From 9d1605bc2a2a8ab2a28b4935e789f525feb878fb Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Tue, 25 Jun 2024 12:43:20 +0300 Subject: [PATCH 01/61] feat: allow cors origin all --- 0.5.1 | 35 +++++++++++++++++++++++++++++++++++ backend/main.py | 14 +++++++++++++- 2 files changed, 48 insertions(+), 1 deletion(-) create mode 100644 0.5.1 diff --git a/0.5.1 b/0.5.1 new file mode 100644 index 0000000..0493645 --- /dev/null +++ b/0.5.1 @@ -0,0 +1,35 @@ +Collecting nixtla + Downloading nixtla-0.5.1-py3-none-any.whl.metadata (4.8 kB) +Requirement already satisfied: httpx in c:\users\user\downloads\ten_academy\week9\scalable_backtesting_infrastructure_for_crypto_trading\backtest\lib\site-packages (from nixtla) (0.27.0) +Requirement already satisfied: pandas in c:\users\user\downloads\ten_academy\week9\scalable_backtesting_infrastructure_for_crypto_trading\backtest\lib\site-packages (from nixtla) (2.2.2) +Requirement already satisfied: pydantic in 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c:\users\user\downloads\ten_academy\week9\scalable_backtesting_infrastructure_for_crypto_trading\backtest\lib\site-packages (from python-dateutil>=2.8.2->pandas->nixtla) (1.16.0) +Requirement already satisfied: exceptiongroup>=1.0.2 in c:\users\user\downloads\ten_academy\week9\scalable_backtesting_infrastructure_for_crypto_trading\backtest\lib\site-packages (from anyio->httpx->nixtla) (1.2.1) +Downloading nixtla-0.5.1-py3-none-any.whl (71 kB) + ---------------------------------------- 71.4/71.4 kB 561.5 kB/s eta 0:00:00 +Downloading utilsforecast-0.1.11-py3-none-any.whl (40 kB) + ---------------------------------------- 40.6/40.6 kB 978.0 kB/s eta 0:00:00 +Downloading tenacity-8.4.1-py3-none-any.whl (27 kB) +Installing collected packages: tenacity, utilsforecast, nixtla +Successfully installed nixtla-0.5.1 tenacity-8.4.1 utilsforecast-0.1.11 diff --git a/backend/main.py b/backend/main.py index 7b46573..c4a6d7e 100644 --- a/backend/main.py +++ b/backend/main.py @@ -1,5 +1,6 @@ from typing import List from fastapi import FastAPI, Depends, HTTPException +from fastapi.middleware.cors import CORSMiddleware from sqlalchemy.orm import Session, joinedload from sqlalchemy.exc import IntegrityError import pandas as pd @@ -9,7 +10,7 @@ from .auth import router as auth_router from kafka_topic.kafka_config import get_kafka_producer, SCENE_TOPIC, RESULT_TOPIC, get_kafka_consumer -from backend.utils.backtest import run_backtest +from scripts.backtesting.main import run_backtest from backend.utils.init_data import initialize_data get_db = database.get_db @@ -21,6 +22,17 @@ app = FastAPI() +# allow all origins +origins = ["*"] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + @app.on_event("startup") # TODO update the code with lifespan dependency def start_kafka_consumer(): threading.Thread(target=consume_scene_parameters).start() From bcac7140965924b6431878e6b673880b92ee94c8 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Tue, 25 Jun 2024 12:56:51 +0300 Subject: [PATCH 02/61] fix: backtesting import errors --- scripts/backtesting/main.py | 19 ++++++++++++++----- 1 file changed, 14 insertions(+), 5 deletions(-) diff --git a/scripts/backtesting/main.py b/scripts/backtesting/main.py index f933e21..3503bab 100644 --- a/scripts/backtesting/main.py +++ b/scripts/backtesting/main.py @@ -1,7 +1,16 @@ import yfinance as yf import backtrader as bt -from util.user_input import get_user_input -from analyzers.metrics_analyzer import MetricsAnalyzer +import os, sys +# from util.user_input import get_user_input +# Assuming this script is two levels deep in the project directory +project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) +if project_root not in sys.path: + sys.path.append(project_root) + +print("The project root is: ", os.getcwd()) + +from scripts.backtesting.analyzers.metrics_analyzer import MetricsAnalyzer +import scripts.backtesting.strategies as strategies def run_backtest(config): initial_cash = config['initial_cash'] @@ -63,6 +72,6 @@ def run_backtest(config): print(f"Winning Trades: {metrics['winning_trades']}") print(f"Losing Trades: {metrics['losing_trades']}") -if __name__ == "__main__": - config = get_user_input() - run_backtest(config) \ No newline at end of file +# if __name__ == "__main__": +# config = get_user_input() +# run_backtest(config) \ No newline at end of file From 0f066e7593bb68abb3c8ddc90eb4c0980c17dd23 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Tue, 25 Jun 2024 12:57:24 +0300 Subject: [PATCH 03/61] fix: space in the files of backtest --- .../backtesting/strategies/{ rsi_strategy.py => rsi_strategy.py} | 0 .../backtesting/strategies/{ sma_strategy.py => sma_strategy.py} | 0 scripts/backtesting/util/{ user_input.py => user_input.py} | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename scripts/backtesting/strategies/{ rsi_strategy.py => rsi_strategy.py} (100%) rename scripts/backtesting/strategies/{ sma_strategy.py => sma_strategy.py} (100%) rename scripts/backtesting/util/{ user_input.py => user_input.py} (100%) diff --git a/scripts/backtesting/strategies/ rsi_strategy.py b/scripts/backtesting/strategies/rsi_strategy.py similarity index 100% rename from scripts/backtesting/strategies/ rsi_strategy.py rename to scripts/backtesting/strategies/rsi_strategy.py diff --git a/scripts/backtesting/strategies/ sma_strategy.py b/scripts/backtesting/strategies/sma_strategy.py similarity index 100% rename from scripts/backtesting/strategies/ sma_strategy.py rename to scripts/backtesting/strategies/sma_strategy.py diff --git a/scripts/backtesting/util/ user_input.py b/scripts/backtesting/util/user_input.py similarity index 100% rename from scripts/backtesting/util/ user_input.py rename to scripts/backtesting/util/user_input.py From 4474aec71e7f8d9adc47f62fd29b6a615650db33 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 10:23:45 +0300 Subject: [PATCH 04/61] feat: add logging to backend --- backend/main.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/backend/main.py b/backend/main.py index c4a6d7e..2994a41 100644 --- a/backend/main.py +++ b/backend/main.py @@ -5,6 +5,10 @@ from sqlalchemy.exc import IntegrityError import pandas as pd import threading +import logging + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) from . import models, schemas, database from .auth import router as auth_router @@ -36,11 +40,13 @@ @app.on_event("startup") # TODO update the code with lifespan dependency def start_kafka_consumer(): threading.Thread(target=consume_scene_parameters).start() + print("Kafka consumer started") @app.on_event("startup") # TODO update the code with lifespan dependency def on_startup(): db = next(get_db()) initialize_data(db) + print("Data initialized") app.include_router(auth_router, prefix="/auth", tags=["auth"]) @@ -95,7 +101,9 @@ def create_scene(scene: schemas.SceneCreate, db: Session = Depends(get_db)): } print("Sending scene parameters to Kafka:", scene_parameters) producer.send(SCENE_TOPIC, scene_parameters) + print("Scene parameters sent to Kafka") producer.flush() + print("Scene flushed successfully:") return db_scene except IntegrityError: @@ -190,15 +198,15 @@ def consume_scene_parameters(): consumer = get_kafka_consumer(SCENE_TOPIC) for message in consumer: scene_parameters = message.value - print(f"Received scene parameters: {scene_parameters}") + logger.info(f"Received scene parameters: {scene_parameters}") existing_results = fetch_existing_backtest(scene_parameters) if existing_results: - print(f"Using existing backtest results for parameters: {scene_parameters}") + logger.info(f"Using existing backtest results for parameters: {scene_parameters}") continue # Run the backtest if no existing results - df = fetch_data(scene_parameters['start_date'], scene_parameters['end_date']) - metrics = run_backtest(scene_parameters, df) + # df = fetch_data(scene_parameters['start_date'], scene_parameters['end_date']) + metrics = run_backtest(scene_parameters) producer = get_kafka_producer() - print(f"Sending backtest results: {metrics}") + logger.info(f"Sending backtest results: {metrics}") producer.send(RESULT_TOPIC, metrics) producer.flush() \ No newline at end of file From 7a60f98b5fc952b62a7d7bc141d50c6804fa30d4 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 10:25:02 +0300 Subject: [PATCH 05/61] feat: update kafka config to accept large files --- kafka_topic/kafka_config.