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/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.) 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/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/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/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`. 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 98596a7..ef4e2d2 100644 --- a/docs/user-guide/factor.md +++ b/docs/user-guide/factor.md @@ -256,16 +256,33 @@ fig = close.plot( ### 介面說明 ```python -results = factor.eval( - prices: Factor, # 價格因子（例如 close 價） - periods: list[int] = (1, 5, 10),# 持有期（單位：bar 數 / 天等） - quantiles: int = 5, # 分層數量（global quantile） - save_path: str | None = None, # 若提供路徑，會輸出評估圖表 PNG +result = factor.eval( + prices: Factor | AggBar, # 價格數據（Factor 或 AggBar） + periods: int = 1, # 持有期（單位：bar 數 / 天等，目前僅支援單一 int） + 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, ) ``` -`results` 回傳一個 `dict`，主要包含： +### 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： - **`ic_series`**: `DataFrame` - index: `end_time` @@ -331,4 +348,3 @@ print(results["spread"]) 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/examples/01_momentum_factor_research.ipynb b/examples/01_momentum_factor_research.ipynb new file mode 100644 index 0000000..d07d074 --- /dev/null +++ b/examples/01_momentum_factor_research.ipynb @@ -0,0 +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": 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" + } + }, + "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..e0b04d0 --- /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=\"1m\")" + ] + }, + { + "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[\"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[\"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", + "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(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 +} diff --git a/examples/03_data_loading_and_exploration.ipynb b/examples/03_data_loading_and_exploration.ipynb new file mode 100644 index 0000000..bd78af6 --- /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: {len(meta.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=\"1m\")\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=\"1m\",\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 +} diff --git a/examples/04_multi_factor_combination.ipynb b/examples/04_multi_factor_combination.ipynb new file mode 100644 index 0000000..3b28997 --- /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=\"1m\")\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(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[\"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[\"total_value\"], 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 +} 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 new file mode 100644 index 0000000..768ab56 --- /dev/null +++ b/examples/README.md @@ -0,0 +1,48 @@ +# 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 | +| [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 + +### 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 +5. **Notebook 05** — Apply universe/checklist masks consistently in analysis and backtests diff --git a/mkdocs.yml b/mkdocs.yml index 86832ca..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 @@ -61,3 +62,4 @@ 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/pyproject.toml b/pyproject.toml index 8dfee8a..f4f2fd9 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" @@ -68,6 +68,7 @@ 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/src/factorium/__init__.py b/src/factorium/__init__.py index 9663364..bfed669 100644 --- a/src/factorium/__init__.py +++ b/src/factorium/__init__.py @@ -29,11 +29,25 @@ >>> 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, + 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 5b49121..e0a6241 100644 --- a/src/factorium/aggbar.py +++ b/src/factorium/aggbar.py @@ -5,16 +5,20 @@ 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 .factors.core import Factor + 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: @@ -38,11 +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): @@ -79,18 +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) @@ -123,7 +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. @@ -156,9 +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. @@ -172,7 +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): @@ -210,13 +213,29 @@ def convert_timestamp(value: Optional[Union[datetime, int, str]]) -> Optional[in 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]: + 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 e0b16eb..