", "print(f\"Loaded {len(agg):,} bars for {len(agg.symbols)} symbols\")\n", "\n", - "session = ResearchSession(agg, default_frequency=\"1min\")" + "session = ResearchSession(agg, default_frequency=\"1m\")" ] }, { @@ -277,12 +277,12 @@ "fig, ax = plt.subplots(figsize=(14, 6))\n", "\n", "eq_neutral = result_neutral.equity_curve.to_pandas()\n", - "eq_neutral[\"ts\"] = pd.to_datetime(eq_neutral[\"start_time\"], unit=\"ms\")\n", - "ax.plot(eq_neutral[\"ts\"], eq_neutral[\"equity\"], label=\"Market Neutral\", linewidth=1.5)\n", + "eq_neutral[\"ts\"] = pd.to_datetime(eq_neutral[\"end_time\"], unit=\"ms\")\n", + "ax.plot(eq_neutral[\"ts\"], eq_neutral[\"total_value\"], label=\"Market Neutral\", linewidth=1.5)\n", "\n", "eq_long = result_long.equity_curve.to_pandas()\n", - "eq_long[\"ts\"] = pd.to_datetime(eq_long[\"start_time\"], unit=\"ms\")\n", - "ax.plot(eq_long[\"ts\"], eq_long[\"equity\"], label=\"Long Only\", linewidth=1.5)\n", + "eq_long[\"ts\"] = pd.to_datetime(eq_long[\"end_time\"], unit=\"ms\")\n", + "ax.plot(eq_long[\"ts\"], eq_long[\"total_value\"], label=\"Long Only\", linewidth=1.5)\n", "\n", "ax.set_xlabel(\"Date\")\n", "ax.set_ylabel(\"Equity\")\n", @@ -337,7 +337,7 @@ "outputs": [], "source": [ "# Visualize autocorrelation for a few symbols\n", - "autocorr.plot.plot_timeseries(symbols=[\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"])\n", + "autocorr.plot(symbols=[\"BTCUSDT\", \"ETHUSDT\", \"SOLUSDT\"])\n", "plt.axhline(y=0, color=\"red\", linestyle=\"--\", alpha=0.5)\n", "plt.title(\"Rolling Return Autocorrelation (60-period window, lag=1)\")\n", "plt.ylabel(\"Autocorrelation\")\n", @@ -391,4 +391,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/examples/03_data_loading_and_exploration.ipynb b/examples/03_data_loading_and_exploration.ipynb index 23d48e0..bd78af6 100644 --- a/examples/03_data_loading_and_exploration.ipynb +++ b/examples/03_data_loading_and_exploration.ipynb @@ -128,7 +128,7 @@ "# Metadata\n", "meta = agg.metadata\n", "print(f\"Number of rows: {meta.num_rows:,}\")\n", - "print(f\"Number of symbols: {meta.num_symbols}\")\n", + "print(f\"Number of symbols: {len(meta.symbols)}\")\n", "print(f\"Time range: {meta.min_time} → {meta.max_time}\")" ] }, @@ -347,7 +347,7 @@ "from factorium import ResearchSession\n", "\n", "# Create from AggBar\n", - "session = ResearchSession(agg, default_frequency=\"1min\")\n", + "session = ResearchSession(agg, default_frequency=\"1m\")\n", "\n", "print(f\"Session symbols: {session.symbols}\")\n", "print(f\"Session columns: {session.cols}\")\n", @@ -367,7 +367,7 @@ "# ResearchSession can also load from files directly\n", "session_from_csv = ResearchSession.from_csv(\n", " output_dir / \"crypto_1min.csv\",\n", - " default_frequency=\"1min\",\n", + " default_frequency=\"1m\",\n", ")\n", "print(f\"Session from CSV: {len(session_from_csv.symbols)} symbols\")" ] @@ -427,4 +427,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/examples/04_multi_factor_combination.ipynb b/examples/04_multi_factor_combination.ipynb index 90e9324..3b28997 100644 --- a/examples/04_multi_factor_combination.ipynb +++ b/examples/04_multi_factor_combination.ipynb @@ -68,7 +68,7 @@ " interval=60_000,\n", ")\n", "\n", - "session = ResearchSession(agg, default_frequency=\"1min\")\n", + "session = ResearchSession(agg, default_frequency=\"1m\")\n", "print(f\"Loaded {len(agg):,} bars for {len(agg.symbols)} symbols\")" ] }, @@ -227,7 +227,7 @@ "rolling_corr = momentum.ts_corr(inv_vol_ranked, window=120)\n", "rolling_corr.name = \"mom_vol_corr\"\n", "\n", - "rolling_corr.plot.plot_timeseries(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", + "rolling_corr.plot(symbols=[\"BTCUSDT\", \"ETHUSDT\"])\n", "plt.axhline(y=0, color=\"red\", linestyle=\"--\", alpha=0.5)\n", "plt.title(\"Rolling Correlation: Momentum vs. Inverse Volatility (120-period window)\")\n", "plt.ylabel(\"Correlation\")\n", @@ -428,10 +428,10 @@ "\n", "for name, result in bt_results.items():\n", " eq = result.equity_curve.to_pandas()\n", - " eq[\"ts\"] = pd.to_datetime(eq[\"start_time\"], unit=\"ms\")\n", + " eq[\"ts\"] = pd.to_datetime(eq[\"end_time\"], unit=\"ms\")\n", " style = \"--\" if name in factors else \"-\"\n", " linewidth = 1.0 if name in factors else 2.0\n", - " ax.plot(eq[\"ts\"], eq[\"equity\"], style, label=name, linewidth=linewidth)\n", + " ax.plot(eq[\"ts\"], eq[\"total_value\"], style, label=name, linewidth=linewidth)\n", "\n", "ax.set_xlabel(\"Date\")\n", "ax.set_ylabel(\"Equity\")\n", @@ -546,4 +546,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/pyproject.toml b/pyproject.toml index 6c1d055..9e959bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -80,6 +80,7 @@ python_files = ["test_*.py"] [dependency-groups] dev = [ "freezegun>=1.5.5", + "jupyterlab>=4.5.4", "mkdocs>=1.6.1", "mkdocs-material>=9.7.1", "mypy>=1.0.0", diff --git a/uv.lock b/uv.lock index 0f05705..b075e29 100644 --- a/uv.lock +++ b/uv.lock @@ -2,7 +2,8 @@ version = 1 revision = 3 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.12'", + "python_full_version >= '3.14'", + "python_full_version >= '3.12' and python_full_version < '3.14'", "python_full_version < '3.12'", ] @@ -139,6 +140,102 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = 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window) - Summary table of all safety patterns - Add tests/factors/test_safe_operations.py with 57 regression tests: - safe_divide: scalar, array, Series, edge cases - Factor division: Factor/Factor, Factor/scalar, rtruediv - Factor.inverse(): near-zero guard - ts_* NaN propagation and window completeness - ts_* degenerate cases (constant values, zero variance) - cs_* NaN propagation (cross-sectional strict mask) - cs_neutralize degenerate case - Math operations (log, sqrt domain safety) - Multi-symbol consistency - EPSILON threshold boundary tests - Update AGENTS.md/GEMINI.md to reference formal docs - Add safe-operations.md to mkdocs.yml navigation Closes #10 --- AGENTS.md | 12 +- docs/dev/safe-operations.md | 250 +++++++++++ mkdocs.yml | 1 + tests/factors/test_safe_operations.py | 584 ++++++++++++++++++++++++++ 4 files changed, 843 insertions(+), 4 deletions(-) create mode 100644 docs/dev/safe-operations.md create mode 100644 tests/factors/test_safe_operations.py diff --git a/AGENTS.md b/AGENTS.md index df54cb9..b0e6024 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -15,6 +15,9 @@ Factorium 是一個量化因子分析與回測框架，主要模組： ## `safe_` 函數模式 +> **完整文檔**: 請參閱 [`docs/dev/safe-operations.md`](docs/dev/safe-operations.md) +> **回歸測試**: [`tests/factors/test_safe_operations.py`](tests/factors/test_safe_operations.py) + 在此專案中，以 `safe_` 開頭的函數（例如：`safe_mean`, `safe_sum`, `safe_div`）旨在確保計算的「嚴格性」與「安全性」，這對於金融因子的計算尤為重要。 ### 共同特點 @@ -28,9 +31,9 @@ Factorium 是一個量化因子分析與回測框架，主要模組： * **資料充裕度檢查**: 如 `safe_corr` 會在計算前確認是否有足夠的有效數據點（例如：多於 2 個）。 3. **safe_div 一致性規範**: - * **閾值**: 使用 `POSITION_EPSILON`（`1e-10`）判斷分母接近 0 的情況。 - * **缺失值回傳**: Pandas 路徑回傳 `np.nan`，Polars 路徑回傳 `null`（建議使用 `pl.lit(None)`）。 - * **語義**: 分母為 0 或 `abs(denominator) <= POSITION_EPSILON` 時視為缺失，避免產生 `inf`。 + * **閾值**: 使用 `EPSILON`（`1e-10`，定義於 `factorium.constants`）判斷分母接近 0 的情況。`POSITION_EPSILON` 是向後相容的別名。 + * **缺失值回傳**: Pandas/NumPy 路徑回傳 `np.nan`，Polars 路徑回傳 `null`（使用 `pl.lit(None)`）。 + * **語義**: 分母為 0 或 `abs(denominator) <= EPSILON` 時視為缺失，避免產生 `inf`。 ### 範例 @@ -71,7 +74,8 @@ docs/ │ └── backtest.md └── dev/ # 開發者文檔 ├── testing.md - └── regression-operators.md + ├── regression-operators.md + └── safe-operations.md ``` ### 本地預覽 diff --git a/docs/dev/safe-operations.md b/docs/dev/safe-operations.md new file mode 100644 index 0000000..2fac7f6 --- /dev/null +++ b/docs/dev/safe-operations.md @@ -0,0 +1,250 @@ +# Safe Operations Semantics + +This document formalizes the **safe operation semantics** used throughout Factorium for +numerical safety in factor calculations and backtesting. These conventions ensure +deterministic, reproducible results and prevent silent errors from corrupting financial signals. + +--- + +## Core Principles + +1. **Strict NaN Propagation** — Any `NaN` (or `null` in Polars) in the input window + causes the entire window result to be `NaN`/`null`. +2. **Division Safety** — Division by values within `EPSILON` of zero returns `NaN`/`null` + instead of `inf`. +3. **Window Completeness** — Rolling operations require a full window; partial windows + produce `NaN`/`null`. + +These rules are intentionally **stricter** than the defaults in Pandas / Polars / NumPy, +which typically skip `NaN` values. In quantitative finance, silently ignoring missing data +can produce misleading signals, so Factorium opts for explicit failure. + +--- + +## Constants + +All numeric thresholds are defined in `factorium.constants`: + +| Constant | Value | Purpose | +|----------|-------|---------| +| `EPSILON` | `1e-10` | Near-zero threshold for safe division and degenerate-case detection | +| `POSITION_EPSILON` | `EPSILON` (alias) | Legacy alias used in `backtest.utils`; identical to `EPSILON` | +| `MIN_PERIODS_PER_YEAR` | `1.0` | Lower bound for `periods_per_year` in metrics | +| `MAX_PERIODS_PER_YEAR` | `525960.0` | Upper bound (minute-level data) | + +```python +# factorium/constants.py +EPSILON = 1e-10 +``` + +--- + +## Safe Division + +The safe division pattern appears in three contexts in the codebase: + +### 1. `backtest.utils.safe_divide(a, b, default=np.nan)` + +A general-purpose safe division function supporting scalar, NumPy array, and Pandas Series inputs. + +**Rules:** +- If `b` is `NaN` → return `default` (default: `np.nan`) +- If `|b| <= EPSILON` → return `default` +- Otherwise → return `a / b` + +```python +from factorium.backtest.utils import safe_divide + +safe_divide(1.0, 0.0) # → np.nan +safe_divide(1.0, 1e-15) # → np.nan (within EPSILON) +safe_divide(1.0, 2.0) # → 0.5 +safe_divide(1.0, np.nan) # → np.nan +``` + +**Supported input types:** + +| Type of `b` | Near-zero detection | NaN detection | +|-------------|-------------------|---------------| +| Scalar (`float`, `int`) | `abs(b) <= EPSILON` | `np.isnan(b)` | +| `np.ndarray` | `np.abs(b) <= EPSILON` | `np.isnan(b)` | +| `pd.Series` | `b.abs() <= EPSILON` | `b.isna()` | + +### 2. Factor Division (`Factor.__truediv__`) + +The `Factor / Factor` and `Factor / scalar` operations use Polars expressions with the +same EPSILON threshold: + +```python +# Polars path (Factor / Factor) +pl.when(pl.col("other").abs() <= EPSILON) + .then(pl.lit(None)) # → null (Polars equivalent of NaN) + .otherwise(pl.col("factor") / pl.col("other")) + +# Polars path (Factor / scalar) +pl.when(pl.lit(other).abs() <= EPSILON) + .then(pl.lit(None)) + .otherwise(pl.col("factor") / pl.lit(other)) +``` + +**Key difference:** The Polars path returns `null` (not `NaN`) for near-zero denominators. +This is consistent with Polars conventions where `null` represents missing data. + +### 3. `MathOpsMixin.inverse()` + +```python +# 1 / factor with safe division +pl.when(pl.col("factor").abs() <= EPSILON) + .then(pl.lit(None)) + .otherwise(1 / pl.col("factor")) +``` + +--- + +## Strict NaN Propagation in Rolling Operations + +All time-series operations (`ts_*`) follow **strict NaN propagation**: if any value within +the rolling window is `NaN`/`null`, or if the window is not full, the result is `NaN`/`null`. + +### Mechanism + +Polars rolling functions control this via the `min_samples` parameter: + +```python +# min_samples=window ensures NaN if window is not full +pl.col("factor").rolling_mean(window_size=window, min_samples=window).over("symbol") +``` + +When `min_samples == window_size`, Polars will return `null` if: +- The window has fewer than `window` non-null values +- Any value in the window is `null` + +### Operations Using This Pattern + +| Operation | Polars Function | EPSILON Check | +|-----------|----------------|---------------| +| `ts_mean` | `rolling_mean(min_samples=window)` | No | +| `ts_std` | `rolling_std(min_samples=window)` | No | +| `ts_sum` | `rolling_sum(min_samples=window)` | No | +| `ts_min` | `rolling_min(min_samples=window)` | No | +| `ts_max` | `rolling_max(min_samples=window)` | No | +| `ts_median` | `rolling_median(min_samples=window)` | No | +| `ts_kurtosis` | `rolling_kurtosis(min_samples=window)` | No | +| `ts_skewness` | `rolling_skew(min_samples=window)` | No | +| `ts_rank` | `rolling_rank(min_samples=window)` | Yes (constant std check) | +| `ts_scale` | min/max + division | Yes (range < EPSILON) | +| `ts_zscore` | mean/std + division | Yes (std < EPSILON) | +| `ts_corr` | manual cov / (std_x × std_y) | Yes (either std < EPSILON) | +| `ts_beta` | manual cov / var | Yes (var < EPSILON) | +| `ts_cv` | std / \|mean\| | Yes (adds 1e-10 bias term) | + +### Explicit NaN-in-Window Mask + +For operations requiring EPSILON checks, an explicit NaN mask is computed: + +```python +nan_in_window = ( + (pl.col("factor").is_null() | pl.col("factor").is_nan()) + .cast(pl.Int64) + .rolling_max(window_size=window, min_samples=window) + .over("symbol") + .fill_null(1) # Treat incomplete windows as having NaN +) +``` + +This mask is `> 0` if **any** value in the window is `NaN` or `null`, or if the window is +not fully populated. Result computation then uses: + +```python +pl.when(nan_in_window > 0).then(pl.lit(None)).otherwise(computed_expr) +``` + +--- + +## Strict NaN Propagation in Cross-Sectional Operations + +Cross-sectional operations (`cs_*`) apply a **strict NaN mask across the entire +cross-section** at each time step: + +```python +# If ANY symbol has NaN at time t, ALL symbols get NaN at time t +nan_mask = (pl.col("factor").is_null() | pl.col("factor").is_nan()).any().over("end_time") +``` + +### Operations Using This Pattern + +| Operation | EPSILON Check | Special Handling | +|-----------|---------------|------------------| +| `cs_rank` | No | Returns rank / count | +| `cs_zscore` | No (std=0 → ±inf, but caught by NaN mask) | — | +| `cs_demean` | No | — | +| `cs_winsorize` | No | Clips to quantile bounds | +| `cs_neutralize` | Yes (var_x < EPSILON → null) | OLS regression | +| `cs_mean` / `cs_median` | No | — | + +--- + +## Degenerate-Case Handling + +Beyond NaN propagation and division safety, specific operations have additional +degenerate-case guards: + +| Operation | Degenerate Condition | Result | +|-----------|---------------------|--------| +| `ts_rank` | `std < EPSILON` (all values identical) | `null` | +| `ts_scale` | `max - min <= EPSILON` (no range) | `null` | +| `ts_zscore` | `std <= EPSILON` (no variance) | `null` | +| `ts_corr` | `std_x <= EPSILON` or `std_y <= EPSILON` | `null` | +| `ts_beta` | `var_x <= EPSILON` | `null` | +| `cs_neutralize` | `var_x <= EPSILON` | `null` | +| `cs_neutralize` (engine) | `std(x) < EPSILON` | `NaN` (NumPy path) | +| `inverse()` | `|factor| <= EPSILON` | `null` | +| `log()` | `factor <= 0` | `null` | +| `sqrt()` | `factor <= 0` | `null` | + +--- + +## Edge Cases + +### Empty Data + +- `safe_divide` with empty `pd.Series` → empty `pd.Series` +- `neutralize_weights` with empty input → empty `pd.Series(dtype=float)` +- Factor operations on empty DataFrames → empty result DataFrame + +### All NaN Input + +- Rolling operations → all `null` output (no valid windows) +- Cross-sectional operations → all `null` output (NaN mask activates) + +### Single-Element Window + +- `ts_std(window=1)` → always `0.0` (single-value std is 0) +- `ts_corr(window=1)` → all `null` (needs window >= 2) +- `ts_beta(window=1)` → all `null` (needs window >= 2) + +### Infinity Handling + +Some operations explicitly replace `inf` / `-inf` with `null`: + +```python +# ts_scale, ts_zscore +z_expr = pl.when(z_expr.is_finite()).then(z_expr).otherwise(pl.lit(None)) + +# ts_jumpiness, ts_vr +result._lf = result._lf.with_columns( + pl.col("factor").replace(float("inf"), None).replace(float("-inf"), None) +) +``` + +--- + +## Summary Table + +| Pattern | Where Used | Threshold | Missing Value | +|---------|-----------|-----------|---------------| +| Safe division | `safe_divide`, `__truediv__`, `inverse` | `EPSILON` (1e-10) | `NaN` (Pandas/NumPy) / `null` (Polars) | +| Strict NaN propagation (rolling) | All `ts_*` operations | `min_samples=window` | `null` | +| Strict NaN propagation (cross-section) | All `cs_*` operations | `.any().over("end_time")` | `null` | +| Variance/std guard | `ts_corr`, `ts_beta`, `ts_rank`, `cs_neutralize` | `EPSILON` | `null` | +| Range guard | `ts_scale` | `EPSILON` | `null` | +| Infinity filter | `ts_zscore`, `ts_scale`, `ts_jumpiness`, `ts_vr` | `is_finite()` | `null` | diff --git a/mkdocs.yml b/mkdocs.yml index 86832ca..9003bf0 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -61,3 +61,4 @@ nav: - 開發者: - 測試策略: dev/testing.md - 回歸運算子設計: dev/regression-operators.md + - Safe Operations 語義: dev/safe-operations.md diff --git a/tests/factors/test_safe_operations.py b/tests/factors/test_safe_operations.py new file mode 100644 index 0000000..c7cc9c3 --- /dev/null +++ b/tests/factors/test_safe_operations.py @@ -0,0 +1,584 @@ +"""Regression tests for safe_* operations and strict NaN propagation semantics. + +These tests lock in the behavior documented in docs/dev/safe-operations.md: +1. Strict NaN propagation (any NaN in window → NaN result) +2. Safe division (near-zero denominator → NaN/null) +3. Window completeness (partial windows → NaN/null) +4. Degenerate-case handling (constant values, zero variance, etc.) + +Reference: Issue #10 +""" + +import numpy as np +import pandas as pd +import polars as pl +import pytest + +from factorium.backtest.utils import safe_divide +from factorium.constants import EPSILON +from factorium.factors.core import Factor + + +# ────────────────────────────────────────────────────────── +# Helpers +# ────────────────────────────────────────────────────────── + + +def _make_factor( + values: list[float], + symbols: list[str] | None = None, + name: str = "test", +) -> Factor: + """Create a Factor from a flat list of values for a single or multiple symbols. + + If symbols is None, uses a single symbol "A" for all values. + If symbols is provided, len(values) must be divisible by len(symbols). + """ + if symbols is None: + symbols = ["A"] + + n_symbols = len(symbols) + n_times = len(values) // n_symbols + assert len(values) == n_symbols * n_times, "values length must be divisible by number of symbols" + + rows = [] + for t in range(n_times): + for s_idx, sym in enumerate(symbols): + rows.append( + { + "start_time": t * 60000, + "end_time": (t + 1) * 60000, + "symbol": sym, + "factor": values[t * n_symbols + s_idx], + } + ) + + df = pl.DataFrame(rows) + return Factor(df, name=name) + + +def _get_values(factor: Factor) -> list[float | None]: + """Extract factor values as a list (None for null).""" + return factor.data["factor"].to_list() + + +def _is_missing(value: float | None) -> bool: + """Check if a value is missing (None/null or NaN). + + Polars uses null (None) for incomplete windows and NaN for windows + contaminated by NaN input. Both represent "missing" in our semantics. + """ + if value is None: + return True + try: + return np.isnan(value) + except (TypeError, ValueError): + return False + + +# ══════════════════════════════════════════════════════════ +# 1. safe_divide (backtest.utils) +# ══════════════════════════════════════════════════════════ + + +class TestSafeDivideScalar: + """Test safe_divide with scalar inputs.""" + + def test_normal_division(self): + assert safe_divide(10.0, 2.0) == 5.0 + + def test_zero_denominator(self): + result = safe_divide(1.0, 0.0) + assert np.isnan(result) + + def test_near_zero_denominator(self): + result = safe_divide(1.0, 1e-15) + assert np.isnan(result) + + def test_epsilon_boundary_below(self): + """Value at exactly EPSILON should return default.""" + result = safe_divide(1.0, EPSILON) + assert np.isnan(result) + + def test_epsilon_boundary_above(self): + """Value above EPSILON should compute normally.""" + result = safe_divide(1.0, EPSILON * 10) + assert not np.isnan(result) + assert result == pytest.approx(1.0 / (EPSILON * 10)) + + def test_nan_denominator(self): + result = safe_divide(1.0, np.nan) + assert np.isnan(result) + + def test_negative_near_zero(self): + result = safe_divide(1.0, -1e-15) + assert np.isnan(result) + + def test_custom_default(self): + result = safe_divide(1.0, 0.0, default=0.0) + assert result == 0.0 + + def test_negative_denominator(self): + assert safe_divide(10.0, -2.0) == -5.0 + + def test_zero_numerator(self): + assert safe_divide(0.0, 2.0) == 0.0 + + +class TestSafeDivideArray: + """Test safe_divide with numpy array inputs.""" + + def test_normal_array(self): + a = np.array([1.0, 2.0, 3.0]) + b = np.array([2.0, 4.0, 6.0]) + result = safe_divide(a, b) + np.testing.assert_array_almost_equal(result, [0.5, 0.5, 0.5]) + + def test_zero_in_array(self): + a = np.array([1.0, 2.0, 3.0]) + b = np.array([2.0, 0.0, 6.0]) + result = safe_divide(a, b) + assert result[0] == pytest.approx(0.5) + assert np.isnan(result[1]) + assert result[2] == pytest.approx(0.5) + + def test_nan_in_array(self): + a = np.array([1.0, 2.0]) + b = np.array([2.0, np.nan]) + result = safe_divide(a, b) + assert result[0] == pytest.approx(0.5) + assert np.isnan(result[1]) + + def test_near_zero_in_array(self): + a = np.array([1.0, 2.0]) + b = np.array([2.0, 1e-15]) + result = safe_divide(a, b) + assert result[0] == pytest.approx(0.5) + assert np.isnan(result[1]) + + +class TestSafeDivideSeries: + """Test safe_divide with pandas Series inputs.""" + + def test_normal_series(self): + a = pd.Series([1.0, 2.0, 3.0]) + b = pd.Series([2.0, 4.0, 6.0]) + result = safe_divide(a, b) + pd.testing.assert_series_equal(result, pd.Series([0.5, 0.5, 0.5])) + + def test_zero_in_series(self): + a = pd.Series([1.0, 2.0, 3.0]) + b = pd.Series([2.0, 0.0, 6.0]) + result = safe_divide(a, b) + assert result.iloc[0] == pytest.approx(0.5) + assert np.isnan(result.iloc[1]) + assert result.iloc[2] == pytest.approx(0.5) + + def test_nan_in_series(self): + a = pd.Series([1.0, 2.0]) + b = pd.Series([2.0, np.nan]) + result = safe_divide(a, b) + assert result.iloc[0] == pytest.approx(0.5) + assert np.isnan(result.iloc[1]) + + def test_empty_series(self): + a = pd.Series(dtype=float) + b = pd.Series(dtype=float) + result = safe_divide(a, b) + assert len(result) == 0 + + +# ══════════════════════════════════════════════════════════ +# 2. Factor Division (safe_div via __truediv__) +# ══════════════════════════════════════════════════════════ + + +class TestFactorDivision: + """Test Factor / Factor and Factor / scalar with EPSILON guard.""" + + def test_normal_factor_division(self): + f1 = _make_factor([10.0, 20.0, 30.0]) + f2 = _make_factor([2.0, 4.0, 5.0]) + result = _get_values(f1 / f2) + assert result == pytest.approx([5.0, 5.0, 6.0]) + + def test_factor_division_by_zero(self): + f1 = _make_factor([10.0, 20.0]) + f2 = _make_factor([2.0, 0.0]) + result = _get_values(f1 / f2) + assert result[0] == pytest.approx(5.0) + assert result[1] is None # Polars null + + def test_factor_division_by_near_zero(self): + f1 = _make_factor([10.0, 20.0]) + f2 = _make_factor([2.0, 1e-15]) + result = _get_values(f1 / f2) + assert result[0] == pytest.approx(5.0) + assert result[1] is None + + def test_scalar_division_by_zero(self): + f1 = _make_factor([10.0, 20.0]) + result = _get_values(f1 / 0.0) + assert all(v is None for v in result) + + def test_scalar_division_by_near_zero(self): + f1 = _make_factor([10.0, 20.0]) + result = _get_values(f1 / 1e-15) + assert all(v is None for v in result) + + def test_scalar_division_normal(self): + f1 = _make_factor([10.0, 20.0]) + result = _get_values(f1 / 2.0) + assert result == pytest.approx([5.0, 10.0]) + + def test_reverse_division(self): + """Test scalar / Factor (rtruediv).""" + f1 = _make_factor([2.0, 0.0, 4.0]) + result = _get_values(10.0 / f1) + assert result[0] == pytest.approx(5.0) + assert result[1] is None # near zero + assert result[2] == pytest.approx(2.5) + + +class TestFactorInverse: + """Test MathOpsMixin.inverse().""" + + def test_normal_inverse(self): + f = _make_factor([2.0, 4.0, 5.0]) + result = _get_values(f.inverse()) + assert result == pytest.approx([0.5, 0.25, 0.2]) + + def test_inverse_near_zero(self): + f = _make_factor([2.0, 0.0, 1e-15]) + result = _get_values(f.inverse()) + assert result[0] == pytest.approx(0.5) + assert result[1] is None + assert result[2] is None + + +# ══════════════════════════════════════════════════════════ +# 3. Strict NaN Propagation — Time-Series Operations +# ══════════════════════════════════════════════════════════ + + +class TestTsNanPropagation: + """Any NaN in window → null output; incomplete window → null output.""" + + def test_ts_mean_nan_in_window(self): + f = _make_factor([1.0, np.nan, 3.0, 4.0, 5.0]) + result = _get_values(f.ts_mean(3)) + # Window [1, NaN, 3] → missing, [NaN, 3, 4] → missing, [3, 4, 5] → 4.0 + assert _is_missing(result[0]) # incomplete window + assert _is_missing(result[1]) # incomplete window + assert _is_missing(result[2]) # NaN in window + assert _is_missing(result[3]) # NaN in window + assert result[4] == pytest.approx(4.0) + + def test_ts_mean_full_clean_window(self): + f = _make_factor([1.0, 2.0, 3.0, 4.0, 5.0]) + result = _get_values(f.ts_mean(3)) + assert _is_missing(result[0]) # window not full + assert _is_missing(result[1]) # window not full + assert result[2] == pytest.approx(2.0) + assert result[3] == pytest.approx(3.0) + assert result[4] == pytest.approx(4.0) + + def test_ts_std_nan_propagation(self): + f = _make_factor([1.0, 2.0, np.nan, 4.0, 5.0]) + result = _get_values(f.ts_std(3)) + assert _is_missing(result[0]) # incomplete window + assert _is_missing(result[1]) # incomplete window + assert _is_missing(result[2]) # NaN in window + assert _is_missing(result[3]) # NaN in window + assert _is_missing(result[4]) # [nan,4,5] → NaN still in window + + def test_ts_sum_incomplete_window(self): + """Partial windows return null.""" + f = _make_factor([1.0, 2.0, 3.0]) + result = _get_values(f.ts_sum(3)) + assert _is_missing(result[0]) + assert _is_missing(result[1]) + assert result[2] == pytest.approx(6.0) + + def test_ts_min_max_nan_propagation(self): + f = _make_factor([1.0, np.nan, 3.0, 4.0]) + result_min = _get_values(f.ts_min(2)) + result_max = _get_values(f.ts_max(2)) + assert _is_missing(result_min[0]) # incomplete + assert _is_missing(result_min[1]) # NaN in window + assert _is_missing(result_min[2]) # NaN in window + assert result_min[3] == pytest.approx(3.0) + assert result_max[3] == pytest.approx(4.0) + + +class TestTsWindowCompleteness: + """Window must have exactly window_size samples.""" + + def test_window_larger_than_data(self): + f = _make_factor([1.0, 2.0]) + result = _get_values(f.ts_mean(5)) + assert all(_is_missing(v) for v in result) + + def test_window_one(self): + f = _make_factor([1.0, 2.0, 3.0]) + result = _get_values(f.ts_mean(1)) + assert result == pytest.approx([1.0, 2.0, 3.0]) + + +class TestTsDegenerateCases: + """Degenerate cases: constant values, zero variance, etc.""" + + def test_ts_zscore_constant_values(self): + """All values identical → std = 0 → null.""" + f = _make_factor([5.0, 5.0, 5.0, 5.0, 5.0]) + result = _get_values(f.ts_zscore(3)) + # std = 0 → should return null for all valid windows + assert result[2] is None + assert result[3] is None + assert result[4] is None + + def test_ts_scale_constant_values(self): + """All values identical → max-min = 0 → null.""" + f = _make_factor([3.0, 3.0, 3.0, 3.0]) + result = _get_values(f.ts_scale(3)) + assert result[2] is None + assert result[3] is None + + def test_ts_rank_constant_values(self): + """All values identical → std < EPSILON → null.""" + f = _make_factor([7.0, 7.0, 7.0, 7.0]) + result = _get_values(f.ts_rank(3)) + assert result[2] is None + assert result[3] is None + + def test_ts_corr_constant_x(self): + """One factor is constant → std = 0 → null.""" + f1 = _make_factor([1.0, 2.0, 3.0, 4.0, 5.0]) + f2 = _make_factor([3.0, 3.0, 3.0, 3.0, 3.0]) + result = _get_values(f1.ts_corr(f2, 3)) + # f2 has zero std → correlation is undefined + assert result[2] is None + assert result[3] is None + assert result[4] is None + + def test_ts_beta_zero_variance(self): + """Regressor has zero variance → beta undefined → null.""" + f1 = _make_factor([1.0, 2.0, 3.0, 4.0]) + f2 = _make_factor([5.0, 5.0, 5.0, 5.0]) + result = _get_values(f1.ts_beta(f2, 3)) + assert result[2] is None + assert result[3] is None + + def test_ts_corr_minimum_window(self): + """ts_corr with window < 2 should return all null.""" + f1 = _make_factor([1.0, 2.0, 3.0]) + f2 = _make_factor([4.0, 5.0, 6.0]) + result = _get_values(f1.ts_corr(f2, 1)) + assert all(v is None for v in result) + + +# ══════════════════════════════════════════════════════════ +# 4. Strict NaN Propagation — Cross-Sectional Operations +# ══════════════════════════════════════════════════════════ + + +class TestCsNanPropagation: + """If ANY symbol has NaN at time t, ALL symbols get null at time t.""" + + def test_cs_rank_with_nan(self): + """One symbol has NaN → entire cross-section is null.""" + f = _make_factor( + [1.0, 2.0, 3.0, np.nan, 5.0, 6.0], + symbols=["A", "B"], + ) + result = _get_values(f.cs_rank()) + # t=0: [1, 2] → valid + assert result[0] is not None + assert result[1] is not None + # t=1: [3, NaN] → both null + assert result[2] is None + assert result[3] is None + # t=2: [5, 6] → valid + assert result[4] is not None + assert result[5] is not None + + def test_cs_zscore_with_nan(self): + f = _make_factor( + [1.0, 2.0, np.nan, 4.0], + symbols=["A", "B"], + ) + result = _get_values(f.cs_zscore()) + # t=0: [1, 2] → valid + assert result[0] is not None + assert result[1] is not None + # t=1: [NaN, 4] → both null + assert result[2] is None + assert result[3] is None + + def test_cs_demean_with_nan(self): + f = _make_factor( + [10.0, np.nan, 30.0, 40.0], + symbols=["A", "B"], + ) + result = _get_values(f.cs_demean()) + # t=0: [10, NaN] → both null + assert result[0] is None + assert result[1] is None + # t=1: [30, 40] → valid + assert result[2] is not None + assert result[3] is not None + + def test_cs_winsorize_with_nan(self): + f = _make_factor( + [1.0, 2.0, 3.0, np.nan, 5.0, 6.0, 7.0, 8.0], + symbols=["A", "B", "C", "D"], + ) + result = _get_values(f.cs_winsorize(0.25)) + # t=0: [1, 2, 3, NaN] → all null + assert all(v is None for v in result[:4]) + # t=1: [5, 6, 7, 8] → all valid + assert all(v is not None for v in result[4:]) + + def test_cs_rank_all_valid(self): + f = _make_factor( + [10.0, 20.0, 30.0, 15.0, 25.0, 35.0], + symbols=["A", "B", "C"], + ) + result = _get_values(f.cs_rank()) + assert all(v is not None for v in result) + + +class TestCsDegenerateCases: + """Cross-sectional degenerate cases.""" + + def test_cs_neutralize_constant_regressor(self): + """Regressor has zero variance → beta undefined → null.""" + f = _make_factor( + [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], + symbols=["A", "B", "C"], + ) + const = _make_factor( + [7.0, 7.0, 7.0, 7.0, 7.0, 7.0], + symbols=["A", "B", "C"], + ) + result = _get_values(f.cs_neutralize(const)) + # Constant regressor → var = 0 → null + assert all(v is None for v in result) + + +# ══════════════════════════════════════════════════════════ +# 5. Math Operations Safety +# ══════════════════════════════════════════════════════════ + + +class TestMathSafety: + """Test math operations with domain-safety guards.""" + + def test_log_positive(self): + f = _make_factor([1.0, np.e, 10.0]) + result = _get_values(f.log()) + assert result[0] == pytest.approx(0.0) + assert result[1] == pytest.approx(1.0) + + def test_log_negative(self): + f = _make_factor([-1.0, 0.0, 1.0]) + result = _get_values(f.log()) + assert result[0] is None # log of negative + assert result[1] is None # log of zero + assert result[2] == pytest.approx(0.0) + + def test_sqrt_negative(self): + f = _make_factor([-4.0, 0.0, 4.0]) + result = _get_values(f.sqrt()) + assert result[0] is None # sqrt of negative + assert result[1] is None # sqrt of zero (factor > 0 is strict) + assert result[2] == pytest.approx(2.0) + + def test_log_with_base(self): + f = _make_factor([1.0, 10.0, 100.0]) + result = _get_values(f.log(base=10)) + assert result[0] == pytest.approx(0.0) + assert result[1] == pytest.approx(1.0) + assert result[2] == pytest.approx(2.0) + + +# ══════════════════════════════════════════════════════════ +# 6. Multi-Symbol Consistency +# ══════════════════════════════════════════════════════════ + + +class TestMultiSymbolConsistency: + """Ensure operations are applied per-symbol for ts_* and per-time for cs_*.""" + + def test_ts_mean_per_symbol(self): + """Each symbol should have independent rolling mean.""" + f = _make_factor( + [1.0, 10.0, 2.0, 20.0, 3.0, 30.0], + symbols=["A", "B"], + ) + result = f.data.sort(["symbol", "end_time"]) + a_vals = result.filter(pl.col("symbol") == "A")["factor"].to_list() + b_vals = result.filter(pl.col("symbol") == "B")["factor"].to_list() + + result_mean = f.ts_mean(2).data.sort(["symbol", "end_time"]) + a_mean = result_mean.filter(pl.col("symbol") == "A")["factor"].to_list() + b_mean = result_mean.filter(pl.col("symbol") == "B")["factor"].to_list() + + assert a_mean[0] is None # incomplete + assert a_mean[1] == pytest.approx(1.5) # mean(1, 2) + assert a_mean[2] == pytest.approx(2.5) # mean(2, 3) + assert b_mean[0] is None + assert b_mean[1] == pytest.approx(15.0) # mean(10, 20) + assert b_mean[2] == pytest.approx(25.0) # mean(20, 30) + + def test_cs_rank_per_time(self): + """Cross-sectional rank is computed per time step.""" + f = _make_factor( + [10.0, 20.0, 30.0, 100.0, 50.0, 1.0], + symbols=["A", "B", "C"], + ) + result = f.cs_rank().data.sort(["end_time", "symbol"]) + vals = result["factor"].to_list() + + # t=0: A=10 (rank 1/3), B=20 (rank 2/3), C=30 (rank 3/3) + assert vals[0] == pytest.approx(1 / 3) + assert vals[1] == pytest.approx(2 / 3) + assert vals[2] == pytest.approx(3 / 3) + + +# ══════════════════════════════════════════════════════════ +# 7. EPSILON Threshold Tests +# ══════════════════════════════════════════════════════════ + + +class TestEpsilonThreshold: + """Test the EPSILON boundary precisely.""" + + def test_epsilon_value(self): + """EPSILON should be 1e-10.""" + assert EPSILON == 1e-10 + + def test_division_at_epsilon(self): + """Division by exactly EPSILON should return null.""" + f1 = _make_factor([1.0]) + f2 = _make_factor([EPSILON]) + result = _get_values(f1 / f2) + assert result[0] is None + + def test_division_just_above_epsilon(self): + """Division by value slightly above EPSILON should succeed.""" + f1 = _make_factor([1.0]) + f2 = _make_factor([EPSILON * 2]) + result = _get_values(f1 / f2) + assert result[0] is not None + assert result[0] == pytest.approx(1.0 / (EPSILON * 2)) + + def test_inverse_at_epsilon(self): + f = _make_factor([EPSILON]) + result = _get_values(f.inverse()) + assert result[0] is None + + def test_inverse_just_above_epsilon(self): + f = _make_factor([EPSILON * 2]) + result = _get_values(f.inverse()) + assert result[0] is not None From 4f44bcd39208a84674602b447ec8b975bb38183f Mon Sep 17 00:00:00 2001 From: novis10813 Date: Fri, 13 Feb 2026 17:37:01 +0800 Subject: [PATCH 05/25] fix: align daily processing boundaries to UTC midnight When _calculate_date_range used datetime.now() directly (with arbitrary time component like 09:32:22 UTC), the day-by-day processing loop in load_aggbar would split the same 1-minute bucket across two adjacent daily DuckDB queries. This produced duplicate bars with partial OHLCV data at every daily boundary (e.g., every day at 09:32 UTC). Fix: snap start/end to UTC midnight boundaries when no explicit dates are provided. This ensures daily query boundaries always align with bar interval bucket boundaries, eliminating duplicate bars. Changes: - loader.py: _calculate_date_range now uses today_midnight + 1 day as end and end - days as start - downloader.py: same alignment for consistency - tests: update freeze_time assertions to expect midnight-aligned dates, add test_midnight_alignment_regardless_of_time --- src/factorium/data/downloader.py | 14 ++++++++++---- src/factorium/data/loader.py | 18 ++++++++++++++---- tests/test_data_loader.py | 30 ++++++++++++++++++++++++------ 3 files changed, 48 insertions(+), 14 deletions(-) diff --git a/src/factorium/data/downloader.py b/src/factorium/data/downloader.py index 7e29078..930799f 100644 --- a/src/factorium/data/downloader.py +++ b/src/factorium/data/downloader.py @@ -211,7 +211,11 @@ def _validate_params(self, data_type: str, market_type: str, futures_type: str) def _calculate_date_range( self, start_date: str | None, end_date: str | None, days: int | None ) -> tuple[datetime, datetime]: - """Calculate date range.""" + """Calculate date range aligned to UTC midnight. + + All returned datetimes are snapped to midnight (00:00:00) to ensure + consistency with the loader's daily processing boundaries. + """ try: if start_date and end_date: try: @@ -226,13 +230,15 @@ def _calculate_date_range( self.logger.error(f"Invalid date format: {str(e)}") raise - end = datetime.now() + # Snap to UTC midnight for consistent daily boundaries + today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) + end = today_midnight + timedelta(days=1) if days: if days < 1: raise ValueError("Days must be greater than 0") - start = end - timedelta(days=days - 1) + start = end - timedelta(days=days) else: - start = end - timedelta(days=6) + start = end - timedelta(days=7) return start, end diff --git a/src/factorium/data/loader.py b/src/factorium/data/loader.py index c88ace9..d87f483 100644 --- a/src/factorium/data/loader.py +++ b/src/factorium/data/loader.py @@ -517,7 +517,14 @@ def load_aggbar( def _calculate_date_range( self, start_date: str | None, end_date: str | None, days: int | None ) -> tuple[datetime, datetime]: - """Calculate date range.""" + """Calculate date range aligned to UTC midnight. + + All returned datetimes are snapped to midnight (00:00:00) to ensure + daily processing boundaries align with bar interval buckets. + Without this alignment, a boundary like 09:32:22 UTC would split + the 09:32 minute-bucket across two adjacent daily queries, + producing duplicate bars with partial OHLCV data. + """ if start_date and end_date: return (datetime.strptime(start_date, "%Y-%m-%d"), datetime.strptime(end_date, "%Y-%m-%d")) @@ -527,11 +534,14 @@ def _calculate_date_range( datetime.strptime(start_date, "%Y-%m-%d") + timedelta(days=days), ) - end = datetime.now() + # Snap to UTC midnight to align daily processing boundaries + today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) + # end = start of tomorrow (exclusive) to include today's full data + end = today_midnight + timedelta(days=1) if days: - start = end - timedelta(days=days - 1) + start = end - timedelta(days=days) else: - start = end - timedelta(days=6) + start = end - timedelta(days=7) return start, end diff --git a/tests/test_data_loader.py b/tests/test_data_loader.py index 1a5c49c..b726062 100644 --- a/tests/test_data_loader.py +++ b/tests/test_data_loader.py @@ -147,19 +147,37 @@ def test_with_start_date_and_days(self, loader): @freeze_time("2024-06-15 12:00:00") def test_with_only_days(self, loader): - """Test with only days specified (should use today as end).""" + """Test with only days specified — aligned to UTC midnight.""" start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=7) - assert end_dt == datetime(2024, 6, 15, 12, 0, 0) - assert start_dt == end_dt - timedelta(days=6) # days-1 = 6 + # end = start of tomorrow (exclusive), start = end - days + assert end_dt == datetime(2024, 6, 16, 0, 0, 0) + assert start_dt == datetime(2024, 6, 9, 0, 0, 0) + # Both must be midnight-aligned + assert start_dt.hour == 0 and start_dt.minute == 0 and start_dt.second == 0 + assert end_dt.hour == 0 and end_dt.minute == 0 and end_dt.second == 0 @freeze_time("2024-06-15 12:00:00") def test_default_7_days(self, loader): - """Test default behavior (no params = 7 days ending today).""" + """Test default behavior (no params = 7 days ending tomorrow midnight).""" start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=None) - assert end_dt == datetime(2024, 6, 15, 12, 0, 0) - assert start_dt == end_dt - timedelta(days=6) + assert end_dt == datetime(2024, 6, 16, 0, 0, 0) + assert start_dt == datetime(2024, 6, 9, 0, 0, 0) + # Both must be midnight-aligned + assert start_dt.hour == 0 and start_dt.minute == 0 and start_dt.second == 0 + assert end_dt.hour == 0 and end_dt.minute == 0 and end_dt.second == 0 + + @freeze_time("2024-06-15 23:59:59") + def test_midnight_alignment_regardless_of_time(self, loader): + """Test that date range is always midnight-aligned, even when called at 23:59:59.""" + start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=3) + + # Should snap to midnight boundaries + assert start_dt == datetime(2024, 6, 13, 0, 0, 0) + assert end_dt == datetime(2024, 6, 16, 0, 0, 0) + assert start_dt.microsecond == 0 + assert end_dt.microsecond == 0 def test_cross_month_range(self, loader): """Test date range crossing month boundary.""" From ef7ddc3792fa17ef028caae8cb67c07e3ecbc72b Mon Sep 17 00:00:00 2001 From: novis10813 Date: Fri, 13 Feb 2026 17:55:54 +0800 Subject: [PATCH 06/25] refactor(data): extract date range calculation to shared utility