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/kafka_topic/kafka_config.py b/kafka_topic/kafka_config.py index f3c78d4..9069b66 100644 --- a/kafka_topic/kafka_config.py +++ b/kafka_topic/kafka_config.py @@ -9,6 +9,7 @@ def get_kafka_producer(): return KafkaProducer( bootstrap_servers=KAFKA_BROKER_URL, + max_request_size=209715200, # 200 MB value_serializer=lambda v: json.dumps(v).encode('utf-8') ) @@ -18,6 +19,7 @@ def get_kafka_consumer(topic): bootstrap_servers=KAFKA_BROKER_URL, auto_offset_reset='earliest', enable_auto_commit=True, + max_partition_fetch_bytes=209715200, group_id='my-group', value_deserializer=lambda x: json.loads(x.decode('utf-8')) ) From ff05cf6bb05710945faffd77303b2e7775c7aeca Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 10:25:45 +0300 Subject: [PATCH 06/61] fix: backtesting features updated to resemble backend --- notebooks/backtesting.ipynb | 147 +++++++++++++++++++++++++++++++++++- notebooks/kafka.ipynb | 138 +++++++++++++++++++++++++-------- scripts/backtesting/main.py | 36 ++++++--- 3 files changed, 277 insertions(+), 44 deletions(-) diff --git a/notebooks/backtesting.ipynb b/notebooks/backtesting.ipynb index 427a63b..8c59331 100644 --- a/notebooks/backtesting.ipynb +++ b/notebooks/backtesting.ipynb @@ -681,6 +681,151 @@ "metrics = run_backtest({\"indicator_name\": \"SMA\", \"period\": 20}, df)\n", "print(metrics)" ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "c:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\notebooks\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "print(os.getcwd())\n", + "os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The project root is: c:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\n" + ] + } + ], + "source": [ + "import yfinance as yf\n", + "import backtrader as bt\n", + "import os, sys\n", + "# from util.user_input import get_user_input\n", + "# Assuming this script is two levels deep in the project directory\n", + "# project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))\n", + "# if project_root not in sys.path:\n", + " # sys.path.append(project_root)\n", + "\n", + "print(\"The project root is: \", os.getcwd())\n", + "\n", + "from scripts.backtesting.analyzers.metrics_analyzer import MetricsAnalyzer\n", + "import scripts.backtesting.strategies as strategies\n", + "\n", + "def run_backtest(config):\n", + " initial_cash = 500\n", + " start_date = config['start_date']\n", + " end_date = config['end_date']\n", + " ticker = config['stock_symbol']\n", + " indicator = config['indicator_symbol']\n", + "\n", + " # Fetch historical data\n", + " try:\n", + " data = yf.download(ticker, start=start_date, end=end_date)\n", + " data_feed = bt.feeds.PandasData(dataname=data)\n", + " except Exception as e:\n", + " print(f\"Error fetching data: {e}\")\n", + " return\n", + "\n", + " # Initialize Cerebro engine\n", + " cerebro = bt.Cerebro()\n", + " cerebro.adddata(data_feed)\n", + " cerebro.broker.setcash(initial_cash)\n", + "\n", + " # Add strategy based on selected indicator\n", + " if indicator == 'SMA':\n", + " from strategies.sma_strategy import SMAStrategy\n", + " cerebro.addstrategy(SMAStrategy)\n", + " elif indicator == 'LSTM':\n", + " from strategies.lstm_strategy import LSTMStrategy\n", + " cerebro.addstrategy(LSTMStrategy)\n", + " elif indicator == 'MACD':\n", + " from strategies.macd_strategy import MACDStrategy\n", + " cerebro.addstrategy(MACDStrategy)\n", + " elif indicator == 'RSI':\n", + " from strategies.rsi_strategy import RSIStrategy\n", + " cerebro.addstrategy(RSIStrategy)\n", + " elif indicator == 'BB':\n", + " from strategies.bollinger_bands_strategy import BollingerBandsStrategy\n", + " cerebro.addstrategy(BollingerBandsStrategy)\n", + " else:\n", + " print(\"Invalid indicator selected.\")\n", + " yield \"Invalid indicator selected.\"\n", + "\n", + " # Add analyzers\n", + " cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, annualized=True)\n", + " cerebro.addanalyzer(MetricsAnalyzer)\n", + "\n", + " # Run backtest\n", + " results = cerebro.run()\n", + " strat = results[0]\n", + "\n", + " # Print results\n", + " print(f\"Initial Cash: {initial_cash}\")\n", + " print(f\"Final Value: {cerebro.broker.getvalue()}\")\n", + " print(f\"Sharpe Ratio: {strat.analyzers.sharperatio.get_analysis()}\")\n", + "\n", + " metrics_analyzer = strat.analyzers.getbyname('MetricsAnalyzer')\n", + " metrics = metrics_analyzer.get_analysis()\n", + " print(f\"Return: {metrics['return']}\")\n", + " print(f\"Total Trades: {metrics['trades']}\")\n", + " print(f\"Winning Trades: {metrics['winning_trades']}\")\n", + " print(f\"Losing Trades: {metrics['losing_trades']}\")\n", + "\n", + " return {\n", + " 'initial_cash': initial_cash,\n", + " 'final_value': cerebro.broker.getvalue(),\n", + " 'sharpe_ratio': strat.analyzers.sharperatio.get_analysis(),\n", + " 'return': metrics['return'],\n", + " 'total_trades': metrics['trades'],\n", + " 'winning_trades': metrics['winning_trades'],\n", + " 'losing_trades': metrics['losing_trades']\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "config = {\n", + " 'start_date': '2020-01-01',\n", + " 'end_date': '2021-01-01',\n", + " 'stock_symbol': 'NVDA',\n", + " 'indicator_symbol': 'SMA'\n", + "}\n", + "run_backtest(config)" + ] } ], "metadata": { @@ -699,7 +844,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/notebooks/kafka.ipynb b/notebooks/kafka.ipynb index 7979345..3c2e1aa 100644 --- a/notebooks/kafka.ipynb +++ b/notebooks/kafka.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -30,17 +30,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Produced!\n" - ] - } - ], + "outputs": [], "source": [ "produce(\"Abubeker\",\"hello\")" ] @@ -51,14 +43,17 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Topics in Kafka cluster:\n", - "_confluent-ksql-ksqldb-server__command_topic\n", - "scene_parameters\n", - "backtest_results\n", - "__consumer_offsets\n" + "ename": "NoBrokersAvailable", + "evalue": "NoBrokersAvailable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNoBrokersAvailable\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 7\u001b[0m\n\u001b[0;32m 4\u001b[0m broker \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg3.10academy.org:9092\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# Create an AdminClient\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m admin_client \u001b[38;5;241m=\u001b[39m \u001b[43mKafkaAdminClient\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mbootstrap_servers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbroker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 9\u001b[0m \u001b[43m \u001b[49m\u001b[43mclient_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmy_client\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\n\u001b[0;32m 10\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# Get the list of topics\u001b[39;00m\n\u001b[0;32m 13\u001b[0m topics \u001b[38;5;241m=\u001b[39m admin_client\u001b[38;5;241m.\u001b[39mlist_topics()\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\admin\\client.py:208\u001b[0m, in \u001b[0;36mKafkaAdminClient.__init__\u001b[1;34m(self, **configs)\u001b[0m\n\u001b[0;32m 205\u001b[0m reporters \u001b[38;5;241m=\u001b[39m [reporter() \u001b[38;5;28;01mfor\u001b[39;00m reporter \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmetric_reporters\u001b[39m\u001b[38;5;124m'\u001b[39m]]\n\u001b[0;32m 206\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metrics \u001b[38;5;241m=\u001b[39m Metrics(metric_config, reporters)\n\u001b[1;32m--> 208\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client \u001b[38;5;241m=\u001b[39m KafkaClient(metrics\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metrics,\n\u001b[0;32m 209\u001b[0m metric_group_prefix\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124madmin\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 210\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig)\n\u001b[0;32m 211\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client\u001b[38;5;241m.\u001b[39mcheck_version(timeout\u001b[38;5;241m=\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version_auto_timeout_ms\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m1000\u001b[39m))\n\u001b[0;32m 213\u001b[0m \u001b[38;5;66;03m# Get auto-discovered version from client if necessary\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\client_async.py:244\u001b[0m, in \u001b[0;36mKafkaClient.__init__\u001b[1;34m(self, **configs)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 243\u001b[0m check_timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version_auto_timeout_ms\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m1000\u001b[39m\n\u001b[1;32m--> 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_version\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_timeout\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\client_async.py:927\u001b[0m, in \u001b[0;36mKafkaClient.check_version\u001b[1;34m(self, node_id, timeout, strict)\u001b[0m\n\u001b[0;32m 924\u001b[0m \u001b[38;5;66;03m# Timeout\u001b[39;00m\n\u001b[0;32m 925\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 926\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m--> 927\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Errors\u001b[38;5;241m.\u001b[39mNoBrokersAvailable()\n", + "\u001b[1;31mNoBrokersAvailable\u001b[0m: NoBrokersAvailable" ] } ], @@ -88,6 +83,56 @@ "admin_client.close()\n" ] }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "from kafka_topic.kafka_config import KAFKA_BROKER_URL, SCENE_TOPIC\n", + "from kafka import KafkaConsumer\n", + "\n", + "a = KafkaConsumer(\n", + " SCENE_TOPIC,\n", + " bootstrap_servers=KAFKA_BROKER_URL,\n", + " auto_offset_reset='earliest',\n", + " enable_auto_commit=True,\n", + " max_partition_fetch_bytes=209715200, # 200 MB\n", + " group_id='my-group',\n", + " value_deserializer=lambda x: json.loads(x.decode('utf-8'))\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from kafka.admin import KafkaAdminClient\n", + "\n", + "# Replace with your Kafka broker address\n", + "broker = 'localhost:29092' # Match the advertised listener\n", + "\n", + "# Create an AdminClient\n", + "admin_client = KafkaAdminClient(\n", + " bootstrap_servers=broker,\n", + " client_id='my-group'\n", + ")\n", + "\n", + "# Get the list of topics\n", + "topics = admin_client.list_topics()\n", + "\n", + "# Print the list of topics\n", + "print(\"Topics in Kafka cluster:\")\n", + "for topic in topics:\n", + " print(topic)\n", + "\n", + "# Close the admin client\n", + "admin_client.close()\n" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -96,7 +141,7 @@ { "data": { "text/plain": [ - "'/home/abubeker_shamil/Scalable_Backtesting_Infrastructure_for_Crypto_Trading/notebooks'" + "'c:\\\\Users\\\\user\\\\Downloads\\\\ten_academy\\\\week9\\\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\\\notebooks'" ] }, "execution_count": 