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 @@ -50,14 +51,15 @@ 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", - constraints: Optional[list] = None, + constraints: list | None = None, + mask: str | None = None, ): """ Initialize the vectorized backtester. @@ -86,23 +88,28 @@ 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() else: self.signal_df = signal - self._result: Optional[BacktestResult] = None + self._result: BacktestResult | None = None def run(self) -> BacktestResult: """ @@ -127,7 +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()") @@ -144,7 +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"}) @@ -162,18 +172,31 @@ 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.drop("_masked_signal") + # Apply constraints for constraint in self.constraints: df = constraint.apply(df) @@ -248,7 +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/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/data/__init__.py b/src/factorium/data/__init__.py index 0573b05..63ba728 100644 --- a/src/factorium/data/__init__.py +++ b/src/factorium/data/__init__.py @@ -11,16 +11,17 @@ 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 __all__ = [ "BinanceDataLoader", @@ -33,4 +34,5 @@ "build_hive_path", "get_market_string", "BINANCE_COLUMNS", + "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 ed7e80b..356270a 100644 --- a/src/factorium/data/downloader.py +++ b/src/factorium/data/downloader.py @@ -4,32 +4,32 @@ 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 -from .parquet import csv_to_parquet, build_hive_path, get_market_string +from .parquet import build_hive_path, csv_to_parquet, get_market_string +from .utils import calculate_date_range 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( @@ -41,41 +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) @@ -86,33 +83,26 @@ 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) - + 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, @@ -122,151 +112,113 @@ 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") - - def _calculate_date_range( - self, - start_date: Optional[str], - end_date: Optional[str], - days: Optional[int] - ) -> Tuple[datetime, datetime]: - """Calculate date range.""" - 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 - - end = datetime.now() - if days: - if days < 1: - raise ValueError("Days must be greater than 0") - start = end - timedelta(days=days-1) - else: - start = end - timedelta(days=6) - - 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: + + + 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).""" # Note: With Hive partitioning, we use temp directories for download # and build the final path using build_hive_path during conversion 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 @@ -274,28 +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 @@ -330,7 +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) @@ -339,58 +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('-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("-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, @@ -398,9 +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 99fb548..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) @@ -120,51 +121,83 @@ def _normalize_timestamps_to_ms(df: pl.DataFrame, ts_unit: str) -> pl.DataFrame: raise ValueError(f"Unsupported timestamp unit: {ts_unit}") -from ..aggbar import AggBar -from .adapters.binance import BinanceAdapter -from .aggregator import BarAggregator -from .cache import BarCache -from .downloader import BinanceDataDownloader -from .metadata import AggBarMetadata -from .parquet import get_market_string, build_hive_path +from ..aggbar import AggBar # noqa: E402 +from .adapters.binance import BinanceAdapter # noqa: E402 +from .aggregator import BarAggregator # noqa: E402 +from .cache import BarCache # noqa: E402 +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: """ - 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, @@ -176,6 +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, @@ -190,24 +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, @@ -290,7 +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: @@ -314,13 +361,19 @@ def load_aggbar( # Initialize components (for trades/aggTrades aggregation) adapter = BinanceAdapter() - aggregator = BarAggregator() - cache = BarCache() if (use_cache and bar_type == "time") else None - market_str = self._get_market_string(market_type, futures_type) + + # Create DuckDB configurator for S3 backend + duckdb_configurator = None + if hasattr(self.storage, "configure_duckdb_s3"): + duckdb_configurator = self.storage.configure_duckdb_s3 + + aggregator = BarAggregator(duckdb_configurator=duckdb_configurator) + cache = BarCache(storage=self.storage) if (use_cache and bar_type == "time") else None + 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) @@ -329,7 +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, @@ -395,8 +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) @@ -410,7 +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, @@ -462,36 +515,16 @@ def load_aggbar( return AggBar(result_df, combined_meta) - def _calculate_date_range( - self, start_date: Optional[str], end_date: Optional[str], days: Optional[int] - ) -> tuple[datetime, datetime]: - """Calculate date range.""" - 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), - ) - - end = datetime.now() - if days: - start = end - timedelta(days=days - 1) - else: - start = end - timedelta(days=6) - - return start, end 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, @@ -537,15 +570,10 @@ 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, - symbols: List[str], + symbols: list[str], data_type: str, market_type: str, futures_type: str, @@ -560,7 +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, @@ -579,11 +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) @@ -652,7 +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, @@ -682,7 +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, @@ -716,14 +741,13 @@ def _load_klines_direct( Returns: AggBar object containing klines OHLCV data with timestamps in milliseconds """ - import duckdb 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( - base_path=self.base_path, + base_path=self.storage.full_path(""), symbols=symbols, data_type=data_type, market_type=market_type, @@ -734,9 +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() @@ -771,7 +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/data/utils.py b/src/factorium/data/utils.py new file mode 100644 index 0000000..147c2b9 --- /dev/null +++ b/src/factorium/data/utils.py @@ -0,0 +1,71 @@ +""" +Shared utilities for data loading and processing. +""" + +import logging +from datetime import datetime, timedelta, timezone + +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 + 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] + + 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_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: + 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(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) + + 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/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 84576fc..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: @@ -61,62 +220,107 @@ class FactorAnalyzer: Analyzer for factor performance and characteristics. """ - def __init__(self, factor: Factor, prices: Union[AggBar, Factor], quantiles: int = 5): + prices: Factor | None # Type annotation for prices attribute + + 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: - 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 else: self.prices = prices - def analyze(self, price_col: str = "close", periods: int = 1) -> FactorAnalysisResult: + 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.""" + 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: """ 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. @@ -138,10 +342,19 @@ def prepare_data(self, periods: Optional[List[int]] = None, price_col: Optional[ # Get price data if price_col is not None and isinstance(self._raw_prices, AggBar): - prices_lf = self._raw_prices.to_polars().lazy().select(["start_time", "end_time", "symbol", price_col]) + # Check if price_col exists in AggBar + 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}") + 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 + # self.prices is a Factor (we've narrowed the type above) prices_lf = self.prices.lazy.rename({"factor": "__price__"}) price_col_name = "__price__" else: @@ -155,6 +368,9 @@ def prepare_data(self, periods: Optional[List[int]] = None, price_col: Optional[ 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 = [] @@ -185,15 +401,14 @@ 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" 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") ) @@ -230,6 +445,42 @@ def calculate_ic_summary(self, method: str = "rank") -> pd.DataFrame: return pd.DataFrame(summary) + def calculate_turnover(self) -> pd.Series: + """ + Calculate factor turnover using rank autocorrelation. + + Method: + 1. For each start_time, calculate cross-sectional rank of factor values + 2. Compute correlation between today's rank and yesterday's rank + 3. turnover = 1 - rank_autocorrelation + + Returns: + pd.Series: Turnover time series indexed by start_time + """ + 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"]) + + # Calculate rank per start_time and get previous rank for each symbol + turnover_df = ( + sorted_data.with_columns(pl.col("factor").rank(method="average").over("start_time").alias("factor_rank")) + .with_columns( + # Get previous period's rank for each symbol + pl.col("factor_rank").shift(1).over("symbol").alias("prev_rank") + ) + .group_by("start_time") + .agg( + # Calculate correlation between current rank and previous rank + pl.corr("factor_rank", "prev_rank").alias("autocorr") + ) + .select([pl.col("start_time"), (1 - pl.col("autocorr")).alias("turnover")]) + .sort("start_time") + ) + + # Convert to pandas Series only at the end + return turnover_df.to_pandas().set_index("start_time")["turnover"] + def calculate_quantile_returns(self, quantiles: int = 5, period: int = 1) -> pd.DataFrame: """ Calculate mean returns for each factor quantile. @@ -241,8 +492,7 @@ 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: @@ -342,3 +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 95ad14e..