function - Create calculate_date_range() in data/utils.py for reusable date logic - Remove duplicate _calculate_date_range() from BinanceDataLoader and BinanceDataDownloader - Use shared get_market_string() from parquet.py instead of local implementation - Update tests to call utility function directly instead of private methods - Export calculate_date_range from data module public API This addresses PR review feedback to consolidate duplicated time calculation logic. --- src/factorium/data/__init__.py | 2 + src/factorium/data/downloader.py | 40 +----------------- src/factorium/data/loader.py | 42 ++----------------- src/factorium/data/utils.py | 70 ++++++++++++++++++++++++++++++++ tests/test_data_loader.py | 41 ++++++++++--------- 5 files changed, 99 insertions(+), 96 deletions(-) create mode 100644 src/factorium/data/utils.py diff --git a/src/factorium/data/__init__.py b/src/factorium/data/__init__.py index e7987fd..63ba728 100644 --- a/src/factorium/data/__init__.py +++ b/src/factorium/data/__init__.py @@ -21,6 +21,7 @@ get_market_string, read_hive_parquet, ) +from .utils import calculate_date_range __all__ = [ "BinanceDataLoader", @@ -33,4 +34,5 @@ "build_hive_path", "get_market_string", "BINANCE_COLUMNS", + "calculate_date_range", ] diff --git a/src/factorium/data/downloader.py b/src/factorium/data/downloader.py index 930799f..356270a 100644 --- a/src/factorium/data/downloader.py +++ b/src/factorium/data/downloader.py @@ -17,6 +17,7 @@ import aiohttp from .parquet import build_hive_path, csv_to_parquet, get_market_string +from .utils import calculate_date_range class BinanceDataDownloader: @@ -82,7 +83,7 @@ async def download_data( days: Number of days to download """ self._validate_params(data_type, market_type, futures_type) - start_date_dt, end_date_dt = self._calculate_date_range(start_date, end_date, days) + start_date_dt, end_date_dt = calculate_date_range(start_date, end_date, days) download_dir = self._setup_download_dir(symbol, data_type, market_type, futures_type) dates = self._generate_date_list(start_date_dt, end_date_dt) @@ -208,43 +209,6 @@ def _validate_params(self, data_type: str, market_type: str, futures_type: str) if market_type == "futures" and futures_type not in ["cm", "um"]: raise ValueError("Invalid futures type") - def _calculate_date_range( - self, start_date: str | None, end_date: str | None, days: int | None - ) -> tuple[datetime, datetime]: - """Calculate date range aligned to UTC midnight. - - All returned datetimes are snapped to midnight (00:00:00) to ensure - consistency with the loader's daily processing boundaries. - """ - try: - if start_date and end_date: - try: - start = datetime.strptime(start_date, "%Y-%m-%d") - end = datetime.strptime(end_date, "%Y-%m-%d") - - if start > end: - raise ValueError("Start date must be earlier than or equal to end date") - - return start, end - except ValueError as e: - self.logger.error(f"Invalid date format: {str(e)}") - raise - - # Snap to UTC midnight for consistent daily boundaries - today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) - end = today_midnight + timedelta(days=1) - if days: - if days < 1: - raise ValueError("Days must be greater than 0") - start = end - timedelta(days=days) - else: - start = end - timedelta(days=7) - - return start, end - - except Exception as e: - self.logger.error(f"Error calculating date range: {str(e)}") - raise def _setup_download_dir(self, symbol: str, data_type: str, market_type: str, futures_type: str = "cm") -> Path: """Setup download directory (kept for backward compatibility, now uses temp dir).""" diff --git a/src/factorium/data/loader.py b/src/factorium/data/loader.py index d87f483..fb93aa4 100644 --- a/src/factorium/data/loader.py +++ b/src/factorium/data/loader.py @@ -128,6 +128,7 @@ def _normalize_timestamps_to_ms(df: pl.DataFrame, ts_unit: str) -> pl.DataFrame: from .downloader import BinanceDataDownloader # noqa: E402 from .metadata import AggBarMetadata # noqa: E402 from .parquet import get_market_string # noqa: E402 +from .utils import calculate_date_range # noqa: E402 class BinanceDataLoader: @@ -336,7 +337,7 @@ def load_aggbar( ) # Calculate date range - start_dt, end_dt = self._calculate_date_range(start_date, end_date, days) + start_dt, end_dt = calculate_date_range(start_date, end_date, days) # Download missing data if force_download: @@ -368,7 +369,7 @@ def load_aggbar( aggregator = BarAggregator(duckdb_configurator=duckdb_configurator) cache = BarCache(storage=self.storage) if (use_cache and bar_type == "time") else None - market_str = self._get_market_string(market_type, futures_type) + market_str = get_market_string(market_type, futures_type) # Collect aggregated data all_dfs: list[pl.DataFrame] = [] @@ -514,36 +515,6 @@ def load_aggbar( return AggBar(result_df, combined_meta) - def _calculate_date_range( - self, start_date: str | None, end_date: str | None, days: int | None - ) -> tuple[datetime, datetime]: - """Calculate date range aligned to UTC midnight. - - All returned datetimes are snapped to midnight (00:00:00) to ensure - daily processing boundaries align with bar interval buckets. - Without this alignment, a boundary like 09:32:22 UTC would split - the 09:32 minute-bucket across two adjacent daily queries, - producing duplicate bars with partial OHLCV data. - """ - if start_date and end_date: - return (datetime.strptime(start_date, "%Y-%m-%d"), datetime.strptime(end_date, "%Y-%m-%d")) - - if start_date and not end_date and days: - return ( - datetime.strptime(start_date, "%Y-%m-%d"), - datetime.strptime(start_date, "%Y-%m-%d") + timedelta(days=days), - ) - - # Snap to UTC midnight to align daily processing boundaries - today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) - # end = start of tomorrow (exclusive) to include today's full data - end = today_midnight + timedelta(days=1) - if days: - start = end - timedelta(days=days) - else: - start = end - timedelta(days=7) - - return start, end def load_aggbar_fast( self, @@ -599,11 +570,6 @@ def load_aggbar_fast( use_cache=use_cache, ) - def _get_market_string(self, market_type: str, futures_type: str) -> str: - """Get market string for cache key.""" - if market_type == "futures": - return f"futures_{futures_type}" - return market_type def _check_all_symbols_exist( self, @@ -777,7 +743,7 @@ def _load_klines_direct( """ adapter = BinanceAdapter() - market_str = self._get_market_string(market_type, futures_type) + market_str = get_market_string(market_type, futures_type) # Build parquet glob pattern parquet_pattern = adapter.build_parquet_glob( diff --git a/src/factorium/data/utils.py b/src/factorium/data/utils.py new file mode 100644 index 0000000..2bd70a3 --- /dev/null +++ b/src/factorium/data/utils.py @@ -0,0 +1,70 @@ +""" +Shared utilities for data loading and processing. +""" + +import logging +from datetime import datetime, timedelta + +logger = logging.getLogger(__name__) + + +def calculate_date_range( + start_date: str | None = None, + end_date: str | None = None, + days: int | None = None, + default_days: int = 7, +) -> tuple[datetime, datetime]: + """Calculate date range aligned to UTC midnight. + + All returned datetimes are snapped to midnight (00:00:00) to ensure + daily processing boundaries align with bar interval buckets. + Without this alignment, a boundary like 09:32:22 UTC would split + the 09:32 minute-bucket across two adjacent daily queries, + producing duplicate bars with partial OHLCV data. + + Priority: + 1. If both start_date and end_date are provided: [start, end] + 2. If start_date and days are provided: [start, start + days] + 3. If neither: [today_midnight - default_days, today_midnight + 1] + 4. If only days: [today_midnight - days, today_midnight + 1] + + Args: + start_date: Start date string (YYYY-MM-DD) + end_date: End date string (YYYY-MM-DD) + days: Number of days + default_days: Default number of days if none specified (default: 7) + + Returns: + tuple[datetime, datetime]: (start_dt, end_dt) snapped to midnight. + """ + try: + if start_date and end_date: + start = datetime.strptime(start_date, "%Y-%m-%d") + end = datetime.strptime(end_date, "%Y-%m-%d") + if start > end: + raise ValueError("Start date must be earlier than or equal to end date") + return start, end + + if start_date and days: + if days < 1: + raise ValueError("Days must be greater than 0") + start = datetime.strptime(start_date, "%Y-%m-%d") + return start, start + timedelta(days=days) + + # Snap to UTC midnight for consistent daily boundaries + today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) + # end = start of tomorrow (exclusive) to include today's full data + end = today_midnight + timedelta(days=1) + + if days: + if days < 1: + raise ValueError("Days must be greater than 0") + start = end - timedelta(days=days) + else: + start = end - timedelta(days=default_days) + + return start, end + + except Exception as e: + logger.error(f"Error calculating date range: {str(e)}") + raise diff --git a/tests/test_data_loader.py b/tests/test_data_loader.py index b726062..5dc0f8d 100644 --- a/tests/test_data_loader.py +++ b/tests/test_data_loader.py @@ -13,7 +13,7 @@ from freezegun import freeze_time from factorium import BinanceDataLoader, AggBar -from factorium.data import build_hive_path +from factorium.data import build_hive_path, calculate_date_range # ============================================================================= @@ -129,26 +129,26 @@ def create_parquet_file(base_path: Path, market: str, data_type: str, symbol: st class TestCalculateDateRange: - """Tests for BinanceDataLoader._calculate_date_range method.""" + """Tests for calculate_date_range utility function.""" - def test_with_start_and_end_date(self, loader): + def test_with_start_and_end_date(self): """Test with both start_date and end_date specified.""" - start_dt, end_dt = loader._calculate_date_range(start_date="2024-01-01", end_date="2024-01-07", days=None) + start_dt, end_dt = calculate_date_range(start_date="2024-01-01", end_date="2024-01-07", days=None) assert start_dt == datetime(2024, 1, 1) assert end_dt == datetime(2024, 1, 7) - def test_with_start_date_and_days(self, loader): + def test_with_start_date_and_days(self): """Test with start_date and days specified.""" - start_dt, end_dt = loader._calculate_date_range(start_date="2024-01-01", end_date=None, days=7) + start_dt, end_dt = calculate_date_range(start_date="2024-01-01", end_date=None, days=7) assert start_dt == datetime(2024, 1, 1) assert end_dt == datetime(2024, 1, 8) # 7 days after start @freeze_time("2024-06-15 12:00:00") - def test_with_only_days(self, loader): + def test_with_only_days(self): """Test with only days specified — aligned to UTC midnight.""" - start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=7) + start_dt, end_dt = calculate_date_range(start_date=None, end_date=None, days=7) # end = start of tomorrow (exclusive), start = end - days assert end_dt == datetime(2024, 6, 16, 0, 0, 0) @@ -158,9 +158,9 @@ def test_with_only_days(self, loader): assert end_dt.hour == 0 and end_dt.minute == 0 and end_dt.second == 0 @freeze_time("2024-06-15 12:00:00") - def test_default_7_days(self, loader): + def test_default_7_days(self): """Test default behavior (no params = 7 days ending tomorrow midnight).""" - start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=None) + start_dt, end_dt = calculate_date_range(start_date=None, end_date=None, days=None) assert end_dt == datetime(2024, 6, 16, 0, 0, 0) assert start_dt == datetime(2024, 6, 9, 0, 0, 0) @@ -169,9 +169,9 @@ def test_default_7_days(self, loader): assert end_dt.hour == 0 and end_dt.minute == 0 and end_dt.second == 0 @freeze_time("2024-06-15 23:59:59") - def test_midnight_alignment_regardless_of_time(self, loader): + def test_midnight_alignment_regardless_of_time(self): """Test that date range is always midnight-aligned, even when called at 23:59:59.""" - start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=3) + start_dt, end_dt = calculate_date_range(start_date=None, end_date=None, days=3) # Should snap to midnight boundaries assert start_dt == datetime(2024, 6, 13, 0, 0, 0) @@ -179,35 +179,36 @@ def test_midnight_alignment_regardless_of_time(self, loader): assert start_dt.microsecond == 0 assert end_dt.microsecond == 0 - def test_cross_month_range(self, loader): + def test_cross_month_range(self): """Test date range crossing month boundary.""" - start_dt, end_dt = loader._calculate_date_range(start_date="2024-01-28", end_date=None, days=10) + start_dt, end_dt = calculate_date_range(start_date="2024-01-28", end_date=None, days=10) assert start_dt == datetime(2024, 1, 28) assert end_dt == datetime(2024, 2, 7) # Crosses into February - def test_cross_year_range(self, loader): + def test_cross_year_range(self): """Test date range crossing year boundary.""" - start_dt, end_dt = loader._calculate_date_range(start_date="2023-12-28", end_date="2024-01-05", days=None) + start_dt, end_dt = calculate_date_range(start_date="2023-12-28", end_date="2024-01-05", days=None) assert start_dt == datetime(2023, 12, 28) assert end_dt == datetime(2024, 1, 5) - def test_single_day_range(self, loader): + def test_single_day_range(self): """Test single day range (start == end).""" - start_dt, end_dt = loader._calculate_date_range(start_date="2024-01-01", end_date="2024-01-01", days=None) + start_dt, end_dt = calculate_date_range(start_date="2024-01-01", end_date="2024-01-01", days=None) assert start_dt == datetime(2024, 1, 1) assert end_dt == datetime(2024, 1, 1) - def test_start_date_with_one_day(self, loader): + def test_start_date_with_one_day(self): """Test start_date with days=1.""" - start_dt, end_dt = loader._calculate_date_range(start_date="2024-01-01", end_date=None, days=1) + start_dt, end_dt = calculate_date_range(start_date="2024-01-01", end_date=None, days=1) assert start_dt == datetime(2024, 1, 1) assert end_dt == datetime(2024, 1, 2) + # ============================================================================= # TestBuildDateFilter - 日期過濾條件測試 # ============================================================================= From 35756a2cebe50054de449f5eb2f320191ada30f9 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Fri, 13 Feb 2026 19:13:40 +0800 Subject: [PATCH 07/25] restore(universe): re-apply checklist pipeline after temporary revert --- src/factorium/__init__.py | 27 +++++ src/factorium/aggbar.py | 18 ++++ src/factorium/backtest/vectorized.py | 30 +++++- src/factorium/factors/analyzer.py | 13 ++- src/factorium/factors/core.py | 3 +- src/factorium/universe/__init__.py | 27 +++++ src/factorium/universe/checklist.py | 25 +++++ src/factorium/universe/filters.py | 79 +++++++++++++++ src/factorium/universe/metadata.py | 99 ++++++++++++++++++ src/factorium/universe/rules.py | 122 +++++++++++++++++++++++ src/factorium/universe/tags.py | 115 +++++++++++++++++++++ src/factorium/universe/universe.py | 25 +++++ tests/universe/__init__.py | 0 tests/universe/test_aggbar_mask.py | 111 +++++++++++++++++++++ tests/universe/test_backtest_mask.py | 102 +++++++++++++++++++ tests/universe/test_checklist_filters.py | 97 ++++++++++++++++++ tests/universe/test_factor_mask.py | 70 +++++++++++++ tests/universe/test_integration.py | 108 ++++++++++++++++++++ tests/universe/test_metadata.py | 92 +++++++++++++++++ tests/universe/test_rules.py | 24 +++++ tests/universe/test_tags.py | 62 ++++++++++++ tests/universe/test_universe_rules.py | 93 +++++++++++++++++ 22 files changed, 1336 insertions(+), 6 deletions(-) create mode 100644 src/factorium/universe/__init__.py create mode 100644 src/factorium/universe/checklist.py create mode 100644 src/factorium/universe/filters.py create mode 100644 src/factorium/universe/metadata.py create mode 100644 src/factorium/universe/rules.py create mode 100644 src/factorium/universe/tags.py create mode 100644 src/factorium/universe/universe.py create mode 100644 tests/universe/__init__.py create mode 100644 tests/universe/test_aggbar_mask.py create mode 100644 tests/universe/test_backtest_mask.py create mode 100644 tests/universe/test_checklist_filters.py create mode 100644 tests/universe/test_factor_mask.py create mode 100644 tests/universe/test_integration.py create mode 100644 tests/universe/test_metadata.py create mode 100644 tests/universe/test_rules.py create mode 100644 tests/universe/test_tags.py create mode 100644 tests/universe/test_universe_rules.py diff --git a/src/factorium/__init__.py b/src/factorium/__init__.py index c91681f..bfed669 100644 --- a/src/factorium/__init__.py +++ b/src/factorium/__init__.py @@ -34,6 +34,20 @@ from .factors.base import BaseFactor from .factors.core import Factor from .research import ResearchSession +from .universe import ( + Checklist, + ExcludeLeveragedTokens, + ExcludeStablecoins, + FilterRule, + MetadataProvider, + MinLiquidity, + MinListingAge, + MinVolume, + SymbolMetadata, + TagFilter, + TagProvider, + Universe, +) __version__ = "0.2.1" @@ -45,4 +59,17 @@ "ResearchSession", # Data loading "BinanceDataLoader", + # Universe and checklist + "FilterRule", + "SymbolMetadata", + "Universe", + "Checklist", + "ExcludeStablecoins", + "ExcludeLeveragedTokens", + "MinListingAge", + "TagFilter", + "MinVolume", + "MinLiquidity", + "MetadataProvider", + "TagProvider", ] diff --git a/src/factorium/aggbar.py b/src/factorium/aggbar.py index ed47db6..e0a6241 100644 --- a/src/factorium/aggbar.py +++ b/src/factorium/aggbar.py @@ -17,6 +17,8 @@ from .bar import BaseBar from .data.metadata import AggBarMetadata from .factors.core import Factor + from .universe.checklist import Checklist + from .universe.universe import Universe class AggBar: @@ -211,6 +213,22 @@ def convert_timestamp(value: datetime | int | str | None) -> int | None: return AggBar(self._data.filter(cond)) + def with_mask( + self, + name: str, + mask_source: "Universe | Checklist", + metadata: dict, + tags: dict[str, list[str]] | None = None, + ) -> "AggBar": + """Add a boolean mask column and return a new AggBar.""" + protected_cols = {"start_time", "end_time", "symbol", "open", "high", "low", "close", "volume"} + if name in protected_cols: + raise ValueError(f"Cannot use protected column name: {name}") + + mask_expr = mask_source.apply(self._data.lazy(), metadata, tags) + new_data = self._data.with_columns(mask_expr.alias(name)) + return AggBar(new_data, metadata=self._metadata) + @property def cols(self) -> list[str]: """Return list of column names.""" diff --git a/src/factorium/backtest/vectorized.py b/src/factorium/backtest/vectorized.py index bf8ca0a..743b12a 100644 --- a/src/factorium/backtest/vectorized.py +++ b/src/factorium/backtest/vectorized.py @@ -59,6 +59,7 @@ def __init__( neutralization: Literal["market", "none"] = "market", frequency: str = "1h", constraints: list | None = None, + mask: str | None = None, ): """ Initialize the vectorized backtester. @@ -87,16 +88,21 @@ def __init__( self.periods_per_year = frequency_to_periods_per_year(frequency) self._periods_per_year = self.periods_per_year # Alias for backward compatibility self.constraints = constraints or [] + self._mask = mask # Convert inputs to Polars DataFrames if isinstance(prices, AggBar): if entry_price not in prices.cols: raise ValueError(f"entry_price '{entry_price}' not found in prices") + if mask is not None and mask not in prices.cols: + raise ValueError(f"mask '{mask}' not found in prices") self.prices_df = prices.to_polars() else: self.prices_df = prices if entry_price not in prices.columns: raise ValueError(f"entry_price '{entry_price}' not found in prices") + if mask is not None and mask not in prices.columns: + raise ValueError(f"mask '{mask}' not found in prices") if isinstance(signal, Factor): self.signal_df = signal.lazy.collect() @@ -145,7 +151,10 @@ def summary(self) -> dict[str, Any]: def _prepare_data(self) -> pl.DataFrame: """Merge prices and signals, shift signals to avoid lookahead bias.""" # Get the entry price column - prices_df = self.prices_df.select(["end_time", "symbol", self.entry_price]).rename({self.entry_price: "price"}) + price_cols = ["end_time", "symbol", self.entry_price] + if self._mask is not None: + price_cols.append(self._mask) + prices_df = self.prices_df.select(price_cols).rename({self.entry_price: "price"}) # Prepare signal data signal_df = self.signal_df.select(["end_time", "symbol", "factor"]).rename({"factor": "signal"}) @@ -163,18 +172,33 @@ def _prepare_data(self) -> pl.DataFrame: def _calculate_weights(self, df: pl.DataFrame) -> pl.DataFrame: """Calculate portfolio weights (cross-sectional).""" + signal_col = "prev_signal" + if self._mask is not None: + signal_col = "_masked_signal" + df = df.with_columns( + pl.when(pl.col(self._mask).fill_null(False)) + .then(pl.col("prev_signal")) + .otherwise(None) + .alias(signal_col) + ) + if self.neutralization == "market": # Market neutral: (signal - mean) / sum(|signal - mean|) from .utils import neutralize_weights_polars - df = neutralize_weights_polars(df, "prev_signal", "end_time") + df = neutralize_weights_polars(df, signal_col, "end_time") else: # long-only # Normalize positive signals to sum to 1 - positive_only = pl.when(pl.col("prev_signal") > 0).then(pl.col("prev_signal")).otherwise(0.0) + positive_only = pl.when(pl.col(signal_col) > 0).then(pl.col(signal_col)).otherwise(0.0) df = df.with_columns( [(positive_only / positive_only.sum().over("end_time")).fill_nan(0.0).fill_null(0.0).alias("weight")] ) + if self._mask is not None: + df = df.with_columns( + pl.when(pl.col(self._mask).fill_null(False)).then(pl.col("weight")).otherwise(0.0).alias("weight") + ).drop("_masked_signal") + # Apply constraints for constraint in self.constraints: df = constraint.apply(df) diff --git a/src/factorium/factors/analyzer.py b/src/factorium/factors/analyzer.py index 7c6c152..b90efc4 100644 --- a/src/factorium/factors/analyzer.py +++ b/src/factorium/factors/analyzer.py @@ -222,10 +222,11 @@ class FactorAnalyzer: prices: Factor | None # Type annotation for prices attribute - def __init__(self, factor: Factor, prices: AggBar | Factor, quantiles: int = 5): + def __init__(self, factor: Factor, prices: AggBar | Factor, quantiles: int = 5, mask: str | None = None): self.factor = factor self.quantiles = quantiles self._raw_prices = prices + self._mask = mask if isinstance(prices, AggBar): try: close_col = prices["close"] @@ -340,7 +341,12 @@ def prepare_data(self, periods: list[int] | None = None, price_col: str | None = if price_col not in self._raw_prices._data.columns: available_cols = ", ".join(sorted(self._raw_prices._data.columns)) raise ValueError(f"Price column '{price_col}' not found in AggBar. Available columns: {available_cols}") - prices_lf = self._raw_prices.to_polars().lazy().select(["start_time", "end_time", "symbol", price_col]) + select_cols = ["start_time", "end_time", "symbol", price_col] + if self._mask is not None: + if self._mask not in self._raw_prices._data.columns: + raise ValueError(f"Mask column '{self._mask}' not found in AggBar") + select_cols.append(self._mask) + prices_lf = self._raw_prices.to_polars().lazy().select(select_cols) price_col_name = price_col elif self.prices is not None: # self.prices is a Factor (we've narrowed the type above) @@ -357,6 +363,9 @@ def prepare_data(self, periods: list[int] | None = None, price_col: str | None = how="inner", ) + if self._mask is not None and isinstance(self._raw_prices, AggBar): + df_lf = df_lf.filter(pl.col(self._mask).fill_null(False)) + # Calculate forward returns for each period # return = (price.shift(-p) / price) - 1.0 return_exprs = [] diff --git a/src/factorium/factors/core.py b/src/factorium/factors/core.py index 871b1ea..2168056 100644 --- a/src/factorium/factors/core.py +++ b/src/factorium/factors/core.py @@ -68,6 +68,7 @@ def eval( quantiles: int = 5, output_dir: str | None = None, price_col: str = "close", + mask: str | None = None, **kwargs, ) -> "FactorAnalysisResult": """ @@ -92,7 +93,7 @@ def eval( """ from .analyzer import FactorAnalyzer - analyzer = FactorAnalyzer(factor=self, prices=prices, quantiles=quantiles) + analyzer = FactorAnalyzer(factor=self, prices=prices, quantiles=quantiles, mask=mask) result = analyzer.analyze(price_col=price_col, periods=periods) diff --git a/src/factorium/universe/__init__.py b/src/factorium/universe/__init__.py new file mode 100644 index 0000000..ffe3b20 --- /dev/null +++ b/src/factorium/universe/__init__.py @@ -0,0 +1,27 @@ +from .checklist import Checklist +from .filters import MinLiquidity, MinVolume, TagFilter +from .metadata import MetadataProvider +from .rules import ( + ExcludeLeveragedTokens, + ExcludeStablecoins, + FilterRule, + MinListingAge, + SymbolMetadata, +) +from .tags import TagProvider +from .universe import Universe + +__all__ = [ + "Checklist", + "ExcludeLeveragedTokens", + "ExcludeStablecoins", + "FilterRule", + "MinLiquidity", + "MinListingAge", + "MinVolume", + "MetadataProvider", + "SymbolMetadata", + "TagFilter", + "TagProvider", + "Universe", +] diff --git a/src/factorium/universe/checklist.py b/src/factorium/universe/checklist.py new file mode 100644 index 0000000..dc0319b --- /dev/null +++ b/src/factorium/universe/checklist.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +import polars as pl + +from .rules import FilterRule, SymbolMetadata + + +class Checklist: + """Research checklist built from inclusion filters.""" + + def __init__(self, filters: list[FilterRule]) -> None: + if not filters: + raise ValueError("Checklist must have at least one filter") + self.filters = filters + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + combined = self.filters[0].apply(df, metadata, tags) + for flt in self.filters[1:]: + combined = combined & flt.apply(df, metadata, tags) + return combined diff --git a/src/factorium/universe/filters.py b/src/factorium/universe/filters.py new file mode 100644 index 0000000..5f8b7c6 --- /dev/null +++ b/src/factorium/universe/filters.py @@ -0,0 +1,79 @@ +from __future__ import annotations + +import polars as pl + +from .rules import SymbolMetadata + + +class TagFilter: + """Filter symbols by include/exclude tag sets.""" + + def __init__(self, include: list[str] | None = None, exclude: list[str] | None = None) -> None: + if include is None and exclude is None: + raise ValueError("At least one of include or exclude must be specified") + self._include = set(include) if include else None + self._exclude = set(exclude) if exclude else None + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + del df + if tags is None: + raise ValueError("TagFilter requires tags, but tags=None was provided") + + included_symbols: set[str] = set() + for sym, meta in metadata.items(): + base_asset = meta.get("base_asset", "") + symbol_tags = set(tags.get(base_asset, [])) + + if self._include is not None and not (symbol_tags & self._include): + continue + + if self._exclude is not None and (symbol_tags & self._exclude): + continue + + included_symbols.add(sym) + + return pl.col("symbol").is_in(included_symbols) + + +class MinVolume: + """Filter symbols by rolling mean traded volume.""" + + def __init__(self, window: int = 20, threshold: float = 1_000_000) -> None: + self._window = window + self._threshold = threshold + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + del df, metadata, tags + rolling_volume = ( + pl.col("volume").rolling_mean(window_size=self._window, min_samples=self._window).over("symbol") + ) + return (rolling_volume >= self._threshold).fill_null(False) + + +class MinLiquidity: + """Filter symbols by rolling mean notional turnover.""" + + def __init__(self, window: int = 20, threshold: float = 100_000) -> None: + self._window = window + self._threshold = threshold + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + del df, metadata, tags + liquidity = pl.col("volume") * pl.col("close") + rolling_liquidity = liquidity.rolling_mean(window_size=self._window, min_samples=self._window).over("symbol") + return (rolling_liquidity >= self._threshold).fill_null(False) diff --git a/src/factorium/universe/metadata.py b/src/factorium/universe/metadata.py new file mode 100644 index 0000000..09ca6cd --- /dev/null +++ b/src/factorium/universe/metadata.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +import asyncio +import json +import time +from pathlib import Path + +import aiohttp + +from .rules import KNOWN_STABLECOINS, LEVERAGED_PATTERNS, SymbolMetadata + + +ENDPOINTS = { + "spot": "https://api.binance.com/api/v3/exchangeInfo", + "um": "https://fapi.binance.com/fapi/v1/exchangeInfo", + "cm": "https://dapi.binance.com/dapi/v1/exchangeInfo", +} + + +class MetadataProvider: + """Fetch and cache symbol metadata from Binance exchangeInfo.""" + + def __init__(self, market: str = "um", cache_dir: str | Path = "./Data/metadata", cache_ttl: int = 86400) -> None: + if market not in ENDPOINTS: + raise ValueError(f"Unsupported market: {market}") + self.market = market + self.cache_dir = Path(cache_dir) + self.cache_ttl = cache_ttl + self._cache_path = self.cache_dir / f"{self.market}_exchange_info.json" + + async def _request_json(self, session: aiohttp.ClientSession, url: str) -> dict: + async with session.get(url) as response: + response.raise_for_status() + return await response.json() + + async def fetch_async(self) -> dict[str, SymbolMetadata]: + cached = self._load_cache() + if cached is not None: + return cached + + endpoint = ENDPOINTS[self.market] + async with aiohttp.ClientSession() as session: + payload = await self._request_json(session, endpoint) + + parsed = self._parse_exchange_info(payload) + self._save_cache(parsed) + return parsed + + def fetch(self) -> dict[str, SymbolMetadata]: + return asyncio.run(self.fetch_async()) + + def _parse_exchange_info(self, data: dict) -> dict[str, SymbolMetadata]: + output: dict[str, SymbolMetadata] = {} + for item in data.get("symbols", []): + symbol = item.get("symbol") + if not symbol: + continue + + base_asset = item.get("baseAsset", "") + onboard_date = item.get("onboardDate") + listing_date = int(onboard_date) if isinstance(onboard_date, (int, float)) else None + + output[symbol] = { + "symbol": symbol, + "base_asset": base_asset, + "quote_asset": item.get("quoteAsset", ""), + "status": item.get("status", ""), + "listing_date": listing_date, + "is_leveraged": bool(LEVERAGED_PATTERNS.search(base_asset)), + "is_stablecoin_pair": base_asset in KNOWN_STABLECOINS, + } + + return output + + def _load_cache(self) -> dict[str, SymbolMetadata] | None: + if not self._cache_path.exists(): + return None + + try: + payload = json.loads(self._cache_path.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError): + return None + + saved_at = payload.get("saved_at") + if not isinstance(saved_at, (int, float)): + return None + + if time.time() - float(saved_at) > self.cache_ttl: + return None + + data = payload.get("data") + if not isinstance(data, dict): + return None + return data + + def _save_cache(self, data: dict[str, SymbolMetadata]) -> None: + self.cache_dir.mkdir(parents=True, exist_ok=True) + payload = {"saved_at": time.time(), "data": data} + self._cache_path.write_text(json.dumps(payload), encoding="utf-8") diff --git a/src/factorium/universe/rules.py b/src/factorium/universe/rules.py new file mode 100644 index 0000000..440a172 --- /dev/null +++ b/src/factorium/universe/rules.py @@ -0,0 +1,122 @@ +from __future__ import annotations + +import re +from typing import Protocol, TypedDict, runtime_checkable + +import polars as pl + + +class SymbolMetadata(TypedDict, total=False): + """Metadata for a single trading symbol.""" + + symbol: str + base_asset: str + quote_asset: str + status: str + listing_date: int + is_leveraged: bool + is_stablecoin_pair: bool + + +@runtime_checkable +class FilterRule(Protocol): + """Shared interface for Universe rules and Checklist filters.""" + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: ... + + +KNOWN_STABLECOINS = frozenset( + { + "USDT", + "USDC", + "BUSD", + "DAI", + "TUSD", + "FDUSD", + "USDP", + "USDD", + "UST", + "FRAX", + "LUSD", + "SUSD", + "GUSD", + "USDJ", + "EUR", + "GBP", + "AEUR", + } +) + + +LEVERAGED_PATTERNS = re.compile(r"(UP|DOWN|BEAR|BULL|[0-9]+[LS])$", re.IGNORECASE) + + +class ExcludeStablecoins: + """Exclude symbols where base asset is a stablecoin.""" + + def __init__(self, extra_stablecoins: set[str] | None = None) -> None: + self._extra = extra_stablecoins or set() + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + del df, tags + all_stables = KNOWN_STABLECOINS | self._extra + excluded_symbols = [ + sym + for sym, meta in metadata.items() + if meta.get("base_asset", "") in all_stables or bool(meta.get("is_stablecoin_pair", False)) + ] + return ~pl.col("symbol").is_in(excluded_symbols) + + +class ExcludeLeveragedTokens: + """Exclude leveraged-token symbols.""" + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + del df, tags + excluded_symbols = [ + sym + for sym, meta in metadata.items() + if bool(meta.get("is_leveraged", False)) or bool(LEVERAGED_PATTERNS.search(meta.get("base_asset", ""))) + ] + return ~pl.col("symbol").is_in(excluded_symbols) + + +class MinListingAge: + """Exclude symbols younger than configured listing days.""" + + def __init__(self, days: int = 90) -> None: + self._min_ms = days * 86_400_000 + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + del df, tags + listing_map: dict[str, int] = {} + for sym, meta in metadata.items(): + listing_date = meta.get("listing_date") + if listing_date is not None: + listing_map[sym] = int(listing_date) + + if not listing_map: + return pl.lit(True) + + listing_expr = pl.col("symbol").replace_strict(listing_map, default=None).cast(pl.Int64, strict=False) + return ((pl.col("start_time") - listing_expr) >= self._min_ms) | listing_expr.is_null() diff --git a/src/factorium/universe/tags.py b/src/factorium/universe/tags.py new file mode 100644 index 0000000..d55dfb1 --- /dev/null +++ b/src/factorium/universe/tags.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import asyncio +import json +import time +from pathlib import Path + +import aiohttp + + +COINGECKO_BASE_URL = "https://api.coingecko.com/api/v3" + + +class TagProvider: + """Fetch and cache token categories from CoinGecko.""" + + def __init__( + self, + cache_dir: str | Path = "./Data/metadata", + cache_ttl: int = 604800, + api_key: str | None = None, + ) -> None: + self.cache_dir = Path(cache_dir) + self.cache_ttl = cache_ttl + self.api_key = api_key + self._cache_path = self.cache_dir / "coingecko_tags.json" + + async def _request_json( + self, + session: aiohttp.ClientSession, + url: str, + params: dict | None = None, + headers: dict[str, str] | None = None, + ) -> dict | list: + async with session.get(url, params=params, headers=headers) as response: + response.raise_for_status() + return await response.json() + + async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[str]]: + requested = [s.upper() for s in symbols] if symbols is not None else None + cached = self._load_cache() + + if cached is not None: + if requested is None: + return cached + if all(sym in cached for sym in requested): + return {sym: cached[sym] for sym in requested} + + headers: dict[str, str] | None = None + if self.api_key: + headers = {"x-cg-pro-api-key": self.api_key} + + async with aiohttp.ClientSession() as session: + raw_list = await self._request_json(session, f"{COINGECKO_BASE_URL}/coins/list", headers=headers) + symbol_to_id: dict[str, str] = {} + for item in raw_list if isinstance(raw_list, list) else []: + symbol = str(item.get("symbol", "")).upper() + coin_id = item.get("id") + if symbol and coin_id and symbol not in symbol_to_id: + symbol_to_id[symbol] = str(coin_id) + + targets = requested or sorted(symbol_to_id.keys()) + result: dict[str, list[str]] = {} if cached is None else dict(cached) + + for symbol in targets: + if symbol in result: + continue + + coin_id = symbol_to_id.get(symbol) + if not coin_id: + continue + + detail = await self._request_json(session, f"{COINGECKO_BASE_URL}/coins/{coin_id}", headers=headers) + categories = detail.get("categories", []) if isinstance(detail, dict) else [] + result[symbol] = [str(tag) for tag in categories] + await asyncio.sleep(0.12) + + self._save_cache(result) + if requested is None: + return result + return {sym: result.get(sym, []) for sym in requested if sym in result} + + def fetch(self, symbols: list[str] | None = None) -> dict[str, list[str]]: + return asyncio.run(self.fetch_async(symbols=symbols)) + + def _load_cache(self) -> dict[str, list[str]] | None: + if not self._cache_path.exists(): + return None + + try: + payload = json.loads(self._cache_path.read_text(encoding="utf-8")) + except (json.JSONDecodeError, OSError): + return None + + saved_at = payload.get("saved_at") + if not isinstance(saved_at, (int, float)): + return None + + if time.time() - float(saved_at) > self.cache_ttl: + return None + + data = payload.get("data") + if not isinstance(data, dict): + return None + + out: dict[str, list[str]] = {} + for key, value in data.items(): + if isinstance(value, list): + out[str(key)] = [str(v) for v in value] + return out + + def _save_cache(self, data: dict[str, list[str]]) -> None: + self.cache_dir.mkdir(parents=True, exist_ok=True) + payload = {"saved_at": time.time(), "data": data} + self._cache_path.write_text(json.dumps(payload), encoding="utf-8") diff --git a/src/factorium/universe/universe.py b/src/factorium/universe/universe.py new file mode 100644 index 0000000..ea57c58 --- /dev/null +++ b/src/factorium/universe/universe.