1, @@ -122,7 +167,23 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NoBrokersAvailable", + "evalue": "NoBrokersAvailable", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNoBrokersAvailable\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mkafka_topic\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkafka_config\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_kafka_producer, SCENE_TOPIC, RESULT_TOPIC, get_kafka_consumer\n\u001b[1;32m----> 2\u001b[0m consumer \u001b[38;5;241m=\u001b[39m \u001b[43mget_kafka_consumer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mSCENE_TOPIC\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\kafka_topic\\kafka_config.py:17\u001b[0m, in \u001b[0;36mget_kafka_consumer\u001b[1;34m(topic)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_kafka_consumer\u001b[39m(topic):\n\u001b[1;32m---> 17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mKafkaConsumer\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[43mtopic\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[43mbootstrap_servers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mKAFKA_BROKER_URL\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mauto_offset_reset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mearliest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[43menable_auto_commit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_partition_fetch_bytes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m209715200\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmy-group\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalue_deserializer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 25\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\consumer\\group.py:356\u001b[0m, in \u001b[0;36mKafkaConsumer.__init__\u001b[1;34m(self, *topics, **configs)\u001b[0m\n\u001b[0;32m 352\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mint\u001b[39m, str_version\u001b[38;5;241m.\u001b[39msplit(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)))\n\u001b[0;32m 353\u001b[0m log\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124muse api_version=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m [tuple] -- \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m as str is deprecated\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 354\u001b[0m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m]), str_version)\n\u001b[1;32m--> 356\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client \u001b[38;5;241m=\u001b[39m KafkaClient(metrics\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metrics, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig)\n\u001b[0;32m 358\u001b[0m \u001b[38;5;66;03m# Get auto-discovered version from client if necessary\u001b[39;00m\n\u001b[0;32m 359\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\client_async.py:244\u001b[0m, in \u001b[0;36mKafkaClient.__init__\u001b[1;34m(self, **configs)\u001b[0m\n\u001b[0;32m 242\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 243\u001b[0m check_timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version_auto_timeout_ms\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m1000\u001b[39m\n\u001b[1;32m--> 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapi_version\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcheck_version\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcheck_timeout\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\client_async.py:927\u001b[0m, in \u001b[0;36mKafkaClient.check_version\u001b[1;34m(self, node_id, timeout, strict)\u001b[0m\n\u001b[0;32m 924\u001b[0m \u001b[38;5;66;03m# Timeout\u001b[39;00m\n\u001b[0;32m 925\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 926\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m--> 927\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m Errors\u001b[38;5;241m.\u001b[39mNoBrokersAvailable()\n", + "\u001b[1;31mNoBrokersAvailable\u001b[0m: NoBrokersAvailable" + ] + } + ], "source": [ "from kafka_topic.kafka_config import get_kafka_producer, SCENE_TOPIC, RESULT_TOPIC, get_kafka_consumer\n", "consumer = get_kafka_consumer(SCENE_TOPIC)" @@ -130,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -139,30 +200,41 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "consumer = get_kafka_consumer(RESULT_TOPIC)" + "consumer = get_kafka_consumer(SCENE_TOPIC)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Received scene parameters: []\n", - "Received scene parameters: []\n", - "Received scene parameters: []\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m message \u001b[38;5;129;01min\u001b[39;00m a:\n\u001b[0;32m 2\u001b[0m scene_parameters \u001b[38;5;241m=\u001b[39m message\u001b[38;5;241m.\u001b[39mvalue\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived scene parameters: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mscene_parameters\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\consumer\\group.py:1193\u001b[0m, in \u001b[0;36mKafkaConsumer.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnext_v1()\n\u001b[0;32m 1192\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnext_v2\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\consumer\\group.py:1201\u001b[0m, in \u001b[0;36mKafkaConsumer.next_v2\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1199\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_message_generator_v2()\n\u001b[0;32m 1200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_iterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1202\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m 1203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\consumer\\group.py:1116\u001b[0m, in \u001b[0;36mKafkaConsumer._message_generator_v2\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1114\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_message_generator_v2\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 1115\u001b[0m timeout_ms \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1000\u001b[39m \u001b[38;5;241m*\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consumer_timeout \u001b[38;5;241m-\u001b[39m time\u001b[38;5;241m.\u001b[39mtime())\n\u001b[1;32m-> 1116\u001b[0m record_map \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoll\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout_ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mupdate_offsets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 1117\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tp, records \u001b[38;5;129;01min\u001b[39;00m six\u001b[38;5;241m.\u001b[39miteritems(record_map):\n\u001b[0;32m 1118\u001b[0m \u001b[38;5;66;03m# Generators are stateful, and it is possible that the tp / records\u001b[39;00m\n\u001b[0;32m 1119\u001b[0m \u001b[38;5;66;03m# here may become stale during iteration -- i.e., we seek to a\u001b[39;00m\n\u001b[0;32m 1120\u001b[0m \u001b[38;5;66;03m# different offset, pause consumption, or lose assignment.\u001b[39;00m\n\u001b[0;32m 1121\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m record \u001b[38;5;129;01min\u001b[39;00m records:\n\u001b[0;32m 1122\u001b[0m \u001b[38;5;66;03m# is_fetchable(tp) should handle assignment changes and offset\u001b[39;00m\n\u001b[0;32m 1123\u001b[0m \u001b[38;5;66;03m# resets; for all other changes (e.g., seeks) we'll rely on the\u001b[39;00m\n\u001b[0;32m 1124\u001b[0m \u001b[38;5;66;03m# outer function destroying the existing iterator/generator\u001b[39;00m\n\u001b[0;32m 1125\u001b[0m \u001b[38;5;66;03m# via self._iterator = None\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\consumer\\group.py:655\u001b[0m, in \u001b[0;36mKafkaConsumer.poll\u001b[1;34m(self, timeout_ms, max_records, update_offsets)\u001b[0m\n\u001b[0;32m 653\u001b[0m remaining \u001b[38;5;241m=\u001b[39m timeout_ms\n\u001b[0;32m 654\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 655\u001b[0m records \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_poll_once\u001b[49m\u001b[43m(\u001b[49m\u001b[43mremaining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_records\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mupdate_offsets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mupdate_offsets\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 656\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m records:\n\u001b[0;32m 657\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m records\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\consumer\\group.py:702\u001b[0m, in \u001b[0;36mKafkaConsumer._poll_once\u001b[1;34m(self, timeout_ms, max_records, update_offsets)\u001b[0m\n\u001b[0;32m 699\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_client\u001b[38;5;241m.\u001b[39mpoll(timeout_ms\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 701\u001b[0m timeout_ms \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(timeout_ms, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_coordinator\u001b[38;5;241m.\u001b[39mtime_to_next_poll() \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m--> 702\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpoll\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout_ms\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout_ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 703\u001b[0m \u001b[38;5;66;03m# after the long poll, we should check whether the group needs to rebalance\u001b[39;00m\n\u001b[0;32m 704\u001b[0m \u001b[38;5;66;03m# prior to returning data so that the group can stabilize faster\u001b[39;00m\n\u001b[0;32m 705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_coordinator\u001b[38;5;241m.