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. @@ -62,45 +63,53 @@ 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, - save_path: Optional[str] = None, + 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) + + result = analyzer.analyze(price_col=price_col, periods=periods) + + # Save results if output directory specified + if output_dir: + result.save(output_dir) - evaluator = FactorEvaluator(self, prices) - return evaluator.run_full_report(periods=periods, quantiles=quantiles, save_path=save_path, **kwargs) + return result 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/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..df37ca2 --- /dev/null +++ b/src/factorium/universe/metadata.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +import json +import time +from pathlib import Path + +import aiohttp + +from ..data.loader import _run_async +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 _run_async(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..cfcdac2 --- /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(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).fill_null(False) diff --git a/src/factorium/universe/tags.py b/src/factorium/universe/tags.py new file mode 100644 index 0000000..2992677 --- /dev/null +++ b/src/factorium/universe/tags.py @@ -0,0 +1,127 @@ +from __future__ import annotations + +import asyncio +import json +import logging +import time +from pathlib import Path + +import aiohttp + +from ..constants import COINGECKO_BASE_URL +from ..data.loader import _run_async + + +logger = logging.getLogger(__name__) + + +class TagProvider: + """Fetch and cache token categories from CoinGecko. + + Note: + ``symbols`` must be explicitly provided to avoid full-market + category fetching from CoinGecko, which can be very slow. + """ + + 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]]: + 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 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 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 + 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) + 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 _run_async(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/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/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.""" 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_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"], 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/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.""" 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() 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 diff --git a/tests/factors/test_analyzer.py b/tests/factors/test_analyzer.py index 0094d35..fba2582 100644 --- a/tests/factors/test_analyzer.py +++ b/tests/factors/test_analyzer.py @@ -198,6 +198,294 @@ 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") + + +def test_calculate_turnover(sample_data): + """Test turnover calculation using rank autocorrelation.""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices, quantiles=5) + + # Prepare data first + analyzer.prepare_data(periods=[1]) + + # Calculate turnover + turnover_series = analyzer.calculate_turnover() + + # Assertions + assert isinstance(turnover_series, pd.Series) + assert turnover_series.index.name == "start_time" + assert len(turnover_series) > 0 + # Turnover should be between 0 and 2 (since 1 - (-1) = 2) + assert turnover_series.min() >= -0.1 # Allow small negative due to correlation + assert turnover_series.max() <= 2.1 + + +def test_analysis_result_has_turnover_fields(sample_data): + """Test that FactorAnalysisResult includes turnover fields.""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices, quantiles=5) + + result = analyzer.analyze(periods=1) + + # Check turnover fields exist + assert hasattr(result, "turnover_series") + assert hasattr(result, "turnover_mean") + + # Check types + assert isinstance(result.turnover_series, pd.Series) + assert isinstance(result.turnover_mean, (float, np.floating)) + + # Check values are reasonable + assert not np.isnan(result.turnover_mean) + + +def test_save_creates_correct_structure(sample_data): + """Test that save() creates correct directory structure.""