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +import polars as pl + +from .rules import FilterRule, SymbolMetadata + + +class Universe: + """Trading universe built from exclusion rules.""" + + def __init__(self, rules: list[FilterRule]) -> None: + if not rules: + raise ValueError("Universe must have at least one rule") + self.rules = rules + + def apply( + self, + df: pl.LazyFrame, + metadata: dict[str, SymbolMetadata], + tags: dict[str, list[str]] | None = None, + ) -> pl.Expr: + combined = self.rules[0].apply(df, metadata, tags) + for rule in self.rules[1:]: + combined = combined & rule.apply(df, metadata, tags) + return combined diff --git a/tests/universe/__init__.py b/tests/universe/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/universe/test_aggbar_mask.py b/tests/universe/test_aggbar_mask.py new file mode 100644 index 0000000..47d7c83 --- /dev/null +++ b/tests/universe/test_aggbar_mask.py @@ -0,0 +1,111 @@ +import polars as pl +import pytest + +from factorium import AggBar +from factorium.universe import ( + Checklist, + ExcludeStablecoins, + MinListingAge, + TagFilter, + Universe, +) + + +DAY_MS = 86_400_000 +BASE_TS = 1_700_000_000_000 + + +class SymbolOnlyRule: + def __init__(self, allowed: set[str]) -> None: + self.allowed = allowed + + def apply(self, df: pl.LazyFrame, metadata: dict, tags: dict[str, list[str]] | None = None) -> pl.Expr: + del df, metadata, tags + return pl.col("symbol").is_in(self.allowed) + + +def _sample_aggbar() -> AggBar: + rows = [] + for i in range(2): + ts = BASE_TS + i * DAY_MS + for symbol, close in [("BTCUSDT", 100.0), ("USDCUSDT", 1.0), ("NEWUSDT", 10.0)]: + rows.append( + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": symbol, + "open": close, + "high": close, + "low": close, + "close": close, + "volume": 1_000.0, + } + ) + return AggBar(pl.DataFrame(rows)) + + +def _metadata() -> dict[str, dict]: + return { + "BTCUSDT": { + "symbol": "BTCUSDT", + "base_asset": "BTC", + "quote_asset": "USDT", + "listing_date": BASE_TS - 500 * DAY_MS, + }, + "USDCUSDT": { + "symbol": "USDCUSDT", + "base_asset": "USDC", + "quote_asset": "USDT", + "is_stablecoin_pair": True, + "listing_date": BASE_TS - 500 * DAY_MS, + }, + "NEWUSDT": {"symbol": "NEWUSDT", "base_asset": "NEW", "quote_asset": "USDT", "listing_date": BASE_TS - DAY_MS}, + } + + +def test_with_mask_returns_new_aggbar_without_mutating_original() -> None: + agg = _sample_aggbar() + masked = agg.with_mask("in_universe", SymbolOnlyRule({"BTCUSDT"}), _metadata()) + + assert "in_universe" not in agg.cols + assert "in_universe" in masked.cols + values = masked.to_polars().filter(pl.col("symbol") == "BTCUSDT")["in_universe"].to_list() + assert values == [True, True] + + +def test_with_mask_rejects_protected_column_name() -> None: + agg = _sample_aggbar() + with pytest.raises(ValueError, match="protected column name"): + agg.with_mask("close", SymbolOnlyRule({"BTCUSDT"}), _metadata()) + + +def test_getitem_factor_does_not_include_mask_column() -> None: + agg = _sample_aggbar().with_mask("in_universe", SymbolOnlyRule({"BTCUSDT"}), _metadata()) + + factor = agg["close"] + assert factor.lazy.collect().columns == ["start_time", "end_time", "symbol", "factor"] + + +def test_universe_and_checklist_masks_can_be_composed() -> None: + agg = _sample_aggbar() + metadata = _metadata() + tags = {"BTC": ["layer1"], "USDC": ["stablecoin"], "NEW": ["meme"]} + + universe = Universe([ExcludeStablecoins(), MinListingAge(days=2)]) + checklist = Checklist([TagFilter(include=["layer1"])]) + + with_universe = agg.with_mask("in_universe", universe, metadata) + with_checklist = with_universe.with_mask("in_checklist", checklist, metadata, tags) + + df = with_checklist.to_polars() + kept = set(df.filter(pl.col("in_universe") & pl.col("in_checklist"))["symbol"].to_list()) + assert kept == {"BTCUSDT"} + + +def test_with_mask_supports_time_varying_rule() -> None: + agg = _sample_aggbar() + metadata = _metadata() + masked = agg.with_mask("old_enough", Universe([MinListingAge(days=2)]), metadata) + + new_values = masked.to_polars().filter(pl.col("symbol") == "NEWUSDT").sort("start_time")["old_enough"].to_list() + assert new_values == [False, True] diff --git a/tests/universe/test_backtest_mask.py b/tests/universe/test_backtest_mask.py new file mode 100644 index 0000000..707a94c --- /dev/null +++ b/tests/universe/test_backtest_mask.py @@ -0,0 +1,102 @@ +import polars as pl + +from factorium import AggBar +from factorium.backtest.vectorized import VectorizedBacktester + + +def _make_prices() -> AggBar: + rows = [] + base_ts = 1_700_000_000_000 + for i in range(6): + ts = base_ts + i * 3_600_000 + rows.extend( + [ + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "A", + "open": 100.0 + i, + "high": 101.0 + i, + "low": 99.0 + i, + "close": 100.0 + i, + "volume": 1_000.0, + "in_universe": True, + }, + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "B", + "open": 50.0 + i, + "high": 51.0 + i, + "low": 49.0 + i, + "close": 50.0 + i, + "volume": 1_000.0, + "in_universe": True, + }, + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "C", + "open": 10.0 + i, + "high": 11.0 + i, + "low": 9.0 + i, + "close": 10.0 + i, + "volume": 1_000.0, + "in_universe": False, + }, + ] + ) + return AggBar(pl.DataFrame(rows)) + + +def _make_signal() -> pl.DataFrame: + rows = [] + base_ts = 1_700_000_000_000 + for i in range(6): + ts = base_ts + i * 3_600_000 + rows.extend( + [ + {"start_time": ts, "end_time": ts + 3_600_000, "symbol": "A", "factor": 3.0 + i}, + {"start_time": ts, "end_time": ts + 3_600_000, "symbol": "B", "factor": 2.0 + i}, + {"start_time": ts, "end_time": ts + 3_600_000, "symbol": "C", "factor": 1.0 + i}, + ] + ) + return pl.DataFrame(rows) + + +def test_backtester_mask_sets_outside_symbols_weight_to_zero() -> None: + prices = _make_prices() + signal = _make_signal() + + bt = VectorizedBacktester(prices=prices, signal=signal, mask="in_universe", neutralization="none") + combined = bt._prepare_data() + weighted = bt._calculate_weights(combined) + + c_weights = weighted.filter((pl.col("symbol") == "C") & (pl.col("prev_signal").is_not_null()))["weight"].to_list() + assert set(c_weights) == {0.0} + + +def test_backtester_mask_applies_before_market_neutralization() -> None: + prices = _make_prices() + signal = _make_signal() + + bt = VectorizedBacktester(prices=prices, signal=signal, mask="in_universe", neutralization="market") + weighted = bt._calculate_weights(bt._prepare_data()) + + sample_time = sorted(set(weighted["end_time"].to_list()))[1] + sample = weighted.filter(pl.col("end_time") == sample_time) + + b_weight = sample.filter(pl.col("symbol") == "B")["weight"].to_list()[0] + c_weight = sample.filter(pl.col("symbol") == "C")["weight"].to_list()[0] + assert b_weight < 0 + assert c_weight == 0.0 + + +def test_backtester_without_mask_keeps_backward_compatible_flow() -> None: + prices = _make_prices() + signal = _make_signal() + + bt = VectorizedBacktester(prices=prices, signal=signal) + result = bt.run() + + assert len(result.equity_curve) > 0 diff --git a/tests/universe/test_checklist_filters.py b/tests/universe/test_checklist_filters.py new file mode 100644 index 0000000..8134446 --- /dev/null +++ b/tests/universe/test_checklist_filters.py @@ -0,0 +1,97 @@ +import polars as pl +import pytest + +from factorium.universe import Checklist, MinLiquidity, MinVolume, TagFilter + + +def _make_panel() -> pl.DataFrame: + rows = [] + base_ts = 1_700_000_000_000 + for i in range(5): + ts = base_ts + i * 3_600_000 + rows.extend( + [ + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "BTCUSDT", + "close": 100.0, + "volume": 20_000.0, + }, + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "DOGEUSDT", + "close": 1.0, + "volume": 100.0, + }, + ] + ) + return pl.DataFrame(rows) + + +def _metadata() -> dict[str, dict]: + return { + "BTCUSDT": {"symbol": "BTCUSDT", "base_asset": "BTC", "quote_asset": "USDT"}, + "DOGEUSDT": {"symbol": "DOGEUSDT", "base_asset": "DOGE", "quote_asset": "USDT"}, + } + + +def _tags() -> dict[str, list[str]]: + return { + "BTC": ["store-of-value", "layer1"], + "DOGE": ["meme"], + } + + +def test_tag_filter_include_mode() -> None: + df = _make_panel().lazy() + expr = TagFilter(include=["store-of-value"]).apply(df, _metadata(), _tags()) + out = _make_panel().lazy().with_columns(expr.alias("keep")).collect() + assert set(out.filter(pl.col("keep"))["symbol"].to_list()) == {"BTCUSDT"} + + +def test_tag_filter_exclude_mode() -> None: + df = _make_panel().lazy() + expr = TagFilter(exclude=["meme"]).apply(df, _metadata(), _tags()) + out = _make_panel().lazy().with_columns(expr.alias("keep")).collect() + assert set(out.filter(pl.col("keep"))["symbol"].to_list()) == {"BTCUSDT"} + + +def test_tag_filter_raises_when_tags_missing() -> None: + with pytest.raises(ValueError, match="requires tags"): + TagFilter(include=["layer1"]).apply(_make_panel().lazy(), _metadata(), tags=None) + + +def test_min_volume_uses_rolling_threshold() -> None: + df = _make_panel().lazy() + expr = MinVolume(window=3, threshold=10_000).apply(df, _metadata()) + out = _make_panel().lazy().with_columns(expr.alias("keep")).collect() + + btc = out.filter(pl.col("symbol") == "BTCUSDT").sort("start_time")["keep"].to_list() + doge = out.filter(pl.col("symbol") == "DOGEUSDT").sort("start_time")["keep"].to_list() + assert btc == [False, False, True, True, True] + assert doge == [False, False, False, False, False] + + +def test_min_liquidity_uses_volume_times_close() -> None: + df = _make_panel().lazy() + expr = MinLiquidity(window=3, threshold=500_000).apply(df, _metadata()) + out = _make_panel().lazy().with_columns(expr.alias("keep")).collect() + + btc = out.filter(pl.col("symbol") == "BTCUSDT").sort("start_time")["keep"].to_list() + doge = out.filter(pl.col("symbol") == "DOGEUSDT").sort("start_time")["keep"].to_list() + assert btc == [False, False, True, True, True] + assert doge == [False, False, False, False, False] + + +def test_checklist_combines_filters_with_and_logic() -> None: + checklist = Checklist([TagFilter(exclude=["meme"]), MinVolume(window=3, threshold=10_000)]) + out = ( + _make_panel() + .lazy() + .with_columns(checklist.apply(_make_panel().lazy(), _metadata(), _tags()).alias("ok")) + .collect() + ) + symbols = set(out.filter(pl.col("ok"))["symbol"].to_list()) + assert symbols == {"BTCUSDT"} diff --git a/tests/universe/test_factor_mask.py b/tests/universe/test_factor_mask.py new file mode 100644 index 0000000..3d3cdcc --- /dev/null +++ b/tests/universe/test_factor_mask.py @@ -0,0 +1,70 @@ +import polars as pl + +from factorium import AggBar +from factorium.factors.analyzer import FactorAnalyzer + + +def _make_aggbar() -> AggBar: + rows = [] + base_ts = 1_700_000_000_000 + for i in range(8): + ts = base_ts + i * 3_600_000 + rows.extend( + [ + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "BTCUSDT", + "open": 100 + i, + "high": 101 + i, + "low": 99 + i, + "close": 100 + i, + "volume": 1_000.0, + "alpha": float(i + 1), + "in_checklist": True, + }, + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "DOGEUSDT", + "open": 10 + i, + "high": 11 + i, + "low": 9 + i, + "close": 10 + i, + "volume": 1_000.0, + "alpha": float(100 - i), + "in_checklist": False, + }, + ] + ) + return AggBar(pl.DataFrame(rows)) + + +def test_factor_eval_mask_is_backward_compatible_when_none() -> None: + agg = _make_aggbar() + factor = agg["alpha"] + + with_mask_none = factor.eval(prices=agg, periods=1, quantiles=2, mask=None) + without_mask = factor.eval(prices=agg, periods=1, quantiles=2) + + assert with_mask_none.ic_series.equals(without_mask.ic_series) + + +def test_factor_analyzer_applies_mask_from_aggbar() -> None: + agg = _make_aggbar() + factor = agg["alpha"] + + analyzer = FactorAnalyzer(factor=factor, prices=agg, quantiles=2, mask="in_checklist") + prepared = analyzer.prepare_data(periods=[1], price_col="close") + + assert set(prepared["symbol"].to_list()) == {"BTCUSDT"} + + +def test_factor_eval_with_mask_changes_universe_used_for_analysis() -> None: + agg = _make_aggbar() + factor = agg["alpha"] + + unmasked = factor.eval(prices=agg, periods=1, quantiles=2) + masked = factor.eval(prices=agg, periods=1, quantiles=2, mask="in_checklist") + + assert len(masked.ic_series) <= len(unmasked.ic_series) diff --git a/tests/universe/test_integration.py b/tests/universe/test_integration.py new file mode 100644 index 0000000..2165dc8 --- /dev/null +++ b/tests/universe/test_integration.py @@ -0,0 +1,108 @@ +import polars as pl + +from factorium import AggBar +from factorium.factors.analyzer import FactorAnalyzer +from factorium.universe import ( + Checklist, + ExcludeStablecoins, + MinListingAge, + MinVolume, + TagFilter, + Universe, +) + + +DAY_MS = 86_400_000 +BASE_TS = 1_700_000_000_000 + + +def _make_aggbar() -> AggBar: + rows = [] + for i in range(10): + ts = BASE_TS + i * DAY_MS + rows.extend( + [ + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "BTCUSDT", + "open": 100 + i, + "high": 101 + i, + "low": 99 + i, + "close": 100 + i, + "volume": 20_000 + i, + "alpha": float(i + 1), + }, + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "USDCUSDT", + "open": 1.0, + "high": 1.0, + "low": 1.0, + "close": 1.0, + "volume": 50_000, + "alpha": float(50 - i), + }, + { + "start_time": ts, + "end_time": ts + 3_600_000, + "symbol": "NEWUSDT", + "open": 10 + i, + "high": 11 + i, + "low": 9 + i, + "close": 10 + i, + "volume": 100 + i, + "alpha": float(100 + i), + }, + ] + ) + return AggBar(pl.DataFrame(rows)) + + +def _metadata() -> dict[str, dict]: + return { + "BTCUSDT": { + "symbol": "BTCUSDT", + "base_asset": "BTC", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": BASE_TS - 365 * DAY_MS, + }, + "USDCUSDT": { + "symbol": "USDCUSDT", + "base_asset": "USDC", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": BASE_TS - 365 * DAY_MS, + "is_stablecoin_pair": True, + }, + "NEWUSDT": { + "symbol": "NEWUSDT", + "base_asset": "NEW", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": BASE_TS - 2 * DAY_MS, + }, + } + + +def test_full_pipeline_universe_checklist_factor_eval() -> None: + bar = _make_aggbar() + metadata = _metadata() + tags = {"BTC": ["layer1"], "USDC": ["stablecoin"], "NEW": ["meme"]} + + universe = Universe([ExcludeStablecoins(), MinListingAge(days=5)]) + checklist = Checklist([TagFilter(include=["layer1"]), MinVolume(window=3, threshold=10_000)]) + + bar = bar.with_mask("in_universe", universe, metadata) + bar = bar.with_mask("in_checklist", checklist, metadata, tags) + + factor = bar["alpha"] + result = factor.eval(prices=bar, periods=1, quantiles=2, mask="in_checklist") + + analyzer = FactorAnalyzer(factor=factor, prices=bar, quantiles=2, mask="in_checklist") + prepared = analyzer.prepare_data(periods=[1], price_col="close") + + assert set(prepared["symbol"].to_list()) == {"BTCUSDT"} + assert result.factor_name == "alpha" diff --git a/tests/universe/test_metadata.py b/tests/universe/test_metadata.py new file mode 100644 index 0000000..4b6e510 --- /dev/null +++ b/tests/universe/test_metadata.py @@ -0,0 +1,92 @@ +import json +from pathlib import Path + +import pytest + +from factorium.universe.metadata import MetadataProvider + + +def test_parse_exchange_info_extracts_symbol_metadata() -> None: + provider = MetadataProvider(market="um") + payload = { + "symbols": [ + { + "symbol": "BTCUSDT", + "baseAsset": "BTC", + "quoteAsset": "USDT", + "status": "TRADING", + "onboardDate": 1_700_000_000_000, + }, + { + "symbol": "BTCUPUSDT", + "baseAsset": "BTCUP", + "quoteAsset": "USDT", + "status": "TRADING", + "onboardDate": 1_700_000_000_000, + }, + { + "symbol": "USDCUSDT", + "baseAsset": "USDC", + "quoteAsset": "USDT", + "status": "TRADING", + }, + ] + } + + out = provider._parse_exchange_info(payload) + + assert out["BTCUSDT"]["listing_date"] == 1_700_000_000_000 + assert out["BTCUSDT"]["is_leveraged"] is False + assert out["BTCUSDT"]["is_stablecoin_pair"] is False + assert out["BTCUPUSDT"]["is_leveraged"] is True + assert out["USDCUSDT"]["is_stablecoin_pair"] is True + + +def test_cache_load_save_and_ttl(tmp_path: Path) -> None: + provider = MetadataProvider(market="um", cache_dir=tmp_path, cache_ttl=60) + sample = { + "BTCUSDT": { + "symbol": "BTCUSDT", + "base_asset": "BTC", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": 1_700_000_000_000, + "is_leveraged": False, + "is_stablecoin_pair": False, + } + } + + provider._save_cache(sample) + loaded = provider._load_cache() + assert loaded == sample + + cache_path = tmp_path / "um_exchange_info.json" + blob = json.loads(cache_path.read_text(encoding="utf-8")) + blob["saved_at"] = 0 + cache_path.write_text(json.dumps(blob), encoding="utf-8") + + assert provider._load_cache() is None + + +def test_fetch_prefers_cache_without_network(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + provider = MetadataProvider(market="um", cache_dir=tmp_path, cache_ttl=3600) + cached = { + "ETHUSDT": { + "symbol": "ETHUSDT", + "base_asset": "ETH", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": 1_700_000_000_000, + "is_leveraged": False, + "is_stablecoin_pair": False, + } + } + provider._save_cache(cached) + + async def should_not_call(*args, **kwargs): + raise AssertionError("network should not be called when cache is valid") + + monkeypatch.setattr(provider, "_request_json", should_not_call) + + out = provider.fetch() + assert out == cached diff --git a/tests/universe/test_rules.py b/tests/universe/test_rules.py new file mode 100644 index 0000000..ff347dc --- /dev/null +++ b/tests/universe/test_rules.py @@ -0,0 +1,24 @@ +import polars as pl +import pytest + +from factorium.universe import Checklist, FilterRule, Universe + + +class DummyRule: + def apply(self, df: pl.LazyFrame, metadata: dict, tags: dict[str, list[str]] | None = None) -> pl.Expr: + return pl.lit(True) + + +def test_filter_rule_runtime_protocol() -> None: + rule = DummyRule() + assert isinstance(rule, FilterRule) + + +def test_universe_requires_at_least_one_rule() -> None: + with pytest.raises(ValueError, match="at least one rule"): + Universe([]) + + +def test_checklist_requires_at_least_one_filter() -> None: + with pytest.raises(ValueError, match="at least one filter"): + Checklist([]) diff --git a/tests/universe/test_tags.py b/tests/universe/test_tags.py new file mode 100644 index 0000000..1827602 --- /dev/null +++ b/tests/universe/test_tags.py @@ -0,0 +1,62 @@ +import json +from pathlib import Path + +import pytest + +from factorium.universe.tags import TagProvider + + +def test_fetch_maps_symbols_to_categories(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + provider = TagProvider(cache_dir=tmp_path, cache_ttl=0) + + async def fake_request_json(session, url, params=None, headers=None): + del session, params, headers + if url.endswith("/coins/list"): + return [ + {"id": "bitcoin", "symbol": "btc", "name": "Bitcoin"}, + {"id": "ethereum", "symbol": "eth", "name": "Ethereum"}, + ] + if url.endswith("/coins/bitcoin"): + return {"categories": ["Layer 1", "Store Of Value"]} + if url.endswith("/coins/ethereum"): + return {"categories": ["Layer 1", "Smart Contract Platform"]} + raise AssertionError(f"unexpected url: {url}") + + async def no_sleep(seconds: float) -> None: + del seconds + + monkeypatch.setattr(provider, "_request_json", fake_request_json) + monkeypatch.setattr("factorium.universe.tags.asyncio.sleep", no_sleep) + + out = provider.fetch(symbols=["BTC", "ETH"]) + assert out["BTC"] == ["Layer 1", "Store Of Value"] + assert out["ETH"] == ["Layer 1", "Smart Contract Platform"] + + +def test_cache_load_save_and_ttl(tmp_path: Path) -> None: + provider = TagProvider(cache_dir=tmp_path, cache_ttl=10) + sample = {"BTC": ["Layer 1"], "ETH": ["Layer 1", "Smart Contract Platform"]} + + provider._save_cache(sample) + loaded = provider._load_cache() + assert loaded == sample + + cache_path = tmp_path / "coingecko_tags.json" + blob = json.loads(cache_path.read_text(encoding="utf-8")) + blob["saved_at"] = 0 + cache_path.write_text(json.dumps(blob), encoding="utf-8") + + assert provider._load_cache() is None + + +def test_fetch_uses_cached_subset(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + provider = TagProvider(cache_dir=tmp_path, cache_ttl=3600) + provider._save_cache({"BTC": ["Layer 1"], "ETH": ["Layer 1"]}) + + async def should_not_call(*args, **kwargs): + raise AssertionError("network should not be called for cached subset") + + monkeypatch.setattr(provider, "_request_json", should_not_call) + + out = provider.fetch(symbols=["BTC"]) + assert out == {"BTC": ["Layer 1"]} diff --git a/tests/universe/test_universe_rules.py b/tests/universe/test_universe_rules.py new file mode 100644 index 0000000..110f79f --- /dev/null +++ b/tests/universe/test_universe_rules.py @@ -0,0 +1,93 @@ +import polars as pl + +from factorium.universe import ExcludeLeveragedTokens, ExcludeStablecoins, MinListingAge, Universe + + +NOW_MS = 1_700_000_000_000 +DAY_MS = 86_400_000 + + +def _sample_df() -> pl.DataFrame: + rows = [ + {"start_time": NOW_MS - 5 * DAY_MS, "end_time": NOW_MS - 5 * DAY_MS + 3_600_000, "symbol": "BTCUSDT"}, + {"start_time": NOW_MS - 5 * DAY_MS, "end_time": NOW_MS - 5 * DAY_MS + 3_600_000, "symbol": "USDCUSDT"}, + {"start_time": NOW_MS - 5 * DAY_MS, "end_time": NOW_MS - 5 * DAY_MS + 3_600_000, "symbol": "BTCUPUSDT"}, + {"start_time": NOW_MS - 5 * DAY_MS, "end_time": NOW_MS - 5 * DAY_MS + 3_600_000, "symbol": "NEWUSDT"}, + {"start_time": NOW_MS + 100 * DAY_MS, "end_time": NOW_MS + 100 * DAY_MS + 3_600_000, "symbol": "NEWUSDT"}, + ] + return pl.DataFrame(rows) + + +def _sample_metadata() -> dict[str, dict]: + return { + "BTCUSDT": { + "symbol": "BTCUSDT", + "base_asset": "BTC", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": NOW_MS - 365 * DAY_MS, + }, + "USDCUSDT": { + "symbol": "USDCUSDT", + "base_asset": "USDC", + "quote_asset": "USDT", + "status": "TRADING", + "is_stablecoin_pair": True, + "listing_date": NOW_MS - 365 * DAY_MS, + }, + "BTCUPUSDT": { + "symbol": "BTCUPUSDT", + "base_asset": "BTCUP", + "quote_asset": "USDT", + "status": "TRADING", + "is_leveraged": True, + "listing_date": NOW_MS - 365 * DAY_MS, + }, + "NEWUSDT": { + "symbol": "NEWUSDT", + "base_asset": "NEW", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": NOW_MS - 10 * DAY_MS, + }, + } + + +def test_exclude_stablecoins_filters_stable_base_assets() -> None: + df = _sample_df().lazy() + metadata = _sample_metadata() + expr = ExcludeStablecoins().apply(df, metadata) + out = _sample_df().lazy().with_columns(expr.alias("keep")).collect() + + stable_keep = out.filter(pl.col("symbol") == "USDCUSDT")["keep"].to_list() + assert stable_keep == [False] + + +def test_exclude_leveraged_tokens_filters_leveraged_symbols() -> None: + df = _sample_df().lazy() + metadata = _sample_metadata() + expr = ExcludeLeveragedTokens().apply(df, metadata) + out = _sample_df().lazy().with_columns(expr.alias("keep")).collect() + + leveraged_keep = out.filter(pl.col("symbol") == "BTCUPUSDT")["keep"].to_list() + assert leveraged_keep == [False] + + +def test_min_listing_age_is_time_varying() -> None: + metadata = _sample_metadata() + df = _sample_df().lazy() + expr = MinListingAge(days=90).apply(df, metadata) + out = _sample_df().lazy().with_columns(expr.alias("keep")).collect() + + new_rows = out.filter(pl.col("symbol") == "NEWUSDT").sort("start_time") + assert new_rows["keep"].to_list() == [False, True] + + +def test_universe_combines_rules_with_and_logic() -> None: + metadata = _sample_metadata() + rules = [ExcludeStablecoins(), ExcludeLeveragedTokens(), MinListingAge(days=90)] + universe = Universe(rules) + + out = _sample_df().lazy().with_columns(universe.apply(_sample_df().lazy(), metadata).alias("in_universe")).collect() + kept_symbols = set(out.filter(pl.col("in_universe"))["symbol"].to_list()) + assert kept_symbols == {"BTCUSDT", "NEWUSDT"} From 58591623ca09d8bbe2aeb99f3ab34ac293d333f7 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 00:13:40 +0800 Subject: [PATCH 08/25] docs(universe): add user guide and integrate mask workflow --- docs/index.md | 1 + .../plans/2026-02-13-universe-docs-pr-plan.md | 199 ++++++++++++++++++ docs/user-guide/backtest.md | 20 +- docs/user-guide/bar.md | 23 ++ docs/user-guide/factor.md | 17 +- docs/user-guide/universe.md | 102 +++++++++ mkdocs.yml | 1 + tests/docs/test_universe_user_docs.py | 52 +++++ 8 files changed, 413 insertions(+), 2 deletions(-) create mode 100644 docs/plans/2026-02-13-universe-docs-pr-plan.md create mode 100644 docs/user-guide/universe.md create mode 100644 tests/docs/test_universe_user_docs.py diff --git a/docs/index.md b/docs/index.md index 5f78859..c0d9cf3 100644 --- a/docs/index.md +++ b/docs/index.md @@ -78,6 +78,7 @@ uv add factorium | [快速開始](getting-started/quickstart.md) | 五分鐘上手教學 | | [資料獲取](getting-started/data-acquisition.md) | 下載與載入市場數據 | | [Bar 聚合](user-guide/bar.md) | 不同類型的 K 線聚合 | +| [Universe 與 Checklist](user-guide/universe.md) | 建立資產池遮罩（Universe / Checklist）並串接因子與回測 | | [Factor 因子](user-guide/factor.md) | 因子計算與運算子 | | [因子分析](user-guide/analyzer.md) | IC / 分層收益等分析工具 | | [策略回測](user-guide/backtest.md) | 向量化回測與權重約束 | diff --git a/docs/plans/2026-02-13-universe-docs-pr-plan.md b/docs/plans/2026-02-13-universe-docs-pr-plan.md new file mode 100644 index 0000000..087ef80 --- /dev/null +++ b/docs/plans/2026-02-13-universe-docs-pr-plan.md @@ -0,0 +1,199 @@ +# Universe User Documentation Implementation Plan + +> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. + +**Goal:** 補齊 universe/checklist 的官方使用文件，讓使用者能從文件直接完成「建立資產池 -> 套 mask -> 因子計算 -> 回測」流程。 + +**Architecture:** 以 `docs/user-guide/universe.md` 作為主入口，並在既有 `bar/factor/backtest/index` 做最小必要串接。先用文件驗收測試定義必要章節與關鍵 API，再逐步補內容，最後以 MkDocs build 驗證可發布品質。 + +**Tech Stack:** MkDocs + Material、Markdown、pytest（文件驗收測試） + +--- + +### Task 1: 建立文件驗收測試骨架 + +**Files:** +- Create: `tests/docs/test_universe_user_docs.py` +- Test: `tests/docs/test_universe_user_docs.py` + +**Step 1: Write the failing test** + +```python +from pathlib import Path + + +def test_universe_guide_file_exists(): + assert Path("docs/user-guide/universe.md").exists() + + +def test_universe_guide_has_required_sections(): + text = Path("docs/user-guide/universe.md").read_text(encoding="utf-8") + required = [ + "# Universe 與 Checklist", + "## 快速流程", + "## 與 AggBar 整合", + "## 與 Factor 整合", + "## 與 Backtest 整合", + "## 常見錯誤", + ] + for section in required: + assert section in text +``` + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/docs/test_universe_user_docs.py -v` +Expected: FAIL（`docs/user-guide/universe.md` 尚不存在） + +**Step 3: Write minimal implementation** + +建立 `docs/user-guide/universe.md`，先放最小標題骨架以滿足測試。 + +**Step 4: Run test to verify it passes** + +Run: `uv run pytest tests/docs/test_universe_user_docs.py -v` +Expected: PASS + +**Step 5: Commit** + +```bash +git add tests/docs/test_universe_user_docs.py docs/user-guide/universe.md +git commit -m "test(docs): add universe user-guide acceptance checks" +``` + +### Task 2: 完成 Universe 主文件內容 + +**Files:** +- Modify: `docs/user-guide/universe.md` +- Test: `tests/docs/test_universe_user_docs.py` + +**Step 1: Write the failing test** + +在 `tests/docs/test_universe_user_docs.py` 增加關鍵片段驗證： +- `AggBar.with_mask(` +- `Factor.eval(..., mask=` +- `Backtester(..., mask=`（以專案實際 API 命名為準） + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/docs/test_universe_user_docs.py -v` +Expected: FAIL（關鍵片段尚未補齊） + +**Step 3: Write minimal implementation** + +在 `docs/user-guide/universe.md` 補齊： +- 概念：universe 與 checklist 的差異 +- 快速流程：資料 -> 遮罩 -> 因子 -> 回測 +- 可直接執行的最小程式片段 +- 常見錯誤（index 對齊、look-ahead、NaN/mask 傳遞） + +**Step 4: Run test to verify it passes** + +Run: `uv run pytest tests/docs/test_universe_user_docs.py -v` +Expected: PASS + +**Step 5: Commit** + +```bash +git add docs/user-guide/universe.md tests/docs/test_universe_user_docs.py +git commit -m "docs(universe): add complete universe/checklist user guide" +``` + +### Task 3: 串接既有文件與導覽 + +**Files:** +- Modify: `mkdocs.yml` +- Modify: `docs/index.md` +- Modify: `docs/user-guide/bar.md` +- Modify: `docs/user-guide/factor.md` +- Modify: `docs/user-guide/backtest.md` +- Test: `tests/docs/test_universe_user_docs.py` + +**Step 1: Write the failing test** + +在 `tests/docs/test_universe_user_docs.py` 新增： +- `mkdocs.yml` 導覽含 `user-guide/universe.md` +- `docs/index.md` 含 universe/checklist 入口描述 +- `bar/factor/backtest` 各至少一處連到 universe 使用方式 + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/docs/test_universe_user_docs.py -v` +Expected: FAIL + +**Step 3: Write minimal implementation** + +更新 `mkdocs.yml` 導覽，並在上述文件加入最小必要交叉連結與示例段落。 + +**Step 4: Run test to verify it passes** + +Run: `uv run pytest tests/docs/test_universe_user_docs.py -v` +Expected: PASS + +**Step 5: Commit** + +```bash +git add mkdocs.yml docs/index.md docs/user-guide/bar.md docs/user-guide/factor.md docs/user-guide/backtest.md tests/docs/test_universe_user_docs.py +git commit -m "docs: wire universe guide into nav and related user guides" +``` + +### Task 4: 發布前驗證 + +**Files:** +- Modify: `docs/user-guide/universe.md`（若 build 警告需微調） + +**Step 1: Write the failing test** + +無新增程式測試；以文件建置作為驗收門檻。 + +**Step 2: Run test to verify it fails** + +Run: `uv run mkdocs build` +Expected: 若有 broken links 或 nav 問題則 FAIL + +**Step 3: Write minimal implementation** + +修正連結、標題層級、程式片段語法標記，直到 build 無錯。 + +**Step 4: Run test to verify it passes** + +Run: `uv run mkdocs build` +Expected: PASS + +**Step 5: Commit** + +```bash +git add docs/ mkdocs.yml +git commit -m "chore(docs): pass mkdocs build for universe documentation" +``` + +### Task 5: PR 自我檢查 + +**Files:** +- Modify: `docs/plans/2026-02-13-universe-docs-pr-plan.md`（若需補充實際執行備註） + +**Step 1: Write the failing test** + +建立自我檢查清單（人工）： +- [ ] 新頁面在導覽可見 +- [ ] 主流程程式片段可讀且命名一致 +- [ ] 交叉連結完整 + +**Step 2: Run test to verify it fails** + +人工審閱，任何一項未滿足即視為 FAIL。 + +**Step 3: Write minimal implementation** + +補齊缺漏內容。 + +**Step 4: Run test to verify it passes** + +再次人工審閱 + `uv run mkdocs build`。 + +**Step 5: Commit** + +```bash +git add docs/ +git commit -m "docs: finalize universe documentation PR checklist" +``` diff --git a/docs/user-guide/backtest.md b/docs/user-guide/backtest.md index 6f83fbc..2e82c2e 100644 --- a/docs/user-guide/backtest.md +++ b/docs/user-guide/backtest.md @@ -9,6 +9,7 @@ - **價格資料 (`prices`)**：使用 `AggBar` 表示的多標的 OHLCV 資料，欄位至少包含 `["start_time", "end_time", "symbol", "open", "high", "low", "close", "volume"]`。 - **信號因子 (`signal`)**：任何 `Factor` 物件，需與 `prices` 在 `end_time, symbol` 上對齊，通常為已做過橫截面處理的排名 / Z-score。 +- **資產池遮罩 (`mask`)**：可選欄位名稱，用來限制哪些標的在該時間點可以持倉。 - **避免前視偏差**：回測時會使用「前一根 bar 的信號」在當前 bar 交易。 - **向量化實作**：內部全部以 Polars 向量化計算完成，再轉成 pandas 計算績效指標。 @@ -83,6 +84,24 @@ print(pandas_result.metrics) --- +## 3.1 使用 Universe / Checklist mask 限制持倉 + +若你已經在 `AggBar` 內建立 mask 欄位（例如 `checklist_mask`），可以在回測時直接指定： + +```python +bt = Backtester( + prices=agg, + signal=momentum, + mask="checklist_mask", + neutralization="market", +) +result = bt.run() +``` + +當 `mask` 為 `False` 或 `null`，該標的在該期權重會被設為 0。這能讓回測與因子分析維持同一套 Universe/Checklist 約束。 + +--- + ## 4. 市場中性與 Long-only 權重 ### 4.1 `neutralization="market"`（市場中性） @@ -175,4 +194,3 @@ print(result.metrics) - 直接使用 `Backtester(prices=agg, signal=signal)`；或 - 透過 `ResearchSession.backtest(signal)`。 5. **查看結果**：讀取 `BacktestResult.metrics`、`equity_curve`、`trades` 等欄位，或將結果轉成 pandas 作進一步分析。 - diff --git a/docs/user-guide/bar.md b/docs/user-guide/bar.md index 2ffbdb3..da91a08 100644 --- a/docs/user-guide/bar.md +++ b/docs/user-guide/bar.md @@ -318,3 +318,26 @@ Bar 聚合使用 DuckDB 的 SQL 引擎，效能特點： 2. **最後一個 Bar**：Volume/Dollar Bar 的最後一個 bar 可能不完整（未達閾值） 3. **空資料處理**：若指定日期無資料，會回傳空的 DataFrame 和零值 metadata 4. **多標的聚合**：每個標的獨立聚合，最後合併 + +--- + +## 與 Universe / Checklist 整合 + +`AggBar` 可透過 `with_mask` 直接掛入資產池遮罩欄位，後續因子與回測可共用同一個欄位名稱： + +```python +from factorium import Universe, ExcludeStablecoins + +universe = Universe(rules=[ExcludeStablecoins()]) +metadata = {} +tags = {} + +agg = agg.with_mask( + name="universe_mask", + mask_source=universe, + metadata=metadata, + tags=tags, +) +``` + +若你要進一步做因子與回測，請參考 [Universe 與 Checklist](universe.md)。 diff --git a/docs/user-guide/factor.md b/docs/user-guide/factor.md index eb48105..d6c95fd 100644 --- a/docs/user-guide/factor.md +++ b/docs/user-guide/factor.md @@ -262,10 +262,26 @@ result = factor.eval( quantiles: int = 5, # 分層數量（per-day cross-sectional quantiles） output_dir: str | None = None, # 若提供路徑，會輸出評估結果到時間戳目錄 price_col: str = "close", # 價格欄位名稱（當 prices 為 AggBar 時使用） + mask: str | None = None, # 選填：限制分析樣本的遮罩欄位名稱 **kwargs, ) -> FactorAnalysisResult ``` +### 與 Universe / Checklist 遮罩整合 + +若你在 `AggBar` 上已建立 Universe/Checklist 遮罩欄位，可以直接傳入 `mask=`： + +```python +result = momentum_20.eval( + prices=agg, + periods=1, + quantiles=5, + mask="checklist_mask", +) +``` + +這可確保分位分組與後續評估都只在可交易資產池上進行，避免把不在策略範圍內的標的納入統計。 + `result` 回傳一個 `FactorAnalysisResult` dataclass，主要包含： - **`factor_name`**: 因子名稱 @@ -358,4 +374,3 @@ if result.ic_summary['ic_ir'] > 1.0: 搭配 `BinanceDataLoader` + `Bar` + `AggBar`，可以很方便地從原始交易資料一路建構到完整的因子研究流程。 - diff --git a/docs/user-guide/universe.md b/docs/user-guide/universe.md new file mode 100644 index 0000000..63d3c77 --- /dev/null +++ b/docs/user-guide/universe.md @@ -0,0 +1,102 @@ +# Universe 與 Checklist + +Universe / Checklist 用來建立資產池遮罩（mask），把因子與回測限制在符合條件的標的集合。 + +- `Universe`：偏向交易規則與市場結構條件（例如排除穩定幣、上市天數）。 +- `Checklist`：偏向研究條件與流動性門檻（例如成交量、流動性、標籤條件）。 + +## 快速流程 + +以下流程示範「建立資產池 -> 套 mask -> 因子計算 -> 回測」。 + +```python +from factorium import ( + Backtester, + Checklist, + Factor, + MinLiquidity, + MinVolume, + Universe, + ExcludeStablecoins, + load_aggbar, +) + +# 1) 載入資料 +bar = load_aggbar("BTCUSDT", start="2023-01-01", end="2024-01-01", timeframe="1d") + +# 2) 建立 Universe / Checklist +universe = Universe(rules=[ExcludeStablecoins()]) +checklist = Checklist(filters=[MinVolume(window=20, threshold=1_000_000), MinLiquidity(window=20, threshold=100_000)]) + +# metadata/tags 可由 MetadataProvider / TagProvider 產生 +metadata = {} +tags = {} + +# 3) 產生 mask 欄位 +bar = bar.with_mask(name="universe_mask", mask_source=universe, metadata=metadata, tags=tags) +bar = bar.with_mask(name="checklist_mask", mask_source=checklist, metadata=metadata, tags=tags) + +# 4) 因子計算使用同一個 mask +factor = Factor("(close / shift(close, 5)) - 1") +result = factor.eval(bar, periods=5, quantiles=5, mask="checklist_mask") + +# 5) 回測使用同一個 mask +bt = Backtester(bar.data, signal=result.data, mask="checklist_mask") +stats = bt.run() +``` + +## 與 AggBar 整合 + +`AggBar.with_mask()` 會把 `Universe` 或 `Checklist` 的條件計算成布林欄位，直接寫回 `AggBar.data`。 + +```python +bar = bar.with_mask( + name="universe_mask", + mask_source=universe, + metadata=metadata, + tags=tags, +) +``` + +注意： + +- `name` 不能覆蓋保留欄位（如 `open`, `close`, `volume`, `symbol`）。 +- `metadata` 需包含與 `symbol` 對應的資訊；若規則依賴 `tags`，必須提供 `tags`。 + +## 與 Factor 整合 + +`Factor.eval` 可透過 `mask` 參數限制有效樣本： + +```python +result = factor.eval( + bar, + periods=5, + quantiles=5, + mask="checklist_mask", +) +``` + +這代表因子分位分組與後續分析只在 `checklist_mask == True` 的資料上進行。 + +## 與 Backtest 整合 + +`Backtester(...)` 支援 `mask=`，用來限制可持倉資產： + +```python +bt = Backtester( + prices=bar.data, + signal=result.data, + holding_period=5, + mask="checklist_mask", +) +stats = bt.run() +``` + +當 mask 為 `False` 或 `null` 時，回測會把該資產權重歸零。 + +## 常見錯誤 + +- `mask` 欄位名稱拼錯，導致 `Factor.eval(..., mask=...)` 或 `Backtester(..., mask=...)` 找不到欄位。 +- `metadata` / `tags` 與 `symbol` 不一致，造成遮罩結果異常。 +- 先用完整資料算 signal，再事後套 mask，可能引入 look-ahead；請在計算流程中一致傳入同一個 mask。 +- 忽略空值處理，讓 `null` 直接進入決策。建議把 mask 欄位維持明確布林語義（`True` 可交易，其他視為不可交易）。 diff --git a/mkdocs.yml b/mkdocs.yml index 9003bf0..c7c5b6e 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -54,6 +54,7 @@ nav: - 五分鐘教學: getting-started/quickstart.md - 使用指南: - Bar 聚合: user-guide/bar.md + - Universe 與 Checklist: user-guide/universe.md - Factor 因子: user-guide/factor.md - 表達式語法: user-guide/parser.md - 因子分析: user-guide/analyzer.md diff --git a/tests/docs/test_universe_user_docs.py b/tests/docs/test_universe_user_docs.py new file mode 100644 index 0000000..58b215c --- /dev/null +++ b/tests/docs/test_universe_user_docs.py @@ -0,0 +1,52 @@ +from pathlib import Path + + +def test_universe_guide_file_exists() -> None: + assert Path("docs/user-guide/universe.md").exists() + + +def test_universe_guide_has_required_sections() -> None: + text = Path("docs/user-guide/universe.md").read_text(encoding="utf-8") + required = [ + "# Universe 與 Checklist", + "## 快速流程", + "## 與 AggBar 整合", + "## 與 Factor 整合", + "## 與 Backtest 整合", + "## 常見錯誤", + ] + for section in required: + assert section in text + + +def test_universe_guide_has_core_api_snippets() -> None: + text = Path("docs/user-guide/universe.md").read_text(encoding="utf-8") + required_snippets = [ + "AggBar.with_mask(", + "Factor.eval", + "mask=", + "Backtester(", + ] + for snippet in required_snippets: + assert snippet in text + + +def test_mkdocs_nav_includes_universe_page() -> None: + text = Path("mkdocs.yml").read_text(encoding="utf-8") + assert "user-guide/universe.md" in text + + +def test_index_mentions_universe_capability() -> None: + text = Path("docs/index.md").read_text(encoding="utf-8") + assert "universe" in text.lower() + assert "checklist" in text.lower() + + +def test_related_guides_reference_universe_workflow() -> None: + bar_text = Path("docs/user-guide/bar.md").read_text(encoding="utf-8") + factor_text = Path("docs/user-guide/factor.md").read_text(encoding="utf-8") + backtest_text = Path("docs/user-guide/backtest.md").read_text(encoding="utf-8") + + assert "with_mask" in bar_text + assert "mask" in factor_text.lower() + assert "mask" in backtest_text.lower() From d5faaaef1b1e48c90471ce6112d1ff8ee1cab3f7 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 08:38:47 +0800 Subject: [PATCH 09/25] docs(examples): add universe checklist workflow notebook --- .../2026-02-13-universe-examples-pr-plan.md | 197 ++++++++++++++++++ examples/05_universe_checklist_workflow.ipynb | 156 ++++++++++++++ examples/README.md | 2 + tests/examples/test_universe_notebook_docs.py | 49 +++++ 4 files changed, 404 insertions(+) create mode 100644 docs/plans/2026-02-13-universe-examples-pr-plan.md create mode 100644 examples/05_universe_checklist_workflow.ipynb create mode 100644 tests/examples/test_universe_notebook_docs.py diff --git a/docs/plans/2026-02-13-universe-examples-pr-plan.md b/docs/plans/2026-02-13-universe-examples-pr-plan.md new file mode 100644 index 0000000..7e4566d --- /dev/null +++ b/docs/plans/2026-02-13-universe-examples-pr-plan.md @@ -0,0 +1,197 @@ +# Universe Example Notebook Implementation Plan + +> **For Claude:** REQUIRED SUB-SKILL: Use superpowers:executing-plans to implement this plan task-by-task. + +**Goal:** 在 `examples/` 新增 universe/checklist 實戰 notebook，並更新 examples 導覽，讓使用者能照著範例重現遮罩化研究流程。 + +**Architecture:** 以新 notebook `examples/05_universe_checklist_workflow.ipynb` 作為單一教學入口，README 只保留高層導引。先建立結構驗收測試（檔案存在、README 收錄、必要章節標題），再逐步填入可執行 cell，最後用 nbconvert 執行驗證。 + +**Tech Stack:** Jupyter Notebook、Python、pytest、nbformat/nbconvert + +--- + +### Task 1: 建立範例驗收測試骨架 + +**Files:** +- Create: `tests/examples/test_universe_notebook_docs.py` +- Test: `tests/examples/test_universe_notebook_docs.py` + +**Step 1: Write the failing test** + +```python +from pathlib import Path + + +def test_universe_notebook_exists(): + assert Path("examples/05_universe_checklist_workflow.ipynb").exists() + + +def test_examples_readme_mentions_universe_notebook(): + text = Path("examples/README.md").read_text(encoding="utf-8") + assert "05_universe_checklist_workflow.ipynb" in text +``` + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: FAIL（新 notebook 尚未建立，README 尚未更新） + +**Step 3: Write minimal implementation** + +建立空白 notebook 檔與 README 最小條目。 + +**Step 4: Run test to verify it passes** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: PASS + +**Step 5: Commit** + +```bash +git add tests/examples/test_universe_notebook_docs.py examples/05_universe_checklist_workflow.ipynb examples/README.md +git commit -m "test(examples): add acceptance checks for universe notebook" +``` + +### Task 2: 定義 notebook 結構與教學章節 + +**Files:** +- Modify: `examples/05_universe_checklist_workflow.ipynb` +- Modify: `tests/examples/test_universe_notebook_docs.py` + +**Step 1: Write the failing test** + +在測試中用 `nbformat` 檢查 notebook markdown 標題至少包含： +- `# Universe Checklist Workflow` +- `## Build Universe and Checklist` +- `## Apply Mask to AggBar` +- `## Evaluate Factor with Mask` +- `## Run Backtest with Mask` + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: FAIL + +**Step 3: Write minimal implementation** + +在 notebook 加入對應章節 markdown cell（先不放完整程式碼）。 + +**Step 4: Run test to verify it passes** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: PASS + +**Step 5: Commit** + +```bash +git add examples/05_universe_checklist_workflow.ipynb tests/examples/test_universe_notebook_docs.py +git commit -m "docs(examples): scaffold universe workflow notebook sections" +``` + +### Task 3: 填入可執行程式流程（最小可重現） + +**Files:** +- Modify: `examples/05_universe_checklist_workflow.ipynb` +- Modify: `tests/examples/test_universe_notebook_docs.py` + +**Step 1: Write the failing test** + +測試新增關鍵 API 片段檢查（以 cell 內容字串比對）： +- `with_mask(` +- `mask=` +- `universe` 或 `checklist` 建立呼叫 + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: FAIL + +**Step 3: Write minimal implementation** + +補齊 notebook code cell： +- 載入資料與必要欄位 +- 建立 universe/checklist +- 產生並套用 mask 到 `AggBar` +- 執行 `Factor.eval(..., mask=...)` +- 執行回測（含 mask 版本） +- 增加與未套 mask 的最小比較表格 + +**Step 4: Run test to verify it passes** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: PASS + +**Step 5: Commit** + +```bash +git add examples/05_universe_checklist_workflow.ipynb tests/examples/test_universe_notebook_docs.py +git commit -m "feat(examples): add executable universe checklist workflow notebook" +``` + +### Task 4: 更新 examples 導覽與執行驗證 + +**Files:** +- Modify: `examples/README.md` +- Modify: `examples/05_universe_checklist_workflow.ipynb`（若執行驗證需修正） + +**Step 1: Write the failing test** + +在 `tests/examples/test_universe_notebook_docs.py` 增加 README 驗收： +- notebook 目的說明 +- 前置條件 +- 建議執行順序（01 -> ... -> 05） + +**Step 2: Run test to verify it fails** + +Run: `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +Expected: FAIL + +**Step 3: Write minimal implementation** + +更新 `examples/README.md`，補齊第 5 本 notebook 的說明與順序。 + +**Step 4: Run test to verify it passes** + +Run: +- `uv run pytest tests/examples/test_universe_notebook_docs.py -v` +- `uv run python -m jupyter nbconvert --to notebook --execute examples/05_universe_checklist_workflow.ipynb --output /tmp/05_universe_checklist_workflow.executed.ipynb` + +Expected: PASS（測試通過，notebook 可從頭執行） + +**Step 5: Commit** + +```bash +git add examples/README.md examples/05_universe_checklist_workflow.ipynb tests/examples/test_universe_notebook_docs.py +git commit -m "docs(examples): document universe notebook and verify execution" +``` + +### Task 5: PR 自我檢查 + +**Files:** +- Modify: `docs/plans/2026-02-13-universe-examples-pr-plan.md`（若需補充執行備註） + +**Step 1: Write the failing test** + +建立人工清單： +- [ ] Notebook 每節都有文字解說與輸出解讀 +- [ ] 程式碼無 look-ahead 寫法 +- [ ] README 與 notebook API 名稱一致 + +**Step 2: Run test to verify it fails** + +人工審閱，任一項不滿足即 FAIL。 + +**Step 3: Write minimal implementation** + +修正文案、變數命名與示例片段。 + +**Step 4: Run test to verify it passes** + +再次人工審閱 + 重新執行測試與 nbconvert。 + +**Step 5: Commit** + +```bash +git add examples/ tests/examples/ +git commit -m "chore(examples): finalize universe notebook PR checklist" +``` diff --git a/examples/05_universe_checklist_workflow.ipynb b/examples/05_universe_checklist_workflow.ipynb new file mode 100644 index 0000000..62896fb --- /dev/null +++ b/examples/05_universe_checklist_workflow.ipynb @@ -0,0 +1,156 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Universe Checklist Workflow\n", + "\n", + "This notebook shows how to apply universe/checklist masks consistently in factor analysis and backtesting." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build Universe and Checklist\n", + "\n", + "We first create a small synthetic multi-symbol dataset, then define metadata/tags and mask rules." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import polars as pl\n", + "\n", + "from factorium import AggBar, Checklist, Universe\n", + "from factorium.backtest import Backtester\n", + "from factorium.universe import ExcludeStablecoins, MinVolume, TagFilter\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DAY_MS = 86_400_000\n", + "BASE_TS = 1_700_000_000_000\n", + "\n", + "rows = []\n", + "for i in range(30):\n", + " ts = BASE_TS + i * DAY_MS\n", + " rows.extend(\n", + " [\n", + " {\"start_time\": ts, \"end_time\": ts + 3_600_000, \"symbol\": \"BTCUSDT\", \"open\": 100 + i, \"high\": 101 + i, \"low\": 99 + i, \"close\": 100 + i, \"volume\": 20_000 + i, \"alpha\": float(i + 1)},\n", + " {\"start_time\": ts, \"end_time\": ts + 3_600_000, \"symbol\": \"USDCUSDT\", \"open\": 1.0, \"high\": 1.0, \"low\": 1.0, \"close\": 1.0, \"volume\": 50_000, \"alpha\": float(50 - i)},\n", + " {\"start_time\": ts, \"end_time\": ts + 3_600_000, \"symbol\": \"NEWUSDT\", \"open\": 10 + i, \"high\": 11 + i, \"low\": 9 + i, \"close\": 10 + i, \"volume\": 500 + i, \"alpha\": float(100 + i)},\n", + " ]\n", + " )\n", + "\n", + "agg = AggBar(pl.DataFrame(rows))\n", + "\n", + "metadata = {\n", + " \"BTCUSDT\": {\"symbol\": \"BTCUSDT\", \"base_asset\": \"BTC\", \"quote_asset\": \"USDT\", \"status\": \"TRADING\", \"listing_date\": BASE_TS - 365 * DAY_MS},\n", + " \"USDCUSDT\": {\"symbol\": \"USDCUSDT\", \"base_asset\": \"USDC\", \"quote_asset\": \"USDT\", \"status\": \"TRADING\", \"listing_date\": BASE_TS - 365 * DAY_MS, \"is_stablecoin_pair\": True},\n", + " \"NEWUSDT\": {\"symbol\": \"NEWUSDT\", \"base_asset\": \"NEW\", \"quote_asset\": \"USDT\", \"status\": \"TRADING\", \"listing_date\": BASE_TS - 30 * DAY_MS},\n", + "}\n", + "tags = {\"BTC\": [\"layer1\"], \"USDC\": [\"stablecoin\"], \"NEW\": [\"meme\"]}\n", + "\n", + "universe = Universe([ExcludeStablecoins()])\n", + "checklist = Checklist([TagFilter(include=[\"layer1\", \"meme\"]), MinVolume(window=5, threshold=1_000)])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Apply Mask to AggBar\n", + "\n", + "Use `AggBar.with_mask(...)` to add boolean columns that can be reused downstream." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agg = agg.with_mask(name=\"in_universe\", mask_source=universe, metadata=metadata, tags=tags)\n", + "agg = agg.with_mask(name=\"in_checklist\", mask_source=checklist, metadata=metadata, tags=tags)\n", + "\n", + "agg.data[[\"symbol\", \"in_universe\", \"in_checklist\"]].head(9)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate Factor with Mask\n", + "\n", + "Use the same mask in `Factor.eval(..., mask=...)` so ranking/evaluation happens only inside your tradable universe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "factor = agg[\"alpha\"]\n", + "masked_result = factor.eval(prices=agg, periods=1, quantiles=2, mask=\"in_checklist\")\n", + "unmasked_result = factor.eval(prices=agg, periods=1, quantiles=2)\n", + "\n", + "print(\"factor:\", masked_result.factor_name)\n", + "masked_rows = sum(len(df) for df in masked_result.quantile_returns.values())\n", + "unmasked_rows = sum(len(df) for df in unmasked_result.quantile_returns.values())\n", + "print(\"masked quantile rows:\", masked_rows)\n", + "print(\"unmasked quantile rows:\", unmasked_rows)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Backtest with Mask\n", + "\n", + "Finally, pass `mask=` into `Backtester(...)` to keep positions inside the same universe constraints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " bt = Backtester(\n", + " prices=agg,\n", + " signal=factor,\n", + " holding_period=3,\n", + " neutralization=\"market\",\n", + " mask=\"in_universe\",\n", + " )\n", + " bt_result = bt.run()\n", + " bt_result.metrics\n", + "except Exception as exc:\n", + " print(\"Backtest run needs enough cross-sectional signals in each bar:\", exc)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/README.md b/examples/README.md index 5cd600b..768ab56 100644 --- a/examples/README.md +++ b/examples/README.md @@ -10,6 +10,7 @@ Interactive Jupyter notebooks demonstrating factor research workflows with Facto | [02 — Mean Reversion Factor](02_mean_reversion_factor.ipynb) | Mean reversion with cross-sectional processing | Z-score distance, volatility normalization, `cs_rank`, `cs_zscore`, `cs_winsorize`, market-neutral vs. long-only backtest, advanced operators (`ts_autocorr`, `ts_kurtosis`, `ts_skewness`) | | [03 — Data Loading & Exploration](03_data_loading_and_exploration.ipynb) | Deep dive into data handling | `BinanceDataLoader`, `AggBar` methods, time-bar intervals (1min/5min/1h), slicing, CSV/Parquet export, `ResearchSession` from files | | [04 — Multi-Factor Combination](04_multi_factor_combination.ipynb) | Combine and select factors | Factor correlations, `ts_corr`, `cs_neutralize`, `CompositeFactor` (equal/custom/z-score), single vs. composite backtest, factor selection workflow | +| [05 — Universe & Checklist Workflow](05_universe_checklist_workflow.ipynb) | Constrain research and backtest to a tradable asset universe | `Universe`, `Checklist`, `AggBar.with_mask`, `Factor.eval(..., mask=...)`, `Backtester(..., mask=...)` | ## Getting Started @@ -44,3 +45,4 @@ If you're new to Factorium, we recommend starting with: 2. **Notebook 01** — Walk through a full factor research workflow 3. **Notebook 02** — Learn about signal processing and cross-sectional transforms 4. **Notebook 04** — Combine multiple factors into a composite signal +5. **Notebook 05** — Apply universe/checklist masks consistently in analysis and backtests diff --git a/tests/examples/test_universe_notebook_docs.py b/tests/examples/test_universe_notebook_docs.py new file mode 100644 index 0000000..477b8c6 --- /dev/null +++ b/tests/examples/test_universe_notebook_docs.py @@ -0,0 +1,49 @@ +from pathlib import Path +import json + + +def test_universe_notebook_exists() -> None: + assert Path("examples/05_universe_checklist_workflow.ipynb").exists() + + +def test_examples_readme_mentions_universe_notebook() -> None: + text = Path("examples/README.md").read_text(encoding="utf-8") + assert "05_universe_checklist_workflow.ipynb" in text + + +def test_universe_notebook_has_required_sections() -> None: + notebook = json.loads(Path("examples/05_universe_checklist_workflow.ipynb").read_text(encoding="utf-8")) + markdown_text = "\n".join( + "".join(cell.get("source", [])) for cell in notebook.get("cells", []) if cell.get("cell_type") == "markdown" + ) + + required = [ + "# Universe Checklist Workflow", + "## Build Universe and Checklist", + "## Apply Mask to AggBar", + "## Evaluate Factor with Mask", + "## Run Backtest with Mask", + ] + for section in required: + assert section in markdown_text + + +def test_universe_notebook_has_core_api_snippets() -> None: + text = Path("examples/05_universe_checklist_workflow.ipynb").read_text(encoding="utf-8") + required_snippets = [ + "with_mask(", + "mask=", + "Universe(", + "Checklist(", + "Backtester(", + ] + for snippet in required_snippets: + assert snippet in text + + +def test_examples_readme_has_universe_guidance() -> None: + text = Path("examples/README.md").read_text(encoding="utf-8") + assert "Universe & Checklist Workflow" in text + assert "Prerequisites" in text + assert "Recommended Reading Order" in text + assert "Notebook 05" in text From 45382048e91e6a0be7ad75b924e16567730d8251 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 13:31:57 +0800 Subject: [PATCH 10/25] test(data): migrate cache tests to storage backend API --- tests/data/test_cache.py | 45 +++++++++++++-------------------- tests/data/test_cache_polars.py | 27 +++++++++----------- 2 files changed, 29 insertions(+), 43 deletions(-) diff --git a/tests/data/test_cache.py b/tests/data/test_cache.py index 9aff2d9..a10302e 100644 --- a/tests/data/test_cache.py +++ b/tests/data/test_cache.py @@ -9,7 +9,6 @@ import pytest from factorium.data.cache import BarCache - from factorium.storage import LocalStorageBackend @@ -20,6 +19,13 @@ def temp_cache_dir(): yield Path(tmpdir) +@pytest.fixture +def cache(temp_cache_dir): + """Create cache using non-deprecated storage API.""" + storage = LocalStorageBackend(str(temp_cache_dir)) + return BarCache(storage=storage, cache_prefix="") + + @pytest.fixture def sample_bar_df(): """Create sample bar DataFrame in Polars format.""" @@ -41,16 +47,13 @@ def sample_bar_df(): class TestBarCache: """Tests for BarCache.""" - def test_cache_initialization(self, temp_cache_dir): + def test_cache_initialization(self, temp_cache_dir, cache): """Test cache initializes correctly.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - assert isinstance(cache.storage, LocalStorageBackend) + assert cache.cache_dir is None assert temp_cache_dir.exists() - def test_cache_miss_returns_none(self, temp_cache_dir): + def test_cache_miss_returns_none(self, cache): """Test that cache miss returns None.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - result = cache.get( exchange="binance", symbols=["BTCUSDT"], @@ -62,10 +65,8 @@ def test_cache_miss_returns_none(self, temp_cache_dir): assert result is None - def test_cache_put_and_get(self, temp_cache_dir, sample_bar_df): + def test_cache_put_and_get(self, cache, sample_bar_df): """Test putting and getting from cache.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - cache.put( df=sample_bar_df, exchange="binance", @@ -88,10 +89,8 @@ def test_cache_put_and_get(self, temp_cache_dir, sample_bar_df): assert result is not None assert len(result) == len(sample_bar_df) - def test_cache_key_different_symbols(self, temp_cache_dir, sample_bar_df): + def test_cache_key_different_symbols(self, cache, sample_bar_df): """Test that different symbols produce different cache keys.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - cache.put( df=sample_bar_df, exchange="binance", @@ -113,10 +112,8 @@ def test_cache_key_different_symbols(self, temp_cache_dir, sample_bar_df): assert result is None - def test_cache_key_different_interval(self, temp_cache_dir, sample_bar_df): + def test_cache_key_different_interval(self, cache, sample_bar_df): """Test that different intervals produce different cache keys.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - cache.put( df=sample_bar_df, exchange="binance", @@ -138,10 +135,8 @@ def test_cache_key_different_interval(self, temp_cache_dir, sample_bar_df): assert result is None - def test_cache_daily_files(self, temp_cache_dir, sample_bar_df): + def test_cache_daily_files(self, temp_cache_dir, cache, sample_bar_df): """Test that cache creates daily files.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - cache.put( df=sample_bar_df, exchange="binance", @@ -156,10 +151,8 @@ def test_cache_daily_files(self, temp_cache_dir, sample_bar_df): assert len(cache_files) == 1 assert "2024-01-15" in cache_files[0].name - def test_get_date_range(self, temp_cache_dir, sample_bar_df): + def test_get_date_range(self, cache, sample_bar_df): """Test getting data for a date range.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - for day in range(1, 4): cache.put( df=sample_bar_df, @@ -184,10 +177,8 @@ def test_get_date_range(self, temp_cache_dir, sample_bar_df): assert result is not None assert len(result) == len(sample_bar_df) * 3 - def test_get_date_range_partial_miss(self, temp_cache_dir, sample_bar_df): + def test_get_date_range_partial_miss(self, cache, sample_bar_df): """Test that partial cache miss returns None for range.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - for day in [1, 3]: cache.put( df=sample_bar_df, @@ -211,10 +202,8 @@ def test_get_date_range_partial_miss(self, temp_cache_dir, sample_bar_df): assert result is None - def test_clear_cache(self, temp_cache_dir, sample_bar_df): + def test_clear_cache(self, cache, sample_bar_df): """Test clearing the cache.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - cache.put( df=sample_bar_df, exchange="binance", diff --git a/tests/data/test_cache_polars.py b/tests/data/test_cache_polars.py index 28b17fc..0706349 100644 --- a/tests/data/test_cache_polars.py +++ b/tests/data/test_cache_polars.py @@ -19,6 +19,13 @@ def temp_cache_dir(): yield Path(tmpdir) +@pytest.fixture +def cache(temp_cache_dir): + """Create cache using non-deprecated storage API.""" + storage = LocalStorageBackend(str(temp_cache_dir)) + return BarCache(storage=storage, cache_prefix="") + + @pytest.fixture def sample_bar_df_polars(): """Create sample bar DataFrame in Polars format.""" @@ -40,10 +47,8 @@ def sample_bar_df_polars(): class TestBarCachePolars: """Tests for BarCache with Polars DataFrames.""" - def test_put_and_get_polars(self, temp_cache_dir, sample_bar_df_polars): + def test_put_and_get_polars(self, cache, sample_bar_df_polars): """Test storing and retrieving Polars DataFrame from cache.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - cache.put( df=sample_bar_df_polars, exchange="binance", @@ -68,10 +73,8 @@ def test_put_and_get_polars(self, temp_cache_dir, sample_bar_df_polars): assert len(result) == len(sample_bar_df_polars) assert result.shape == sample_bar_df_polars.shape - def test_get_returns_none_when_not_cached(self, temp_cache_dir): + def test_get_returns_none_when_not_cached(self, cache): """Test that get returns None if data not in cache.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - result = cache.get( exchange="binance", symbols=["BTCUSDT"], @@ -83,10 +86,8 @@ def test_get_returns_none_when_not_cached(self, temp_cache_dir): assert result is None - def test_get_range_returns_polars(self, temp_cache_dir, sample_bar_df_polars): + def test_get_range_returns_polars(self, cache, sample_bar_df_polars): """Test get_range returns concatenated Polars DataFrame.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - # Store data for 3 consecutive days for day in range(1, 4): cache.put( @@ -113,10 +114,8 @@ def test_get_range_returns_polars(self, temp_cache_dir, sample_bar_df_polars): assert isinstance(result, pl.DataFrame) assert len(result) == len(sample_bar_df_polars) * 3 - def test_get_range_returns_none_if_any_missing(self, temp_cache_dir, sample_bar_df_polars): + def test_get_range_returns_none_if_any_missing(self, cache, sample_bar_df_polars): """Test get_range returns None if any day missing from range.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - # Store data for days 1 and 3, but skip day 2 for day in [1, 3]: cache.put( @@ -142,10 +141,8 @@ def test_get_range_returns_none_if_any_missing(self, temp_cache_dir, sample_bar_ # Should return None because day 2 is missing assert result is None - def test_put_and_get_preserves_data_types(self, temp_cache_dir): + def test_put_and_get_preserves_data_types(self, cache): """Test that data types are preserved through cache round-trip.""" - cache = BarCache(storage=LocalStorageBackend(str(temp_cache_dir))) - df = pl.DataFrame( { "symbol": ["BTCUSDT", "ETHUSDT"], From 22fe3d2130b83f2ac6108e9785719f4b8f85ab7e Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:19:43 +0800 Subject: [PATCH 11/25] fix(data): align date range to UTC midnight --- src/factorium/data/utils.py | 4 ++-- tests/data/test_timestamp_utils.py | 21 +++++++++++++++++++++ 2 files changed, 23 insertions(+), 2 deletions(-) diff --git a/src/factorium/data/utils.py b/src/factorium/data/utils.py index 2bd70a3..5feb482 100644 --- a/src/factorium/data/utils.py +++ b/src/factorium/data/utils.py @@ -3,7 +3,7 @@ """ import logging -from datetime import datetime, timedelta +from datetime import datetime, timedelta, timezone logger = logging.getLogger(__name__) @@ -52,7 +52,7 @@ def calculate_date_range( return start, start + timedelta(days=days) # Snap to UTC midnight for consistent daily boundaries - today_midnight = datetime.now().replace(hour=0, minute=0, second=0, microsecond=0) + today_midnight = datetime.now(timezone.utc).replace(hour=0, minute=0, second=0, microsecond=0) # end = start of tomorrow (exclusive) to include today's full data end = today_midnight + timedelta(days=1) diff --git a/tests/data/test_timestamp_utils.py b/tests/data/test_timestamp_utils.py index 6187d76..362cdfc 100644 --- a/tests/data/test_timestamp_utils.py +++ b/tests/data/test_timestamp_utils.py @@ -1,12 +1,16 @@ # tests/data/test_timestamp_utils.py +from datetime import datetime, timedelta, timezone + import polars as pl import pytest +from factorium.data import utils as data_utils from factorium.data.loader import ( _convert_to_target_unit, _detect_timestamp_unit, _normalize_timestamps_to_ms, ) +from factorium.data.utils import calculate_date_range def test_detect_timestamp_unit_seconds(): @@ -39,6 +43,23 @@ def test_convert_to_target_unit_invalid_unit(): _convert_to_target_unit(1704067200000, "invalid") +def test_calculate_date_range_uses_utc_midnight(monkeypatch): + class FakeDateTime(datetime): + @classmethod + def now(cls, tz=None): + if tz is None: + return cls(2026, 2, 14, 23, 30, tzinfo=timezone(timedelta(hours=8))) + return cls(2026, 2, 14, 15, 30, tzinfo=timezone.utc).astimezone(tz) + + monkeypatch.setattr(data_utils, "datetime", FakeDateTime) + + start, end = calculate_date_range(days=1) + + expected_today_midnight = datetime(2026, 2, 14, 0, 0, tzinfo=timezone.utc) + assert start == expected_today_midnight + assert end == expected_today_midnight + timedelta(days=1) + + class TestNormalizeTimestampsToMs: """Tests for _normalize_timestamps_to_ms function.""" From a02281e5db30e292dedd1168c482913432405ce0 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:19:43 +0800 Subject: [PATCH 12/25] fix(universe): use run_async wrappers and warn on full tag fetch --- src/factorium/universe/metadata.py | 5 +-- src/factorium/universe/tags.py | 16 ++++++++-- tests/universe/test_metadata.py | 33 ++++++++++++++++++++ tests/universe/test_tags.py | 50 ++++++++++++++++++++++++++++++ 4 files changed, 100 insertions(+), 4 deletions(-) diff --git a/src/factorium/universe/metadata.py b/src/factorium/universe/metadata.py index 09ca6cd..01e6b6a 100644 --- a/src/factorium/universe/metadata.py +++ b/src/factorium/universe/metadata.py @@ -1,6 +1,5 @@ from __future__ import annotations -import asyncio import json import time from pathlib import Path @@ -47,7 +46,9 @@ async def fetch_async(self) -> dict[str, SymbolMetadata]: return parsed def fetch(self) -> dict[str, SymbolMetadata]: - return asyncio.run(self.fetch_async()) + from ..data.loader import _run_async + + return _run_async(self.fetch_async()) def _parse_exchange_info(self, data: dict) -> dict[str, SymbolMetadata]: output: dict[str, SymbolMetadata] = {} diff --git a/src/factorium/universe/tags.py b/src/factorium/universe/tags.py index d55dfb1..67c6498 100644 --- a/src/factorium/universe/tags.py +++ b/src/factorium/universe/tags.py @@ -2,6 +2,7 @@ import asyncio import json +import logging import time from pathlib import Path @@ -9,10 +10,16 @@ COINGECKO_BASE_URL = "https://api.coingecko.com/api/v3" +logger = logging.getLogger(__name__) class TagProvider: - """Fetch and cache token categories from CoinGecko.""" + """Fetch and cache token categories from CoinGecko. + + Note: + Calling ``fetch``/``fetch_async`` with ``symbols=None`` performs + full-market category fetching and can be slow for large universes. + """ def __init__( self, @@ -46,6 +53,9 @@ async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[ if all(sym in cached for sym in requested): return {sym: cached[sym] for sym in requested} + if requested is None: + logger.warning("Fetching tags for all symbols may take a long time; pass symbols to limit scope.") + headers: dict[str, str] | None = None if self.api_key: headers = {"x-cg-pro-api-key": self.api_key} @@ -81,7 +91,9 @@ async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[ return {sym: result.get(sym, []) for sym in requested if sym in result} def fetch(self, symbols: list[str] | None = None) -> dict[str, list[str]]: - return asyncio.run(self.fetch_async(symbols=symbols)) + from ..data.loader import _run_async + + return _run_async(self.fetch_async(symbols=symbols)) def _load_cache(self) -> dict[str, list[str]] | None: if not self._cache_path.exists(): diff --git a/tests/universe/test_metadata.py b/tests/universe/test_metadata.py index 4b6e510..51f0e48 100644 --- a/tests/universe/test_metadata.py +++ b/tests/universe/test_metadata.py @@ -3,6 +3,7 @@ import pytest +import factorium.data.loader as data_loader from factorium.universe.metadata import MetadataProvider @@ -90,3 +91,35 @@ async def should_not_call(*args, **kwargs): out = provider.fetch() assert out == cached + + +def test_metadata_fetch_uses_run_async(monkeypatch: pytest.MonkeyPatch) -> None: + provider = MetadataProvider(market="um") + expected = { + "BTCUSDT": { + "symbol": "BTCUSDT", + "base_asset": "BTC", + "quote_asset": "USDT", + "status": "TRADING", + "listing_date": 1_700_000_000_000, + "is_leveraged": False, + "is_stablecoin_pair": False, + } + } + + async def fake_fetch_async() -> dict[str, dict[str, object]]: + return expected + + called = {"value": False} + + def fake_run_async(coro): + called["value"] = True + coro.close() + return expected + + monkeypatch.setattr(provider, "fetch_async", fake_fetch_async) + monkeypatch.setattr(data_loader, "_run_async", fake_run_async) + + out = provider.fetch() + assert out == expected + assert called["value"] is True diff --git a/tests/universe/test_tags.py b/tests/universe/test_tags.py index 1827602..309e477 100644 --- a/tests/universe/test_tags.py +++ b/tests/universe/test_tags.py @@ -3,6 +3,7 @@ import pytest +import factorium.data.loader as data_loader from factorium.universe.tags import TagProvider @@ -60,3 +61,52 @@ async def should_not_call(*args, **kwargs): out = provider.fetch(symbols=["BTC"]) assert out == {"BTC": ["Layer 1"]} + + +def test_tags_fetch_uses_run_async(monkeypatch: pytest.MonkeyPatch) -> None: + provider = TagProvider() + expected = {"BTC": ["Layer 1"]} + + async def fake_fetch_async(symbols=None): + del symbols + return expected + + called = {"value": False} + + def fake_run_async(coro): + called["value"] = True + coro.close() + return expected + + monkeypatch.setattr(provider, "fetch_async", fake_fetch_async) + monkeypatch.setattr(data_loader, "_run_async", fake_run_async) + + out = provider.fetch(symbols=["BTC"]) + assert out == expected + assert called["value"] is True + + +def test_fetch_warns_when_symbols_is_none( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path, caplog: pytest.LogCaptureFixture +) -> None: + provider = TagProvider(cache_dir=tmp_path, cache_ttl=0) + + async def fake_request_json(session, url, params=None, headers=None): + del session, params, headers + if url.endswith("/coins/list"): + return [{"id": "bitcoin", "symbol": "btc", "name": "Bitcoin"}] + if url.endswith("/coins/bitcoin"): + return {"categories": ["Layer 1"]} + raise AssertionError(f"unexpected url: {url}") + + async def no_sleep(seconds: float) -> None: + del seconds + + monkeypatch.setattr(provider, "_request_json", fake_request_json) + monkeypatch.setattr("factorium.universe.tags.asyncio.sleep", no_sleep) + + caplog.set_level("WARNING") + out = provider.fetch(symbols=None) + + assert "BTC" in out + assert "may take a long time" in caplog.text From a8b82d3093fe586138181399956f3a80a921a918 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:19:43 +0800 Subject: [PATCH 13/25] fix(analyzer): re-prepare data when requested periods are missing --- src/factorium/factors/analyzer.py | 9 +++++++-- tests/factors/test_analyzer.py | 14 ++++++++++++++ 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/src/factorium/factors/analyzer.py b/src/factorium/factors/analyzer.py index b90efc4..2488959 100644 --- a/src/factorium/factors/analyzer.py +++ b/src/factorium/factors/analyzer.py @@ -243,8 +243,13 @@ def __init__(self, factor: Factor, prices: AggBar | Factor, quantiles: int = 5, def _ensure_data_prepared(self, periods: list[int] | None = None, price_col: str | None = None) -> None: """Ensure data is prepared. Auto-calls prepare_data() if needed.""" - if not hasattr(self, "_clean_data"): - logger.info("Data not prepared. Auto-calling prepare_data()...") + has_missing_period = bool( + periods + and hasattr(self, "_clean_data") + and any(f"period_{p}" not in self._clean_data.columns for p in periods) + ) + if not hasattr(self, "_clean_data") or has_missing_period: + logger.info("Data not prepared or missing requested periods. Auto-calling prepare_data()...") self.prepare_data(periods=periods, price_col=price_col) def analyze(self, price_col: str = "close", periods: int | list[int] = 1) -> FactorAnalysisResult: diff --git a/tests/factors/test_analyzer.py b/tests/factors/test_analyzer.py index 3eab3c1..fba2582 100644 --- a/tests/factors/test_analyzer.py +++ b/tests/factors/test_analyzer.py @@ -475,3 +475,17 @@ def test_analyze_empty_periods_list_raises_error(sample_data): with pytest.raises(ValueError, match="Periods list cannot be empty"): analyzer.analyze(periods=[]) + + +def test_ensure_data_prepared_reprepare_when_period_missing(sample_data): + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + analyzer = FactorAnalyzer(factor, prices) + + analyzer.prepare_data(periods=[1]) + assert "period_1" in analyzer._clean_data.columns + assert "period_5" not in analyzer._clean_data.columns + + analyzer._ensure_data_prepared(periods=[1, 5]) + assert "period_5" in analyzer._clean_data.columns From f14a06109d6bbcc8fca65a8923e4eb3ee128d17d Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:19:43 +0800 Subject: [PATCH 14/25] refactor(backtest): remove redundant mask reapplication --- src/factorium/backtest/vectorized.py | 4 +--- tests/backtest/test_vectorized.py | 35 ++++++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 3 deletions(-) diff --git a/src/factorium/backtest/vectorized.py b/src/factorium/backtest/vectorized.py index 743b12a..52a30d7 100644 --- a/src/factorium/backtest/vectorized.py +++ b/src/factorium/backtest/vectorized.py @@ -195,9 +195,7 @@ def _calculate_weights(self, df: pl.DataFrame) -> pl.DataFrame: ) if self._mask is not None: - df = df.with_columns( - pl.when(pl.col(self._mask).fill_null(False)).then(pl.col("weight")).otherwise(0.0).alias("weight") - ).drop("_masked_signal") + df = df.drop("_masked_signal") # Apply constraints for constraint in self.constraints: diff --git a/tests/backtest/test_vectorized.py b/tests/backtest/test_vectorized.py index 1d69066..c8ae6ab 100644 --- a/tests/backtest/test_vectorized.py +++ b/tests/backtest/test_vectorized.py @@ -170,6 +170,41 @@ def test_long_only_weights_sum_to_one(self): if ws > 0: assert abs(ws - 1.0) < 1e-10 + def test_calculate_weights_masked_assets_remain_zero_after_neutralize(self): + timestamps = [1704067200000, 1704070800000, 1704074400000] + rows = [] + for i, ts in enumerate(timestamps): + for symbol, base_price, in_universe in [ + ("A", 100.0, True), + ("B", 80.0, True), + ("C", 60.0, False), + ]: + price = base_price * (1 + 0.01 * i) + rows.append( + { + "start_time": ts, + "end_time": ts + 3600000, + "symbol": symbol, + "open": price, + "high": price, + "low": price, + "close": price, + "volume": 1000.0, + "in_universe": in_universe, + } + ) + + prices = AggBar(pl.DataFrame(rows)) + signal = prices["close"].cs_rank() + bt = VectorizedBacktester(prices=prices, signal=signal, neutralization="market", mask="in_universe") + + combined = bt._prepare_data() + weighted = bt._calculate_weights(combined) + + masked = weighted.filter(~pl.col("in_universe").fill_null(False)) + assert masked["weight"].abs().max() == 0.0 + assert "_masked_signal" not in weighted.columns + class TestMetricsCalculation: """Tests for metrics calculation.""" From 5b5b2d28846519de1633183f39e6586f0e5da259 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:37:25 +0800 Subject: [PATCH 15/25] fix(data): make explicit end_date inclusive by day --- src/factorium/data/utils.py | 7 ++++--- tests/test_data_loader.py | 21 ++++++++++----------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/src/factorium/data/utils.py b/src/factorium/data/utils.py index 5feb482..147c2b9 100644 --- a/src/factorium/data/utils.py +++ b/src/factorium/data/utils.py @@ -23,7 +23,7 @@ def calculate_date_range( producing duplicate bars with partial OHLCV data. Priority: - 1. If both start_date and end_date are provided: [start, end] + 1. If both start_date and end_date are provided: [start, end + 1 day) 2. If start_date and days are provided: [start, start + days] 3. If neither: [today_midnight - default_days, today_midnight + 1] 4. If only days: [today_midnight - days, today_midnight + 1] @@ -40,9 +40,10 @@ def calculate_date_range( try: if start_date and end_date: start = datetime.strptime(start_date, "%Y-%m-%d") - end = datetime.strptime(end_date, "%Y-%m-%d") - if start > end: + end_inclusive = datetime.strptime(end_date, "%Y-%m-%d") + if start > end_inclusive: raise ValueError("Start date must be earlier than or equal to end date") + end = end_inclusive + timedelta(days=1) return start, end if start_date and days: diff --git a/tests/test_data_loader.py b/tests/test_data_loader.py index 5dc0f8d..e7a28a8 100644 --- a/tests/test_data_loader.py +++ b/tests/test_data_loader.py @@ -8,7 +8,7 @@ import pyarrow as pa import pyarrow.parquet as pq from pathlib import Path -from datetime import datetime, timedelta +from datetime import datetime, timedelta, timezone from unittest.mock import patch, MagicMock, AsyncMock from freezegun import freeze_time @@ -136,7 +136,7 @@ def test_with_start_and_end_date(self): start_dt, end_dt = calculate_date_range(start_date="2024-01-01", end_date="2024-01-07", days=None) assert start_dt == datetime(2024, 1, 1) - assert end_dt == datetime(2024, 1, 7) + assert end_dt == datetime(2024, 1, 8) def test_with_start_date_and_days(self): """Test with start_date and days specified.""" @@ -151,8 +151,8 @@ def test_with_only_days(self): start_dt, end_dt = calculate_date_range(start_date=None, end_date=None, days=7) # end = start of tomorrow (exclusive), start = end - days - assert end_dt == datetime(2024, 6, 16, 0, 0, 0) - assert start_dt == datetime(2024, 6, 9, 0, 0, 0) + assert end_dt == datetime(2024, 6, 16, 0, 0, 0, tzinfo=timezone.utc) + assert start_dt == datetime(2024, 6, 9, 0, 0, 0, tzinfo=timezone.utc) # Both must be midnight-aligned assert start_dt.hour == 0 and start_dt.minute == 0 and start_dt.second == 0 assert end_dt.hour == 0 and end_dt.minute == 0 and end_dt.second == 0 @@ -162,8 +162,8 @@ def test_default_7_days(self): """Test default behavior (no params = 7 days ending tomorrow midnight).""" start_dt, end_dt = calculate_date_range(start_date=None, end_date=None, days=None) - assert end_dt == datetime(2024, 6, 16, 0, 0, 0) - assert start_dt == datetime(2024, 6, 9, 0, 0, 0) + assert end_dt == datetime(2024, 6, 16, 0, 0, 0, tzinfo=timezone.utc) + assert start_dt == datetime(2024, 6, 9, 0, 0, 0, tzinfo=timezone.utc) # Both must be midnight-aligned assert start_dt.hour == 0 and start_dt.minute == 0 and start_dt.second == 0 assert end_dt.hour == 0 and end_dt.minute == 0 and end_dt.second == 0 @@ -174,8 +174,8 @@ def test_midnight_alignment_regardless_of_time(self): start_dt, end_dt = calculate_date_range(start_date=None, end_date=None, days=3) # Should snap to midnight boundaries - assert start_dt == datetime(2024, 6, 13, 0, 0, 0) - assert end_dt == datetime(2024, 6, 16, 0, 0, 0) + assert start_dt == datetime(2024, 6, 13, 0, 0, 0, tzinfo=timezone.utc) + assert end_dt == datetime(2024, 6, 16, 0, 0, 0, tzinfo=timezone.utc) assert start_dt.microsecond == 0 assert end_dt.microsecond == 0 @@ -191,14 +191,14 @@ def test_cross_year_range(self): start_dt, end_dt = calculate_date_range(start_date="2023-12-28", end_date="2024-01-05", days=None) assert start_dt == datetime(2023, 12, 28) - assert end_dt == datetime(2024, 1, 5) + assert end_dt == datetime(2024, 1, 6) def test_single_day_range(self): """Test single day range (start == end).""" start_dt, end_dt = calculate_date_range(start_date="2024-01-01", end_date="2024-01-01", days=None) assert start_dt == datetime(2024, 1, 1) - assert end_dt == datetime(2024, 1, 1) + assert end_dt == datetime(2024, 1, 2) def test_start_date_with_one_day(self): """Test start_date with days=1.""" @@ -208,7 +208,6 @@ def test_start_date_with_one_day(self): assert end_dt == datetime(2024, 1, 2) - # ============================================================================= # TestBuildDateFilter - 日期過濾條件測試 # ============================================================================= From df90c680a1c5e455600742927f2ff84a7ca8384f Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:37:25 +0800 Subject: [PATCH 16/25] fix(universe): exclude missing listing dates in MinListingAge --- src/factorium/universe/rules.py | 4 ++-- tests/universe/test_universe_rules.py | 14 ++++++++++++++ 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/src/factorium/universe/rules.py b/src/factorium/universe/rules.py index 440a172..cfcdac2 100644 --- a/src/factorium/universe/rules.py +++ b/src/factorium/universe/rules.py @@ -116,7 +116,7 @@ def apply( listing_map[sym] = int(listing_date) if not listing_map: - return pl.lit(True) + return pl.lit(False) listing_expr = pl.col("symbol").replace_strict(listing_map, default=None).cast(pl.Int64, strict=False) - return ((pl.col("start_time") - listing_expr) >= self._min_ms) | listing_expr.is_null() + return ((pl.col("start_time") - listing_expr) >= self._min_ms).fill_null(False) diff --git a/tests/universe/test_universe_rules.py b/tests/universe/test_universe_rules.py index 110f79f..bf60098 100644 --- a/tests/universe/test_universe_rules.py +++ b/tests/universe/test_universe_rules.py @@ -91,3 +91,17 @@ def test_universe_combines_rules_with_and_logic() -> None: out = _sample_df().lazy().with_columns(universe.apply(_sample_df().lazy(), metadata).alias("in_universe")).collect() kept_symbols = set(out.filter(pl.col("in_universe"))["symbol"].to_list()) assert kept_symbols == {"BTCUSDT", "NEWUSDT"} + + +def test_min_listing_age_excludes_symbol_when_listing_date_missing() -> None: + metadata = _sample_metadata() + metadata["NEWUSDT"].pop("listing_date") + + out = ( + _sample_df() + .lazy() + .with_columns(MinListingAge(days=90).apply(_sample_df().lazy(), metadata).alias("keep")) + .collect() + ) + new_rows = out.filter(pl.col("symbol") == "NEWUSDT").sort("start_time") + assert new_rows["keep"].to_list() == [False, False] From e37420b963c286f36f6423f07e456bdabe9e6a14 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:37:25 +0800 Subject: [PATCH 17/25] fix(universe): require symbols and handle CoinGecko symbol collisions --- src/factorium/universe/tags.py | 26 ++++++++--------- tests/universe/test_tags.py | 52 +++++++++++++++++++--------------- 2 files changed, 42 insertions(+), 36 deletions(-) diff --git a/src/factorium/universe/tags.py b/src/factorium/universe/tags.py index 67c6498..b00d9f8 100644 --- a/src/factorium/universe/tags.py +++ b/src/factorium/universe/tags.py @@ -17,8 +17,8 @@ class TagProvider: """Fetch and cache token categories from CoinGecko. Note: - Calling ``fetch``/``fetch_async`` with ``symbols=None`` performs - full-market category fetching and can be slow for large universes. + ``symbols`` must be explicitly provided to avoid full-market + category fetching from CoinGecko, which can be very slow. """ def __init__( @@ -44,18 +44,16 @@ async def _request_json( return await response.json() async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[str]]: - requested = [s.upper() for s in symbols] if symbols is not None else None + if symbols is None: + raise ValueError("symbols must be provided to avoid fetching the entire CoinGecko database") + + requested = [s.upper() for s in symbols] cached = self._load_cache() if cached is not None: - if requested is None: - return cached if all(sym in cached for sym in requested): return {sym: cached[sym] for sym in requested} - if requested is None: - logger.warning("Fetching tags for all symbols may take a long time; pass symbols to limit scope.") - headers: dict[str, str] | None = None if self.api_key: headers = {"x-cg-pro-api-key": self.api_key} @@ -66,10 +64,14 @@ async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[ for item in raw_list if isinstance(raw_list, list) else []: symbol = str(item.get("symbol", "")).upper() coin_id = item.get("id") - if symbol and coin_id and symbol not in symbol_to_id: - symbol_to_id[symbol] = str(coin_id) + if not symbol or not coin_id: + continue + + coin_id_str = str(coin_id) + if symbol not in symbol_to_id or coin_id_str == symbol.lower(): + symbol_to_id[symbol] = coin_id_str - targets = requested or sorted(symbol_to_id.keys()) + targets = requested result: dict[str, list[str]] = {} if cached is None else dict(cached) for symbol in targets: @@ -86,8 +88,6 @@ async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[ await asyncio.sleep(0.12) self._save_cache(result) - if requested is None: - return result return {sym: result.get(sym, []) for sym in requested if sym in result} def fetch(self, symbols: list[str] | None = None) -> dict[str, list[str]]: diff --git a/tests/universe/test_tags.py b/tests/universe/test_tags.py index 309e477..20fd684 100644 --- a/tests/universe/test_tags.py +++ b/tests/universe/test_tags.py @@ -34,6 +34,32 @@ async def no_sleep(seconds: float) -> None: assert out["ETH"] == ["Layer 1", "Smart Contract Platform"] +def test_fetch_prefers_canonical_id_on_symbol_collision(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None: + provider = TagProvider(cache_dir=tmp_path, cache_ttl=0) + + async def fake_request_json(session, url, params=None, headers=None): + del session, params, headers + if url.endswith("/coins/list"): + return [ + {"id": "btc-token", "symbol": "btc", "name": "Some BTC Token"}, + {"id": "btc", "symbol": "btc", "name": "Canonical BTC"}, + ] + if url.endswith("/coins/btc"): + return {"categories": ["Store Of Value"]} + if url.endswith("/coins/btc-token"): + raise AssertionError("should use canonical id when collision exists") + raise AssertionError(f"unexpected url: {url}") + + async def no_sleep(seconds: float) -> None: + del seconds + + monkeypatch.setattr(provider, "_request_json", fake_request_json) + monkeypatch.setattr("factorium.universe.tags.asyncio.sleep", no_sleep) + + out = provider.fetch(symbols=["BTC"]) + assert out["BTC"] == ["Store Of Value"] + + def test_cache_load_save_and_ttl(tmp_path: Path) -> None: provider = TagProvider(cache_dir=tmp_path, cache_ttl=10) sample = {"BTC": ["Layer 1"], "ETH": ["Layer 1", "Smart Contract Platform"]} @@ -86,27 +112,7 @@ def fake_run_async(coro): assert called["value"] is True -def test_fetch_warns_when_symbols_is_none( - monkeypatch: pytest.MonkeyPatch, tmp_path: Path, caplog: pytest.LogCaptureFixture -) -> None: +def test_fetch_raises_when_symbols_is_none(tmp_path: Path) -> None: provider = TagProvider(cache_dir=tmp_path, cache_ttl=0) - - async def fake_request_json(session, url, params=None, headers=None): - del session, params, headers - if url.endswith("/coins/list"): - return [{"id": "bitcoin", "symbol": "btc", "name": "Bitcoin"}] - if url.endswith("/coins/bitcoin"): - return {"categories": ["Layer 1"]} - raise AssertionError(f"unexpected url: {url}") - - async def no_sleep(seconds: float) -> None: - del seconds - - monkeypatch.setattr(provider, "_request_json", fake_request_json) - monkeypatch.setattr("factorium.universe.tags.asyncio.sleep", no_sleep) - - caplog.set_level("WARNING") - out = provider.fetch(symbols=None) - - assert "BTC" in out - assert "may take a long time" in caplog.text + with pytest.raises(ValueError, match="symbols must be provided"): + provider.fetch(symbols=None) From a2555416772ee89d645259626e2ee273c71b8dc1 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Sat, 14 Feb 2026 15:37:25 +0800 Subject: [PATCH 18/25] test(factors): normalize minute literal style in safe ops helper --- tests/factors/test_safe_operations.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/factors/test_safe_operations.py b/tests/factors/test_safe_operations.py index c7cc9c3..3b78ad7 100644 --- a/tests/factors/test_safe_operations.py +++ b/tests/factors/test_safe_operations.py @@ -46,8 +46,8 @@ def _make_factor( for s_idx, sym in enumerate(symbols): rows.append( { - "start_time": t * 60000, - "end_time": (t + 1) * 60000, + "start_time": t * 60_000, + "end_time": (t + 1) * 60_000, "symbol": sym, "factor": values[t * n_symbols + s_idx], } From d759e39e4de6e906992841bb6ef88e8fdfb6bfde Mon Sep 17 00:00:00 2001 From: novis10813 Date: Mon, 16 Feb 2026 18:42:16 +0800 Subject: [PATCH 19/25] fix: put coingecko url into constants.py --- src/factorium/constants.py | 4 ++++ src/factorium/universe/tags.