\u001b[39mneed_rejoin():\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\client_async.py:602\u001b[0m, in \u001b[0;36mKafkaClient.poll\u001b[1;34m(self, timeout_ms, future)\u001b[0m\n\u001b[0;32m 599\u001b[0m timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(timeout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mretry_backoff_ms\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 600\u001b[0m timeout \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(\u001b[38;5;241m0\u001b[39m, timeout) \u001b[38;5;66;03m# avoid negative timeouts\u001b[39;00m\n\u001b[1;32m--> 602\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_poll\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 604\u001b[0m \u001b[38;5;66;03m# called without the lock to avoid deadlock potential\u001b[39;00m\n\u001b[0;32m 605\u001b[0m \u001b[38;5;66;03m# if handlers need to acquire locks\u001b[39;00m\n\u001b[0;32m 606\u001b[0m responses\u001b[38;5;241m.\u001b[39mextend(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fire_pending_completed_requests())\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\kafka\\client_async.py:634\u001b[0m, in \u001b[0;36mKafkaClient._poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 631\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_register_send_sockets()\n\u001b[0;32m 633\u001b[0m start_select \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m--> 634\u001b[0m ready \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_selector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 635\u001b[0m end_select \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m 636\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sensors:\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\selectors.py:324\u001b[0m, in \u001b[0;36mSelectSelector.select\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 322\u001b[0m ready \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m 323\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 324\u001b[0m r, w, _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_select\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_readers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_writers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 325\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mInterruptedError\u001b[39;00m:\n\u001b[0;32m 326\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ready\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\selectors.py:315\u001b[0m, in \u001b[0;36mSelectSelector._select\u001b[1;34m(self, r, w, _, timeout)\u001b[0m\n\u001b[0;32m 314\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_select\u001b[39m(\u001b[38;5;28mself\u001b[39m, r, w, _, timeout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m--> 315\u001b[0m r, w, x \u001b[38;5;241m=\u001b[39m \u001b[43mselect\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mselect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 316\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r, w \u001b[38;5;241m+\u001b[39m x, []\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ - "for message in consumer:\n", + "for message in a:\n", " scene_parameters = message.value\n", " print(f\"Received scene parameters: {scene_parameters}\")" ] @@ -184,7 +256,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.0" } }, "nbformat": 4, diff --git a/scripts/backtesting/main.py b/scripts/backtesting/main.py index 3503bab..9916b01 100644 --- a/scripts/backtesting/main.py +++ b/scripts/backtesting/main.py @@ -1,7 +1,6 @@ import yfinance as yf import backtrader as bt import os, sys -# from util.user_input import get_user_input # Assuming this script is two levels deep in the project directory project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) if project_root not in sys.path: @@ -11,14 +10,15 @@ from scripts.backtesting.analyzers.metrics_analyzer import MetricsAnalyzer import scripts.backtesting.strategies as strategies +from scripts.backtesting.util.user_input import get_user_input def run_backtest(config): - initial_cash = config['initial_cash'] + initial_cash = 500 start_date = config['start_date'] end_date = config['end_date'] - ticker = config['ticker'] - indicator = config['indicator'] - + ticker = config['stock_symbol'] + indicator = config['indicator_symbol'] + print("The config is: ",ticker, start_date, end_date, indicator) # Fetch historical data try: data = yf.download(ticker, start=start_date, end=end_date) @@ -45,12 +45,12 @@ def run_backtest(config): elif indicator == 'RSI': from strategies.rsi_strategy import RSIStrategy cerebro.addstrategy(RSIStrategy) - elif indicator == 'Bollinger Bands': + elif indicator == 'BB': from strategies.bollinger_bands_strategy import BollingerBandsStrategy cerebro.addstrategy(BollingerBandsStrategy) else: print("Invalid indicator selected.") - return + yield "Invalid indicator selected." # Add analyzers cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, annualized=True) @@ -72,6 +72,22 @@ def run_backtest(config): print(f"Winning Trades: {metrics['winning_trades']}") print(f"Losing Trades: {metrics['losing_trades']}") -# if __name__ == "__main__": -# config = get_user_input() -# run_backtest(config) \ No newline at end of file + # return { + # 'initial_cash': initial_cash, + # 'final_value': cerebro.broker.getvalue(), + # 'sharpe_ratio': strat.analyzers.sharperatio.get_analysis(), + # 'return': metrics['return'], + # 'total_trades': metrics['trades'], + # 'winning_trades': metrics['winning_trades'], + # 'losing_trades': metrics['losing_trades'] + # } + +if __name__ == "__main__": + # config = { + # 'start_date': '2020-01-01', + # 'end_date': '2021-01-01', + # 'stock_symbol': 'NVDA', + # 'indicator_symbol': 'SMA' + # } + config = get_user_input() + run_backtest(config) \ No newline at end of file From 6367ac0a3dc52ebaff103f14817f96e3a27429a9 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 17:01:29 +0300 Subject: [PATCH 07/61] move: move frontend folder from updated to frontend --- frontend/.gitignore | 30 +- frontend/README.md | 74 +- frontend/index.html | 15 - frontend/package-lock.json | 19794 +++++++++++++--- frontend/package.json | 47 +- frontend/postcss.config.js | 6 - frontend/public/analyticshelp.svg | 3 - frontend/public/analyticslogout.svg | 4 - frontend/public/avatar-1@2x.png | Bin 6412 -> 0 bytes frontend/public/avatar@2x.png | Bin 16556 -> 0 bytes .../public/favicon.ico | Bin frontend/public/icon.svg | 3 - .../public/index.html | 0 frontend/public/logo.svg | 17 - .../public/logo192.png | Bin .../public/logo512.png | Bin .../public/manifest.json | 0 frontend/public/notification.svg | 4 - .../public/robots.txt | 0 frontend/public/setupaccount-setup.svg | 3 - frontend/public/short-arrow.svg | 3 - frontend/public/trailing-icon@2x.png | Bin 899 -> 0 bytes frontend/public/vite.svg | 1 - {updated_frontend => frontend}/src/App.js | 0 frontend/src/App.jsx | 66 - frontend/src/components/Auth0FormsLogin.jsx | 103 - frontend/src/components/Auth0FormsSignup.jsx | 90 - .../src/components/BacktestForm.js | 0 frontend/src/components/DesktopDefault.jsx | 44 - frontend/src/components/DesktopDefault1.jsx | 61 - frontend/src/components/InputFields.jsx | 148 - .../src/components/Login.js | 0 .../src/components/Navbar.js | 0 frontend/src/components/ResultData.jsx | 372 - .../src/components/Signup.js | 0 frontend/src/global.css | 14 - {updated_frontend => frontend}/src/index.css | 0 {updated_frontend => frontend}/src/index.js | 0 frontend/src/index.jsx | 20 - frontend/src/pages/Backtest.jsx | 79 - frontend/src/pages/Login.jsx | 11 - frontend/src/pages/Result.jsx | 47 - frontend/src/pages/Signup.jsx | 11 - frontend/src/reportWebVitals.jsx | 13 - frontend/tailwind.config.js | 75 +- frontend/vite.config.mjs | 10 - updated_frontend/.gitignore | 23 - updated_frontend/README.md | 70 - updated_frontend/package-lock.json | 18470 -------------- updated_frontend/package.json | 45 - updated_frontend/tailwind.config.js | 14 - 51 files changed, 16227 insertions(+), 23563 deletions(-) delete mode 100644 frontend/index.html delete mode 100644 frontend/postcss.config.js delete mode 100644 frontend/public/analyticshelp.svg delete mode 100644 frontend/public/analyticslogout.svg delete mode 100644 frontend/public/avatar-1@2x.png delete mode 100644 frontend/public/avatar@2x.png rename {updated_frontend => frontend}/public/favicon.ico (100%) delete mode 100644 frontend/public/icon.svg rename {updated_frontend => frontend}/public/index.html (100%) delete mode 100644 frontend/public/logo.svg rename {updated_frontend => frontend}/public/logo192.png (100%) rename {updated_frontend => frontend}/public/logo512.png (100%) rename {updated_frontend => frontend}/public/manifest.json (100%) delete mode 100644 frontend/public/notification.svg rename {updated_frontend => frontend}/public/robots.txt (100%) delete mode 100644 frontend/public/setupaccount-setup.svg delete mode 100644 frontend/public/short-arrow.svg delete mode 100644 frontend/public/trailing-icon@2x.png delete mode 100644 frontend/public/vite.svg rename {updated_frontend => frontend}/src/App.js (100%) delete mode 100644 frontend/src/App.jsx delete mode 100644 frontend/src/components/Auth0FormsLogin.jsx delete mode 100644 frontend/src/components/Auth0FormsSignup.jsx rename {updated_frontend => frontend}/src/components/BacktestForm.js (100%) delete mode 100644 frontend/src/components/DesktopDefault.jsx delete mode 100644 frontend/src/components/DesktopDefault1.jsx delete mode 100644 frontend/src/components/InputFields.jsx rename {updated_frontend => frontend}/src/components/Login.js (100%) rename {updated_frontend => frontend}/src/components/Navbar.js (100%) delete mode 100644 frontend/src/components/ResultData.jsx rename {updated_frontend => frontend}/src/components/Signup.js (100%) delete mode 100644 frontend/src/global.css rename {updated_frontend => frontend}/src/index.css (100%) rename {updated_frontend => frontend}/src/index.js (100%) delete mode 100644 frontend/src/index.jsx delete mode 100644 frontend/src/pages/Backtest.jsx delete mode 100644 frontend/src/pages/Login.jsx delete mode 100644 frontend/src/pages/Result.jsx delete mode 100644 frontend/src/pages/Signup.jsx delete mode 100644 frontend/src/reportWebVitals.jsx delete mode 100644 frontend/vite.config.mjs delete mode 100644 updated_frontend/.gitignore delete mode 100644 updated_frontend/README.md delete mode 100644 updated_frontend/package-lock.json delete mode 100644 updated_frontend/package.json delete mode 100644 updated_frontend/tailwind.config.js diff --git a/frontend/.gitignore b/frontend/.gitignore index 9d2f2a8..4d29575 100644 --- a/frontend/.gitignore +++ b/frontend/.gitignore @@ -1,35 +1,23 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. -# Dependencies +# dependencies /node_modules +/.pnp +.pnp.js -# Production +# testing +/coverage + +# production /build -# Misc +# misc .DS_Store .env.local .env.development.local .env.test.local .env.production.local -# Logs npm-debug.log* yarn-debug.log* yarn-error.log* - -# Runtime data -pids -*.pid -*.seed -*.log - -# Editor directories and files -.idea -.vscode/ -*.swp -*.swo -*.swn - -# OS-specific -Thumbs.db - diff --git a/frontend/README.md b/frontend/README.md index ad1da1d..58beeac 100644 --- a/frontend/README.md +++ b/frontend/README.md @@ -1,10 +1,70 @@ +# Getting Started with Create React App - # Backtest +This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app). - Note: Please ensure you have installed nodejs +## Available Scripts - To preview and run the project on your device: - 1) Open project folder in Visual Studio Code - 2) In the terminal, run `npm install` - 3) Run `npm start` to view project in browser - \ No newline at end of file +In the project directory, you can run: + +### `npm start` + +Runs the app in the development mode.\ +Open [http://localhost:3000](http://localhost:3000) to view it in your browser. + +The page will reload when you make changes.\ +You may also see any lint errors in the console. + +### `npm test` + +Launches the test runner in the interactive watch mode.\ +See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information. + +### `npm run build` + +Builds the app for production to the `build` folder.\ +It correctly bundles React in production mode and optimizes the build for the best performance. + +The build is minified and the filenames include the hashes.\ +Your app is ready to be deployed! + +See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information. + +### `npm run eject` + +**Note: this is a one-way operation. Once you `eject`, you can't go back!** + +If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. + +Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own. + +You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it. + +## Learn More + +You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started). + +To learn React, check out the [React documentation](https://reactjs.org/). + +### Code Splitting + +This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting) + +### Analyzing the Bundle Size + +This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size) + +### Making a Progressive Web App + +This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app) + +### Advanced Configuration + +This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration) + +### Deployment + +This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment) + +### `npm run build` fails to minify + +This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify) diff --git a/frontend/index.html b/frontend/index.html deleted file mode 100644 index aef74be..0000000 --- a/frontend/index.html +++ /dev/null @@ -1,15 +0,0 @@ - - - - - - - - - Backtest - - -
- - - diff --git a/frontend/package-lock.json b/frontend/package-lock.json index e3d4b37..679ed77 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1,35 +1,27 @@ { - "name": "backtest", + "name": "frontend", "version": "0.1.0", "lockfileVersion": 3, "requires": true, "packages": { "": { - "name": "backtest", + "name": "frontend", "version": "0.1.0", "dependencies": { - "@testing-library/jest-dom": "^5.16.5", + "@testing-library/jest-dom": "^5.17.0", "@testing-library/react": "^13.4.0", "@testing-library/user-event": "^13.5.0", - "@types/jest": "^29.5.0", - "@types/react": "^18.0.28", - "@types/react-dom": "^18.0.11", - "prop-types": "^15.8.1", - "react": "^18.2.0", - "react-dom": "^18.2.0", - "react-router-dom": "^6.2.1", - "web-vitals": "^2.1.4" + "formik": "^2.4.6", + "react": "^18.3.1", + "react-dom": "^18.3.1", + "react-router-dom": "^6.23.1", + "react-scripts": "5.0.1", + "web-vitals": "^2.1.4", + "yup": "^1.4.0" }, "devDependencies": { - "@vitejs/plugin-react": "^4.2.1", - 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a/updated_frontend/public/index.html b/frontend/public/index.html similarity index 100% rename from updated_frontend/public/index.html rename to frontend/public/index.html diff --git a/frontend/public/logo.svg b/frontend/public/logo.svg deleted file mode 100644 index 82358e6..0000000 --- a/frontend/public/logo.svg +++ /dev/null @@ -1,17 +0,0 @@ - - - - - - - - - - - - - - - - - diff --git a/updated_frontend/public/logo192.png b/frontend/public/logo192.png similarity index 100% rename from updated_frontend/public/logo192.png rename to frontend/public/logo192.png diff --git a/updated_frontend/public/logo512.png b/frontend/public/logo512.png similarity index 100% rename from updated_frontend/public/logo512.png rename to frontend/public/logo512.png diff --git a/updated_frontend/public/manifest.json b/frontend/public/manifest.json similarity index 100% rename from updated_frontend/public/manifest.json rename to frontend/public/manifest.json diff --git a/frontend/public/notification.svg b/frontend/public/notification.svg deleted file mode 100644 index 6a02278..0000000 --- a/frontend/public/notification.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/updated_frontend/public/robots.txt b/frontend/public/robots.txt similarity index 100% rename from updated_frontend/public/robots.txt rename to frontend/public/robots.txt diff --git a/frontend/public/setupaccount-setup.svg b/frontend/public/setupaccount-setup.svg deleted file mode 100644 index 23ac56e..0000000 --- a/frontend/public/setupaccount-setup.svg +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/frontend/public/short-arrow.svg b/frontend/public/short-arrow.svg deleted file mode 100644 index 1c665f5..0000000 --- a/frontend/public/short-arrow.svg +++ /dev/null @@ -1,3 +0,0 @@ - - - diff --git a/frontend/public/trailing-icon@2x.png b/frontend/public/trailing-icon@2x.png deleted file mode 100644 index 07fc03c31e02d5f6b9efa4e682e35117415de040..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 899 zcmV-}1AP36P)Tq+-|$~a+iI32?L$k-^`oKUiR+Z+kNlt+XXV2OeT}b{5vjSHY|$V5VaGIHJ~l>2+dt370$*M46|V|;Ht-;!yD&kzaK@(g zuE;$UfzRtrk>42i=R=&u$0RtAEAD%xS*|i*6ZUMwAR7g3D)Su0h=oCa(cPop57aZ? zMD8Fy+l#Kxm+)pwb>c^Qu))#Le#x1el&$>ihDpI^E(=_TN`tEncmO*C)$YNTYT0K^ z-8+Dt&9KpGpn&ICwbx;1S+$Q~hwD<;H(Cu9Am3Fx7KkA_#!?HpQ(Z#?MmRq#_#dGG zsk!{JYNzh>dT3YBVv<*9*3Z*Q*TvEW%g`JX) z+5ozN)s>jFbUuHH$wr*GJ|S(Stv#jmU9}Q(dB25t1#6BpMq6+ z@}}?SGhnbqO057Lgiwo#rPDo2;_fz9ha8Y-R1WHOnI Z=MVOqpUz^I9u)up002ovPDHLkV1f)jm`eZv diff --git a/frontend/public/vite.svg b/frontend/public/vite.svg deleted file mode 100644 index e7b8dfb..0000000 --- a/frontend/public/vite.svg +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/updated_frontend/src/App.js b/frontend/src/App.js similarity index 100% rename from updated_frontend/src/App.js rename to frontend/src/App.js diff --git a/frontend/src/App.jsx b/frontend/src/App.jsx deleted file mode 100644 index 925be93..0000000 --- a/frontend/src/App.jsx +++ /dev/null @@ -1,66 +0,0 @@ -import { useEffect } from "react"; -import { - Routes, - Route, - useNavigationType, - useLocation, -} from "react-router-dom"; -import Login from "./pages/Login"; -import Backtest from "./pages/Backtest"; -import Signup from "./pages/Signup"; -import Result from "./pages/Result"; - -function App() { - const action = useNavigationType(); - const location = useLocation(); - const pathname = location.pathname; - - useEffect(() => { - if (action !== "POP") { - window.scrollTo(0, 0); - } - }, [action, pathname]); - - useEffect(() => { - let title = ""; - let metaDescription = ""; - - switch (pathname) { - case "/": - title = ""; - metaDescription = ""; - break; - case "/backtest": - title = ""; - metaDescription = ""; - break; - case "/signup": - title = ""; - metaDescription = ""; - break; - } - - if (title) { - document.title = title; - } - - if (metaDescription) { - const metaDescriptionTag = document.querySelector( - 'head > meta[name="description"]' - ); - if (metaDescriptionTag) { - metaDescriptionTag.content = metaDescription; - } - } - }, [pathname]); - - return ( - - } /> - } /> - } /> - } /> - - ); -} -export default App; diff --git a/frontend/src/components/Auth0FormsLogin.jsx b/frontend/src/components/Auth0FormsLogin.jsx deleted file mode 100644 index d25648d..0000000 --- a/frontend/src/components/Auth0FormsLogin.jsx +++ /dev/null @@ -1,103 +0,0 @@ -import PropTypes from "prop-types"; - -const Auth0FormsLogin = ({ className = "" }) => { - return ( -
-