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + analyzer = FactorAnalyzer(factor, prices, quantiles=5) + result = analyzer.analyze(periods=1) + + import tempfile + from pathlib import Path + import json + + with tempfile.TemporaryDirectory() as tmpdir: + result.save(tmpdir) + + # Find the created experiment folder + exp_dirs = list(Path(tmpdir).glob("*_*")) + assert len(exp_dirs) == 1 + exp_dir = exp_dirs[0] + + # Check folder name format: YYYYMMDD_HHMMSS_factorname + folder_name = exp_dir.name + parts = folder_name.split("_") + assert len(parts) >= 3 + assert parts[0].isdigit() and len(parts[0]) == 8 # YYYYMMDD + assert parts[1].isdigit() and len(parts[1]) == 6 # HHMMSS + + # Check CSV files exist + assert (exp_dir / "ic_series.csv").exists() + assert (exp_dir / "ic_summary.csv").exists() + assert (exp_dir / "turnover.csv").exists() + assert (exp_dir / "quantile_returns_period_1.csv").exists() + + # cumulative_returns may be None + if result.cumulative_returns is not None: + assert (exp_dir / "cumulative_returns_period_1.csv").exists() + + # Check config.json exists and has correct structure + config_path = exp_dir / "config.json" + assert config_path.exists() + + with open(config_path) as f: + config = json.load(f) + + assert config["factor_name"] == result.factor_name + assert config["periods"] == result.periods + assert config["quantiles"] == result.quantiles + assert "created_at" in config + + # Check plots directory exists + plots_dir = exp_dir / "plots" + assert plots_dir.exists() + assert plots_dir.is_dir() + + +def test_save_generates_plots(sample_data): + """Test that save() generates plot files.""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + analyzer = FactorAnalyzer(factor, prices, quantiles=5) + result = analyzer.analyze(periods=1) + + import tempfile + from pathlib import Path + + with tempfile.TemporaryDirectory() as tmpdir: + result.save(tmpdir) + + exp_dirs = list(Path(tmpdir).glob("*_*")) + exp_dir = exp_dirs[0] + plots_dir = exp_dir / "plots" + + # Check plot files exist + assert (plots_dir / "ic_timeseries.png").exists() + assert (plots_dir / "ic_distribution.png").exists() + assert (plots_dir / "quantile_returns_period_1.png").exists() + + +def test_analyze_multi_horizon_returns_list_periods(sample_data): + """analyze(periods=[1, 5]) 應回傳 list periods。""" + from factorium.factors.analyzer import FactorAnalysisResult + + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices) + result = analyzer.analyze(periods=[1, 5]) + + assert isinstance(result, FactorAnalysisResult) + assert result.periods == [1, 5] + + +def test_analyze_multi_horizon_ic_summary_structure(sample_data): + """multi-horizon 時 ic_summary 應為 dict[int, dict]。""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices) + result = analyzer.analyze(periods=[1, 5]) + + # ic_summary 應為 {1: {...}, 5: {...}} + assert isinstance(result.ic_summary, dict) + assert 1 in result.ic_summary + assert 5 in result.ic_summary + assert "mean_ic" in result.ic_summary[1] + assert "mean_ic" in result.ic_summary[5] + + +def test_analyze_single_horizon_consistent_format(sample_data): + """單一 horizon 時格式與多 horizon 一致（統一為 dict[int, dict]）。""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices) + result = analyzer.analyze(periods=1) + + # periods 現在總是 list[int] + assert isinstance(result.periods, list) + assert result.periods == [1] + # ic_summary 現在總是 dict[int, dict[str, float]] + assert isinstance(result.ic_summary, dict) + assert 1 in result.ic_summary + assert "mean_ic" in result.ic_summary[1] + assert isinstance(result.ic_summary[1]["mean_ic"], float) + + +def test_repr_multi_horizon(sample_data): + """multi-horizon __repr__ 應顯示所有 period 的 IC。""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices) + result = analyzer.analyze(periods=[1, 5]) + + repr_str = repr(result) + assert "my_factor" in repr_str + assert "[1, 5]" in repr_str + # 應該有多個 period 的 IC 資訊 + assert "Period 1" in repr_str + assert "Period 5" in repr_str + + +def test_save_multi_horizon_creates_per_period_files(sample_data): + """multi-horizon save 應為每個 period 建立 quantile_returns 檔案。""" + import tempfile + from pathlib import Path + + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + analyzer = FactorAnalyzer(factor, prices, quantiles=2) + result = analyzer.analyze(periods=[1, 5]) + + with tempfile.TemporaryDirectory() as tmpdir: + result.save(tmpdir) + + exp_dirs = list(Path(tmpdir).glob("*_*")) + assert len(exp_dirs) == 1 + exp_dir = exp_dirs[0] + + # 應有 quantile_returns_period_1.csv, quantile_returns_period_5.csv + assert (exp_dir / "quantile_returns_period_1.csv").exists() + assert (exp_dir / "quantile_returns_period_5.csv").exists() + + # 應有 ic_summary.csv 包含多行 + ic_summary_path = exp_dir / "ic_summary.csv" + assert ic_summary_path.exists() + + # 應有 config.json 包含 periods + config_path = exp_dir / "config.json" + assert config_path.exists() + + +def test_plot_ic_decay(sample_data): + """multi-horizon 應支援 plot_ic_decay() 繪製 IC decay 曲線。""" + import matplotlib.figure as mpl_figure + + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices) + analyzer.prepare_data(periods=[1, 3, 5]) + + fig = analyzer.plot_ic_decay(periods=[1, 3, 5]) + assert isinstance(fig, mpl_figure.Figure) + + +def test_save_multi_horizon_includes_ic_decay_plot(sample_data): + """multi-horizon save 應包含 ic_decay.png。""" + import tempfile + from pathlib import Path + + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + analyzer = FactorAnalyzer(factor, prices, quantiles=2) + result = analyzer.analyze(periods=[1, 3, 5]) + + with tempfile.TemporaryDirectory() as tmpdir: + result.save(tmpdir) + + exp_dirs = list(Path(tmpdir).glob("*_*")) + exp_dir = exp_dirs[0] + plots_dir = exp_dir / "plots" + + assert (plots_dir / "ic_decay.png").exists() + + +def test_analyze_empty_periods_list_raises_error(sample_data): + """analyze(periods=[]) 應拋出 ValueError。""" + agg = AggBar(sample_data) + factor = agg["my_factor"] + prices = agg["close"] + + analyzer = FactorAnalyzer(factor, prices) + + 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 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_safe_operations.py b/tests/factors/test_safe_operations.py new file mode 100644 index 0000000..3b78ad7 --- /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 * 60_000, + "end_time": (t + 1) * 60_000, + "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 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 0c5221b..e7a28a8 100644 --- a/tests/test_data_loader.py +++ b/tests/test_data_loader.py @@ -8,12 +8,12 @@ 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 from factorium import BinanceDataLoader, AggBar -from factorium.data import build_hive_path +from factorium.data import build_hive_path, calculate_date_range # ============================================================================= @@ -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: @@ -129,62 +129,80 @@ 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) + assert end_dt == datetime(2024, 1, 8) - 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): - """Test with only days specified (should use today as end).""" - start_dt, end_dt = loader._calculate_date_range(start_date=None, end_date=None, days=7) + def test_with_only_days(self): + """Test with only days specified — aligned to UTC midnight.""" + start_dt, end_dt = 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, 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 @freeze_time("2024-06-15 12:00:00") - def test_default_7_days(self, loader): - """Test default behavior (no params = 7 days ending today).""" - 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) - - def test_cross_month_range(self, loader): + 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, 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 + + @freeze_time("2024-06-15 23:59:59") + 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 = 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, 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 + + 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) + assert end_dt == datetime(2024, 1, 6) - 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) + assert end_dt == datetime(2024, 1, 2) - 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) @@ -205,7 +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): @@ -225,7 +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)) @@ -244,7 +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", @@ -259,7 +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 = ( @@ -292,7 +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 = ( @@ -588,18 +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 = [] 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..375b710 --- /dev/null +++ b/tests/universe/test_metadata.py @@ -0,0 +1,125 @@ +import json +from pathlib import Path + +import pytest + +from factorium.universe import metadata as metadata_module +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 + + +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(metadata_module, "_run_async", fake_run_async) + + out = provider.fetch() + assert out == expected + assert called["value"] is True 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..8122f00 --- /dev/null +++ b/tests/universe/test_tags.py @@ -0,0 +1,119 @@ +import json +from pathlib import Path + +import pytest + +import factorium.data.loader as data_loader +from factorium.universe import tags as tags_module +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_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"]} + + 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"]} + + +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(tags_module, "_run_async", fake_run_async) + + out = provider.fetch(symbols=["BTC"]) + assert out == expected + assert called["value"] is True + + +def test_fetch_raises_when_symbols_is_none(tmp_path: Path) -> None: + provider = TagProvider(cache_dir=tmp_path, cache_ttl=0) + with pytest.raises(ValueError, match="symbols must be provided"): + provider.fetch(symbols=None) diff --git a/tests/universe/test_universe_rules.py b/tests/universe/test_universe_rules.py new file mode 100644 index 0000000..bf60098 --- /dev/null +++ b/tests/universe/test_universe_rules.py @@ -0,0 +1,107 @@ +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"} + + +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] diff --git a/uv.lock b/uv.lock index d8a6b16..c6df8b1 100644 --- a/uv.lock +++ b/uv.lock @@ -2,8 +2,12 @@ version = 1 revision = 3 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.12'", - "python_full_version < '3.12'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version 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