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/src/factorium/constants.py b/src/factorium/constants.py index 42e9115..b7120ec 100644 --- a/src/factorium/constants.py +++ b/src/factorium/constants.py @@ -15,9 +15,13 @@ MIN_PERIODS_PER_YEAR = 1.0 MAX_PERIODS_PER_YEAR = 365.25 * 24 * 60 # Minutes in a year +# External API URLs +COINGECKO_BASE_URL = "https://api.coingecko.com/api/v3" + __all__ = [ "EPSILON", "SECONDS_PER_YEAR", "MIN_PERIODS_PER_YEAR", "MAX_PERIODS_PER_YEAR", + "COINGECKO_BASE_URL", ] diff --git a/src/factorium/universe/tags.py b/src/factorium/universe/tags.py index b00d9f8..2edba28 100644 --- a/src/factorium/universe/tags.py +++ b/src/factorium/universe/tags.py @@ -8,8 +8,8 @@ import aiohttp +from ..constants import COINGECKO_BASE_URL -COINGECKO_BASE_URL = "https://api.coingecko.com/api/v3" logger = logging.getLogger(__name__) From 28a2244816ae2fe252c2d7aae544f67d7799a791 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Mon, 16 Feb 2026 20:46:59 +0800 Subject: [PATCH 20/25] fix(test): correct mock target and missing imports in universe tests - Move _run_async import to module level in metadata.py and tags.py - Patch local module reference in tests to correctly mock _run_async - Fix NameError in test_metadata.py due to missing import --- src/factorium/universe/metadata.py | 3 +-- src/factorium/universe/tags.py | 4 ++-- tests/universe/test_metadata.py | 4 ++-- tests/universe/test_tags.py | 3 ++- 4 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/factorium/universe/metadata.py b/src/factorium/universe/metadata.py index 01e6b6a..df37ca2 100644 --- a/src/factorium/universe/metadata.py +++ b/src/factorium/universe/metadata.py @@ -6,6 +6,7 @@ import aiohttp +from ..data.loader import _run_async from .rules import KNOWN_STABLECOINS, LEVERAGED_PATTERNS, SymbolMetadata @@ -46,8 +47,6 @@ async def fetch_async(self) -> dict[str, SymbolMetadata]: return parsed def fetch(self) -> dict[str, SymbolMetadata]: - from ..data.loader import _run_async - return _run_async(self.fetch_async()) def _parse_exchange_info(self, data: dict) -> dict[str, SymbolMetadata]: diff --git a/src/factorium/universe/tags.py b/src/factorium/universe/tags.py index 2edba28..2992677 100644 --- a/src/factorium/universe/tags.py +++ b/src/factorium/universe/tags.py @@ -9,6 +9,8 @@ import aiohttp from ..constants import COINGECKO_BASE_URL +from ..data.loader import _run_async + logger = logging.getLogger(__name__) @@ -91,8 +93,6 @@ async def fetch_async(self, symbols: list[str] | None = None) -> dict[str, list[ return {sym: result.get(sym, []) for sym in requested if sym in result} def fetch(self, symbols: list[str] | None = None) -> dict[str, list[str]]: - from ..data.loader import _run_async - return _run_async(self.fetch_async(symbols=symbols)) def _load_cache(self) -> dict[str, list[str]] | None: diff --git a/tests/universe/test_metadata.py b/tests/universe/test_metadata.py index 51f0e48..375b710 100644 --- a/tests/universe/test_metadata.py +++ b/tests/universe/test_metadata.py @@ -3,7 +3,7 @@ import pytest -import factorium.data.loader as data_loader +from factorium.universe import metadata as metadata_module from factorium.universe.metadata import MetadataProvider @@ -118,7 +118,7 @@ def fake_run_async(coro): return expected monkeypatch.setattr(provider, "fetch_async", fake_fetch_async) - monkeypatch.setattr(data_loader, "_run_async", fake_run_async) + monkeypatch.setattr(metadata_module, "_run_async", fake_run_async) out = provider.fetch() assert out == expected diff --git a/tests/universe/test_tags.py b/tests/universe/test_tags.py index 20fd684..8122f00 100644 --- a/tests/universe/test_tags.py +++ b/tests/universe/test_tags.py @@ -4,6 +4,7 @@ import pytest import factorium.data.loader as data_loader +from factorium.universe import tags as tags_module from factorium.universe.tags import TagProvider @@ -105,7 +106,7 @@ def fake_run_async(coro): return expected monkeypatch.setattr(provider, "fetch_async", fake_fetch_async) - monkeypatch.setattr(data_loader, "_run_async", fake_run_async) + monkeypatch.setattr(tags_module, "_run_async", fake_run_async) out = provider.fetch(symbols=["BTC"]) assert out == expected From 1ed34108439bda1a5f513dc7f84424326339baf0 Mon Sep 17 00:00:00 2001 From: novis10813 Date: Tue, 17 Feb 2026 21:32:34 +0800 Subject: [PATCH 21/25] chore(release): bump version to 0.4.0 --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 9e959bb..93958d0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [project] name = "factorium" -version = "0.3.2" +version = "0.4.0" description = "A quantitative factor analysis library for financial research" readme = "README.md" requires-python = ">=3.11" From b9aace45cdd4d1be4662b9c87bde1ce348da5cec Mon Sep 17 00:00:00 2001 From: novis10813 Date: Tue, 17 Feb 2026 22:05:48 +0800 Subject: [PATCH 22/25] chore(release): add CHANGELOG for v0.4.0 --- CHANGELOG.md | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 CHANGELOG.md diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..fc98014 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,27 @@ +# Changelog + +All notable changes to this project will be documented in this file. + +## [0.4.0] - 2026-02-17 +### Added +- VectorizedBacktester: standardized `signal -> exposure -> weight` pipeline and portfolio schemes (market‑neutral, long‑only, top‑N patterns). (see #5) +- Factor analysis: multi‑horizon IC decay and flexible targets for `FactorAnalyzer` / reports. (see #4) +- Factor correlation utilities and clustering analysis (correlation matrix + visualizations). (see #6) +- Factor orthogonalization utilities (`cs_neutralize` / residual‑based orthogonalization). (see #7) +- Additional backtest metrics: Sortino ratio, Calmar ratio, win rate and improved metrics handling. +- New example notebooks demonstrating multi‑factor workflows and orthogonalization (`examples/04_multi_factor_combination.ipynb`). +- Extensive unit and integration tests for backtest, factor ops, and Polars paths. + +### Changed +- `Backtester` is now an alias for `VectorizedBacktester` (Polars‑based implementation). +- Internal refactors and Polars migration improvements for TS/CS operators and analyzer. + +### Fixed +- Various bug fixes and test stabilizations across data loading and backtest path. + +### Notes +- Backward compatibility: No breaking API changes expected for typical user workflows. See `docs/dev/migration-guide.md` for migration notes if you rely on internal/edge APIs. + +--- + +(Full changelog & commit list available in the release PR.) From 8b1c00a5f1eff34c6efd1de1164b890ed5118c3b Mon Sep 17 00:00:00 2001 From: novis10813 Date: Tue, 17 Feb 2026 22:06:29 +0800 Subject: [PATCH 23/25] docs(release): add 0.4.0 release notes --- docs/release-notes/0.4.0.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 docs/release-notes/0.4.0.md diff --git a/docs/release-notes/0.4.0.md b/docs/release-notes/0.4.0.md new file mode 100644 index 0000000..d6e45d5 --- /dev/null +++ b/docs/release-notes/0.4.0.md @@ -0,0 +1,15 @@ +# Release notes — v0.4.0 + +Released: 2026-02-17 (pending merge) + +## Highlights +- Vectorized backtester: clearer `signal → exposure → weights` pipeline and new portfolio schemes (market‑neutral, long‑only, top‑N). +- Multi‑horizon IC decay, factor correlation matrix + clustering, and orthogonalization utilities. +- New backtest metrics: Sortino, Calmar, win rate. + +## Migration notes +- No breaking changes expected for standard user APIs. If you rely on internal or edge-case APIs, please review the detailed CHANGELOG and tests. + +## Examples & docs +- See `examples/04_multi_factor_combination.ipynb` for correlation and orthogonalization workflows. +- Full changelog: `CHANGELOG.md`. From a25e6b4cd83b456d723fc6dacba8d3a85de76b4a Mon Sep 17 00:00:00 2001 From: novis10813 Date: Tue, 17 Feb 2026 22:16:07 +0800 Subject: [PATCH 24/25] chore(lock): regenerate uv.lock for Python 3.13 (fix s3transfer parse) --- uv.lock | 1547 +++++++++++++++++++++++++++---------------------------- 1 file changed, 763 insertions(+), 784 deletions(-) diff --git a/uv.lock b/uv.lock index 3d563a9..c6df8b1 100644 --- a/uv.lock +++ b/uv.lock @@ -2,9 +2,12 @@ version = 1 revision = 3 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.14'", - "python_full_version >= '3.12' and python_full_version < '3.14'", - "python_full_version < '3.12'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.14' and sys_platform == 'win32'", + "python_full_version < '3.14' and sys_platform == 'emscripten'", + "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] [[package]] @@ -27,7 +30,7 @@ wheels = [ [[package]] name = "aiohttp" -version = "3.13.2" +version = "3.13.3" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, @@ -38,93 +41,93 @@ dependencies = [ { name = "propcache" }, { name = "yarl" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/1c/ce/3b83ebba6b3207a7135e5fcaba49706f8a4b6008153b4e30540c982fae26/aiohttp-3.13.2.tar.gz", hash = "sha256:40176a52c186aefef6eb3cad2cdd30cd06e3afbe88fe8ab2af9c0b90f228daca", size = 7837994, upload-time = "2025-10-28T20:59:39.937Z" } -wheels = [ - { url = "https://files.pythonhosted.org/packages/35/74/b321e7d7ca762638cdf8cdeceb39755d9c745aff7a64c8789be96ddf6e96/aiohttp-3.13.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:4647d02df098f6434bafd7f32ad14942f05a9caa06c7016fdcc816f343997dd0", size = 743409, 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src/factorium/backtest/constraints.py | 4 +- src/factorium/backtest/metrics.py | 5 +- src/factorium/backtest/portfolio.py | 7 +- src/factorium/backtest/utils.py | 17 +- src/factorium/backtest/vectorized.py | 23 +- src/factorium/data/__init__.py | 8 +- src/factorium/data/adapters/base.py | 2 +- src/factorium/data/adapters/binance.py | 6 +- src/factorium/data/aggregator.py | 64 +++-- src/factorium/data/cache.py | 12 +- src/factorium/data/downloader.py | 191 ++++++------- src/factorium/data/loader.py | 164 +++++++---- src/factorium/data/parquet.py | 45 ++- src/factorium/factors/__init__.py | 13 +- src/factorium/factors/analyzer.py | 303 ++++++++++++++++++--- src/factorium/factors/base.py | 22 +- src/factorium/factors/composite.py | 12 +- src/factorium/factors/core.py | 58 ++-- src/factorium/factors/engine.py | 16 +- src/factorium/factors/mixins/__init__.py | 2 +- src/factorium/factors/mixins/cs_ops.py | 10 +- src/factorium/factors/mixins/math_ops.py | 27 +- src/factorium/factors/mixins/ts_ops.py | 8 +- src/factorium/factors/operators.py | 64 ++++- src/factorium/factors/parser.py | 141 +++++----- src/factorium/factors/plotting.py | 46 ++-- src/factorium/factors/plotting_analyzer.py | 51 +++- src/factorium/research/__init__.py | 2 +- src/factorium/research/report.py | 25 +- src/factorium/research/session.py | 67 +++-- src/factorium/storage/__init__.py | 12 +- src/factorium/storage/base.py | 3 +- src/factorium/storage/local.py | 27 +- src/factorium/storage/s3.py | 157 +++++++++++ tests/backtest/test_backtester.py | 12 +- tests/conftest.py | 2 +- tests/data/test_loader_fast.py | 24 +- tests/data/test_loader_klines.py | 20 +- tests/factors/test_analyzer.py | 7 +- tests/factors/test_analyzer_polars.py | 18 ++ tests/factors/test_base_polars.py | 6 +- tests/factors/test_engine.py | 2 +- tests/factors/test_factor_eval.py | 67 +++++ tests/factors/test_math_ops_polars.py | 6 +- tests/factors/test_ts_ops_polars.py | 4 +- tests/mixins/test_ts_ops.py | 4 +- tests/storage/conftest.py | 74 +++++ tests/storage/test_factory.py | 20 +- tests/storage/test_integration.py | 94 +++++++ tests/storage/test_local.py | 50 ++++ tests/storage/test_s3.py | 119 ++++++++ tests/storage/test_s3_integration.py | 127 +++++++++ tests/test_data_loader.py | 20 +- tests/test_evaluation.py | 72 +++-- tests/test_plotting.py | 2 +- 59 files changed, 1799 insertions(+), 646 deletions(-) create mode 100644 src/factorium/storage/s3.py create mode 100644 tests/factors/test_factor_eval.py create mode 100644 tests/storage/conftest.py create mode 100644 tests/storage/test_integration.py create mode 100644 tests/storage/test_s3.py create mode 100644 tests/storage/test_s3_integration.py diff --git a/src/factorium/__init__.py b/src/factorium/__init__.py index 989f35c..bfed669 100644 --- a/src/factorium/__init__.py +++ b/src/factorium/__init__.py @@ -29,10 +29,10 @@ >>> ranked = momentum.rank() """ -from .factors.core import Factor -from .factors.base import BaseFactor from .aggbar import AggBar from .data import BinanceDataLoader +from .factors.base import BaseFactor +from .factors.core import Factor from .research import ResearchSession from .universe import ( Checklist, diff --git a/src/factorium/aggbar.py b/src/factorium/aggbar.py index bd1b7c6..e0a6241 100644 --- a/src/factorium/aggbar.py +++ b/src/factorium/aggbar.py @@ -5,14 +5,17 @@ across multiple symbols in long format. """ +from datetime import datetime +from pathlib import Path +from typing import TYPE_CHECKING, Optional, Union, cast + +import numpy as np import pandas as pd import polars as pl -import numpy as np -from typing import Union, List, Optional, TYPE_CHECKING -from pathlib import Path -from datetime import datetime if TYPE_CHECKING: + from .bar import BaseBar + from .data.metadata import AggBarMetadata from .factors.core import Factor from .universe.checklist import Checklist from .universe.universe import Universe @@ -39,11 +42,10 @@ class AggBar: def __init__( self, - data: Union[List["BaseBar"], pd.DataFrame, pl.DataFrame], + data: list["BaseBar"] | pd.DataFrame | pl.DataFrame, metadata: Optional["AggBarMetadata"] = None, ): # Import here to avoid circular imports - from .data.metadata import AggBarMetadata # Convert to Polars if isinstance(data, list): @@ -80,18 +82,18 @@ def _compute_metadata(self) -> "AggBarMetadata": return AggBarMetadata( symbols=sorted(self._data["symbol"].unique().to_list()), - min_time=self._data["start_time"].min(), - max_time=self._data["end_time"].max(), + min_time=cast(int, self._data["start_time"].min()), + max_time=cast(int, self._data["end_time"].max()), num_rows=len(self._data), ) @classmethod - def from_bars(cls, bars: List) -> "AggBar": + def from_bars(cls, bars: list) -> "AggBar": """Create AggBar from a list of BaseBar objects.""" return cls(bars) @classmethod - def from_df(cls, df: Union[pd.DataFrame, pl.DataFrame]) -> "AggBar": + def from_df(cls, df: pd.DataFrame | pl.DataFrame) -> "AggBar": """Create AggBar from a DataFrame.""" return cls(df) @@ -124,7 +126,7 @@ def to_parquet(self, path: Path) -> Path: self._data.write_parquet(path) return path - def __getitem__(self, key: Union[str, List[str]]) -> Union["Factor", "AggBar"]: + def __getitem__(self, key: str | list[str]) -> Union["Factor", "AggBar"]: """ Get a column as a Factor or multiple columns as a new AggBar. @@ -157,9 +159,9 @@ def __getitem__(self, key: Union[str, List[str]]) -> Union["Factor", "AggBar"]: def slice( self, - start: Optional[Union[datetime, int, str]] = None, - end: Optional[Union[datetime, int, str]] = None, - symbols: Optional[List[str]] = None, + start: datetime | int | str | None = None, + end: datetime | int | str | None = None, + symbols: list[str] | None = None, ) -> "AggBar": """ Slice data by time range and/or symbols. @@ -173,7 +175,7 @@ def slice( New AggBar with filtered data """ - def convert_timestamp(value: Optional[Union[datetime, int, str]]) -> Optional[int]: + def convert_timestamp(value: datetime | int | str | None) -> int | None: if value is None: return None if isinstance(value, str): @@ -228,12 +230,12 @@ def with_mask( return AggBar(new_data, metadata=self._metadata) @property - def cols(self) -> List[str]: + def cols(self) -> list[str]: """Return list of column names.""" return self._data.columns @property - def symbols(self) -> List[str]: + def symbols(self) -> list[str]: """Return list of unique symbols from metadata.""" return self._metadata.symbols diff --git a/src/factorium/backtest/__init__.py b/src/factorium/backtest/__init__.py index d07bcda..0b7ac77 100644 --- a/src/factorium/backtest/__init__.py +++ b/src/factorium/backtest/__init__.py @@ -1,17 +1,18 @@ from .backtester import ( IterativeBacktester as LegacyBacktester, +) +from .backtester import ( IterativeBacktestResult as LegacyBacktestResult, ) -from .metrics import calculate_metrics -from .portfolio import Portfolio -from .vectorized import VectorizedBacktester, BacktestResult from .constraints import ( - WeightConstraint, - MaxPositionConstraint, LongOnlyConstraint, - MaxGrossExposureConstraint, MarketNeutralConstraint, + MaxGrossExposureConstraint, + MaxPositionConstraint, + WeightConstraint, ) +from .metrics import calculate_metrics +from .portfolio import Portfolio from .utils import ( MAX_PERIODS_PER_YEAR, MIN_PERIODS_PER_YEAR, @@ -21,6 +22,7 @@ normalize_weights, parse_frequency_to_seconds, ) +from .vectorized import BacktestResult, VectorizedBacktester # Backward compatibility: Backtester is now an alias for VectorizedBacktester Backtester = VectorizedBacktester diff --git a/src/factorium/backtest/backtester.py b/src/factorium/backtest/backtester.py index 2c830e4..ea30ac6 100644 --- a/src/factorium/backtest/backtester.py +++ b/src/factorium/backtest/backtester.py @@ -2,10 +2,9 @@ from __future__ import annotations -from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Dict, Literal, Optional, Union - import warnings +from dataclasses import dataclass +from typing import TYPE_CHECKING, Any, Literal import numpy as np import pandas as pd @@ -27,7 +26,7 @@ class IterativeBacktestResult: equity_curve: pd.Series returns: pd.Series - metrics: Dict[str, float] + metrics: dict[str, float] trades: pd.DataFrame portfolio_history: pd.DataFrame @@ -64,10 +63,10 @@ class IterativeBacktester: def __init__( self, - prices: "AggBar", - signal: "Factor", + prices: AggBar, + signal: Factor, entry_price: str = "close", - transaction_cost: Union[float, tuple[float, float]] = 0.0003, + transaction_cost: float | tuple[float, float] = 0.0003, initial_capital: float = 10000.0, full_rebalance: bool = False, neutralization: Literal["market", "none"] = "market", @@ -98,8 +97,8 @@ def __init__( self._validate_inputs() - self._portfolio: Optional[Portfolio] = None - self._result: Optional[IterativeBacktestResult] = None + self._portfolio: Portfolio | None = None + self._result: IterativeBacktestResult | None = None self._price_map: dict[Any, Any] = {} self._signal_map: dict[Any, Any] = {} @@ -167,9 +166,9 @@ def _calculate_target_holdings( def _generate_orders( self, target_holdings: pd.Series, - current_holdings: Dict[str, float], - ) -> Dict[str, float]: - orders: Dict[str, float] = {} + current_holdings: dict[str, float], + ) -> dict[str, float]: + orders: dict[str, float] = {} all_symbols = set(target_holdings.index) | set(current_holdings.keys()) for symbol in all_symbols: @@ -266,7 +265,7 @@ def _build_result(self) -> IterativeBacktestResult: return self._result - def summary(self) -> Dict[str, Any]: + def summary(self) -> dict[str, Any]: if self._result is None: raise RuntimeError("Must call run() before summary()") @@ -277,7 +276,7 @@ def summary(self) -> Dict[str, Any]: **self._result.metrics, } - def plot_equity(self, figsize: tuple[float, float] = (12, 6)) -> "matplotlib.figure.Figure": + def plot_equity(self, figsize: tuple[float, float] = (12, 6)) -> matplotlib.figure.Figure: import matplotlib.pyplot as plt if self._result is None: diff --git a/src/factorium/backtest/constraints.py b/src/factorium/backtest/constraints.py index f46f11a..cda6aae 100644 --- a/src/factorium/backtest/constraints.py +++ b/src/factorium/backtest/constraints.py @@ -4,10 +4,10 @@ Provides constraints for position sizing and weight bounds. """ -from typing import Optional, Dict -import polars as pl from abc import ABC, abstractmethod +import polars as pl + class WeightConstraint(ABC): """ diff --git a/src/factorium/backtest/metrics.py b/src/factorium/backtest/metrics.py index b82157d..cffa222 100644 --- a/src/factorium/backtest/metrics.py +++ b/src/factorium/backtest/metrics.py @@ -1,6 +1,5 @@ """Performance metrics calculation for backtesting.""" -from typing import Dict import numpy as np import pandas as pd @@ -11,7 +10,7 @@ def calculate_metrics( returns: pd.Series, risk_free_rate: float = 0.0, periods_per_year: float = 365.0 * 24, -) -> Dict[str, float]: +) -> dict[str, float]: if not MIN_PERIODS_PER_YEAR <= periods_per_year <= MAX_PERIODS_PER_YEAR: raise ValueError( f"periods_per_year must be between {MIN_PERIODS_PER_YEAR} and {MAX_PERIODS_PER_YEAR}, " @@ -99,5 +98,5 @@ def calculate_metrics( "var_95": var_95, "cvar_95": cvar_95, "win_rate": float(win_rate), - "profit_factor": profit_factor, + "profit_factor": float(profit_factor), } diff --git a/src/factorium/backtest/portfolio.py b/src/factorium/backtest/portfolio.py index de3f212..e7aa884 100644 --- a/src/factorium/backtest/portfolio.py +++ b/src/factorium/backtest/portfolio.py @@ -3,7 +3,6 @@ from __future__ import annotations from dataclasses import dataclass, field -from typing import Dict, List import pandas as pd @@ -16,9 +15,9 @@ class Portfolio: initial_capital: float = 10000.0 cash: float = field(init=False) - positions: Dict[str, float] = field(default_factory=dict) - history: List[Dict] = field(default_factory=list) - trade_log: List[Dict] = field(default_factory=list) + positions: dict[str, float] = field(default_factory=dict) + history: list[dict] = field(default_factory=list) + trade_log: list[dict] = field(default_factory=list) def __post_init__(self) -> None: self.cash = self.initial_capital diff --git a/src/factorium/backtest/utils.py b/src/factorium/backtest/utils.py index 012966c..9ca52aa 100644 --- a/src/factorium/backtest/utils.py +++ b/src/factorium/backtest/utils.py @@ -1,7 +1,6 @@ """Utility functions for backtesting.""" import re -from typing import Union import numpy as np import pandas as pd @@ -14,6 +13,14 @@ SECONDS_PER_YEAR, ) +# Re-export constants for backward compatibility +__all__ = [ + "MAX_PERIODS_PER_YEAR", + "MIN_PERIODS_PER_YEAR", + "POSITION_EPSILON", + "SECONDS_PER_YEAR", +] + # Backward compatibility alias POSITION_EPSILON = EPSILON @@ -153,10 +160,10 @@ def neutralize_weights_polars( def safe_divide( - a: Union[float, np.ndarray, pd.Series], - b: Union[float, np.ndarray, pd.Series], + a: float | np.ndarray | pd.Series, + b: float | np.ndarray | pd.Series, default: float = np.nan, -) -> Union[float, np.ndarray, pd.Series]: +) -> float | np.ndarray | pd.Series: """ Safe division that returns default when denominator is near zero. @@ -172,7 +179,7 @@ def safe_divide( """ # Handle scalar if isinstance(b, (int, float, np.floating, np.integer)): - if np.isnan(b) or abs(b) <= EPSILON: + if np.isnan(float(b)) or abs(float(b)) <= EPSILON: return default return a / b diff --git a/src/factorium/backtest/vectorized.py b/src/factorium/backtest/vectorized.py index 540f9a6..52a30d7 100644 --- a/src/factorium/backtest/vectorized.py +++ b/src/factorium/backtest/vectorized.py @@ -1,16 +1,17 @@ """Vectorized backtester using Polars for performance.""" from dataclasses import dataclass -from typing import Optional, Literal, Dict, Any, Union +from typing import Any, Literal + import numpy as np import pandas as pd import polars as pl from ..aggbar import AggBar -from ..factors.core import Factor from ..constants import EPSILON -from .utils import frequency_to_periods_per_year +from ..factors.core import Factor from .metrics import calculate_metrics +from .utils import frequency_to_periods_per_year @dataclass @@ -19,7 +20,7 @@ class BacktestResult: equity_curve: pl.DataFrame # columns: [end_time, total_value] returns: pl.DataFrame # columns: [end_time, return] - metrics: Dict[str, float] + metrics: dict[str, float] trades: pl.DataFrame # columns: [end_time, symbol, qty, price, cost] portfolio_history: pl.DataFrame # columns: [end_time, cash, market_value, total_value] @@ -40,7 +41,7 @@ class BacktestResultPandas: equity_curve: pd.DataFrame returns: pd.DataFrame - metrics: Dict[str, float] + metrics: dict[str, float] trades: pd.DataFrame portfolio_history: pd.DataFrame @@ -50,10 +51,10 @@ class VectorizedBacktester: def __init__( self, - prices: Union[AggBar, pl.DataFrame], - signal: Union[Factor, pl.DataFrame], + prices: AggBar | pl.DataFrame, + signal: Factor | pl.DataFrame, entry_price: str = "close", - transaction_cost: Union[float, tuple[float, float]] = 0.0003, + transaction_cost: float | tuple[float, float] = 0.0003, initial_capital: float = 10000.0, neutralization: Literal["market", "none"] = "market", frequency: str = "1h", @@ -108,7 +109,7 @@ def __init__( else: self.signal_df = signal - self._result: Optional[BacktestResult] = None + self._result: BacktestResult | None = None def run(self) -> BacktestResult: """ @@ -133,7 +134,7 @@ def run(self) -> BacktestResult: self._result = self._build_result(portfolio_history, combined) return self._result - def summary(self) -> Dict[str, Any]: + def summary(self) -> dict[str, Any]: """Return a summary of backtest results.""" if self._result is None: raise RuntimeError("Must call run() before summary()") @@ -270,7 +271,7 @@ def _calculate_equity(self, df: pl.DataFrame) -> pl.DataFrame: return equity.select(["end_time", "cash", "market_value", "total_value"]) - def _calculate_metrics(self, equity_history: pl.DataFrame) -> Dict[str, float]: + def _calculate_metrics(self, equity_history: pl.DataFrame) -> dict[str, float]: """Calculate performance metrics.""" # Convert to pandas for metrics calculation equity_pd = equity_history.to_pandas() diff --git a/src/factorium/data/__init__.py b/src/factorium/data/__init__.py index 9cc77a3..63ba728 100644 --- a/src/factorium/data/__init__.py +++ b/src/factorium/data/__init__.py @@ -11,15 +11,15 @@ from .aggregator import BarAggregator from .cache import BarCache -from .loader import BinanceDataLoader from .downloader import BinanceDataDownloader +from .loader import BinanceDataLoader from .metadata import AggBarMetadata from .parquet import ( - csv_to_parquet, - read_hive_parquet, + BINANCE_COLUMNS, build_hive_path, + csv_to_parquet, get_market_string, - BINANCE_COLUMNS, + read_hive_parquet, ) from .utils import calculate_date_range diff --git a/src/factorium/data/adapters/base.py b/src/factorium/data/adapters/base.py index ed667ef..f11bea3 100644 --- a/src/factorium/data/adapters/base.py +++ b/src/factorium/data/adapters/base.py @@ -52,7 +52,7 @@ def column_mappings(self) -> dict[str, ColumnMapping]: @abstractmethod def build_parquet_glob( self, - base_path: Path, + base_path: str, symbols: list[str], data_type: str, market_type: str, diff --git a/src/factorium/data/adapters/binance.py b/src/factorium/data/adapters/binance.py index 63d45f8..312b636 100644 --- a/src/factorium/data/adapters/binance.py +++ b/src/factorium/data/adapters/binance.py @@ -1,7 +1,5 @@ """Binance exchange adapter.""" -from pathlib import Path - from .base import BaseExchangeAdapter, ColumnMapping @@ -51,7 +49,7 @@ def _get_market_string(self, market_type: str, futures_type: str = "") -> str: def build_parquet_glob( self, - base_path: Path, + base_path: str, symbols: list[str], data_type: str, market_type: str, @@ -71,7 +69,7 @@ def build_parquet_glob( # For multiple symbols, we'll filter in SQL instead symbol_pattern = "symbol=*" - return str(base_path / f"market={market_str}" / f"data_type={data_type}" / symbol_pattern / "**/*.parquet") + return f"{base_path}/market={market_str}/data_type={data_type}/{symbol_pattern}/**/*.parquet" def get_download_url( self, diff --git a/src/factorium/data/aggregator.py b/src/factorium/data/aggregator.py index b019947..1e3d6ae 100644 --- a/src/factorium/data/aggregator.py +++ b/src/factorium/data/aggregator.py @@ -1,14 +1,22 @@ """DuckDB-based bar aggregator for high-performance OHLCV aggregation.""" import logging +from collections.abc import Callable +from typing import TYPE_CHECKING, cast import duckdb import polars as pl from .adapters.base import ColumnMapping +if TYPE_CHECKING: + from .metadata import AggBarMetadata + logger = logging.getLogger(__name__) +# Type alias for DuckDB connection configurator +DuckDBConfigurator = Callable[[duckdb.DuckDBPyConnection], None] + class BarAggregator: """High-performance bar aggregator using DuckDB SQL. @@ -20,8 +28,32 @@ class BarAggregator: - Memory efficient: aggregation happens in DuckDB - Fast: uses DuckDB's vectorized execution engine - Scalable: handles large datasets that don't fit in memory + + Args: + duckdb_configurator: Optional callback to configure DuckDB connection + (e.g., for S3 credentials). Called before queries. """ + def __init__(self, duckdb_configurator: DuckDBConfigurator | None = None): + """Initialize the aggregator. + + Args: + duckdb_configurator: Optional function to configure DuckDB connection. + Used for S3 backends to set credentials/endpoint. + """ + self._duckdb_configurator = duckdb_configurator + + def _get_connection(self) -> duckdb.DuckDBPyConnection: + """Get a configured DuckDB connection. + + Returns: + A DuckDB connection, optionally configured via the configurator. + """ + conn = duckdb.connect() + if self._duckdb_configurator: + self._duckdb_configurator(conn) + return conn + def _compute_metadata(self, df: pl.DataFrame) -> "AggBarMetadata": """Compute metadata from aggregated DataFrame. @@ -43,8 +75,8 @@ def _compute_metadata(self, df: pl.DataFrame) -> "AggBarMetadata": return AggBarMetadata( symbols=symbols, - min_time=min_time, - max_time=max_time, + min_time=cast(int, min_time), + max_time=cast(int, max_time), num_rows=num_rows, ) @@ -158,7 +190,7 @@ def aggregate_time_bars( """ try: - with duckdb.connect() as conn: + with self._get_connection() as conn: df = conn.execute(query).pl() metadata = self._compute_metadata(df) return df, metadata @@ -268,7 +300,7 @@ def aggregate_tick_bars( """ try: - with duckdb.connect() as conn: + with self._get_connection() as conn: df = conn.execute(query).pl() metadata = self._compute_metadata(df) return df, metadata @@ -355,25 +387,25 @@ def aggregate_volume_bars( -- Recursive CTE to compute greedy bar assignments (Greedy Packing algorithm) greedy AS ( -- Base case: first row - SELECT + SELECT seq, volume, volume AS running_volume, CAST(0 AS BIGINT) AS bar_id FROM numbered WHERE seq = 1 - + UNION ALL - + -- Recursive case: process remaining rows SELECT r.seq, r.volume, - CASE + CASE WHEN g.running_volume >= {interval_volume} THEN r.volume -- Reset after threshold ELSE g.running_volume + r.volume -- Continue accumulating END AS running_volume, - CASE + CASE WHEN g.running_volume >= {interval_volume} THEN g.bar_id + 1 -- New bar ELSE g.bar_id -- Same bar END AS bar_id @@ -413,7 +445,7 @@ def aggregate_volume_bars( """ try: - with duckdb.connect() as conn: + with self._get_connection() as conn: df = conn.execute(query).pl() metadata = self._compute_metadata(df) return df, metadata @@ -498,25 +530,25 @@ def aggregate_dollar_bars( -- Recursive CTE to compute greedy bar assignments (Greedy Packing algorithm) greedy AS ( -- Base case: first row - SELECT + SELECT seq, dollar_volume, dollar_volume AS running_dollar, CAST(0 AS BIGINT) AS bar_id FROM numbered WHERE seq = 1 - + UNION ALL - + -- Recursive case: process remaining rows SELECT r.seq, r.dollar_volume, - CASE + CASE WHEN g.running_dollar >= {interval_dollar} THEN r.dollar_volume -- Reset after threshold ELSE g.running_dollar + r.dollar_volume -- Continue accumulating END AS running_dollar, - CASE + CASE WHEN g.running_dollar >= {interval_dollar} THEN g.bar_id + 1 -- New bar ELSE g.bar_id -- Same bar END AS bar_id @@ -556,7 +588,7 @@ def aggregate_dollar_bars( """ try: - with duckdb.connect() as conn: + with self._get_connection() as conn: df = conn.execute(query).pl() metadata = self._compute_metadata(df) return df, metadata diff --git a/src/factorium/data/cache.py b/src/factorium/data/cache.py index 520c88d..f93016b 100644 --- a/src/factorium/data/cache.py +++ b/src/factorium/data/cache.py @@ -4,9 +4,11 @@ import json from datetime import datetime, timedelta from pathlib import Path +from typing import cast + import polars as pl -from ..storage import StorageBackend, LocalStorageBackend +from ..storage import LocalStorageBackend, StorageBackend class BarCache: @@ -23,6 +25,9 @@ class BarCache: Each day is stored as a separate Parquet file for efficient partial updates. """ + storage: StorageBackend + cache_dir: Path | None # for backward compatibility only + def __init__( self, storage: "StorageBackend | None" = None, @@ -36,6 +41,7 @@ def __init__( storage: StorageBackend instance. If None, creates LocalStorageBackend. cache_prefix: Prefix path for cache files within storage. cache_dir: DEPRECATED. Use storage parameter instead. + When using storage (or default), cache_dir attribute is set to None. """ if cache_dir is not None: # Backward compatibility @@ -52,9 +58,11 @@ def __init__( elif storage is None: self.storage = LocalStorageBackend("./Data") self.cache_prefix = cache_prefix + self.cache_dir = None # not used when storage is the source of truth else: self.storage = storage self.cache_prefix = cache_prefix + self.cache_dir = None # not used when storage is the source of truth if self.cache_prefix: self.storage.makedirs(self.cache_prefix) @@ -111,7 +119,7 @@ def get( cache_path = self._get_cache_path(exchange, symbols, interval_ms, data_type, market_type, date) if self.storage.exists(cache_path): - return self.storage.read_parquet(cache_path) + return cast(pl.DataFrame, self.storage.read_parquet(cache_path)) return None def get_range( diff --git a/src/factorium/data/downloader.py b/src/factorium/data/downloader.py index 77e2d72..356270a 100644 --- a/src/factorium/data/downloader.py +++ b/src/factorium/data/downloader.py @@ -4,18 +4,14 @@ Provides async download functionality for Binance Vision historical data. """ +import argparse import asyncio -import aiohttp -import aiofiles -import os -from datetime import datetime, timedelta -from pathlib import Path -import logging -from typing import Optional, Tuple, List import hashlib -import zipfile -import argparse +import logging import tempfile +import zipfile +from datetime import datetime, timedelta +from pathlib import Path import aiofiles # type: ignore[import-untyped] import aiohttp @@ -27,13 +23,13 @@ class BinanceDataDownloader: """ Asynchronous downloader for Binance Vision historical data. - + Args: base_path: Base directory for data storage max_concurrent_downloads: Maximum number of concurrent downloads retry_attempts: Number of retry attempts for failed downloads retry_delay: Delay between retries in seconds - + Example: >>> downloader = BinanceDataDownloader(base_path="./Data") >>> await downloader.download_data( @@ -45,41 +41,38 @@ class BinanceDataDownloader: ... end_date="2024-01-31" ... ) """ - + def __init__( self, base_path: str = "./Data", max_concurrent_downloads: int = 5, retry_attempts: int = 3, - retry_delay: int = 1 + retry_delay: int = 1, ): self.base_path = Path(base_path) self.max_concurrent_downloads = max_concurrent_downloads self.retry_attempts = retry_attempts self.retry_delay = retry_delay self._setup_logging() - + def _setup_logging(self): """Setup logging configuration.""" - logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(levelname)s - %(message)s' - ) + logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") self.logger = logging.getLogger(__name__) - + async def download_data( self, symbol: str, data_type: str, market_type: str, - futures_type: str = 'cm', - start_date: Optional[str] = None, - end_date: Optional[str] = None, - days: Optional[int] = None + futures_type: str = "cm", + start_date: str | None = None, + end_date: str | None = None, + days: int | None = None, ) -> None: """ Download data for specified parameters. - + Args: symbol: Trading symbol (e.g., BTCUSDT, BTCUSD_PERP) data_type: Data type (trades/klines/aggTrades) @@ -93,30 +86,23 @@ async def download_data( start_date_dt, end_date_dt = calculate_date_range(start_date, end_date, days) download_dir = self._setup_download_dir(symbol, data_type, market_type, futures_type) dates = self._generate_date_list(start_date_dt, end_date_dt) - + tasks = [] semaphore = asyncio.Semaphore(self.max_concurrent_downloads) - + async with aiohttp.ClientSession() as session: for date in dates: task = asyncio.create_task( self._download_single_day( - session, - symbol, - data_type, - market_type, - futures_type, - date, - download_dir, - semaphore + session, symbol, data_type, market_type, futures_type, date, download_dir, semaphore ) ) tasks.append(task) - + await asyncio.gather(*tasks) - + self._update_readme() - + async def _download_single_day( self, session: aiohttp.ClientSession, @@ -126,105 +112,101 @@ async def _download_single_day( futures_type: str, date: datetime, download_dir: Path, - semaphore: asyncio.Semaphore + semaphore: asyncio.Semaphore, ) -> None: """Download single day data and convert to Parquet with Hive partitioning.""" async with semaphore: date_str = date.strftime("%Y-%m-%d") self.logger.info(f"Processing data for {date_str}") - + filename = self._build_filename(symbol, data_type, date_str) checksum_filename = f"{filename}.CHECKSUM" base_url = self._build_base_url(market_type, data_type, symbol, futures_type) - + for attempt in range(self.retry_attempts): try: # Use temp directory for download with tempfile.TemporaryDirectory() as temp_dir: temp_path = Path(temp_dir) data_file_path = temp_path / filename - + if await self._download_file(session, f"{base_url}/{filename}", data_file_path): checksum_file_path = temp_path / checksum_filename - if await self._download_file(session, f"{base_url}/{checksum_filename}", checksum_file_path): + if await self._download_file( + session, f"{base_url}/{checksum_filename}", checksum_file_path + ): if await self._verify_checksum(data_file_path, checksum_file_path): # Extract ZIP to temp directory await self._extract_zip(data_file_path) - + # Find extracted CSV file - csv_filename = filename.replace('.zip', '.csv') + csv_filename = filename.replace(".zip", ".csv") csv_path = temp_path / csv_filename - + if csv_path.exists(): # Build Hive partition path and convert to Parquet market = get_market_string(market_type, futures_type) hive_path = build_hive_path( - self.base_path, market, data_type, symbol, - date.year, date.month, date.day + self.base_path, market, data_type, symbol, date.year, date.month, date.day ) csv_to_parquet(csv_path, hive_path, data_type=data_type) self.logger.info(f"✓ Successfully processed {filename} -> {hive_path}") return else: self.logger.error(f"CSV file not found after extraction: {csv_path}") - + self.logger.warning(f"Attempt {attempt + 1} failed for {date_str}") await asyncio.sleep(self.retry_delay) - + except Exception as e: self.logger.error(f"Error processing {date_str}: {str(e)}") await asyncio.sleep(self.retry_delay) - + self.logger.error(f"Failed to download data for {date_str} after {self.retry_attempts} attempts") - - async def _download_file( - self, - session: aiohttp.ClientSession, - url: str, - file_path: Path - ) -> bool: + + async def _download_file(self, session: aiohttp.ClientSession, url: str, file_path: Path) -> bool: """Download a single file.""" try: async with session.get(url) as response: if response.status == 200: - async with aiofiles.open(file_path, 'wb') as f: + async with aiofiles.open(file_path, "wb") as f: await f.write(await response.read()) return True return False except Exception as e: self.logger.error(f"Error downloading {url}: {str(e)}") return False - + async def _verify_checksum(self, data_file: Path, checksum_file: Path) -> bool: """Verify file checksum.""" try: - async with aiofiles.open(checksum_file, 'r') as f: + async with aiofiles.open(checksum_file) as f: expected_checksum = (await f.read()).split()[0] - - async with aiofiles.open(data_file, 'rb') as f: + + async with aiofiles.open(data_file, "rb") as f: file_content = await f.read() actual_checksum = hashlib.sha256(file_content).hexdigest() - - return expected_checksum == actual_checksum + + return bool(expected_checksum == actual_checksum) except Exception as e: self.logger.error(f"Error verifying checksum: {str(e)}") return False - + async def _extract_zip(self, zip_file: Path) -> None: """Extract ZIP file.""" try: - with zipfile.ZipFile(zip_file, 'r') as zip_ref: + with zipfile.ZipFile(zip_file, "r") as zip_ref: zip_ref.extractall(zip_file.parent) except Exception as e: self.logger.error(f"Error extracting {zip_file}: {str(e)}") - + def _validate_params(self, data_type: str, market_type: str, futures_type: str) -> None: """Validate parameters.""" - if data_type not in ['trades', 'klines', 'aggTrades', 'bookTicker', 'bookDepth']: + if data_type not in ["trades", "klines", "aggTrades", "bookTicker", "bookDepth"]: raise ValueError("Invalid data type") - if market_type not in ['spot', 'futures']: + if market_type not in ["spot", "futures"]: raise ValueError("Invalid market type") - if market_type == 'futures' and futures_type not in ['cm', 'um']: + if market_type == "futures" and futures_type not in ["cm", "um"]: raise ValueError("Invalid futures type") @@ -235,8 +217,8 @@ def _setup_download_dir(self, symbol: str, data_type: str, market_type: str, fut temp_dir = self.base_path / ".temp" temp_dir.mkdir(parents=True, exist_ok=True) return temp_dir - - def _generate_date_list(self, start_date: datetime, end_date: datetime) -> List[datetime]: + + def _generate_date_list(self, start_date: datetime, end_date: datetime) -> list[datetime]: """Generate list of dates.""" dates = [] current = start_date @@ -244,28 +226,28 @@ def _generate_date_list(self, start_date: datetime, end_date: datetime) -> List[ dates.append(current) current += timedelta(days=1) return dates - + def _build_filename(self, symbol: str, data_type: str, date_str: str) -> str: """Build filename.""" if data_type == "klines": return f"{symbol}-1m-{date_str}.zip" return f"{symbol}-{data_type}-{date_str}.zip" - - def _build_base_url(self, market_type: str, data_type: str, symbol: str, futures_type: str = 'cm') -> str: + + def _build_base_url(self, market_type: str, data_type: str, symbol: str, futures_type: str = "cm") -> str: """Build base URL.""" if market_type == "futures": market_path = f"futures/{futures_type}" else: market_path = "spot" - + base_url = f"https://data.binance.vision/data/{market_path}/daily/{data_type}/{symbol}" - + # Klines have an additional interval subdirectory if data_type == "klines": base_url = f"{base_url}/1m" - + return base_url - + def _update_readme(self) -> None: """Update README file.""" readme_content = f"""# Binance Market Data @@ -300,7 +282,7 @@ def _update_readme(self) -> None: - futures: Futures market data (CM/UM) - spot: Spot market data -Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} +Last updated: {datetime.now().strftime("%Y-%m-%d %H:%M:%S")} """ with open(self.base_path / "README.md", "w") as f: f.write(readme_content) @@ -309,58 +291,59 @@ def _update_readme(self) -> None: def parse_args(): """Parse command line arguments.""" parser = argparse.ArgumentParser( - description='Download Binance market data', + description="Download Binance market data", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: # Download 7 days of futures trades data (CM) python -m factorium.data.downloader -s BTCUSD_PERP -t trades -m futures -f cm -d 7 - + # Download specific date range of futures trades data (UM) python -m factorium.data.downloader -s BTCUSDT -t trades -m futures -f um -r 2024-01-01:2024-01-31 - + # Download spot market data python -m factorium.data.downloader -s BTCUSDT -t klines -m spot -r 2024-01-01:2024-01-31 - """ + """, ) - - parser.add_argument('-s', '--symbol', default='BTCUSD_PERP', help='Trading symbol') - parser.add_argument('-t', '--data-type', default='trades', - choices=['trades', 'klines', 'aggTrades', 'bookTicker', 'bookDepth']) - parser.add_argument('-m', '--market-type', default='futures', choices=['spot', 'futures']) - parser.add_argument('-f', '--futures-type', default='cm', choices=['cm', 'um']) - parser.add_argument('-d', '--days', type=int, default=7) - parser.add_argument('-p', '--path', default='./Data') - parser.add_argument('-r', '--date-range', help='Date range YYYY-MM-DD:YYYY-MM-DD') - parser.add_argument('--max-concurrent', type=int, default=5) - parser.add_argument('--retry-attempts', type=int, default=3) - parser.add_argument('--retry-delay', type=int, default=1) - + + parser.add_argument("-s", "--symbol", default="BTCUSD_PERP", help="Trading symbol") + parser.add_argument( + "-t", "--data-type", default="trades", choices=["trades", "klines", "aggTrades", "bookTicker", "bookDepth"] + ) + parser.add_argument("-m", "--market-type", default="futures", choices=["spot", "futures"]) + parser.add_argument("-f", "--futures-type", default="cm", choices=["cm", "um"]) + parser.add_argument("-d", "--days", type=int, default=7) + parser.add_argument("-p", "--path", default="./Data") + parser.add_argument("-r", "--date-range", help="Date range YYYY-MM-DD:YYYY-MM-DD") + parser.add_argument("--max-concurrent", type=int, default=5) + parser.add_argument("--retry-attempts", type=int, default=3) + parser.add_argument("--retry-delay", type=int, default=1) + return parser.parse_args() async def main(): args = parse_args() - + try: downloader = BinanceDataDownloader( base_path=args.path, max_concurrent_downloads=args.max_concurrent, retry_attempts=args.retry_attempts, - retry_delay=args.retry_delay + retry_delay=args.retry_delay, ) - + start_date = None end_date = None if args.date_range: try: - start_date, end_date = args.date_range.split(':') + start_date, end_date = args.date_range.split(":") datetime.strptime(start_date, "%Y-%m-%d") datetime.strptime(end_date, "%Y-%m-%d") except ValueError: print("Error: Invalid date range format. Use YYYY-MM-DD:YYYY-MM-DD") return - + await downloader.download_data( symbol=args.symbol, data_type=args.data_type, @@ -368,9 +351,9 @@ async def main(): futures_type=args.futures_type, start_date=start_date, end_date=end_date, - days=args.days if not args.date_range else None + days=args.days if not args.date_range else None, ) - + except Exception as e: print(f"Error: {str(e)}") return diff --git a/src/factorium/data/loader.py b/src/factorium/data/loader.py index cfb3651..fb93aa4 100644 --- a/src/factorium/data/loader.py +++ b/src/factorium/data/loader.py @@ -8,12 +8,13 @@ import logging from datetime import datetime, timedelta from pathlib import Path -from typing import Optional, List, Literal +from typing import Literal, cast import duckdb -import pandas as pd import polars as pl +from ..storage import get_storage_backend + def _run_async(coro): """ @@ -22,7 +23,7 @@ def _run_async(coro): This is necessary for Jupyter notebooks which already have a running event loop. """ try: - loop = asyncio.get_running_loop() + asyncio.get_running_loop() except RuntimeError: # No running loop, use asyncio.run() return asyncio.run(coro) @@ -132,40 +133,71 @@ def _normalize_timestamps_to_ms(df: pl.DataFrame, ts_unit: str) -> pl.DataFrame: class BinanceDataLoader: """ - Data loader for Binance market data with automatic download. + Data loader for Binance market data with automatic download. - Uses DuckDB to query Parquet files stored in Hive partition format. + Uses DuckDB to query Parquet files stored in Hive partition format. - Args: - base_path: Base directory for data storage - max_concurrent_downloads: Maximum number of concurrent downloads - retry_attempts: Number of retry attempts for failed downloads - retry_delay: Delay between retries in seconds - - Example: - >>> loader = BinanceDataLoader() - >>> agg = loader.load_aggbar( - ... symbols=["BTCUSDT"], - ... data_type="aggTrades", - ... market_type="futures", - ... futures_type="um", - ... start_date="2024-01-01", - ... days=7, - ... bar_type="time", - ... interval=60_000, - ... ) + Args: + backend: Storage backend type - "local" or "s3" + path: For local: base directory. For S3: "bucket/prefix" + base_path: DEPRECATED. Use backend='local' and path instead. + max_concurrent_downloads: Maximum number of concurrent downloads + retry_attempts: Number of retry attempts for failed downloads + retry_delay: Delay between retries in seconds + + Example: + >>> loader = BinanceDataLoader() + >>> agg = loader.load_aggbar( + ... symbols=["BTCUSDT"], + ... data_type="aggTrades", + ... market_type="futures", + ... futures_type="um", + ... start_date="2024-01-01", + ... days=7, + ... bar_type="time", + ... interval=60_000, + ... ) + + + Note: + When using backend='s3', data must already exist in S3. + Automatic download to S3 is not yet supported. """ def __init__( self, - base_path: str = "./Data", + backend: str = "local", + path: str = "./Data", + *, + base_path: str | None = None, # Deprecated max_concurrent_downloads: int = 5, retry_attempts: int = 3, retry_delay: int = 1, ): - self.base_path = Path(base_path) + """... + + Args: + backend: Storage backend type - "local" or "s3" + path: For local: base directory. For S3: "bucket/prefix" + base_path: DEPRECATED. Use backend='local' and path instead. + ... + """ + if base_path is not None: + import warnings + + warnings.warn( + "base_path is deprecated, use backend='local' and path instead", + DeprecationWarning, + stacklevel=2, + ) + backend = "local" + path = base_path + + self.storage = get_storage_backend(backend, path) + self.backend = backend + self.base_path = Path(path) if backend == "local" else None self.downloader = BinanceDataDownloader( - base_path=base_path, + base_path=path, max_concurrent_downloads=max_concurrent_downloads, retry_attempts=retry_attempts, retry_delay=retry_delay, @@ -177,6 +209,23 @@ def _setup_logging(self): logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") self.logger = logging.getLogger(__name__) + def _build_parquet_relative_path(self, market: str, data_type: str, symbol: str, date: datetime) -> str: + """Build the relative path for a Parquet file. + + Args: + market: Market string (e.g., "futures_um", "spot") + data_type: Data type (e.g., "aggTrades", "klines") + symbol: Trading symbol + date: Date for the partition + + Returns: + Relative path to the Parquet file (e.g., "market=.../data.parquet") + """ + return ( + f"market={market}/data_type={data_type}/symbol={symbol}/" + f"year={date.year}/month={date.month:02d}/day={date.day:02d}/data.parquet" + ) + def _check_all_files_exist( self, symbol: str, @@ -191,24 +240,21 @@ def _check_all_files_exist( current = start_dt while current < end_dt: - hive_path = build_hive_path( - self.base_path, market, data_type, symbol, current.year, current.month, current.day - ) - parquet_file = hive_path / "data.parquet" - if not parquet_file.exists(): + relative_path = self._build_parquet_relative_path(market, data_type, symbol, current) + if not self.storage.exists(relative_path): return False current += timedelta(days=1) return True def load_aggbar( self, - symbols: str | List[str], + symbols: str | list[str], data_type: str, market_type: str, futures_type: str = "um", - start_date: Optional[str] = None, - end_date: Optional[str] = None, - days: Optional[int] = None, + start_date: str | None = None, + end_date: str | None = None, + days: int | None = None, bar_type: Literal["time", "tick", "volume", "dollar"] = "time", interval: float = 60_000, force_download: bool = False, @@ -326,8 +372,8 @@ def load_aggbar( market_str = get_market_string(market_type, futures_type) # Collect aggregated data - all_dfs: List[pl.DataFrame] = [] - all_metadata: List[AggBarMetadata] = [] + all_dfs: list[pl.DataFrame] = [] + all_metadata: list[AggBarMetadata] = [] current = start_dt # For non-time bars, process entire date range at once (no daily chunking) @@ -336,7 +382,7 @@ def load_aggbar( end_ts = int(end_dt.timestamp() * 1000) parquet_pattern = adapter.build_parquet_glob( - base_path=self.base_path, + base_path=self.storage.full_path(""), symbols=symbols, data_type=data_type, market_type=market_type, @@ -402,8 +448,8 @@ def load_aggbar( # Compute metadata for cached data cached_meta = AggBarMetadata( symbols=cached_df["symbol"].unique().sort().to_list(), - min_time=cached_df["start_time"].min(), - max_time=cached_df["end_time"].max(), + min_time=cast(int, cached_df["start_time"].min()), + max_time=cast(int, cached_df["end_time"].max()), num_rows=len(cached_df), ) all_metadata.append(cached_meta) @@ -417,7 +463,7 @@ def load_aggbar( day_end_ts = int((current + timedelta(days=1)).timestamp() * 1000) parquet_pattern = adapter.build_parquet_glob( - base_path=self.base_path, + base_path=self.storage.full_path(""), symbols=symbols, data_type=data_type, market_type=market_type, @@ -472,13 +518,13 @@ def load_aggbar( def load_aggbar_fast( self, - symbols: List[str], + symbols: list[str], data_type: str, market_type: str, futures_type: str = "um", - start_date: Optional[str] = None, - end_date: Optional[str] = None, - days: Optional[int] = None, + start_date: str | None = None, + end_date: str | None = None, + days: int | None = None, interval_ms: int = 60_000, force_download: bool = False, use_cache: bool = True, @@ -527,7 +573,7 @@ def load_aggbar_fast( def _check_all_symbols_exist( self, - symbols: List[str], + symbols: list[str], data_type: str, market_type: str, futures_type: str, @@ -542,7 +588,7 @@ def _check_all_symbols_exist( def _find_missing_files( self, - symbols: List[str], + symbols: list[str], data_type: str, market_type: str, futures_type: str, @@ -561,11 +607,8 @@ def _find_missing_files( symbol_missing: list[datetime] = [] current = start_dt while current < end_dt: - hive_path = build_hive_path( - self.base_path, market, data_type, symbol, current.year, current.month, current.day - ) - parquet_file = hive_path / "data.parquet" - if not parquet_file.exists(): + relative_path = self._build_parquet_relative_path(market, data_type, symbol, current) + if not self.storage.exists(relative_path): symbol_missing.append(current) current += timedelta(days=1) @@ -634,7 +677,7 @@ def _group_consecutive_dates(self, dates: list[datetime]) -> list[tuple[datetime def _download_all_symbols( self, - symbols: List[str], + symbols: list[str], data_type: str, market_type: str, futures_type: str, @@ -664,7 +707,7 @@ async def download_all(): def _load_klines_direct( self, - symbols: List[str], + symbols: list[str], data_type: str, market_type: str, futures_type: str, @@ -698,14 +741,13 @@ def _load_klines_direct( Returns: AggBar object containing klines OHLCV data with timestamps in milliseconds """ - import duckdb adapter = BinanceAdapter() market_str = get_market_string(market_type, futures_type) # Build parquet glob pattern parquet_pattern = adapter.build_parquet_glob( - base_path=self.base_path, + base_path=self.storage.full_path(""), symbols=symbols, data_type=data_type, market_type=market_type, @@ -716,9 +758,13 @@ def _load_klines_direct( # Use a single DuckDB connection for all queries con = duckdb.connect(":memory:") + # Configure S3 settings if using S3 backend + if hasattr(self.storage, "configure_duckdb_s3"): + self.storage.configure_duckdb_s3(con) + sample_query = f""" - SELECT open_time - FROM read_parquet('{parquet_pattern}', hive_partitioning=true) + SELECT open_time + FROM read_parquet('{parquet_pattern}', hive_partitioning=true) LIMIT 1 """ sample_result = con.execute(sample_query).fetchone() @@ -753,7 +799,7 @@ def _load_klines_direct( # quote_volume, count, taker_buy_volume, taker_buy_quote_volume query = f""" - SELECT + SELECT open_time as start_time, close_time as end_time, symbol, diff --git a/src/factorium/data/parquet.py b/src/factorium/data/parquet.py index 62eca70..c59bfd8 100644 --- a/src/factorium/data/parquet.py +++ b/src/factorium/data/parquet.py @@ -4,14 +4,13 @@ Provides functions for CSV to Parquet conversion and optimized reading via DuckDB. """ -import pyarrow as pa -import pyarrow.csv as pv -import pyarrow.parquet as pq -import duckdb +import logging from pathlib import Path -from typing import Optional, List + +import duckdb import pandas as pd -import logging +import pyarrow.csv as pv +import pyarrow.parquet as pq logger = logging.getLogger(__name__) @@ -37,7 +36,7 @@ def _detect_has_header(csv_path: Path) -> bool: Detect if CSV has a header by checking if first row contains numeric values. Binance data without headers will have numeric first column (trade id). """ - with open(csv_path, 'r') as f: + with open(csv_path) as f: first_line = f.readline().strip() if not first_line: return False @@ -55,28 +54,28 @@ def csv_to_parquet( output_dir: Path, compression: str = 'zstd', filename: str = 'data.parquet', - data_type: Optional[str] = None, + data_type: str | None = None, ) -> Path: """ Convert CSV file to Parquet format in target directory. - + Automatically detects if CSV has headers. For headerless Binance CSVs, uses predefined column names based on data_type. - + Args: csv_path: Path to input CSV file output_dir: Directory to write Parquet file (will be created if needed) compression: Compression codec ('zstd', 'snappy', 'gzip', or None) filename: Output filename data_type: Binance data type (aggTrades, trades, klines) for headerless CSVs - + Returns: Path to created Parquet file """ output_dir.mkdir(parents=True, exist_ok=True) - + has_header = _detect_has_header(csv_path) - + if has_header: # CSV has headers, read normally table = pv.read_csv(csv_path) @@ -91,7 +90,7 @@ def csv_to_parquet( # Fallback: read with auto-generated column names logger.warning(f"No column definitions for data_type={data_type}, using auto-generated names") table = pv.read_csv(csv_path) - + out_path = output_dir / filename pq.write_table(table, out_path, compression=compression) logger.debug(f"Converted {csv_path} -> {out_path}") @@ -100,20 +99,20 @@ def csv_to_parquet( def read_hive_parquet( base_path: str, - columns: Optional[List[str]] = None, - where: Optional[str] = None, + columns: list[str] | None = None, + where: str | None = None, ) -> pd.DataFrame: """ Read Parquet files with Hive partitioning via DuckDB. - + Args: base_path: Glob pattern to Parquet files (e.g., 'Data/market=*/**/*.parquet') columns: Optional list of columns to select where: Optional WHERE clause (without 'WHERE' keyword) - + Returns: DataFrame with query results - + Example: >>> df = read_hive_parquet( ... 'Data/market=futures_um/data_type=klines/**/*.parquet', @@ -141,7 +140,7 @@ def build_hive_path( ) -> Path: """ Build Hive-style partition path. - + Args: base_path: Base data directory market: Market type (futures_cm, futures_um, spot) @@ -150,7 +149,7 @@ def build_hive_path( year: Year month: Month (1-12) day: Day (1-31) - + Returns: Path to partition directory """ @@ -168,11 +167,11 @@ def build_hive_path( def get_market_string(market_type: str, futures_type: str = '') -> str: """ Get combined market string for Hive partition. - + Args: market_type: 'spot' or 'futures' futures_type: 'cm' or 'um' (only for futures) - + Returns: Market string: 'spot', 'futures_cm', or 'futures_um' """ diff --git a/src/factorium/factors/__init__.py b/src/factorium/factors/__init__.py index 7e3eaa1..8f4bf02 100644 --- a/src/factorium/factors/__init__.py +++ b/src/factorium/factors/__init__.py @@ -9,15 +9,14 @@ - operators: Functional operators for factor expressions """ -from .core import Factor -from .base import BaseFactor -from .parser import FactorExpressionParser -from .analyzer import FactorAnalyzer, FactorAnalysisResult -from .engine import PolarsEngine -from .composite import CompositeFactor - # Import all operators from . import operators +from .analyzer import FactorAnalysisResult, FactorAnalyzer +from .base import BaseFactor +from .composite import CompositeFactor +from .core import Factor +from .engine import PolarsEngine +from .parser import FactorExpressionParser __all__ = [ "Factor", diff --git a/src/factorium/factors/analyzer.py b/src/factorium/factors/analyzer.py index cae1179..2488959 100644 --- a/src/factorium/factors/analyzer.py +++ b/src/factorium/factors/analyzer.py @@ -1,12 +1,14 @@ -import pandas as pd -import polars as pl -import numpy as np import logging from dataclasses import dataclass -from typing import Union, List, Optional, Dict, Any -from .core import Factor -from ..aggbar import AggBar +from typing import Any + import matplotlib.figure as mpl_figure +import numpy as np +import pandas as pd +import polars as pl + +from ..aggbar import AggBar +from .core import Factor logger = logging.getLogger(__name__) @@ -18,23 +20,30 @@ class FactorAnalysisResult: Attributes: factor_name: Name of the analyzed factor - periods: Analysis periods (forward return horizons) + periods: Analysis periods (forward return horizons) - always a list quantiles: Number of quantiles used ic_series: Information Coefficient time series - ic_summary: Summary statistics of IC (mean, std, ir, t-stat) - quantile_returns: Mean returns by quantile + ic_summary: Summary statistics of IC, keyed by period + Dict[int, Dict[str, float]] with mean_ic, ic_std, ic_ir, t-stat + turnover_series: Turnover time series (1 - rank autocorrelation) + turnover_mean: Average turnover across all periods + quantile_returns: Mean returns by quantile, keyed by period + Dict[int, pd.DataFrame] cumulative_returns: Cumulative returns by quantile (if available) + Dict[int, pd.DataFrame] or None """ factor_name: str - periods: int + periods: list[int] quantiles: int ic_series: pd.DataFrame - ic_summary: Dict[str, float] - quantile_returns: pd.DataFrame - cumulative_returns: Optional[pd.DataFrame] = None + ic_summary: dict[int, dict[str, float]] + turnover_series: pd.Series + turnover_mean: float + quantile_returns: dict[int, pd.DataFrame] + cumulative_returns: dict[int, pd.DataFrame] | None = None - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> dict[str, Any]: """Convert to dictionary for backward compatibility.""" return { "factor_name": self.factor_name, @@ -42,18 +51,168 @@ def to_dict(self) -> Dict[str, Any]: "quantiles": self.quantiles, "ic_series": self.ic_series, "ic_summary": self.ic_summary, + "turnover_series": self.turnover_series, + "turnover_mean": self.turnover_mean, "quantile_returns": self.quantile_returns, "cumulative_returns": self.cumulative_returns, } def __repr__(self) -> str: - ic = self.ic_summary - return f"""FactorAnalysisResult: {self.factor_name} - Periods: {self.periods}, Quantiles: {self.quantiles} - Mean IC: {ic.get("mean_ic", 0):.4f} - IC Std: {ic.get("ic_std", 0):.4f} - IC IR: {ic.get("ic_ir", 0):.4f} -""" + lines = [f"FactorAnalysisResult: {self.factor_name}"] + lines.append(f" Periods: {self.periods}, Quantiles: {self.quantiles}") + for p in self.periods: + ic = self.ic_summary.get(p, {}) + lines.append(f" Period {p}: IC={ic.get('mean_ic', 0):.4f}, IR={ic.get('ic_ir', 0):.4f}") + lines.append(f" Turnover: {self.turnover_mean:.4f}") + return "\n".join(lines) + "\n" + + def save(self, output_dir: str) -> None: + """ + Save analysis results to directory with timestamp. + + Creates structure (single horizon): + {output_dir}/ + └── YYYYMMDD_HHMMSS_{factor_name}/ + ├── config.json + ├── ic_series.csv + ├── ic_summary.csv + ├── turnover.csv + ├── quantile_returns.csv + ├── cumulative_returns.csv + └── plots/ + ├── ic_distribution.png + ├── ic_timeseries.png + ├── quantile_returns.png + └── cumulative_returns.png + + Multi-horizon structure (periods=[1, 5, 20]): + {output_dir}/ + └── YYYYMMDD_HHMMSS_{factor_name}/ + ├── config.json + ├── ic_series.csv # columns: period_1, period_5, period_20 + ├── ic_summary.csv # rows indexed by period + ├── turnover.csv + ├── quantile_returns_period_1.csv # per-period files + ├── quantile_returns_period_5.csv + ├── quantile_returns_period_20.csv + ├── cumulative_returns_period_1.csv + ├── cumulative_returns_period_5.csv + ├── cumulative_returns_period_20.csv + └── plots/ + ├── ic_distribution.png + ├── ic_timeseries.png + ├── ic_decay.png # IC decay curve (multi-horizon only) + ├── quantile_returns_period_1.png + ├── quantile_returns_period_5.png + ├── quantile_returns_period_20.png + ├── cumulative_returns_period_1.png + ├── cumulative_returns_period_5.png + └── cumulative_returns_period_20.png + + Args: + output_dir: Base directory for experiment outputs + """ + import json + from datetime import datetime + from pathlib import Path + + import matplotlib.pyplot as plt + + from .plotting_analyzer import FactorAnalyzerPlotter + + # Create timestamped folder + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + folder_name = f"{timestamp}_{self.factor_name}" + exp_path = Path(output_dir) / folder_name + exp_path.mkdir(parents=True, exist_ok=True) + + # Create plots subdirectory + plots_path = exp_path / "plots" + plots_path.mkdir(exist_ok=True) + + # Save CSV files + self.ic_series.to_csv(exp_path / "ic_series.csv") + + # Convert ic_summary to DataFrame for CSV (always dict[int, dict[str, float]] now) + ic_summary_df = pd.DataFrame(self.ic_summary).T + ic_summary_df.index.name = "period" + ic_summary_df.to_csv(exp_path / "ic_summary.csv") + + self.turnover_series.to_csv(exp_path / "turnover.csv", header=True) + + # Handle quantile_returns (always dict[int, pd.DataFrame] now) + for p, df in self.quantile_returns.items(): + df.to_csv(exp_path / f"quantile_returns_period_{p}.csv") + + if self.cumulative_returns is not None: + # Always dict[int, pd.DataFrame] now + for p, df in self.cumulative_returns.items(): + df.to_csv(exp_path / f"cumulative_returns_period_{p}.csv") + + # Save plots + plotter = FactorAnalyzerPlotter() + + # IC time series plot + try: + fig_ic_ts = plotter.plot_ic_ts(self.ic_series) + fig_ic_ts.savefig(plots_path / "ic_timeseries.png", dpi=150, bbox_inches="tight") + plt.close(fig_ic_ts) + except Exception as e: + logger.warning(f"Failed to generate IC timeseries plot: {e}") + + # IC distribution plot + try: + fig_ic_hist = plotter.plot_ic_hist(self.ic_series) + fig_ic_hist.savefig(plots_path / "ic_distribution.png", dpi=150, bbox_inches="tight") + plt.close(fig_ic_hist) + except Exception as e: + logger.warning(f"Failed to generate IC distribution plot: {e}") + + # Quantile returns plot (always per-period now) + for p, df in self.quantile_returns.items(): + try: + fig_qret = plotter.plot_quantile_returns(df) + fig_qret.savefig(plots_path / f"quantile_returns_period_{p}.png", dpi=150, bbox_inches="tight") + plt.close(fig_qret) + except Exception as e: + logger.warning(f"Failed to generate quantile returns plot for period {p}: {e}") + + # Cumulative returns plot (if available, always per-period now) + if self.cumulative_returns is not None: + for p, df in self.cumulative_returns.items(): + try: + fig_cumret = plotter.plot_cumulative_returns(df) + fig_cumret.savefig(plots_path / f"cumulative_returns_period_{p}.png", dpi=150, bbox_inches="tight") + plt.close(fig_cumret) + except Exception as e: + logger.warning(f"Failed to generate cumulative returns plot for period {p}: {e}") + + # IC decay plot (multi-horizon only) + if isinstance(self.periods, list) and len(self.periods) > 1: + try: + fig_decay = plotter.plot_ic_decay(self.ic_summary) + fig_decay.savefig(plots_path / "ic_decay.png", dpi=150, bbox_inches="tight") + plt.close(fig_decay) + except Exception as e: + logger.warning(f"Failed to generate IC decay plot: {e}") + + # Save config.json + config = { + "factor_name": self.factor_name, + "periods": self.periods, + "quantiles": self.quantiles, + "created_at": datetime.now().isoformat(), + "data_range": { + "start": str(self.ic_series.index.min()), + "end": str(self.ic_series.index.max()), + "n_observations": len(self.ic_series), + }, + } + + with open(exp_path / "config.json", "w") as f: + json.dump(config, f, indent=2) + + logger.info(f"Results saved to {exp_path}") class FactorAnalyzer: @@ -70,7 +229,12 @@ def __init__(self, factor: Factor, prices: AggBar | Factor, quantiles: int = 5, self._mask = mask if isinstance(prices, AggBar): try: - self.prices = prices["close"] + close_col = prices["close"] + if isinstance(close_col, Factor): + self.prices = close_col + else: + # close column is not a Factor (AggBar), skip + self.prices = None except KeyError: # If 'close' is not there, we'll wait for price_col in prepare_data self.prices = None @@ -92,45 +256,71 @@ def analyze(self, price_col: str = "close", periods: int | list[int] = 1) -> Fac """ Run full factor analysis. + Args: + price_col: Column name for prices. + periods: Single period (int) or list of periods for multi-horizon analysis. + Returns: FactorAnalysisResult with IC series, summary, and quantile returns + + Raises: + ValueError: If periods is an empty list. """ + # Normalize periods to list for internal processing + periods_list = [periods] if isinstance(periods, int) else periods + + # Validate periods + if not periods_list: + raise ValueError("Periods list cannot be empty.") + # Prepare data - self.prepare_data(price_col=price_col, periods=[periods]) + self.prepare_data(price_col=price_col, periods=periods_list) # Calculate IC ic_series = self.calculate_ic() ic_summary_df = self.calculate_ic_summary() - # Convert IC summary to dict for single period as expected by FactorAnalysisResult - col = f"period_{periods}" - ic_summary = { - "mean_ic": ic_summary_df.loc["mean", col] if col in ic_summary_df.columns else 0.0, - "ic_std": ic_summary_df.loc["std", col] if col in ic_summary_df.columns else 0.0, - "ic_ir": ic_summary_df.loc["ic_ir", col] if col in ic_summary_df.columns else 0.0, - "t-stat": ic_summary_df.loc["t-stat", col] if col in ic_summary_df.columns else 0.0, - } + # Build ic_summary - always use dict[int, dict[str, float]] format + ic_summary: dict[int, dict[str, float]] = {} + for p in periods_list: + col = f"period_{p}" + ic_summary[p] = { + "mean_ic": float(ic_summary_df.loc["mean", col]) if col in ic_summary_df.columns else 0.0, + "ic_std": float(ic_summary_df.loc["std", col]) if col in ic_summary_df.columns else 0.0, + "ic_ir": float(ic_summary_df.loc["ic_ir", col]) if col in ic_summary_df.columns else 0.0, + "t-stat": float(ic_summary_df.loc["t-stat", col]) if col in ic_summary_df.columns else 0.0, + } - # Calculate quantile returns - quantile_returns = self.calculate_quantile_returns(quantiles=self.quantiles, period=periods) + # Calculate quantile returns - always use dict[int, pd.DataFrame] format + quantile_returns: dict[int, pd.DataFrame] = { + p: self.calculate_quantile_returns(quantiles=self.quantiles, period=p) for p in periods_list + } - # Calculate cumulative returns (optional) + # Calculate cumulative returns (optional) - always use dict[int, pd.DataFrame] format try: - cumulative_returns = self.calculate_cumulative_returns(quantiles=self.quantiles, period=periods) + cumulative_returns: dict[int, pd.DataFrame] | None = { + p: self.calculate_cumulative_returns(quantiles=self.quantiles, period=p) for p in periods_list + } except Exception: cumulative_returns = None + # Calculate turnover + turnover_series = self.calculate_turnover() + turnover_mean = float(turnover_series.mean()) + return FactorAnalysisResult( factor_name=self.factor.name, - periods=periods, + periods=periods_list, quantiles=self.quantiles, ic_series=ic_series, ic_summary=ic_summary, + turnover_series=turnover_series, + turnover_mean=turnover_mean, quantile_returns=quantile_returns, cumulative_returns=cumulative_returns, ) - def prepare_data(self, periods: Optional[List[int]] = None, price_col: Optional[str] = None) -> pl.DataFrame: + def prepare_data(self, periods: list[int] | None = None, price_col: str | None = None) -> pl.DataFrame: """ Prepare data for analysis by aligning factor values with future returns. @@ -164,7 +354,7 @@ def prepare_data(self, periods: Optional[List[int]] = None, price_col: Optional[ prices_lf = self._raw_prices.to_polars().lazy().select(select_cols) price_col_name = price_col elif self.prices is not None: - # self.prices is a Factor + # self.prices is a Factor (we've narrowed the type above) prices_lf = self.prices.lazy.rename({"factor": "__price__"}) price_col_name = "__price__" else: @@ -218,7 +408,7 @@ def calculate_ic(self, method: str = "rank") -> pd.DataFrame: ic_df = ( self._clean_data.group_by("start_time") - .agg([pl.corr("factor", col, method=corr_method).alias(col) for col in period_cols]) + .agg([pl.corr("factor", col, method=corr_method).alias(col) for col in period_cols]) # type: ignore[call-overload] .sort("start_time") ) @@ -402,3 +592,38 @@ def plot_cumulative_returns( cum_ret = self.calculate_cumulative_returns(quantiles=quantiles, period=period, long_short=long_short) plotter = FactorAnalyzerPlotter() return plotter.plot_cumulative_returns(cum_ret) + + def plot_ic_decay(self, periods: list[int] | None = None, method: str = "rank") -> mpl_figure.Figure: + """ + Plot IC decay curve across multiple horizons. + + Args: + periods: List of periods to plot. If None, uses all available periods. + method: 'rank' for Spearman, 'normal' for Pearson. + + Returns: + matplotlib Figure + """ + from .plotting_analyzer import FactorAnalyzerPlotter + + ic_summary_df = self.calculate_ic_summary(method=method) + + # Build ic_summary dict for plotting + if periods is None: + # Extract periods from available columns + periods = [int(c.replace("period_", "")) for c in ic_summary_df.columns if c.startswith("period_")] + + ic_summary = {} + for p in periods: + col = f"period_{p}" + if col in ic_summary_df.columns: + ic_summary[p] = { + "mean_ic": float(ic_summary_df.loc["mean", col]), + "ic_ir": float(ic_summary_df.loc["ic_ir", col]), + } + + if not ic_summary: + raise ValueError("No IC data available for the specified periods.") + + plotter = FactorAnalyzerPlotter() + return plotter.plot_ic_decay(ic_summary) diff --git a/src/factorium/factors/base.py b/src/factorium/factors/base.py index 6773722..2bcd548 100644 --- a/src/factorium/factors/base.py +++ b/src/factorium/factors/base.py @@ -1,14 +1,16 @@ -from typing import Union, Optional, Callable, TYPE_CHECKING +from collections.abc import Callable +from typing import TYPE_CHECKING, Union try: from typing import Self except ImportError: - from typing_extensions import Self + from typing import Self from abc import ABC -import pandas as pd -import polars as pl from pathlib import Path + import numpy as np +import pandas as pd +import polars as pl from ..constants import EPSILON @@ -17,9 +19,7 @@ class BaseFactor(ABC): - def __init__( - self, data: Union["AggBar", pd.DataFrame, pl.DataFrame, pl.LazyFrame, Path], name: Optional[str] = None - ): + def __init__(self, data: Union["AggBar", pd.DataFrame, pl.DataFrame, pl.LazyFrame, Path], name: str | None = None): self._name = name or "factor" self._lf = self._to_lazy(data) @@ -153,7 +153,7 @@ def _from_polars(self, pl_df: pl.DataFrame, name: str) -> Self: return self.__class__(pl_df, name) def _binary_op( - self, other: Union["BaseFactor", float], op_func: Callable, op_name: str, scalar_suffix: Optional[str] = None + self, other: Union["BaseFactor", float], op_func: Callable, op_name: str, scalar_suffix: str | None = None ) -> Self: if isinstance(other, self.__class__): # Use Polars LazyFrame join for factor-factor operations @@ -343,10 +343,10 @@ def __gt__(self, other: Union["BaseFactor", float]) -> Self: def __ge__(self, other: Union["BaseFactor", float]) -> Self: return self._comparison_op(other, lambda x, y: x >= y, ">=") - def __eq__(self, other: Union["BaseFactor", float]) -> Self: + def __eq__(self, other: Union["BaseFactor", float]) -> Self: # type: ignore[override] return self._comparison_op(other, lambda x, y: x == y, "==") - def __ne__(self, other: Union["BaseFactor", float]) -> Self: + def __ne__(self, other: Union["BaseFactor", float]) -> Self: # type: ignore[override] return self._comparison_op(other, lambda x, y: x != y, "!=") def __len__(self) -> int: @@ -356,4 +356,4 @@ def __len__(self) -> int: which is much faster than collecting the full dataset but still requires execution. Avoid calling in tight loops. """ - return self._lf.select(pl.len()).collect().item() + return int(self._lf.select(pl.len()).collect().item()) diff --git a/src/factorium/factors/composite.py b/src/factorium/factors/composite.py index 57e5980..b08347a 100644 --- a/src/factorium/factors/composite.py +++ b/src/factorium/factors/composite.py @@ -4,7 +4,6 @@ Allows combining multiple factors using weighted combinations. """ -from typing import List, Dict, Optional import polars as pl from .core import Factor @@ -28,8 +27,8 @@ class CompositeFactor: def __init__( self, - factors: List[Factor], - weights: Optional[List[float]] = None, + factors: list[Factor], + weights: list[float] | None = None, name: str = "composite", ): if len(factors) == 0: @@ -46,7 +45,7 @@ def __init__( self.name = name @classmethod - def from_equal_weights(cls, factors: List[Factor], name: str = "composite") -> "CompositeFactor": + def from_equal_weights(cls, factors: list[Factor], name: str = "composite") -> "CompositeFactor": """ Create composite with equal weights. @@ -60,7 +59,7 @@ def from_equal_weights(cls, factors: List[Factor], name: str = "composite") -> " return cls(factors, weights=None, name=name) @classmethod - def from_weights(cls, factors: List[Factor], weights: List[float], name: str = "composite") -> "CompositeFactor": + def from_weights(cls, factors: list[Factor], weights: list[float], name: str = "composite") -> "CompositeFactor": """ Create composite with custom weights. @@ -80,7 +79,7 @@ def from_weights(cls, factors: List[Factor], weights: List[float], name: str = " return cls(factors, weights=weights, name=name) @classmethod - def from_zscore(cls, factors: List[Factor], name: str = "composite_zscore") -> "CompositeFactor": + def from_zscore(cls, factors: list[Factor], name: str = "composite_zscore") -> "CompositeFactor": """ Create composite by standardizing factors first (z-score). @@ -94,7 +93,6 @@ def from_zscore(cls, factors: List[Factor], name: str = "composite_zscore") -> " Returns: CompositeFactor with z-score normalized factors """ - import numpy as np # Standardize each factor standardized = [] diff --git a/src/factorium/factors/core.py b/src/factorium/factors/core.py index de51b6d..2168056 100644 --- a/src/factorium/factors/core.py +++ b/src/factorium/factors/core.py @@ -1,17 +1,18 @@ -import pandas as pd -import matplotlib.figure as mpl_figure - -from typing import Union, Optional, List, Tuple, Dict, Any, TYPE_CHECKING -from pathlib import Path from datetime import datetime +from pathlib import Path +from typing import TYPE_CHECKING, Union + +import matplotlib.figure as mpl_figure +import pandas as pd from .base import BaseFactor +from .mixins.cs_ops import CrossSectionalOpsMixin from .mixins.math_ops import MathOpsMixin from .mixins.ts_ops import TimeSeriesOpsMixin -from .mixins.cs_ops import CrossSectionalOpsMixin if TYPE_CHECKING: from ..aggbar import AggBar + from .analyzer import FactorAnalysisResult class Factor(CrossSectionalOpsMixin, TimeSeriesOpsMixin, MathOpsMixin, BaseFactor): @@ -33,11 +34,11 @@ class Factor(CrossSectionalOpsMixin, TimeSeriesOpsMixin, MathOpsMixin, BaseFacto >>> ranked.plot(plot_type='timeseries') """ - def __init__(self, data: Union["AggBar", pd.DataFrame, Path], name: Optional[str] = None): + def __init__(self, data: Union["AggBar", pd.DataFrame, Path], name: str | None = None): super().__init__(data, name) @classmethod - def from_expression(cls, expr: str, context: Dict[str, "Factor"]) -> "Factor": + def from_expression(cls, expr: str, context: dict[str, "Factor"]) -> "Factor": """ Create a Factor from an expression string. @@ -62,36 +63,35 @@ def from_expression(cls, expr: str, context: Dict[str, "Factor"]) -> "Factor": def eval( self, - prices: "Factor", - periods: List[int] = [1, 5, 10], + prices: Union["Factor", "AggBar"], + periods: int = 1, # MVP 僅支援單一窗口 quantiles: int = 5, output_dir: str | None = None, price_col: str = "close", mask: str | None = None, **kwargs, - ) -> Dict[str, Any]: + ) -> "FactorAnalysisResult": """ - Run a full evaluation report for the factor. + Evaluate factor's predictive power (Evaluation Layer). Args: - prices: A Factor object containing price data (e.g., close prices) - periods: List of holding periods to evaluate (e.g., [1, 5, 10] days) - quantiles: Number of quantiles for layer testing - save_path: Path to save the evaluation report plot (e.g., 'report.png') - **kwargs: Additional arguments passed to the evaluator + prices: Price data (Factor or AggBar) + periods: Prediction horizon (currently only supports single int) + quantiles: Number of quantiles for layer analysis (default 5) + output_dir: Experiment output directory (creates timestamped folder if specified) + price_col: Price column name (default "close") Returns: - Dictionary containing evaluation metrics: - - ic_mean: Mean IC for each period - - ic_ir: IC Information Ratio for each period - - turnover_mean: Average factor turnover - - layer_returns: Average returns for each quantile - - spread: Long-short spread (Top - Bottom quantile) + FactorAnalysisResult: Complete evaluation metrics including IC, ICIR, t-stat, + turnover, quantile returns, and cumulative returns Example: - >>> factor.eval(prices=close_factor, periods=[1, 5, 20], save_path='eval.png') + >>> momentum = ts_returns(close, 20) + >>> result = momentum.eval(prices, output_dir="./experiments") + >>> print(result.ic_summary) + {'mean_ic': 0.05, 'ic_ir': 1.2, 't_stat': 3.5, ...} """ - from .evaluation import FactorEvaluator + from .analyzer import FactorAnalyzer analyzer = FactorAnalyzer(factor=self, prices=prices, quantiles=quantiles, mask=mask) @@ -106,10 +106,10 @@ def eval( def plot( self, plot_type: str = "timeseries", - symbols: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - figsize: Tuple[int, int] = (12, 6), + symbols: list[str] | None = None, + start_time: datetime | None = None, + end_time: datetime | None = None, + figsize: tuple[int, int] = (12, 6), **kwargs, ) -> mpl_figure.Figure: """ diff --git a/src/factorium/factors/engine.py b/src/factorium/factors/engine.py index cde2914..84825d4 100644 --- a/src/factorium/factors/engine.py +++ b/src/factorium/factors/engine.py @@ -1,8 +1,8 @@ """Polars-based computation engine for high-performance factor operations.""" -import polars as pl -import pandas as pd import numpy as np +import pandas as pd +import polars as pl from ..constants import EPSILON @@ -171,7 +171,7 @@ def cs_winsorize( upper_bound = pl.col(value_col).quantile(1 - limits).over(time_col) # Clip values between bounds - winsorize_expr = pl.col(value_col).clip(min_bound=lower_bound, max_bound=upper_bound) + winsorize_expr = pl.col(value_col).clip(lower_bound=lower_bound, upper_bound=upper_bound) return df.with_columns(pl.when(has_nan).then(None).otherwise(winsorize_expr).alias(value_col)) @@ -240,17 +240,17 @@ def least_squares_batch(batch_df): try: # Solve least squares: y = [x 1] * [beta alpha]^T - A = np.vstack([x, np.ones(len(x))]).T - beta_alpha, _, _, _ = np.linalg.lstsq(A, y, rcond=None) - residuals = y - A @ beta_alpha + design_matrix = np.vstack([x, np.ones(len(x))]).T + beta_alpha, _, _, _ = np.linalg.lstsq(design_matrix, y, rcond=None) + residuals = y - design_matrix @ beta_alpha batch_pd["residual"] = residuals except Exception: batch_pd["residual"] = np.nan return batch_pd - # Apply least-squares per time period - result = df.map_batches(least_squares_batch, schema={**df.schema, "residual": pl.Float64}) + # Apply least-squares per time period using group_by + map_groups + result = df.group_by("end_time", maintain_order=True).map_groups(least_squares_batch) # Select and rename back to factor result = result.select(["start_time", "end_time", "symbol", "residual"]).rename({"residual": value_col}) diff --git a/src/factorium/factors/mixins/__init__.py b/src/factorium/factors/mixins/__init__.py index 0d1a07f..c32fd37 100644 --- a/src/factorium/factors/mixins/__init__.py +++ b/src/factorium/factors/mixins/__init__.py @@ -7,8 +7,8 @@ - CrossSectionalOpsMixin: Cross-sectional operations (rank, mean, etc.) """ +from .cs_ops import CrossSectionalOpsMixin from .math_ops import MathOpsMixin from .ts_ops import TimeSeriesOpsMixin -from .cs_ops import CrossSectionalOpsMixin __all__ = ["MathOpsMixin", "TimeSeriesOpsMixin", "CrossSectionalOpsMixin"] diff --git a/src/factorium/factors/mixins/cs_ops.py b/src/factorium/factors/mixins/cs_ops.py index 7e5d864..fe89f0f 100644 --- a/src/factorium/factors/mixins/cs_ops.py +++ b/src/factorium/factors/mixins/cs_ops.py @@ -1,12 +1,10 @@ -from typing import List, Union - try: from typing import Self except ImportError: - from typing_extensions import Self + from typing import Self -import numbers import polars as pl + from ...constants import EPSILON @@ -46,12 +44,12 @@ def cs_demean(self) -> Self: ) return self.