-

Welcome To

-

Crypto Back Testing

-

Platform

-

- -
- Input Your Email and Password to Continue -
-
-
- Assistive text -
-
-
-
-
-
-
-
- Email address -
-
-
- -
-
-
-
-
- -
-
-
-
-
-
-
-
-
-
-
- -
- Edit -
-
-
-
-
-
-
- Assistive text -
-
-
-
-
- Forgot password? -
-
- -
-
- Don’t have an account? -
-
- Sign up -
-
-
- ); -}; - -Auth0FormsLogin.propTypes = { - className: PropTypes.string, -}; - -export default Auth0FormsLogin; -//1-103 working \ No newline at end of file diff --git a/frontend/src/components/Auth0FormsSignup.jsx b/frontend/src/components/Auth0FormsSignup.jsx deleted file mode 100644 index b661384..0000000 --- a/frontend/src/components/Auth0FormsSignup.jsx +++ /dev/null @@ -1,90 +0,0 @@ -import PropTypes from "prop-types"; - -const Auth0FormsSignup = ({ className = "" }) => { - return ( -
-

-

Welcome To

-

Crypto Back Testing

-

Platform

-

- -
- It Only Takes a Second to Have Your Account Ready! -
-
- -
-
-
- Label -
-
-
-
-
-
- Assistive text -
-
-
-
-
- -
-
-
-
-
-
-
- max.mustermann@company.com -
-
-
-
-
- -
- Edit -
-
-
-
-
- -
-
- Already have an account? -
-
- Login -
-
-
- ); -}; - -Auth0FormsSignup.propTypes = { - className: PropTypes.string, -}; - -export default Auth0FormsSignup; diff --git a/updated_frontend/src/components/BacktestForm.js b/frontend/src/components/BacktestForm.js similarity index 100% rename from updated_frontend/src/components/BacktestForm.js rename to frontend/src/components/BacktestForm.js diff --git a/frontend/src/components/DesktopDefault.jsx b/frontend/src/components/DesktopDefault.jsx deleted file mode 100644 index 8bb24d8..0000000 --- a/frontend/src/components/DesktopDefault.jsx +++ /dev/null @@ -1,44 +0,0 @@ -import PropTypes from "prop-types"; - -const DesktopDefault = ({ className = "", default1, addressValue }) => { - return ( -
-
- {default1} -
-
-
- -
-
-
-
- {addressValue} -
-
-
-
- -
-
-
- ); -}; - -DesktopDefault.propTypes = { - className: PropTypes.string, - default1: PropTypes.string, - addressValue: PropTypes.string, -}; - -export default DesktopDefault; diff --git a/frontend/src/components/DesktopDefault1.jsx b/frontend/src/components/DesktopDefault1.jsx deleted file mode 100644 index 96f3ac8..0000000 --- a/frontend/src/components/DesktopDefault1.jsx +++ /dev/null @@ -1,61 +0,0 @@ -import { useMemo } from "react"; -import PropTypes from "prop-types"; - -const DesktopDefault1 = ({ - className = "", - default1, - contentPlaceholder, - propAlignSelf, - propFlex, -}) => { - const desktopDefaultStyle = useMemo(() => { - return { - alignSelf: propAlignSelf, - flex: propFlex, - }; - }, [propAlignSelf, propFlex]); - - return ( -
-
- {default1} -
-
-
- -
- -
- -
-
-
- ); -}; - -DesktopDefault1.propTypes = { - className: PropTypes.string, - default1: PropTypes.string, - contentPlaceholder: PropTypes.string, - - /** Style props */ - propAlignSelf: PropTypes.any, - propFlex: PropTypes.any, -}; - -export default DesktopDefault1; diff --git a/frontend/src/components/InputFields.jsx b/frontend/src/components/InputFields.jsx deleted file mode 100644 index 34e82c9..0000000 --- a/frontend/src/components/InputFields.jsx +++ /dev/null @@ -1,148 +0,0 @@ -import DesktopDefault1 from "./DesktopDefault1"; -import DesktopDefault from "./DesktopDefault"; -import PropTypes from "prop-types"; - -const InputFields = ({ className = "" }) => { - return ( -
-
-
- -
-
- - -
-
- - -
-
-
-
-
-
-
-
-

- Choose Your Stock -

-
{`Choose The Stock Name `}
-
-
-
- Stock -
-
-
- -
-
- -
- -
- -
-
-
-
-
-
- - Metric - -
- {" "} - Choose the Metric -
-
- -
-
-
-

- Choose Start and End Date -

-
- - -
-
-
-

- Portfolio -

-
-
-
- -
-
-
-
-
- ); -}; - -InputFields.propTypes = { - className: PropTypes.string, -}; - -export default InputFields; diff --git a/updated_frontend/src/components/Login.js b/frontend/src/components/Login.js similarity index 100% rename from updated_frontend/src/components/Login.js rename to frontend/src/components/Login.js diff --git a/updated_frontend/src/components/Navbar.js b/frontend/src/components/Navbar.js similarity index 100% rename from updated_frontend/src/components/Navbar.js rename to frontend/src/components/Navbar.js diff --git a/frontend/src/components/ResultData.jsx b/frontend/src/components/ResultData.jsx deleted file mode 100644 index c507209..0000000 --- a/frontend/src/components/ResultData.jsx +++ /dev/null @@ -1,372 +0,0 @@ -// import PropTypes from "prop-types"; - -// const ResultData = ({ className = "" }) => { -// return ( -//
-//
-//
-//
-// -//
-//
-// {/* User profile and notification */} -//
-//
-//
-//
-//
-//
-//

-// Backtest Results -//

-//
-//
-//
-//
-//

-// Number of Trades -//

-//
-// 100 -//
-//
-//
-//

-// Winning Trades -//

-//
-// 70 -//
-//
- -//
-//

-// Losing Trades -//

-//
-// 30 -//
-//
-//
-//

-// Max Drawdown -//

-//
-// 15% -//
-//
-//
-//

-// Sharpe Ratio -//

-//
-// 0.8 -//
-//
-//
-//
-//
-//
-//
-//
-// ); -// }; - -// ResultData.propTypes = { -// className: PropTypes.string, -// }; - -// export default ResultData; - -// import PropTypes from "prop-types"; - -// const ResultData = ({ className = "" }) => { -// return ( -//
-//
-//
-//
-// -//
-//
-// {/* User profile and notification */} -//
-//
-//
-//
-//
-//
-//

-// Backtest Results -//

-//
-//
-//
-//
-//

-// Number of Trades -//

-//
-// 100 -//
-//
-//
-//

-// Winning Trades -//

-//
-//
-//
-//
-//

-// Losing Trades -//

-//
-// 30 -//
-//
-//
-//

-// Max Drawdown -//

-//
-// 15% -//
-//
-//
-//

-// Sharpe Ratio -//

-//
-// 0.8 -//
-//
-//
-//
-//
-//
-//
-// ); -// }; - -// ResultData.propTypes = { -// className: PropTypes.string, -// }; - -// export default ResultData; -//93-180 working - -// import PropTypes from "prop-types"; -// const ResultData = ({ className = "" }) => { -// return ( -//
-//
-//
-//
-// -//
-//
-// {/* User profile and notification */} -//
-//
-//
-//
-//
-//
-//

-// Backtest Results -//

-//
-//
-//
-//
-//

-// Number of Trades -//

-//
-// 100 -//
-//
-//
-//

-// Winning Trades -//

-//
-// 70 -//
-//
-//
-//

-// Losing Trades -//

-//
-// 30 -//
-//
-//
-//

-// Max Drawdown -//

-//
-// 15% -//
-//
-//
-//

-// Sharpe Ratio -//

-//
-// 0.8 -//
-//
-//
-//
-//
-//
-//
-//
- -// ); -// }; - -// ResultData.propTypes = { -// className: PropTypes.string, -// }; - -// export default ResultData; -// 181-273 updated - -import PropTypes from 'prop-types'; - -const ResultData = ({ className = "" }) => { - return ( -
-
-
- -
- {/* User profile and notification */} -
-
-
-
-
-
-

- Backtest Results -

-
-
-
-

- Number of Trades -

-
-
- 100 -
-
-
-
-

- Winning Trades -

-
-
- 70 -
-
-
-
-

- Losing Trades -

-
-
- 30 -
-
-
-
-

- Max Drawdown -

-
-
- 15% -
-
-
-
-

- Sharpe Ratio -

-
-
- 0.8 -
-
-
-
-
-
-
-
- ); -}; - -ResultData.propTypes = { - className: PropTypes.string, -}; - -export default ResultData; \ No newline at end of file diff --git a/updated_frontend/src/components/Signup.js b/frontend/src/components/Signup.js similarity index 100% rename from updated_frontend/src/components/Signup.js rename to frontend/src/components/Signup.js diff --git a/frontend/src/global.css b/frontend/src/global.css deleted file mode 100644 index 4f2f1d4..0000000 --- a/frontend/src/global.css +++ /dev/null @@ -1,14 +0,0 @@ -@import url("https://fonts.googleapis.com/css2?family=PoetsenOne:wght@400&display=swap"); -@import url("https://fonts.googleapis.com/css2?family=Roboto:wght@300;400;500;600&display=swap"); -@import url("https://fonts.googleapis.com/css2?family=Inter:wght@500&display=swap"); -@tailwind base; -@tailwind components; -@tailwind utilities; -body { - @apply leading-[normal] m-0; -} -*, -::before, -::after { - border-width: 0; -} diff --git a/updated_frontend/src/index.css b/frontend/src/index.css similarity index 100% rename from updated_frontend/src/index.css rename to frontend/src/index.css diff --git a/updated_frontend/src/index.js b/frontend/src/index.js similarity index 100% rename from updated_frontend/src/index.js rename to frontend/src/index.js diff --git a/frontend/src/index.jsx b/frontend/src/index.jsx deleted file mode 100644 index 76e3112..0000000 --- a/frontend/src/index.jsx +++ /dev/null @@ -1,20 +0,0 @@ -import React from "react"; -import { createRoot } from "react-dom/client"; -import App from "./App"; -import reportWebVitals from "./reportWebVitals"; -import { BrowserRouter } from "react-router-dom"; -import "./global.css"; - -const container = document.getElementById("root"); -const root = createRoot(container); - -root.render( - - - -); - -// If you want to start measuring performance in your app, pass a function -// to log results (for example: reportWebVitals(console.log)) -// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals -reportWebVitals(); diff --git a/frontend/src/pages/Backtest.jsx b/frontend/src/pages/Backtest.jsx deleted file mode 100644 index e123f79..0000000 --- a/frontend/src/pages/Backtest.jsx +++ /dev/null @@ -1,79 +0,0 @@ -import InputFields from "../components/InputFields"; - -const Backtest = () => { - return ( -
-
-
-
- -
-
-
-
- -
- Backtesting -
-
-
-
-
-
-
-
- -
- Help -
-
-
-
-
- -
- Logout -
-
-
-
-
-
-
- -
-
- -
-
-
-
-
- ); -}; - -export default Backtest; diff --git a/frontend/src/pages/Login.jsx b/frontend/src/pages/Login.jsx deleted file mode 100644 index f31606d..0000000 --- a/frontend/src/pages/Login.jsx +++ /dev/null @@ -1,11 +0,0 @@ -import Auth0FormsLogin from "../components/Auth0FormsLogin"; - -const Login = () => { - return ( -
- -
- ); -}; - -export default Login; diff --git a/frontend/src/pages/Result.jsx b/frontend/src/pages/Result.jsx deleted file mode 100644 index cfcffb0..0000000 --- a/frontend/src/pages/Result.jsx +++ /dev/null @@ -1,47 +0,0 @@ -// import ResultData from "../components/ResultData"; - -// const Result = () => { -// return ( -//
-//
-// {/* Sidebar content */} -//
-//
-//
-// -//
-//
-//
-// ); -// }; - -// export default Result; - -import ResultData from "../components/ResultData"; - -const Result = () => { - return ( -
-
-
-
- -
- {/* Sidebar content */} -
- {/* Sidebar content */} -
-
-
- -
-
-
- ); -}; - -export default Result; \ No newline at end of file diff --git a/frontend/src/pages/Signup.jsx b/frontend/src/pages/Signup.jsx deleted file mode 100644 index 0b6bdf2..0000000 --- a/frontend/src/pages/Signup.jsx +++ /dev/null @@ -1,11 +0,0 @@ -import Auth0FormsSignup from "../components/Auth0FormsSignup"; - -const Signup = () => { - return ( -
- -
- ); -}; - -export default Signup; diff --git a/frontend/src/reportWebVitals.jsx b/frontend/src/reportWebVitals.jsx deleted file mode 100644 index 9ecd33f..0000000 --- a/frontend/src/reportWebVitals.jsx +++ /dev/null @@ -1,13 +0,0 @@ -const reportWebVitals = (onPerfEntry) => { - if (onPerfEntry && onPerfEntry instanceof Function) { - import("web-vitals").then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => { - getCLS(onPerfEntry); - getFID(onPerfEntry); - getFCP(onPerfEntry); - getLCP(onPerfEntry); - getTTFB(onPerfEntry); - }); - } -}; - -export default reportWebVitals; diff --git a/frontend/tailwind.config.js b/frontend/tailwind.config.js index 86c8ef8..413c7ae 100644 --- a/frontend/tailwind.config.js +++ b/frontend/tailwind.config.js @@ -1,69 +1,14 @@ /** @type {import('tailwindcss').Config} */ module.exports = { - content: ["./src/**/*.{js,jsx,ts,tsx}"], + content: [ + "./src/**/*.{js,jsx,ts,tsx}", + ], theme: { - extend: { - colors: { - "white-background-primary": "#fff", - "cta-color": "#635dff", - "text-primary": "#2d333a", - mediumslateblue: "#7d78ff", - link: "#3f59e4", - "primary-500": "#6200ee", - border: "#c2c8d0", - textsecondary: "#6f7780", - "primary-color": "#007bad", - "light-theme-background-bg": "#fbfcfe", - "color-variants-purple-5": "#613de4", - "wireframe-100": "#f2f4f7", - "light-theme-border-and-divider-border-and-divider": "#cdd5e9", - lightgray: "#d4d4d4", - "light-theme-text-primary-body": "#111112", - "light-theme-background-surfaces": "#f5f7fc", - }, - spacing: {}, - fontFamily: { - "subtitle-1": "Roboto", - "label-base-16-24": "Inter", - poetsenone: "PoetsenOne", - }, - borderRadius: { - "31xl": "50px", - }, - }, - fontSize: { - base: "16px", - xs: "12px", - "5xl": "24px", - lgi: "19px", - "2xs": "11px", - "13xl": "32px", - "7xl": "26px", - sm: "14px", - inherit: "inherit", - }, - screens: { - lg: { - max: "1200px", - }, - mq900: { - raw: "screen and (max-width: 900px)", - }, - mq750: { - raw: "screen and (max-width: 750px)", - }, - mq675: { - raw: "screen and (max-width: 675px)", - }, - mq525: { - raw: "screen and (max-width: 525px)", - }, - mq450: { - raw: "screen and (max-width: 450px)", - }, - }, + extend: {}, }, - corePlugins: { - preflight: false, - }, -}; + plugins: [ + require('daisyui'), + + ], +} + diff --git a/frontend/vite.config.mjs b/frontend/vite.config.mjs deleted file mode 100644 index 06b326c..0000000 --- a/frontend/vite.config.mjs +++ /dev/null @@ -1,10 +0,0 @@ -import { defineConfig } from "vite"; -import react from "@vitejs/plugin-react"; - -// https://vitejs.dev/config/ -export default defineConfig({ - build: { - outDir: "build", - }, - plugins: [react()], -}); diff --git a/updated_frontend/.gitignore b/updated_frontend/.gitignore deleted file mode 100644 index 4d29575..0000000 --- a/updated_frontend/.gitignore +++ /dev/null @@ -1,23 +0,0 @@ -# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. - -# dependencies -/node_modules -/.pnp -.pnp.js - -# testing -/coverage - -# production -/build - -# misc -.DS_Store -.env.local -.env.development.local -.env.test.local -.env.production.local - -npm-debug.log* -yarn-debug.log* -yarn-error.log* diff --git a/updated_frontend/README.md b/updated_frontend/README.md deleted file mode 100644 index 58beeac..0000000 --- a/updated_frontend/README.md +++ /dev/null @@ -1,70 +0,0 @@ -# Getting Started with Create React App - -This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app). - -## Available Scripts - -In the project directory, you can run: - -### `npm start` - -Runs the app in the development mode.\ -Open [http://localhost:3000](http://localhost:3000) to view it in your browser. - -The page will reload when you make changes.\ -You may also see any lint errors in the console. - -### `npm test` - -Launches the test runner in the interactive watch mode.\ -See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information. - -### `npm run build` - -Builds the app for production to the `build` folder.\ -It correctly bundles React in production mode and optimizes the build for the best performance. - -The build is minified and the filenames include the hashes.\ -Your app is ready to be deployed! - -See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information. - -### `npm run eject` - -**Note: this is a one-way operation. Once you `eject`, you can't go back!