__class__(result_lf, "cs_demean") - def cs_winsorize(self, limits: Union[float, List[float]] = 0.025) -> Self: + def cs_winsorize(self, limits: float | list[float] = 0.025) -> Self: """ Cross-sectional winsorization. Strict: Returns NaN if any input is NaN. Limits can be a single float (applied to both sides) or [lower, upper]. """ - if isinstance(limits, numbers.Real): + if isinstance(limits, (int, float)): lower_lim = upper_lim = limits else: lower_lim, upper_lim = limits diff --git a/src/factorium/factors/mixins/math_ops.py b/src/factorium/factors/mixins/math_ops.py index 441e880..9af05e5 100644 --- a/src/factorium/factors/mixins/math_ops.py +++ b/src/factorium/factors/mixins/math_ops.py @@ -1,12 +1,11 @@ -import polars as pl - from math import nan -from typing import Optional, Union + +import polars as pl try: from typing import Self except ImportError: - from typing_extensions import Self + from typing import Self from ...constants import EPSILON @@ -29,7 +28,7 @@ def inverse(self) -> Self: ) return self.__class__(result_lf, f"inverse({self.name})") - def log(self, base: Optional[float] = None) -> Self: + def log(self, base: float | None = None) -> Self: if base is None: result_lf = self._lf.with_columns( pl.when(pl.col("factor") > 0).then(pl.col("factor").log()).otherwise(None).alias("factor") @@ -62,7 +61,7 @@ def signed_log1p(self) -> Self: result_lf = self._lf.with_columns((pl.col("factor").sign() * pl.col("factor").abs().log1p()).alias("factor")) return self.__class__(result_lf, f"signed_log1p({self.name})") - def signed_pow(self, exponent: Union[Self, float]) -> Self: + def signed_pow(self, exponent: Self | float) -> Self: if isinstance(exponent, self.__class__): # Factor-factor path result_lf = self._lf.join(exponent._lf, on=["start_time", "end_time", "symbol"], suffix="_exp") @@ -78,7 +77,7 @@ def signed_pow(self, exponent: Union[Self, float]) -> Self: ) return self.__class__(result_lf, f"signed_pow({self.name},{exponent})") - def pow(self, exponent: Union[Self, float]) -> Self: + def pow(self, exponent: Self | float) -> Self: if isinstance(exponent, self.__class__): # Factor-factor path result_lf = self._lf.join(exponent._lf, on=["start_time", "end_time", "symbol"], suffix="_exp") @@ -90,19 +89,19 @@ def pow(self, exponent: Union[Self, float]) -> Self: result_lf = self._lf.with_columns(pl.col("factor").pow(pl.lit(exponent)).alias("factor")) return self.__class__(result_lf, f"pow({self.name},{exponent})") - def add(self, other: Union[Self, float]) -> Self: + def add(self, other: Self | float) -> Self: return self.__add__(other) - def sub(self, other: Union[Self, float]) -> Self: + def sub(self, other: Self | float) -> Self: return self.__sub__(other) - def mul(self, other: Union[Self, float]) -> Self: + def mul(self, other: Self | float) -> Self: return self.__mul__(other) - def div(self, other: Union[Self, float]) -> Self: + def div(self, other: Self | float) -> Self: return self.__truediv__(other) - def where(self, cond: Self, other: Union[Self, float] = nan) -> Self: + def where(self, cond: Self, other: Self | float = nan) -> Self: if not isinstance(cond, self.__class__): raise ValueError(f"Condition must be a Factor, got {type(cond)}") @@ -127,7 +126,7 @@ def where(self, cond: Self, other: Union[Self, float] = nan) -> Self: result_lf = result_lf.select(["start_time", "end_time", "symbol", "factor"]) return self.__class__(result_lf, f"where({self.name})") - def max(self, other: Union[Self, float]) -> Self: + def max(self, other: Self | float) -> Self: if isinstance(other, self.__class__): # Factor-factor path result_lf = self._lf.join(other._lf, on=["start_time", "end_time", "symbol"], suffix="_other") @@ -141,7 +140,7 @@ def max(self, other: Union[Self, float]) -> Self: result_lf = self._lf.with_columns(pl.max_horizontal(pl.col("factor"), pl.lit(other)).alias("factor")) return self.__class__(result_lf, f"max({self.name},{other})") - def min(self, other: Union[Self, float]) -> Self: + def min(self, other: Self | float) -> Self: if isinstance(other, self.__class__): # Factor-factor path result_lf = self._lf.join(other._lf, on=["start_time", "end_time", "symbol"], suffix="_other") diff --git a/src/factorium/factors/mixins/ts_ops.py b/src/factorium/factors/mixins/ts_ops.py index b388af8..02abc29 100644 --- a/src/factorium/factors/mixins/ts_ops.py +++ b/src/factorium/factors/mixins/ts_ops.py @@ -1,10 +1,9 @@ try: from typing import Self except ImportError: - from typing_extensions import Self + from typing import Self import numpy as np -import pandas as pd import polars as pl from scipy.stats import cauchy, norm, uniform @@ -496,8 +495,9 @@ def ts_corr(self, other: Self, window: int) -> Self: std_x = pl.col("factor").rolling_std(window_size=window, min_samples=window, ddof=1).over("symbol") std_y = pl.col("factor_y").rolling_std(window_size=window, min_samples=window, ddof=1).over("symbol") - corr_expr = pl.when((std_x <= EPSILON) | (std_y <= EPSILON)) - corr_expr = corr_expr.then(pl.lit(None)).otherwise(cov_xy / (std_x * std_y)) + corr_expr: pl.Expr = ( + pl.when((std_x <= EPSILON) | (std_y <= EPSILON)).then(pl.lit(None)).otherwise(cov_xy / (std_x * std_y)) + ) corr_expr = pl.when(nan_in_window > 0).then(pl.lit(None)).otherwise(corr_expr) result_lf = joined.with_columns(corr_expr.alias("factor")).drop("factor_y") diff --git a/src/factorium/factors/operators.py b/src/factorium/factors/operators.py index f7e88ef..e707824 100644 --- a/src/factorium/factors/operators.py +++ b/src/factorium/factors/operators.py @@ -5,7 +5,7 @@ enabling expression-based factor construction similar to alpha101. """ -from typing import TYPE_CHECKING, Union, List +from typing import TYPE_CHECKING, Union, cast import numpy as np @@ -178,7 +178,7 @@ def cs_demean(factor: "Factor") -> "Factor": return factor.cs_demean() -def cs_winsorize(factor: "Factor", limits: Union[float, List[float]] = 0.025) -> "Factor": +def cs_winsorize(factor: "Factor", limits: float | list[float] = 0.025) -> "Factor": """Functional version of factor.cs_winsorize(limits)""" return factor.cs_winsorize(limits) @@ -253,7 +253,7 @@ def pow(factor: "Factor", exponent: Union["Factor", float]) -> "Factor": return factor.pow(exponent) -def where(factor: "Factor", cond: "Factor", other: Union["Factor", float] = None) -> "Factor": +def where(factor: "Factor", cond: "Factor", other: Union["Factor", float, None] = None) -> "Factor": """Functional version of factor.where(cond, other)""" if other is None: other = np.nan @@ -281,20 +281,62 @@ def reverse(factor: "Factor") -> "Factor": def add(factor1: Union["Factor", float], factor2: Union["Factor", float]) -> "Factor": - """Functional version of factor1 + factor2""" - return factor1 + factor2 + """Functional version of factor1 + factor2. + + At least one argument must be a Factor. The result is always a Factor. + """ + from .core import Factor as FactorClass + + if isinstance(factor1, FactorClass): + return cast("Factor", factor1 + factor2) + elif isinstance(factor2, FactorClass): + return cast("Factor", factor2 + factor1) + else: + raise TypeError("At least one argument must be a Factor") def sub(factor1: Union["Factor", float], factor2: Union["Factor", float]) -> "Factor": - """Functional version of factor1 - factor2""" - return factor1 - factor2 + """Functional version of factor1 - factor2. + + At least one argument must be a Factor. The result is always a Factor. + """ + from .core import Factor as FactorClass + + if isinstance(factor1, FactorClass): + return cast("Factor", factor1 - factor2) + elif isinstance(factor2, FactorClass): + # float - Factor = -(Factor - float) + return cast("Factor", (factor2 - factor1).reverse()) + else: + raise TypeError("At least one argument must be a Factor") def mul(factor1: Union["Factor", float], factor2: Union["Factor", float]) -> "Factor": - """Functional version of factor1 * factor2""" - return factor1 * factor2 + """Functional version of factor1 * factor2. + + At least one argument must be a Factor. The result is always a Factor. + """ + from .core import Factor as FactorClass + + if isinstance(factor1, FactorClass): + return cast("Factor", factor1 * factor2) + elif isinstance(factor2, FactorClass): + return cast("Factor", factor2 * factor1) + else: + raise TypeError("At least one argument must be a Factor") def div(factor1: Union["Factor", float], factor2: Union["Factor", float]) -> "Factor": - """Functional version of factor1 / factor2""" - return factor1 / factor2 + """Functional version of factor1 / factor2. + + At least one argument must be a Factor. The result is always a Factor. + """ + from .core import Factor as FactorClass + + if isinstance(factor1, FactorClass): + return cast("Factor", factor1 / factor2) + elif isinstance(factor2, FactorClass): + # float / Factor = (1/Factor) * float = Factor.pow(-1) * float + return cast("Factor", factor2.pow(-1) * factor1) + else: + raise TypeError("At least one argument must be a Factor") diff --git a/src/factorium/factors/parser.py b/src/factorium/factors/parser.py index e9f3b7e..b800383 100644 --- a/src/factorium/factors/parser.py +++ b/src/factorium/factors/parser.py @@ -5,23 +5,23 @@ enabling string-based factor construction similar to alpha101. """ -from typing import Dict, Union, Any, List, TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Union, cast if TYPE_CHECKING: from .core import Factor from pyparsing import ( - Word, - alphas, - alphanums, - nums, - Optional, - Group, + Combine, Forward, - infix_notation, + Group, OpAssoc, + Optional, ParseException, Suppress, - Combine, + Word, + alphanums, + alphas, + infix_notation, + nums, one_of, ) @@ -34,59 +34,62 @@ class FactorExpressionParser: """ Parser for functional-style factor expressions. - + Supports: - Function calls: ts_delta(close, 20) - Variables: close, volume (resolved from context) - Numbers: 20, 3.14 - Binary operators: +, -, *, / (with proper precedence) - Parentheses: (expression) - + Example: >>> parser = FactorExpressionParser() - >>> result = parser.parse("ts_delta(close, 20) / ts_shift(close, 20)", + >>> result = parser.parse("ts_delta(close, 20) / ts_shift(close, 20)", ... context={'close': close_factor}) """ - + def __init__(self): """Initialize the parser with grammar rules.""" # Define basic tokens identifier = Word(alphas + "_", alphanums + "_") - + # Numbers (integers and floats) integer = Combine(Optional("-") + Word(nums)) float_number = Combine( - Optional("-") + Word(nums) + "." + Word(nums) + - Optional(one_of("e E") + Optional(one_of("+ -")) + Word(nums)) + Optional("-") + + Word(nums) + + "." + + Word(nums) + + Optional(one_of("e E") + Optional(one_of("+ -")) + Word(nums)) ) number = float_number | integer - + # Function call: function_name(arg1, arg2, ...) function_call = Forward() - + # Expression (forward declaration for recursion) expression = Forward() - + # Argument list - each argument is its own Group to isolate results arg_list = Group(expression) + (Suppress(",") + Group(expression))[...] - + # Function call definition function_call <<= Group( - identifier.set_results_name("func_name") + - Suppress("(") + - Optional(arg_list).set_results_name("args") + - Suppress(")") + identifier.set_results_name("func_name") + + Suppress("(") + + Optional(arg_list).set_results_name("args") + + Suppress(")") ) - + # Variable or number - wrap in Group to isolate names atom = function_call | Group(identifier.set_results_name("variable")) | Group(number.set_results_name("number")) - + # Parenthesized expression paren_expr = Suppress("(") + expression + Suppress(")") - + # Primary factor (atom or parenthesized expression) factor = paren_expr | atom - + # Define operator precedence expression <<= infix_notation( factor, @@ -95,26 +98,26 @@ def __init__(self): (one_of("+ -"), 2, OpAssoc.LEFT, self._make_binary_op), ], ) - + self.parser = expression - + def _make_binary_op(self, tokens): """Action for infix operators - returns a dict structure""" # tokens[0] is a list: [left, op, right, op, right...] # Because we used OpAssoc.LEFT, tokens[0] contains the matched tokens - matched = tokens[0] + matched = tokens[0] res = matched[0] for i in range(1, len(matched), 2): op = matched[i] - right = matched[i+1] + right = matched[i + 1] res = {"type": "binary_op", "op": op, "left": res, "right": right} return res - - - def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", float, int]: + + def _evaluate(self, node: Any, context: dict[str, "Factor"]) -> Union["Factor", float, int]: """Evaluate a parsed expression node.""" # Handle Factor objects directly from .core import Factor + if isinstance(node, Factor): return node @@ -122,22 +125,24 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", if hasattr(node, "as_dict"): try: node_dict = node.as_dict() - except: + except Exception: node_dict = {} - + # If it's empty but has content, it might be a list-like ParseResults if not node_dict and hasattr(node, "__iter__") and not isinstance(node, (str, dict)): if len(node) == 1: return self._evaluate(node[0], context) - + # Check if it's a binary operation (from infix_notation) node_type = node_dict.get("type") if node_type == "binary_op": op = node_dict["op"] left = self._evaluate(node_dict["left"], context) right = self._evaluate(node_dict["right"], context) - print(f"DEBUG: binary_op {op}, left: {getattr(left, 'name', left)}, right: {getattr(right, 'name', right)}") - + print( + f"DEBUG: binary_op {op}, left: {getattr(left, 'name', left)}, right: {getattr(right, 'name', right)}" + ) + if op == "+": return operators.add(left, right) elif op == "-": @@ -148,17 +153,17 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", return operators.div(left, right) else: raise ValueError(f"Unknown binary operator: {op}") - + # Check if it's a function call if "func_name" in node_dict: func_name = node_dict["func_name"] args_val = node_dict.get("args") - + if not hasattr(operators, func_name): raise ValueError(f"Unknown function: {func_name}") - + op_func = getattr(operators, func_name) - + # Evaluate arguments if args_val is None: eval_args = [] @@ -166,16 +171,16 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", eval_args = [self._evaluate(arg, context) for arg in args_val] else: eval_args = [self._evaluate(args_val, context)] - - return op_func(*eval_args) - + + return cast("Factor | float | int", op_func(*eval_args)) + # Check if it's a variable if "variable" in node_dict: var_name = node_dict["variable"] if var_name not in context: raise ValueError(f"Undefined variable: {var_name}") return context[var_name] - + # Check if it's a number if "number" in node_dict: num_str = str(node_dict["number"]) @@ -193,7 +198,7 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", op = node["op"] left = self._evaluate(node["left"], context) right = self._evaluate(node["right"], context) - + if op == "+": return operators.add(left, right) elif op == "-": @@ -204,22 +209,26 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", return operators.div(left, right) else: raise ValueError(f"Unknown binary operator: {op}") - + if "func_name" in node: func_name = node["func_name"] args_list = node.get("args", []) if not hasattr(operators, func_name): raise ValueError(f"Unknown function: {func_name}") op_func = getattr(operators, func_name) - eval_args = [self._evaluate(arg, context) for arg in args_list] if isinstance(args_list, list) else [self._evaluate(args_list, context)] - return op_func(*eval_args) - + eval_args = ( + [self._evaluate(arg, context) for arg in args_list] + if isinstance(args_list, list) + else [self._evaluate(args_list, context)] + ) + return cast("Factor | float | int", op_func(*eval_args)) + if "variable" in node: var_name = node["variable"] if var_name not in context: raise ValueError(f"Undefined variable: {var_name}") return context[var_name] - + if "number" in node: num_str = str(node["number"]) try: @@ -232,15 +241,15 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", node_list = list(node) if len(node_list) == 1: return self._evaluate(node_list[0], context) - + # If we have a list that didn't match anything above, it might be raw tokens of a function call # This shouldn't happen with our current grammar but let's be safe return self._evaluate(node_list[0], context) - + # Handle direct values if isinstance(node, (int, float, Factor)): return node - + if isinstance(node, str): if node in context: return context[node] @@ -252,21 +261,21 @@ def _evaluate(self, node: Any, context: Dict[str, "Factor"]) -> Union["Factor", if hasattr(operators, node): raise ValueError(f"Function '{node}' used without parentheses") raise ValueError(f"Undefined variable: {node}") - + # If we get here, it's an unexpected type raise ValueError(f"Unexpected node type: {type(node)}, value: {node}") - - def parse(self, expr: str, context: Dict[str, "Factor"]) -> "Factor": + + def parse(self, expr: str, context: dict[str, "Factor"]) -> "Factor": """ Parse and evaluate a factor expression. - + Args: expr: Expression string (e.g., "ts_delta(close, 20) / ts_shift(close, 20)") context: Dictionary mapping variable names to Factor objects - + Returns: Factor: The resulting factor from the expression - + Raises: ParseException: If the expression cannot be parsed ValueError: If there's an error in evaluation (undefined variable, etc.) @@ -274,12 +283,12 @@ def parse(self, expr: str, context: Dict[str, "Factor"]) -> "Factor": try: parsed = self.parser.parse_string(expr, parse_all=True) result = self._evaluate(parsed, context) - + from .core import Factor + if not isinstance(result, Factor): raise ValueError(f"Expression did not evaluate to a Factor, got {type(result)}") - + return result except ParseException as e: raise ValueError(f"Failed to parse expression '{expr}': {e}") from e - diff --git a/src/factorium/factors/plotting.py b/src/factorium/factors/plotting.py index 34f3fa4..8555634 100644 --- a/src/factorium/factors/plotting.py +++ b/src/factorium/factors/plotting.py @@ -4,12 +4,12 @@ Provides FactorPlotter class for visualizing factor data with various plot types. """ -from typing import Optional, List, Tuple, TYPE_CHECKING from datetime import datetime -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt +from typing import TYPE_CHECKING + import matplotlib.figure as mpl_figure +import matplotlib.pyplot as plt +import pandas as pd if TYPE_CHECKING: from .core import Factor @@ -37,9 +37,9 @@ def __init__(self, factor: "Factor"): def _filter_data( self, - symbols: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, + symbols: list[str] | None = None, + start_time: datetime | None = None, + end_time: datetime | None = None, ) -> pd.DataFrame: """ Filter data by symbols and time range. @@ -71,10 +71,10 @@ def _filter_data( def plot_timeseries( self, - symbols: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - figsize: Tuple[int, int] = (12, 6), + symbols: list[str] | None = None, + start_time: datetime | None = None, + end_time: datetime | None = None, + figsize: tuple[int, int] = (12, 6), **kwargs, ) -> mpl_figure.Figure: """ @@ -117,10 +117,10 @@ def plot_timeseries( def plot_heatmap( self, - symbols: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - figsize: Tuple[int, int] = (14, 8), + symbols: list[str] | None = None, + start_time: datetime | None = None, + end_time: datetime | None = None, + figsize: tuple[int, int] = (14, 8), **kwargs, ) -> mpl_figure.Figure: """ @@ -186,10 +186,10 @@ def plot_heatmap( def plot_distribution( self, - symbols: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - figsize: Tuple[int, int] = (12, 6), + symbols: list[str] | None = None, + start_time: datetime | None = None, + end_time: datetime | None = None, + figsize: tuple[int, int] = (12, 6), dist_type: str = "histogram", **kwargs, ) -> mpl_figure.Figure: @@ -260,10 +260,10 @@ def plot_distribution( def plot( self, plot_type: str = "timeseries", - symbols: Optional[List[str]] = None, - start_time: Optional[datetime] = None, - end_time: Optional[datetime] = None, - figsize: Tuple[int, int] = (12, 6), + symbols: list[str] | None = None, + start_time: datetime | None = None, + end_time: datetime | None = None, + figsize: tuple[int, int] = (12, 6), **kwargs, ) -> mpl_figure.Figure: """ diff --git a/src/factorium/factors/plotting_analyzer.py b/src/factorium/factors/plotting_analyzer.py index 21b1fcd..3815025 100644 --- a/src/factorium/factors/plotting_analyzer.py +++ b/src/factorium/factors/plotting_analyzer.py @@ -2,10 +2,10 @@ Plotting utilities for FactorAnalyzer. """ -from typing import Tuple -import pandas as pd -import matplotlib.pyplot as plt + import matplotlib.figure as mpl_figure +import matplotlib.pyplot as plt +import pandas as pd class FactorAnalyzerPlotter: @@ -13,7 +13,7 @@ class FactorAnalyzerPlotter: Plotting utility for FactorAnalyzer results. """ - def plot_ic_ts(self, ic_data: pd.DataFrame, figsize: Tuple[int, int] = (12, 6)) -> mpl_figure.Figure: + def plot_ic_ts(self, ic_data: pd.DataFrame, figsize: tuple[int, int] = (12, 6)) -> mpl_figure.Figure: """ Plot time series of IC. @@ -33,7 +33,7 @@ def plot_ic_ts(self, ic_data: pd.DataFrame, figsize: Tuple[int, int] = (12, 6)) plt.tight_layout() return fig - def plot_ic_hist(self, ic_data: pd.DataFrame, figsize: Tuple[int, int] = (10, 6)) -> mpl_figure.Figure: + def plot_ic_hist(self, ic_data: pd.DataFrame, figsize: tuple[int, int] = (10, 6)) -> mpl_figure.Figure: """ Plot histogram of IC. @@ -54,7 +54,7 @@ def plot_ic_hist(self, ic_data: pd.DataFrame, figsize: Tuple[int, int] = (10, 6) return fig def plot_quantile_returns( - self, quantile_stats: pd.DataFrame, figsize: Tuple[int, int] = (10, 6) + self, quantile_stats: pd.DataFrame, figsize: tuple[int, int] = (10, 6) ) -> mpl_figure.Figure: """ Plot bar chart of mean returns per quantile. @@ -79,7 +79,7 @@ def plot_quantile_returns( plt.tight_layout() return fig - def plot_cumulative_returns(self, cum_ret: pd.DataFrame, figsize: Tuple[int, int] = (12, 6)) -> mpl_figure.Figure: + def plot_cumulative_returns(self, cum_ret: pd.DataFrame, figsize: tuple[int, int] = (12, 6)) -> mpl_figure.Figure: """ Plot cumulative returns of quantiles. @@ -98,3 +98,40 @@ def plot_cumulative_returns(self, cum_ret: pd.DataFrame, figsize: Tuple[int, int ax.grid(True, alpha=0.3) plt.tight_layout() return fig + + def plot_ic_decay(self, ic_summary: dict[int, dict[str, float]]) -> mpl_figure.Figure: + """ + Plot IC decay curve across horizons. + + Args: + ic_summary: Dict mapping period -> {"mean_ic": float, "ic_ir": float, ...} + + Returns: + matplotlib Figure + """ + periods = sorted(ic_summary.keys()) + mean_ics = [ic_summary[p]["mean_ic"] for p in periods] + ic_irs = [ic_summary[p]["ic_ir"] for p in periods] + + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) + + # Mean IC decay + ax1.bar(range(len(periods)), mean_ics, color="steelblue", alpha=0.7) + ax1.set_xticks(range(len(periods))) + ax1.set_xticklabels([str(p) for p in periods]) + ax1.set_xlabel("Horizon (periods)") + ax1.set_ylabel("Mean IC") + ax1.set_title("IC Decay by Horizon") + ax1.axhline(y=0, color="gray", linestyle="--", alpha=0.5) + + # IC IR decay + ax2.bar(range(len(periods)), ic_irs, color="darkorange", alpha=0.7) + ax2.set_xticks(range(len(periods))) + ax2.set_xticklabels([str(p) for p in periods]) + ax2.set_xlabel("Horizon (periods)") + ax2.set_ylabel("IC IR") + ax2.set_title("IC Information Ratio by Horizon") + ax2.axhline(y=0, color="gray", linestyle="--", alpha=0.5) + + fig.tight_layout() + return fig diff --git a/src/factorium/research/__init__.py b/src/factorium/research/__init__.py index 8b7ccee..8290fd3 100644 --- a/src/factorium/research/__init__.py +++ b/src/factorium/research/__init__.py @@ -1,4 +1,4 @@ -from .session import ResearchSession from .report import FactorReport +from .session import ResearchSession __all__ = ["ResearchSession", "FactorReport"] diff --git a/src/factorium/research/report.py b/src/factorium/research/report.py index 1de77d6..dd72b07 100644 --- a/src/factorium/research/report.py +++ b/src/factorium/research/report.py @@ -4,16 +4,14 @@ Combines factor analysis and backtest results into comprehensive reports. """ -from typing import Dict, Any, Optional, Union, TYPE_CHECKING +from typing import TYPE_CHECKING, Any, Union if TYPE_CHECKING: - from .session import ResearchSession from ..factors.analyzer import FactorAnalysisResult -import polars as pl -import pandas as pd + from .session import ResearchSession -from ..factors.core import Factor from ..backtest.vectorized import BacktestResult +from ..factors.core import Factor class FactorReport: @@ -73,14 +71,14 @@ def generate( def __init__( self, factor: Factor, - analysis: Union[Dict[str, Any], "FactorAnalysisResult"], + analysis: Union[dict[str, Any], "FactorAnalysisResult"], backtest: BacktestResult, ): self.factor = factor self.analysis = analysis self.backtest = backtest - def summary(self) -> Dict[str, Any]: + def summary(self) -> dict[str, Any]: """ Generate summary combining analysis and backtest metrics. @@ -98,7 +96,7 @@ def summary(self) -> Dict[str, Any]: "backtest_metrics": self.backtest.metrics, } - def to_dict(self) -> Dict[str, Any]: + def to_dict(self) -> dict[str, Any]: """Convert report to dictionary.""" if hasattr(self.analysis, "to_dict"): analysis_dict = self.analysis.to_dict() @@ -116,7 +114,7 @@ def to_dict(self) -> Dict[str, Any]: def __repr__(self) -> str: """String representation of report.""" summary = self.summary() - ic = summary["ic_summary"] + ic_summary = summary["ic_summary"] metrics = summary["backtest_metrics"] def fmt_float(val: Any, fmt: str) -> str: @@ -124,11 +122,18 @@ def fmt_float(val: Any, fmt: str) -> str: return f"{val:{fmt}}" return "N/A" + # ic_summary is now dict[int, dict[str, float]], get first period's stats + if ic_summary: + first_period = next(iter(ic_summary.keys())) + ic = ic_summary.get(first_period, {}) + else: + ic = {} + return f"""FactorReport: {summary["factor_name"]} IC Summary: Mean IC: {fmt_float(ic.get("mean_ic"), ".4f")} IC Std: {fmt_float(ic.get("ic_std"), ".4f")} - + Backtest Metrics: Total Return: {fmt_float(metrics.get("total_return"), ".2%")} Annual Return: {fmt_float(metrics.get("annual_return"), ".2%")} diff --git a/src/factorium/research/session.py b/src/factorium/research/session.py index f85f7b8..3b54bd2 100644 --- a/src/factorium/research/session.py +++ b/src/factorium/research/session.py @@ -9,15 +9,20 @@ >>> print(result.metrics) """ -from typing import Optional, Union, Dict, Any, List, Callable -import polars as pl -import pandas as pd +from collections.abc import Callable from pathlib import Path +from typing import TYPE_CHECKING + +import pandas as pd +import polars as pl from ..aggbar import AggBar +from ..backtest.vectorized import BacktestResult, VectorizedBacktester from ..factors.core import Factor from ..factors.parser import FactorExpressionParser -from ..backtest.vectorized import VectorizedBacktester, BacktestResult + +if TYPE_CHECKING: + from ..factors.analyzer import FactorAnalysisResult class ResearchSession: @@ -42,7 +47,7 @@ class ResearchSession: def __init__( self, - data: Union[AggBar, pd.DataFrame, pl.DataFrame], + data: AggBar | pd.DataFrame | pl.DataFrame, default_frequency: str = "1h", default_initial_capital: float = 10000.0, default_transaction_cost: float = 0.0003, @@ -55,20 +60,20 @@ def __init__( self.default_frequency = default_frequency self.default_initial_capital = default_initial_capital self.default_transaction_cost = default_transaction_cost - self._factors: Dict[str, Factor] = {} # Cache for created factors + self._factors: dict[str, Factor] = {} # Cache for created factors self._parser = FactorExpressionParser() @property - def symbols(self) -> List[str]: + def symbols(self) -> list[str]: """Return list of symbols in the data.""" return self.data.symbols @property - def cols(self) -> List[str]: + def cols(self) -> list[str]: """Return list of columns in the data.""" return self.data.cols - def create_factor(self, expr: Union[str, Callable[[AggBar], Factor]], name: Optional[str] = None) -> Factor: + def create_factor(self, expr: str | Callable[[AggBar], Factor], name: str | None = None) -> Factor: """ Create and cache a factor from expression or callable. @@ -84,8 +89,8 @@ def create_factor(self, expr: Union[str, Callable[[AggBar], Factor]], name: Opti >>> session.create_factor("ts_mean(close, 20)", "ma20") >>> session.create_factor(lambda agg: agg["close"].ts_return(20), "ret_20d") """ - # Generate cache key - cache_key = name or (expr if isinstance(expr, str) else id(expr)) + # Generate cache key (always use str) + cache_key: str = name if name else (expr if isinstance(expr, str) else str(id(expr))) # Return cached if exists if cache_key in self._factors: @@ -100,10 +105,13 @@ def create_factor(self, expr: Union[str, Callable[[AggBar], Factor]], name: Opti raise TypeError(f"Expected Factor for column {expr}, got {type(res)}") factor = res else: - # Build context for parser - context = { - col: self.data[col] for col in self.data.cols if col not in ["start_time", "end_time", "symbol"] - } + # Build context for parser (only include Factor types) + context: dict[str, Factor] = {} + for col in self.data.cols: + if col not in ["start_time", "end_time", "symbol"]: + item = self.data[col] + if isinstance(item, Factor): + context[col] = item factor = self._parser.parse(expr, context) if name: @@ -121,26 +129,26 @@ def create_factor(self, expr: Union[str, Callable[[AggBar], Factor]], name: Opti return factor @classmethod - def from_csv(cls, path: Union[str, Path], **kwargs) -> "ResearchSession": + def from_csv(cls, path: str | Path, **kwargs) -> "ResearchSession": """Create ResearchSession from CSV file.""" aggbar = AggBar.from_csv(Path(path)) return cls(aggbar, **kwargs) @classmethod - def from_parquet(cls, path: Union[str, Path], **kwargs) -> "ResearchSession": + def from_parquet(cls, path: str | Path, **kwargs) -> "ResearchSession": """Create ResearchSession from Parquet file.""" df = pl.read_parquet(path) aggbar = AggBar.from_df(df) return cls(aggbar, **kwargs) @classmethod - def from_df(cls, df: Union[pd.DataFrame, pl.DataFrame], **kwargs) -> "ResearchSession": + def from_df(cls, df: pd.DataFrame | pl.DataFrame, **kwargs) -> "ResearchSession": """Create ResearchSession from DataFrame.""" aggbar = AggBar.from_df(df) return cls(aggbar, **kwargs) @classmethod - def load(cls, path: Union[str, Path], **kwargs) -> "ResearchSession": + def load(cls, path: str | Path, **kwargs) -> "ResearchSession": """ Auto-detect format and load data. @@ -209,7 +217,8 @@ def quick_report( backtest = self.backtest(factor) # Format report - ic_summary = analysis.ic_summary + # ic_summary is now always dict[int, dict[str, float]] + ic_stats = analysis.ic_summary.get(periods, {}) metrics = backtest.metrics report = f""" @@ -217,9 +226,9 @@ def quick_report( {"=" * 60} IC Analysis (periods={periods}): - Mean IC: {ic_summary.get("mean_ic", 0):.4f} - IC Std: {ic_summary.get("ic_std", 0):.4f} - IC IR: {ic_summary.get("ic_ir", 0):.4f} + Mean IC: {ic_stats.get("mean_ic", 0):.4f} + IC Std: {ic_stats.get("ic_std", 0):.4f} + IC IR: {ic_stats.get("ic_ir", 0):.4f} Backtest Performance: Total Return: {metrics.get("total_return", 0):.2%} @@ -260,9 +269,9 @@ def backtest( signal: Factor, neutralization: str = "market", entry_price: str = "close", - frequency: Optional[str] = None, - initial_capital: Optional[float] = None, - transaction_cost: Optional[float] = None, + frequency: str | None = None, + initial_capital: float | None = None, + transaction_cost: float | None = None, ) -> BacktestResult: """ Run backtest with given signal. @@ -301,9 +310,9 @@ def backtest( def slice( self, - start: Optional[Union[int, str]] = None, - end: Optional[Union[int, str]] = None, - symbols: Optional[List[str]] = None, + start: int | str | None = None, + end: int | str | None = None, + symbols: list[str] | None = None, ) -> "ResearchSession": """ Create new session with subset of data. diff --git a/src/factorium/storage/__init__.py b/src/factorium/storage/__init__.py index f2bb7fa..bca6a5e 100644 --- a/src/factorium/storage/__init__.py +++ b/src/factorium/storage/__init__.py @@ -1,9 +1,18 @@ # src/factorium/storage/__init__.py """Storage backend abstraction layer.""" +from typing import TYPE_CHECKING + from .base import StorageBackend from .local import LocalStorageBackend +# Optional S3 backend +S3StorageBackend: type | None = None +try: + from .s3 import S3StorageBackend # type: ignore[no-redef] +except ImportError: + pass + def get_storage_backend(backend: str = "local", path: str = "./Data") -> StorageBackend: """Factory function to create storage backend instances. @@ -27,7 +36,6 @@ def get_storage_backend(backend: str = "local", path: str = "./Data") -> Storage if backend == "local": return LocalStorageBackend(path) elif backend == "s3": - # S3 backend will be implemented in Task 4 try: from .s3 import S3StorageBackend except ImportError: @@ -41,4 +49,4 @@ def get_storage_backend(backend: str = "local", path: str = "./Data") -> Storage raise ValueError(f"Unknown backend: {backend}. Supported: 'local', 's3'") -__all__ = ["StorageBackend", "LocalStorageBackend", "get_storage_backend"] +__all__ = ["StorageBackend", "LocalStorageBackend", "S3StorageBackend", "get_storage_backend"] diff --git a/src/factorium/storage/base.py b/src/factorium/storage/base.py index 3ab0d95..3be082a 100644 --- a/src/factorium/storage/base.py +++ b/src/factorium/storage/base.py @@ -2,7 +2,6 @@ """Storage backend abstraction layer.""" from abc import ABC, abstractmethod -from typing import List import polars as pl @@ -35,7 +34,7 @@ def exists(self, path: str) -> bool: ... @abstractmethod - def glob(self, pattern: str) -> List[str]: + def glob(self, pattern: str) -> list[str]: """List files matching a glob pattern.""" ... diff --git a/src/factorium/storage/local.py b/src/factorium/storage/local.py index 51de597..b081c1d 100644 --- a/src/factorium/storage/local.py +++ b/src/factorium/storage/local.py @@ -2,7 +2,6 @@ """Local filesystem storage backend.""" from pathlib import Path -from typing import List import polars as pl @@ -17,11 +16,29 @@ def __init__(self, base_path: str): self.base_path.mkdir(parents=True, exist_ok=True) def _resolve_path(self, path: str) -> Path: - return self.base_path / path + """Resolve relative path to absolute path within base_path. + + Raises: + ValueError: If path is absolute or attempts directory traversal + """ + # Prevent absolute path override + if Path(path).is_absolute(): + raise ValueError(f"Path must be relative, got: {path}") + + resolved = (self.base_path / path).resolve() + base_resolved = self.base_path.resolve() + + # Ensure the resolved path is within the base path (cross-platform) + try: + resolved.relative_to(base_resolved) + except ValueError: + raise ValueError(f"Path traversal detected: {path}") + + return resolved def full_path(self, path: str) -> str: """Get absolute path string for DuckDB queries.""" - return str(self._resolve_path(path)) + return str(self._resolve_path(path).resolve()) def read_parquet(self, path: str) -> pl.DataFrame: return pl.read_parquet(self._resolve_path(path)) @@ -34,12 +51,12 @@ def write_parquet(self, df: pl.DataFrame, path: str) -> None: def exists(self, path: str) -> bool: return self._resolve_path(path).exists() - def glob(self, pattern: str) -> List[str]: + def glob(self, pattern: str) -> list[str]: matches = list(self.base_path.glob(pattern)) return [str(m.relative_to(self.base_path)) for m in matches] def delete(self, path: str) -> None: - self._resolve_path(path).unlink() + self._resolve_path(path).unlink(missing_ok=True) def makedirs(self, path: str) -> None: self._resolve_path(path).mkdir(parents=True, exist_ok=True) diff --git a/src/factorium/storage/s3.py b/src/factorium/storage/s3.py new file mode 100644 index 0000000..c407920 --- /dev/null +++ b/src/factorium/storage/s3.py @@ -0,0 +1,157 @@ +# src/factorium/storage/s3.py +"""S3 storage backend using DuckDB for Parquet operations.""" + +import io +import os + +import polars as pl + +from .base import StorageBackend + +# Check for boto3 at import time +try: + import boto3 + from botocore.exceptions import ClientError +except ImportError: + raise ImportError("S3 backend requires boto3. Install with: pip install factorium[s3]") + + +class S3StorageBackend(StorageBackend): + """Storage backend for Amazon S3. + + Supports custom S3-compatible endpoints (MinIO, LocalStack) via AWS_ENDPOINT_URL. + """ + + def __init__(self, bucket: str, prefix: str = ""): + self.bucket = bucket + self.prefix = prefix.strip("/") + + # Support custom endpoint for MinIO/LocalStack + self._endpoint_url = os.environ.get("AWS_ENDPOINT_URL") + if self._endpoint_url: + self._s3_client = boto3.client("s3", endpoint_url=self._endpoint_url) + else: + self._s3_client = boto3.client("s3") + + def _build_key(self, path: str) -> str: + """Build full S3 key from relative path.""" + if not path: + return self.prefix + if self.prefix: + return f"{self.prefix}/{path}" + return path + + def full_path(self, path: str) -> str: + """Get full S3 URI for DuckDB queries.""" + key = self._build_key(path) + return f"s3://{self.bucket}/{key}" + + def configure_duckdb_s3(self, con) -> None: + """Configure DuckDB connection for S3 access. + + DuckDB requires explicit S3 configuration, it doesn't read AWS_ENDPOINT_URL. + + This method should be called on any DuckDB connection that will access S3 URIs + before executing queries, especially when using custom endpoints like MinIO. + + Args: + con: A DuckDB connection object + + Example: + >>> storage = S3StorageBackend("my-bucket") + >>> con = duckdb.connect(":memory:") + >>> storage.configure_duckdb_s3(con) + >>> df = con.execute("SELECT * FROM read_parquet('s3://...')").pl() + """ + access_key = os.environ.get("AWS_ACCESS_KEY_ID", "") + secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY", "") + region = os.environ.get("AWS_REGION", "us-east-1") + + con.execute(f"SET s3_access_key_id='{access_key}'") + con.execute(f"SET s3_secret_access_key='{secret_key}'") + con.execute(f"SET s3_region='{region}'") + + if self._endpoint_url: + # For MinIO/LocalStack: configure custom endpoint + # Remove http:// or https:// prefix for DuckDB + endpoint = self._endpoint_url.replace("http://", "").replace("https://", "") + con.execute(f"SET s3_endpoint='{endpoint}'") + con.execute("SET s3_use_ssl=false") + con.execute("SET s3_url_style='path'") + + def read_parquet(self, path: str) -> pl.DataFrame: + """Read a Parquet file from S3 using DuckDB. + + Note: + Requires AWS credentials to be configured via environment variables + (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION) or ~/.aws/credentials. + For MinIO/LocalStack, set AWS_ENDPOINT_URL. + """ + import duckdb + + uri = self.full_path(path) + con = duckdb.connect(":memory:") + try: + self.configure_duckdb_s3(con) + result = con.execute(f"SELECT * FROM read_parquet('{uri}')").pl() + finally: + con.close() + return result + + def write_parquet(self, df: pl.DataFrame, path: str) -> None: + """Write a Polars DataFrame to S3.""" + key = self._build_key(path) + + buffer = io.BytesIO() + df.write_parquet(buffer) + buffer.seek(0) + + self._s3_client.put_object(Bucket=self.bucket, Key=key, Body=buffer.getvalue()) + + def exists(self, path: str) -> bool: + """Check if object exists in S3.""" + key = self._build_key(path) + try: + self._s3_client.head_object(Bucket=self.bucket, Key=key) + return True + except ClientError as e: + if e.response["Error"]["Code"] == "404": + return False + raise + + def glob(self, pattern: str) -> list[str]: + """List objects matching pattern in S3.""" + import fnmatch + + prefix_parts = [] + for part in pattern.split("/"): + if "*" in part or "?" in part: + break + prefix_parts.append(part) + + list_prefix = self._build_key("/".join(prefix_parts)) if prefix_parts else self.prefix + + paginator = self._s3_client.get_paginator("list_objects_v2") + matches = [] + + for page in paginator.paginate(Bucket=self.bucket, Prefix=list_prefix): + for obj in page.get("Contents", []): + key = obj["Key"] + if self.prefix: + rel_path = key[len(self.prefix) + 1 :] if key.startswith(self.prefix + "/") else key + else: + rel_path = key + + if fnmatch.fnmatch(rel_path, pattern): + matches.append(rel_path) + + return matches + + def delete(self, path: str) -> None: + """Delete an object from S3.""" + key = self._build_key(path) + self._s3_client.delete_object(Bucket=self.bucket, Key=key) + + def makedirs(self, path: str) -> None: + """No-op for S3 (directories are virtual).""" + pass diff --git a/tests/backtest/test_backtester.py b/tests/backtest/test_backtester.py index 2b28773..ccd450c 100644 --- a/tests/backtest/test_backtester.py +++ b/tests/backtest/test_backtester.py @@ -140,7 +140,7 @@ class TestBacktester: @pytest.fixture def sample_data(self): dates = pd.date_range(start="2025-01-01", periods=20, freq="1h") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) rows = [] for i, ts in enumerate(timestamps): @@ -246,7 +246,7 @@ class TestEdgeCases: def test_single_symbol_backtest(self): """Single asset should work without cross-sectional operations failing.""" dates = pd.date_range(start="2025-01-01", periods=20, freq="1h") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) rows = [] for i, ts in enumerate(timestamps): @@ -306,7 +306,7 @@ class TestVectorizedBacktesterIntegration: def sample_data(self): np.random.seed(42) dates = pd.date_range(start="2025-01-01", periods=20, freq="1h") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) rows = [] for i, ts in enumerate(timestamps): @@ -398,7 +398,7 @@ def test_cash_never_negative(self): # BTC starts cheap, becomes very expensive # ETH stays cheap dates = pd.date_range(start="2025-01-01", periods=10, freq="1h") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) rows = [] for i, ts in enumerate(timestamps): for symbol in ["BTC", "ETH"]: @@ -446,7 +446,7 @@ class TestMissingPriceHandling: def test_missing_price_symbol_excluded_from_holdings(self): """Symbols with missing prices should be excluded from target holdings.""" dates = pd.date_range(start="2025-01-01", periods=10, freq="1h") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) rows = [] for i, ts in enumerate(timestamps): @@ -504,7 +504,7 @@ class TestLegacyBacktester: @pytest.fixture def sample_data(self): dates = pd.date_range(start="2025-01-01", periods=20, freq="1h") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) rows = [] for i, ts in enumerate(timestamps): diff --git a/tests/conftest.py b/tests/conftest.py index 679cd9a..247dfd3 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -19,7 +19,7 @@ def sample_aggbar(): Includes positive, negative, and zero values to test various math edge cases. """ dates = pd.date_range(start="2025-01-01", periods=10, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) common_cols = { "start_time": timestamps, diff --git a/tests/data/test_loader_fast.py b/tests/data/test_loader_fast.py index 3c12a32..65a9fb2 100644 --- a/tests/data/test_loader_fast.py +++ b/tests/data/test_loader_fast.py @@ -58,7 +58,7 @@ class TestLoadAggbarTimeBars: def test_returns_aggbar(self, sample_hive_data): """Test that load_aggbar returns AggBar instance.""" - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( @@ -78,7 +78,7 @@ def test_returns_aggbar(self, sample_hive_data): def test_loads_multiple_symbols(self, sample_hive_data): """Test loading multiple symbols.""" - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( @@ -98,7 +98,7 @@ def test_loads_multiple_symbols(self, sample_hive_data): def test_uses_cache_when_enabled(self, sample_hive_data, tmp_path): """Test that cache is used when enabled.""" - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) with patch("factorium.data.loader.BarCache") as MockCache: mock_cache_instance = MagicMock() @@ -123,7 +123,7 @@ def test_uses_cache_when_enabled(self, sample_hive_data, tmp_path): def test_cache_hit_skips_aggregation(self, sample_hive_data): """Test that cache hit skips DuckDB aggregation.""" - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) cached_df = pl.DataFrame( { @@ -167,7 +167,7 @@ def test_cache_hit_skips_aggregation(self, sample_hive_data): def test_different_intervals(self, sample_hive_data): """Test that different intervals produce different bar counts.""" - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result_1m = loader.load_aggbar( @@ -198,7 +198,7 @@ def test_different_intervals(self, sample_hive_data): def test_raises_on_no_data(self, sample_hive_data): """Test that ValueError is raised when no data is found.""" - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): with pytest.raises(ValueError, match="No data found"): @@ -217,7 +217,7 @@ def test_raises_on_no_data(self, sample_hive_data): class TestIncrementalDownload: def test_find_missing_files_all_exist(self, sample_hive_data): - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) missing = loader._find_missing_files( symbols=["BTCUSDT", "ETHUSDT"], data_type="aggTrades", @@ -229,7 +229,7 @@ def test_find_missing_files_all_exist(self, sample_hive_data): assert missing == {} def test_find_missing_files_partial_missing(self, sample_hive_data): - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) missing = loader._find_missing_files( symbols=["BTCUSDT"], data_type="aggTrades", @@ -242,7 +242,7 @@ def test_find_missing_files_partial_missing(self, sample_hive_data): assert len(missing["BTCUSDT"]) == 6 def test_find_missing_files_new_symbol(self, sample_hive_data): - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) missing = loader._find_missing_files( symbols=["BTCUSDT", "NEWCOIN"], data_type="aggTrades", @@ -256,7 +256,7 @@ def test_find_missing_files_new_symbol(self, sample_hive_data): assert "BTCUSDT" not in missing def test_group_consecutive_dates(self, sample_hive_data): - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) dates = [datetime(2024, 1, 1), datetime(2024, 1, 2), datetime(2024, 1, 5), datetime(2024, 1, 6)] ranges = loader._group_consecutive_dates(dates) @@ -266,11 +266,11 @@ def test_group_consecutive_dates(self, sample_hive_data): assert ranges[1] == (datetime(2024, 1, 5), datetime(2024, 1, 6)) def test_group_consecutive_dates_empty(self, sample_hive_data): - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) assert loader._group_consecutive_dates([]) == [] def test_download_missing_only_called(self, sample_hive_data): - loader = BinanceDataLoader(base_path=sample_hive_data) + loader = BinanceDataLoader(path=sample_hive_data) with patch.object(loader, "_find_missing_files") as mock_find: mock_find.return_value = {"NEWCOIN": [datetime(2024, 1, 5)]} diff --git a/tests/data/test_loader_klines.py b/tests/data/test_loader_klines.py index a2193ad..21ad9af 100644 --- a/tests/data/test_loader_klines.py +++ b/tests/data/test_loader_klines.py @@ -114,7 +114,7 @@ class TestLoadKlines: def test_returns_aggbar_for_klines(self, sample_klines_data): """Test that load_aggbar returns AggBar instance for klines.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( @@ -132,7 +132,7 @@ def test_returns_aggbar_for_klines(self, sample_klines_data): def test_klines_has_all_columns(self, sample_klines_data): """Test that klines data has all expected columns.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( @@ -164,7 +164,7 @@ def test_klines_has_all_columns(self, sample_klines_data): def test_klines_bypasses_aggregation(self, sample_klines_data): """Test that klines loading bypasses BarAggregator.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch("factorium.data.loader.BarAggregator") as MockAggregator: mock_agg_instance = MockAggregator.return_value @@ -192,7 +192,7 @@ def test_klines_bypasses_aggregation(self, sample_klines_data): def test_klines_loads_multiple_symbols(self, sample_klines_data): """Test loading klines for multiple symbols.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( @@ -210,7 +210,7 @@ def test_klines_loads_multiple_symbols(self, sample_klines_data): def test_klines_resample_to_5m(self, sample_klines_data): """Test resampling 1m klines to 5m.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result_1m = loader.load_aggbar( @@ -240,7 +240,7 @@ def test_klines_resample_to_5m(self, sample_klines_data): def test_klines_resample_to_1h(self, sample_klines_data): """Test resampling 1m klines to 1h.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result_1m = loader.load_aggbar( @@ -270,7 +270,7 @@ def test_klines_resample_to_1h(self, sample_klines_data): def test_klines_raises_on_non_time_bar_type(self, sample_klines_data): """Test that klines raises error for non-time bar types.""" - loader = BinanceDataLoader(base_path=sample_klines_data) + loader = BinanceDataLoader(path=sample_klines_data) with patch.object(loader, "_check_all_symbols_exist", return_value=True): with pytest.raises(ValueError, match="only supports bar_type='time'"): @@ -289,7 +289,7 @@ def test_klines_raises_on_non_time_bar_type(self, sample_klines_data): def test_load_aggbar_klines_auto_detects_timestamp_unit(self, sample_klines_data_microseconds): """Verify klines loading auto-detects and handles microsecond timestamps.""" tmpdir = sample_klines_data_microseconds - loader = BinanceDataLoader(base_path=tmpdir) + loader = BinanceDataLoader(path=tmpdir) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( @@ -310,7 +310,7 @@ def test_load_aggbar_klines_microseconds_with_resample(self, sample_klines_data_ before resampling, which assumes millisecond timestamps. """ tmpdir = sample_klines_data_microseconds - loader = BinanceDataLoader(base_path=tmpdir) + loader = BinanceDataLoader(path=tmpdir) with patch.object(loader, "_check_all_symbols_exist", return_value=True): # Load 1m data @@ -346,7 +346,7 @@ def test_load_aggbar_klines_microseconds_with_resample(self, sample_klines_data_ def test_load_aggbar_klines_timestamps_normalized_to_ms(self, sample_klines_data_microseconds): """Verify that output timestamps are normalized to milliseconds.""" tmpdir = sample_klines_data_microseconds - loader = BinanceDataLoader(base_path=tmpdir) + loader = BinanceDataLoader(path=tmpdir) with patch.object(loader, "_check_all_symbols_exist", return_value=True): result = loader.load_aggbar( diff --git a/tests/factors/test_analyzer.py b/tests/factors/test_analyzer.py index 37c9160..fba2582 100644 --- a/tests/factors/test_analyzer.py +++ b/tests/factors/test_analyzer.py @@ -198,8 +198,11 @@ def test_analyze_returns_dataclass(sample_data): assert isinstance(result, FactorAnalysisResult) assert result.factor_name == "my_factor" - assert result.periods == 1 - assert "mean_ic" in result.ic_summary + # periods is always a list now + assert result.periods == [1] + # ic_summary is always dict[int, dict[str, float]] now + assert 1 in result.ic_summary + assert "mean_ic" in result.ic_summary[1] assert hasattr(result, "to_dict") diff --git a/tests/factors/test_analyzer_polars.py b/tests/factors/test_analyzer_polars.py index c038c3e..02af699 100644 --- a/tests/factors/test_analyzer_polars.py +++ b/tests/factors/test_analyzer_polars.py @@ -72,3 +72,21 @@ def test_calculate_quantile_returns_uses_polars_internally(sample_data): assert isinstance(q_ret, pd.DataFrame) # Should not use pandas groupby mock_groupby.assert_not_called() + + +def test_prepare_data_validates_price_col(sample_data): + """Test that prepare_data raises clear error when price_col doesn't exist in AggBar.""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + + # Create analyzer with AggBar as prices + analyzer = FactorAnalyzer(factor, agg) + + # Try to use non-existent column + with pytest.raises(ValueError, match="Price column 'nonexistent' not found"): + analyzer.prepare_data(periods=[1], price_col="nonexistent") + + # Should work with existing column + df = analyzer.prepare_data(periods=[1], price_col="close") + assert isinstance(df, pl.DataFrame) + assert len(df) > 0 diff --git a/tests/factors/test_base_polars.py b/tests/factors/test_base_polars.py index 54b5e3f..213250b 100644 --- a/tests/factors/test_base_polars.py +++ b/tests/factors/test_base_polars.py @@ -20,7 +20,7 @@ def sample_pandas_df(): """Create sample factor data as pandas DataFrame.""" np.random.seed(42) dates = pd.date_range("2024-01-01", periods=20, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) data = [] for symbol in ["BTCUSDT", "ETHUSDT"]: @@ -537,8 +537,8 @@ def test_len_avoids_full_collection(self): n_rows = 100_000 df = pd.DataFrame( { - "start_time": pd.date_range("2020-01-01", periods=n_rows, freq="1min").astype(np.int64) // 10**6, - "end_time": (pd.date_range("2020-01-01", periods=n_rows, freq="1min").astype(np.int64) // 10**6) + "start_time": pd.date_range("2020-01-01", periods=n_rows, freq="1min").astype("datetime64[ms]").astype(np.int64), + "end_time": (pd.date_range("2020-01-01", periods=n_rows, freq="1min").astype("datetime64[ms]").astype(np.int64)) + 60000, "symbol": ["A"] * n_rows, "factor": np.arange(n_rows, dtype=float), diff --git a/tests/factors/test_engine.py b/tests/factors/test_engine.py index 1050193..314b77b 100644 --- a/tests/factors/test_engine.py +++ b/tests/factors/test_engine.py @@ -15,7 +15,7 @@ def sample_factor_df(): n_rows = 100 dates = pd.date_range("2024-01-01", periods=50, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) return pl.DataFrame( { diff --git a/tests/factors/test_factor_eval.py b/tests/factors/test_factor_eval.py new file mode 100644 index 0000000..83aa6cc --- /dev/null +++ b/tests/factors/test_factor_eval.py @@ -0,0 +1,67 @@ +import pytest +import tempfile +from pathlib import Path +from factorium import AggBar +from factorium.factors import Factor +from factorium.factors.analyzer import FactorAnalysisResult + + +@pytest.fixture +def sample_factor_and_prices(): + """Fixture providing sample factor and price data for testing.""" + import pandas as pd + import numpy as np + + dates = pd.date_range("2023-01-01", periods=20, freq="D") + symbols = ["AAPL", "GOOGL", "MSFT"] + + data = [] + for date in dates: + for symbol in symbols: + data.append( + { + "start_time": int(date.timestamp() * 1000), + "end_time": int((date + pd.Timedelta(days=1)).timestamp() * 1000), + "symbol": symbol, + "close": np.random.randn() * 10 + 100, # Random prices around 100 + "my_factor": np.random.randn(), # Random factor values + } + ) + + df = pd.DataFrame(data) + agg = AggBar(df) + factor = agg["my_factor"] + prices = agg["close"] + + return factor, prices + + +def test_factor_eval_returns_analysis_result(sample_factor_and_prices): + """Test that Factor.eval() returns FactorAnalysisResult.""" + factor, prices = sample_factor_and_prices + + result = factor.eval(prices, periods=1, quantiles=5) + + assert isinstance(result, FactorAnalysisResult) + assert result.factor_name == factor.name + # periods is now always list[int] + assert result.periods == [1] + assert result.quantiles == 5 + assert hasattr(result, "turnover_series") + assert hasattr(result, "turnover_mean") + + +def test_factor_eval_with_output_dir(sample_factor_and_prices): + """Test that Factor.eval() creates output when output_dir is specified.""" + factor, prices = sample_factor_and_prices + + with tempfile.TemporaryDirectory() as tmpdir: + result = factor.eval(prices, periods=1, output_dir=tmpdir) + + # Check that experiment folder was created + exp_dirs = list(Path(tmpdir).glob("*_*")) + assert len(exp_dirs) == 1 + + # Check config.json exists + config_path = exp_dirs[0] / "config.json" + assert config_path.exists() diff --git a/tests/factors/test_math_ops_polars.py b/tests/factors/test_math_ops_polars.py index ef88814..7f4d994 100644 --- a/tests/factors/test_math_ops_polars.py +++ b/tests/factors/test_math_ops_polars.py @@ -19,7 +19,7 @@ def sample_pandas_df(): """Create sample factor data as pandas DataFrame.""" np.random.seed(42) dates = pd.date_range("2024-01-01", periods=20, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) data = [] for symbol in ["BTCUSDT", "ETHUSDT"]: @@ -59,7 +59,7 @@ def positive_factor_pandas(): """Create factor data with positive values only.""" np.random.seed(42) dates = pd.date_range("2024-01-01", periods=20, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) data = [] for symbol in ["BTCUSDT", "ETHUSDT"]: @@ -87,7 +87,7 @@ def mixed_factor_pandas(): """Create factor with mixed positive/negative values and some zeros.""" np.random.seed(42) dates = pd.date_range("2024-01-01", periods=20, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) data = [] for symbol in ["BTCUSDT", "ETHUSDT"]: diff --git a/tests/factors/test_ts_ops_polars.py b/tests/factors/test_ts_ops_polars.py index 84a85ef..1eca15a 100644 --- a/tests/factors/test_ts_ops_polars.py +++ b/tests/factors/test_ts_ops_polars.py @@ -33,7 +33,7 @@ def sample_polars_factor_data(): - Mix of regular and edge case values """ dates = pd.date_range("2025-01-01", periods=10, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) common_cols = { "start_time": timestamps, @@ -73,7 +73,7 @@ def sample_polars_factor_with_nan(sample_polars_factor_data): def sample_polars_factor_constant(): """Create factor data with constant values for std/var edge cases.""" dates = pd.date_range("2025-01-01", periods=10, freq="1min") - timestamps = dates.astype(np.int64) // 10**6 + timestamps = dates.astype("datetime64[ms]").astype(np.int64) common_cols = { "start_time": timestamps, diff --git a/tests/mixins/test_ts_ops.py b/tests/mixins/test_ts_ops.py index be032f3..588ee1e 100644 --- a/tests/mixins/test_ts_ops.py +++ b/tests/mixins/test_ts_ops.py @@ -317,8 +317,8 @@ def _factory(series: pd.Series, symbol: str = "BTCUSDT"): df = pd.DataFrame( { - "start_time": series.index.view(np.int64) // 10**6, - "end_time": (series.index.view(np.int64) // 10**6) + 60000, + "start_time": series.index.astype("datetime64[ms]").astype(np.int64), + "end_time": (series.index.astype("datetime64[ms]").astype(np.int64)) + 60000, "symbol": symbol, "factor": series.values, } diff --git a/tests/storage/conftest.py b/tests/storage/conftest.py new file mode 100644 index 0000000..444389d --- /dev/null +++ b/tests/storage/conftest.py @@ -0,0 +1,74 @@ +"""Fixtures for storage tests.""" + +import os +import pytest + +# MinIO test settings +MINIO_ENDPOINT = os.environ.get("MINIO_ENDPOINT", "localhost:9000") +MINIO_ACCESS_KEY = os.environ.get("MINIO_ACCESS_KEY", "minioadmin") +MINIO_SECRET_KEY = os.environ.get("MINIO_SECRET_KEY", "minioadmin") +MINIO_TEST_BUCKET = os.environ.get("MINIO_TEST_BUCKET", "factorium-test") + + +try: + from botocore.exceptions import EndpointConnectionError +except ImportError: + EndpointConnectionError = Exception + +def is_minio_available() -> bool: + """Check if MinIO is available for testing.""" + try: + import boto3 + from botocore.exceptions import ClientError + + s3 = boto3.client( + "s3", + endpoint_url=f"http://{MINIO_ENDPOINT}", + aws_access_key_id=MINIO_ACCESS_KEY, + aws_secret_access_key=MINIO_SECRET_KEY, + ) + s3.list_buckets() + return True + except (ImportError, EndpointConnectionError, Exception): + return False + + +# Skip marker for tests requiring MinIO +requires_minio = pytest.mark.skipif(not is_minio_available(), reason="MinIO not available") + + +@pytest.fixture(scope="session") +def minio_client(): + """Create a boto3 client connected to MinIO.""" + import boto3 + + return boto3.client( + "s3", + endpoint_url=f"http://{MINIO_ENDPOINT}", + aws_access_key_id=MINIO_ACCESS_KEY, + aws_secret_access_key=MINIO_SECRET_KEY, + ) + + +@pytest.fixture(scope="session") +def minio_test_bucket(minio_client): + """Create and return test bucket, clean up after tests.""" + bucket = MINIO_TEST_BUCKET + + # Create bucket if not exists + try: + minio_client.head_bucket(Bucket=bucket) + except: + minio_client.create_bucket(Bucket=bucket) + + yield bucket + + # Cleanup: delete all objects and bucket + try: + paginator = minio_client.get_paginator("list_objects_v2") + for page in paginator.paginate(Bucket=bucket): + for obj in page.get("Contents", []): + minio_client.delete_object(Bucket=bucket, Key=obj["Key"]) + minio_client.delete_bucket(Bucket=bucket) + except: + pass # Ignore cleanup errors diff --git a/tests/storage/test_factory.py b/tests/storage/test_factory.py index de3dc71..483ea7b 100644 --- a/tests/storage/test_factory.py +++ b/tests/storage/test_factory.py @@ -1,8 +1,14 @@ # tests/storage/test_factory.py import tempfile from pathlib import Path +from unittest.mock import patch import pytest +try: + import boto3 + has_boto3 = True +except ImportError: + has_boto3 = False from factorium.storage import get_storage_backend, LocalStorageBackend @@ -31,9 +37,11 @@ def test_unknown_backend_raises_error(self, temp_dir): with pytest.raises(ValueError, match="Unknown backend"): get_storage_backend("unknown", str(temp_dir)) - def test_s3_backend_placeholder(self): - """S3 backend should raise ImportError until implemented.""" - # This test will be updated when S3 backend is implemented - # For now, just test error handling - with pytest.raises((ValueError, ImportError)): - get_storage_backend("s3", "my-bucket/path") + @pytest.mark.skipif(not has_boto3, reason="boto3 not installed") + def test_s3_backend_creates_instance(self): + """S3 backend should create S3StorageBackend instance.""" + with patch("factorium.storage.s3.boto3"): + from factorium.storage.s3 import S3StorageBackend + + backend = get_storage_backend("s3", "my-bucket/path") + assert isinstance(backend, S3StorageBackend) diff --git a/tests/storage/test_integration.py b/tests/storage/test_integration.py new file mode 100644 index 0000000..bedf6e7 --- /dev/null +++ b/tests/storage/test_integration.py @@ -0,0 +1,94 @@ +"""Integration tests for storage backends with data loading.""" + +import tempfile +from datetime import datetime +from pathlib import Path + +import polars as pl +import pytest + +from factorium.storage import get_storage_backend, LocalStorageBackend +from factorium.data.cache import BarCache + + +class TestStorageIntegration: + """Integration tests for storage with cache.""" + + @pytest.fixture + def temp_dir(self): + with tempfile.TemporaryDirectory() as tmpdir: + yield Path(tmpdir) + + def test_cache_with_local_backend(self, temp_dir): + """BarCache should work with LocalStorageBackend.""" + backend = LocalStorageBackend(str(temp_dir)) + cache = BarCache(storage=backend, cache_prefix=".cache") + + # Create test data + df = pl.DataFrame( + { + "start_time": [1704067200000], + "symbol": ["BTCUSDT"], + "open": [42000.0], + "high": [42500.0], + "low": [41800.0], + "close": [42200.0], + "volume": [100.0], + } + ) + + # Store in cache + cache.put( + df=df, + exchange="binance", + symbols=["BTCUSDT"], + interval_ms=60000, + data_type="aggTrades", + market_type="futures_um", + date=datetime(2024, 1, 1), + ) + + # Retrieve from cache + result = cache.get( + exchange="binance", + symbols=["BTCUSDT"], + interval_ms=60000, + data_type="aggTrades", + market_type="futures_um", + date=datetime(2024, 1, 1), + ) + + assert result is not None + assert len(result) == 1 + assert result["symbol"][0] == "BTCUSDT" + + def test_factory_creates_correct_backend(self, temp_dir): + """get_storage_backend should create correct backend type.""" + local_backend = get_storage_backend("local", str(temp_dir)) + assert isinstance(local_backend, LocalStorageBackend) + + def test_local_backend_full_path(self, temp_dir): + """LocalStorageBackend.full_path should return absolute path.""" + backend = LocalStorageBackend(str(temp_dir)) + full_path = backend.full_path("test/file.parquet") + + assert str(temp_dir) in full_path + assert "test/file.parquet" in full_path + assert Path(full_path).is_absolute() + + def test_storage_round_trip(self, temp_dir): + """Data should survive write/read cycle through storage.""" + backend = LocalStorageBackend(str(temp_dir)) + + original_df = pl.DataFrame( + { + "a": [1, 2, 3], + "b": ["x", "y", "z"], + "c": [1.1, 2.2, 3.3], + } + ) + + backend.write_parquet(original_df, "test.parquet") + loaded_df = backend.read_parquet("test.parquet") + + assert original_df.equals(loaded_df) diff --git a/tests/storage/test_local.py b/tests/storage/test_local.py index 74b8133..16c74e2 100644 --- a/tests/storage/test_local.py +++ b/tests/storage/test_local.py @@ -75,5 +75,55 @@ def test_makedirs_creates_directory(self, backend, temp_dir): def test_full_path_returns_absolute_path(self, backend, temp_dir): """full_path() should return absolute path for DuckDB.""" full = backend.full_path("some/file.parquet") + assert Path(full).is_absolute(), f"Expected absolute path, got: {full}" assert str(temp_dir) in full assert "some/file.parquet" in full + + def test_full_path_returns_absolute_path_with_relative_base(self): + """full_path should return absolute path even when base_path is relative.""" + # Create backend with relative path + backend = LocalStorageBackend("./test_data") + + # Get full path + result = backend.full_path("some/file.parquet") + + # Should be absolute + assert Path(result).is_absolute(), f"Expected absolute path, got: {result}" + + # Should contain the resolved base path + expected_base = Path("./test_data").resolve() + assert str(expected_base) in result + + def test_full_path_returns_absolute_path_with_absolute_base(self): + """full_path should return absolute path when base_path is absolute.""" + # Create backend with absolute path + base_path = Path("/tmp/test_data").resolve() + backend = LocalStorageBackend(str(base_path)) + + # Get full path + result = backend.full_path("some/file.parquet") + + # Should be absolute + assert Path(result).is_absolute(), f"Expected absolute path, got: {result}" + + # Should contain the base path + assert str(base_path) in result + + def test_resolve_path_rejects_absolute_paths(self, backend): + """_resolve_path() should reject absolute paths.""" + with pytest.raises(ValueError, match="Path must be relative"): + backend._resolve_path("/etc/passwd") + + def test_resolve_path_rejects_directory_traversal(self, backend): + """_resolve_path() should reject directory traversal attempts.""" + with pytest.raises(ValueError, match="Path traversal detected"): + backend._resolve_path("../outside.txt") + + with pytest.raises(ValueError, match="Path traversal detected"): + backend._resolve_path("subdir/../../../root.txt") + + def test_delete_nonexistent_file_does_not_raise(self, backend): + """delete() should not raise when file doesn't exist.""" + # This should not raise an exception + backend.delete("nonexistent.parquet") + assert not backend.exists("nonexistent.parquet") diff --git a/tests/storage/test_s3.py b/tests/storage/test_s3.py new file mode 100644 index 0000000..d72d099 --- /dev/null +++ b/tests/storage/test_s3.py @@ -0,0 +1,119 @@ +# tests/storage/test_s3.py +"""Tests for S3StorageBackend using mocks.""" + +import io +import pytest +from unittest.mock import MagicMock, patch, PropertyMock +import polars as pl +try: + import boto3 + has_boto3 = True +except ImportError: + has_boto3 = False + + +@pytest.mark.skipif(not has_boto3, reason="boto3 not installed") +class TestS3StorageBackend: + """Tests for S3StorageBackend with mocked boto3.""" + + @pytest.fixture + def mock_boto3(self): + """Mock boto3 module.""" + with patch("factorium.storage.s3.boto3") as mock: + # Setup default mock behavior + mock.client.return_value = MagicMock() + yield mock + + @pytest.fixture + def backend(self, mock_boto3): + """Create S3StorageBackend with mocked boto3.""" + from factorium.storage.s3 import S3StorageBackend + + return S3StorageBackend(bucket="test-bucket", prefix="data") + + def test_full_path_with_prefix(self, backend): + """full_path should return correct S3 URI with prefix.""" + assert backend.full_path("cache/file.parquet") == "s3://test-bucket/data/cache/file.parquet" + + def test_full_path_without_prefix(self, mock_boto3): + """full_path should work without prefix.""" + from factorium.storage.s3 import S3StorageBackend + + backend = S3StorageBackend(bucket="test-bucket", prefix="") + assert backend.full_path("file.parquet") == "s3://test-bucket/file.parquet" + + def test_exists_returns_true_for_existing_object(self, backend, mock_boto3): + """exists() should return True when object exists.""" + mock_boto3.client.return_value.head_object.return_value = {} + assert backend.exists("existing.parquet") is True + + def test_exists_returns_false_for_missing_object(self, backend, mock_boto3): + """exists() should return False when object doesn't exist.""" + from botocore.exceptions import ClientError + + mock_boto3.client.return_value.head_object.side_effect = ClientError( + {"Error": {"Code": "404", "Message": "Not Found"}}, "HeadObject" + ) + assert backend.exists("missing.parquet") is False + + def test_delete_calls_delete_object(self, backend, mock_boto3): + """delete() should call S3 delete_object.""" + backend.delete("to_delete.parquet") + mock_boto3.client.return_value.delete_object.assert_called_once_with( + Bucket="test-bucket", Key="data/to_delete.parquet" + ) + + def test_makedirs_is_noop(self, backend): + """makedirs() should be a no-op for S3.""" + # Should not raise + backend.makedirs("some/path") + + def test_write_parquet_uploads_to_s3(self, backend, mock_boto3): + """write_parquet() should upload parquet data to S3.""" + df = pl.DataFrame({"a": [1, 2, 3]}) + backend.write_parquet(df, "test.parquet") + + # Verify put_object was called + mock_boto3.client.return_value.put_object.assert_called_once() + call_kwargs = mock_boto3.client.return_value.put_object.call_args[1] + assert call_kwargs["Bucket"] == "test-bucket" + assert call_kwargs["Key"] == "data/test.parquet" + + def test_read_parquet_uses_duckdb(self, backend, mock_boto3): + """read_parquet() should use DuckDB with correct S3 URI.""" + import sys + + mock_duckdb = MagicMock() + mock_con = MagicMock() + mock_duckdb.connect.return_value = mock_con + mock_con.execute.return_value.pl.return_value = pl.DataFrame({"a": [1]}) + + with patch.dict("sys.modules", {"duckdb": mock_duckdb}): + result = backend.read_parquet("test.parquet") + + mock_duckdb.connect.assert_called_once_with(":memory:") + # Check that at least one execute call contains the S3 URI + execute_calls = [str(call) for call in mock_con.execute.call_args_list] + assert any("s3://test-bucket/data/test.parquet" in call for call in execute_calls) + mock_con.close.assert_called_once() + + def test_glob_returns_matching_files(self, backend, mock_boto3): + """glob() should return files matching pattern.""" + # Mock paginator response + mock_paginator = MagicMock() + mock_boto3.client.return_value.get_paginator.return_value = mock_paginator + mock_paginator.paginate.return_value = [ + { + "Contents": [ + {"Key": "data/cache/file1.parquet"}, + {"Key": "data/cache/file2.parquet"}, + {"Key": "data/other/file3.parquet"}, + ] + } + ] + + matches = backend.glob("cache/*.parquet") + + assert len(matches) == 2 + assert "cache/file1.parquet" in matches + assert "cache/file2.parquet" in matches diff --git a/tests/storage/test_s3_integration.py b/tests/storage/test_s3_integration.py new file mode 100644 index 0000000..8ff3241 --- /dev/null +++ b/tests/storage/test_s3_integration.py @@ -0,0 +1,127 @@ +"""Integration tests for S3StorageBackend with real MinIO. + +These tests require MinIO to be running. They will be skipped if MinIO is not available. + +To run locally: + docker-compose -f docker-compose.minio.yml up -d + uv run pytest tests/storage/test_s3_integration.py -v + docker-compose -f docker-compose.minio.yml down +""" + +import os +import uuid + +import polars as pl +import pytest + +from .conftest import ( + requires_minio, + MINIO_ENDPOINT, + MINIO_ACCESS_KEY, + MINIO_SECRET_KEY, +) + + +@requires_minio +class TestS3StorageBackendIntegration: + """Integration tests for S3StorageBackend with real MinIO.""" + + @pytest.fixture + def s3_backend(self, minio_test_bucket, monkeypatch): + """Create S3StorageBackend connected to MinIO.""" + # Use monkeypatch to set environment variables (auto-restored after test) + monkeypatch.setenv("AWS_ACCESS_KEY_ID", MINIO_ACCESS_KEY) + monkeypatch.setenv("AWS_SECRET_ACCESS_KEY", MINIO_SECRET_KEY) + monkeypatch.setenv("AWS_ENDPOINT_URL", f"http://{MINIO_ENDPOINT}") + monkeypatch.setenv("AWS_REGION", "us-east-1") + + from factorium.storage.s3 import S3StorageBackend + + prefix = f"test-{uuid.uuid4().hex[:8]}" + backend = S3StorageBackend(bucket=minio_test_bucket, prefix=prefix) + + yield backend + + # Cleanup: delete test prefix + # (handled by minio_test_bucket fixture at session end) + + def test_write_and_read_parquet(self, s3_backend): + """Should write and read Parquet files through MinIO.""" + df = pl.DataFrame( + { + "a": [1, 2, 3], + "b": ["x", "y", "z"], + "c": [1.1, 2.2, 3.3], + } + ) + + s3_backend.write_parquet(df, "test/data.parquet") + result = s3_backend.read_parquet("test/data.parquet") + + assert result.equals(df) + + def test_exists_returns_true_for_existing_object(self, s3_backend): + """exists() should return True for existing objects in MinIO.""" + df = pl.DataFrame({"a": [1]}) + s3_backend.write_parquet(df, "exists_test.parquet") + + assert s3_backend.exists("exists_test.parquet") is True + + def test_exists_returns_false_for_missing_object(self, s3_backend): + """exists() should return False for non-existing objects.""" + assert s3_backend.exists("nonexistent.parquet") is False + + def test_glob_finds_matching_files(self, s3_backend): + """glob() should find files matching pattern in MinIO.""" + df = pl.DataFrame({"a": [1]}) + s3_backend.write_parquet(df, "glob_test/file1.parquet") + s3_backend.write_parquet(df, "glob_test/file2.parquet") + s3_backend.write_parquet(df, "other/file3.parquet") + + matches = s3_backend.glob("glob_test/*.parquet") + + assert len(matches) == 2 + assert all("glob_test" in m for m in matches) + + def test_delete_removes_object(self, s3_backend): + """delete() should remove object from MinIO.""" + df = pl.DataFrame({"a": [1]}) + s3_backend.write_parquet(df, "delete_test.parquet") + assert s3_backend.exists("delete_test.parquet") + + s3_backend.delete("delete_test.parquet") + + assert not s3_backend.exists("delete_test.parquet") + + def test_full_path_returns_s3_uri(self, s3_backend, minio_test_bucket): + """full_path() should return proper S3 URI.""" + path = s3_backend.full_path("some/file.parquet") + + assert path.startswith("s3://") + assert minio_test_bucket in path + assert "some/file.parquet" in path + + def test_makedirs_is_noop(self, s3_backend): + """makedirs() should not raise for S3 (virtual directories).""" + # Should not raise + s3_backend.makedirs("some/deep/path") + + def test_large_dataframe_roundtrip(self, s3_backend): + """Should handle larger DataFrames correctly.""" + import numpy as np + + # Create a larger DataFrame (~1MB) + n_rows = 50000 + df = pl.DataFrame( + { + "id": list(range(n_rows)), + "value": np.random.randn(n_rows), + "category": [f"cat_{i % 100}" for i in range(n_rows)], + } + ) + + s3_backend.write_parquet(df, "large_test.parquet") + result = s3_backend.read_parquet("large_test.parquet") + + assert len(result) == n_rows + assert result.schema == df.schema diff --git a/tests/test_data_loader.py b/tests/test_data_loader.py index 00d0617..e7a28a8 100644 --- a/tests/test_data_loader.py +++ b/tests/test_data_loader.py @@ -56,7 +56,7 @@ def sample_trades_df(): @pytest.fixture def loader(): """Create a BinanceDataLoader instance.""" - return BinanceDataLoader(base_path="./test_data") + return BinanceDataLoader(path="./test_data") @pytest.fixture @@ -69,7 +69,7 @@ def temp_data_dir(): @pytest.fixture def loader_with_temp_dir(temp_data_dir): """Create a BinanceDataLoader with temporary directory.""" - return BinanceDataLoader(base_path=str(temp_data_dir)) + return BinanceDataLoader(path=str(temp_data_dir)) def create_mock_df(symbol: str, seed: int = 42) -> pd.DataFrame: @@ -223,7 +223,7 @@ class TestCheckAllFilesExist: def test_all_files_exist(self, temp_data_dir): """Test when all required files exist.""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) # Create parquet files for 3 days for i in range(3): @@ -243,7 +243,7 @@ def test_all_files_exist(self, temp_data_dir): def test_some_files_missing(self, temp_data_dir): """Test when some files are missing.""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) # Create files for day 1 and 3, skip day 2 create_parquet_file(temp_data_dir, "futures_um", "aggTrades", "BTCUSDT", datetime(2024, 1, 1)) @@ -262,7 +262,7 @@ def test_some_files_missing(self, temp_data_dir): def test_no_files_exist(self, temp_data_dir): """Test when no files exist.""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) result = loader._check_all_files_exist( symbol="BTCUSDT", @@ -277,7 +277,7 @@ def test_no_files_exist(self, temp_data_dir): def test_checks_correct_path_structure(self, temp_data_dir): """Test that check uses correct Hive partition path.""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) # Create file with correct structure expected_path = ( @@ -310,7 +310,7 @@ def test_checks_correct_path_structure(self, temp_data_dir): def test_spot_market_path(self, temp_data_dir): """Test file check for spot market (different path).""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) # Create file for spot market expected_path = ( @@ -606,18 +606,18 @@ def test_default_base_path(self): def test_custom_base_path(self, temp_data_dir): """Test custom base_path is set correctly.""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) assert loader.base_path == temp_data_dir def test_downloader_is_created(self, temp_data_dir): """Test that BinanceDataDownloader is created.""" - loader = BinanceDataLoader(base_path=str(temp_data_dir)) + loader = BinanceDataLoader(path=str(temp_data_dir)) assert loader.downloader is not None def test_download_settings_passed_to_downloader(self, temp_data_dir): """Test that download settings are passed to downloader.""" loader = BinanceDataLoader( - base_path=str(temp_data_dir), max_concurrent_downloads=10, retry_attempts=5, retry_delay=2 + path=str(temp_data_dir), max_concurrent_downloads=10, retry_attempts=5, retry_delay=2 ) assert loader.downloader.max_concurrent_downloads == 10 diff --git a/tests/test_evaluation.py b/tests/test_evaluation.py index 3e27b20..b502005 100644 --- a/tests/test_evaluation.py +++ b/tests/test_evaluation.py @@ -2,47 +2,63 @@ import numpy as np import pytest from factorium.factors.core import Factor +from factorium.factors.analyzer import FactorAnalysisResult + def test_factor_evaluation_flow(): # Create dummy price data (upward trend) - dates = pd.date_range('2023-01-01', periods=10, freq='D') - symbols = ['AAPL', 'MSFT', 'GOOG'] - + dates = pd.date_range("2023-01-01", periods=10, freq="D") + symbols = ["AAPL", "MSFT", "GOOG"] + price_data = [] for d in dates: for s in symbols: # Price increases over time p = 100 + (d - dates[0]).days * 2 + np.random.randn() price_data.append([d, d, s, p]) - - prices_df = pd.DataFrame(price_data, columns=['start_time', 'end_time', 'symbol', 'factor']) - prices_factor = Factor(prices_df, name='close') - - # Create dummy signal factor + + prices_df = pd.DataFrame(price_data, columns=["start_time", "end_time", "symbol", "factor"]) + prices_factor = Factor(prices_df, name="close") + + # Create dummy signal factor signal_data = [] for i, d in enumerate(dates): for s in symbols: sig = np.random.randn() signal_data.append([d, d, s, sig]) - - signal_df = pd.DataFrame(signal_data, columns=['start_time', 'end_time', 'symbol', 'factor']) - signal_factor = Factor(signal_df, name='signal') - - # Run eval method with plot + + signal_df = pd.DataFrame(signal_data, columns=["start_time", "end_time", "symbol", "factor"]) + signal_factor = Factor(signal_df, name="signal") + + # Run eval method with output_dir import os - plot_path = "test_eval_plot.png" - results = signal_factor.eval(prices_factor, periods=[1, 2], quantiles=2, save_path=plot_path) - + import tempfile + import shutil + + output_dir = tempfile.mkdtemp() + result = signal_factor.eval(prices_factor, periods=1, quantiles=2, output_dir=output_dir) + # Assertions - assert 'ic_mean' in results - assert 'ic_ir' in results - assert 'layer_returns' in results - assert 'turnover_mean' in results - assert isinstance(results['ic_mean'], pd.Series) - assert 1 in results['layer_returns'] - assert 2 in results['layer_returns'] - - # Check if plot was saved - assert os.path.exists(plot_path) - if os.path.exists(plot_path): - os.remove(plot_path) # Cleanup + assert isinstance(result, FactorAnalysisResult) + # ic_summary is now always dict[int, dict[str, float]] + assert 1 in result.ic_summary + assert "mean_ic" in result.ic_summary[1] + assert "ic_ir" in result.ic_summary[1] + assert isinstance(result.ic_series, pd.DataFrame) + assert isinstance(result.turnover_mean, float) + # quantile_returns is now dict[int, pd.DataFrame] + assert isinstance(result.quantile_returns, dict) + assert 1 in result.quantile_returns + assert len(result.quantile_returns[1].columns) == 2 # quantiles=2 + + # Check if output directory was created and has files + assert os.path.exists(output_dir) + # Should have created a timestamped subdirectory + subdirs = [d for d in os.listdir(output_dir) if os.path.isdir(os.path.join(output_dir, d))] + assert len(subdirs) == 1 + exp_dir = os.path.join(output_dir, subdirs[0]) + assert os.path.exists(os.path.join(exp_dir, "ic_series.csv")) + assert os.path.exists(os.path.join(exp_dir, "plots")) + + # Cleanup + shutil.rmtree(output_dir) diff --git a/tests/test_plotting.py b/tests/test_plotting.py index f262982..02182dc 100644 --- a/tests/test_plotting.py +++ b/tests/test_plotting.py @@ -20,7 +20,7 @@ def sample_aggbar_for_plotting(): """ # Generate more time points for better visualization dates = pd.date_range(start="2025-01-01", periods=50, freq="1h") - timestamps = (dates.astype(np.int64) // 10**6).astype(int) + timestamps = (dates.astype("datetime64[ms]").astype(np.int64)).astype(int) symbols = ["BTCUSDT", "ETHUSDT", "BNBUSDT", "SOLUSDT"] data_list = []