** - -If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project. - -Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own. - -You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it. - -## Learn More - -You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started). - -To learn React, check out the [React documentation](https://reactjs.org/). - -### Code Splitting - -This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting) - -### Analyzing the Bundle Size - -This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size) - -### Making a Progressive Web App - -This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app) - -### Advanced Configuration - -This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration) - -### Deployment - -This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment) - -### `npm run build` fails to minify - -This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify) diff --git a/updated_frontend/package-lock.json b/updated_frontend/package-lock.json deleted file mode 100644 index 679ed77..0000000 --- a/updated_frontend/package-lock.json +++ /dev/null @@ -1,18470 +0,0 @@ -{ - "name": "frontend", - "version": "0.1.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "frontend", - "version": "0.1.0", - "dependencies": { - "@testing-library/jest-dom": "^5.17.0", - "@testing-library/react": "^13.4.0", - "@testing-library/user-event": "^13.5.0", - "formik": "^2.4.6", - "react": "^18.3.1", - "react-dom": "^18.3.1", - 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"integrity": "sha512-RAH822pAdBgcNMAfWnCBU3CFZcfZ/i1eZjwFU/dsLKumyuuP3niueg2UAukXYF0E2AAoc82ZSSf9J0WQBinzHA==", - "engines": { - "node": ">=12.20" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - } - } -} diff --git a/updated_frontend/package.json b/updated_frontend/package.json deleted file mode 100644 index bef948f..0000000 --- a/updated_frontend/package.json +++ /dev/null @@ -1,45 +0,0 @@ -{ - "name": "frontend", - "version": "0.1.0", - "private": true, - "dependencies": { - "@testing-library/jest-dom": "^5.17.0", - "@testing-library/react": "^13.4.0", - "@testing-library/user-event": "^13.5.0", - "formik": "^2.4.6", - "react": "^18.3.1", - "react-dom": "^18.3.1", - "react-router-dom": "^6.23.1", - "react-scripts": "5.0.1", - "web-vitals": "^2.1.4", - "yup": "^1.4.0" - }, - "scripts": { - "start": "react-scripts start", - "build": "react-scripts build", - "test": "react-scripts test", - "eject": "react-scripts eject" - }, - "eslintConfig": { - "extends": [ - "react-app", - "react-app/jest" - ] - }, - "browserslist": { - "production": [ - ">0.2%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 1 chrome version", - "last 1 firefox version", - "last 1 safari version" - ] - }, - "devDependencies": { - "daisyui": "^4.12.2", - "tailwindcss": "^3.4.4" - } -} diff --git a/updated_frontend/tailwind.config.js b/updated_frontend/tailwind.config.js deleted file mode 100644 index 413c7ae..0000000 --- a/updated_frontend/tailwind.config.js +++ /dev/null @@ -1,14 +0,0 @@ -/** @type {import('tailwindcss').Config} */ -module.exports = { - content: [ - "./src/**/*.{js,jsx,ts,tsx}", - ], - theme: { - extend: {}, - }, - plugins: [ - require('daisyui'), - - ], -} - From 91ac90b3c9ba8ecb8d992c556dc36962ab325cfd Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 18:33:38 +0300 Subject: [PATCH 08/61] feat:update consumer in backtest implementation --- backend/main.py | 53 ++++++++++++++++++++++++++++++++++--------------- 1 file changed, 37 insertions(+), 16 deletions(-) diff --git a/backend/main.py b/backend/main.py index 2994a41..25e476b 100644 --- a/backend/main.py +++ b/backend/main.py @@ -6,6 +6,7 @@ import pandas as pd import threading import logging +import time logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) @@ -91,11 +92,13 @@ def create_scene(scene: schemas.SceneCreate, db: Session = Depends(get_db)): # Send scene parameters to Kafka scene_parameters = { + 'scene_id': db_scene.id, 'period': db_scene.period, + 'initial_cash': 500, 'indicator_name': db_scene.indicator.name, - 'indicator_symbol': db_scene.indicator.symbol, + 'indicator': db_scene.indicator.symbol, 'stock_name': db_scene.stock.name, - 'stock_symbol': db_scene.stock.symbol, + 'ticker': db_scene.stock.symbol, 'start_date': db_scene.start_date.strftime('%Y-%m-%d'), 'end_date': db_scene.end_date.strftime('%Y-%m-%d') } @@ -134,6 +137,9 @@ def read_scene(scene_id: int, db: Session = Depends(get_db)): @app.get('/scenes/', response_model=List[schemas.Scene]) def read_scenes(skip: int = 0, limit: int = 10, db: Session = Depends(get_db)): scenes = db.query(models.Scene).offset(skip).limit(limit).all() + for scene in scenes: + for backtest in scene.backtests: + logger.info(f"Backtest Result: {backtest.__dict__}") return scenes @app.post('/backtests/{scene_id}', response_model=List[schemas.BacktestResult]) @@ -195,18 +201,33 @@ def fetch_existing_backtest(scene_parameters): return None def consume_scene_parameters(): + db = next(get_db()) consumer = get_kafka_consumer(SCENE_TOPIC) - for message in consumer: - scene_parameters = message.value - logger.info(f"Received scene parameters: {scene_parameters}") - existing_results = fetch_existing_backtest(scene_parameters) - if existing_results: - logger.info(f"Using existing backtest results for parameters: {scene_parameters}") - continue - # Run the backtest if no existing results - # df = fetch_data(scene_parameters['start_date'], scene_parameters['end_date']) - metrics = run_backtest(scene_parameters) - producer = get_kafka_producer() - logger.info(f"Sending backtest results: {metrics}") - producer.send(RESULT_TOPIC, metrics) - producer.flush() \ No newline at end of file + producer = get_kafka_producer() + while True: + try: + for message in consumer: + scene_parameters = message.value + logger.info(f"Received scene parameters: {scene_parameters}") + existing_results = fetch_existing_backtest(scene_parameters) + if existing_results: + logger.info(f"Using existing backtest results for parameters: {scene_parameters}") + continue + # Run the backtest if no existing results + # df = fetch_data(scene_parameters['start_date'], scene_parameters['end_date']) + metrics = run_backtest(scene_parameters) + logger.info(f"Sending backtest results: {metrics}") + # Attach backtest results to scene + scene_id = scene_parameters['scene_id'] + + db_backtest_result = models.BacktestResult(scene_id=scene_id, **metrics) + db.add(db_backtest_result) + db.commit() + db.refresh(db_backtest_result) + + producer.send(RESULT_TOPIC, metrics) + producer.flush() + logger.info("Backtest results sent to Kafka") + except Exception as e: + logger.error(f"Error in consumer loop: {e}") + time.sleep(5) # Sleep for a while before retrying \ No newline at end of file From adb0092d4e4e086eb7ced22091db808210ba4b45 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 18:34:07 +0300 Subject: [PATCH 09/61] fix: update backtest results for backtest --- backend/models.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/backend/models.py b/backend/models.py index 2b2b000..1490c62 100644 --- a/backend/models.py +++ b/backend/models.py @@ -41,14 +41,15 @@ class Scene(Base): class BacktestResult(Base): __tablename__ = 'backtest_results' + # __table_args__ = (UniqueConstraint('scene_id', 'indicator_id', 'stock_id', name='_backtest_uc'),) id = Column(Integer, primary_key=True, index=True) scene_id = Column(Integer, ForeignKey('scenes.id')) - gross_profit = Column(Float, nullable=False) - net_profit = Column(Float, nullable=False) - number_of_trades = Column(Integer, nullable=False) + initial_cash = Column(Float, nullable=False) + final_value = Column(Float, nullable=False) + percentage_return = Column(Float) + total_trades = Column(Integer) winning_trades = Column(Integer) losing_trades = Column(Integer) - max_drawdown = Column(Float) sharpe_ratio = Column(Float) created_at = Column(TIMESTAMP, server_default=text('CURRENT_TIMESTAMP')) scene = relationship('Scene', back_populates='backtests') From 2d6cdb02564924c988f386719003e62a94170af7 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 18:34:40 +0300 Subject: [PATCH 10/61] fix: update backtest results schema --- backend/schemas.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/backend/schemas.py b/backend/schemas.py index 481bf5f..174ffc6 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -76,13 +76,13 @@ class Config: class BacktestResultBase(BaseModel): scene_id: int - gross_profit: float - net_profit: float - number_of_trades: int - winning_trades: Optional[int] = None - losing_trades: Optional[int] = None - max_drawdown: Optional[float] = None - sharpe_ratio: Optional[float] = None + initial_cash: float + final_value: float + percentage_return: float + total_trades: int + winning_trades: int + losing_trades: int + sharpe_ratio: float class BacktestResultCreate(BacktestResultBase): pass From f3505eee9eea75c0ce9c6a5c6739f8cc8d66834d Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 18:34:55 +0300 Subject: [PATCH 11/61] test: playground updates --- notebooks/backtesting.ipynb | 129 +++++++++++++++++++++++++++++++++--- 1 file changed, 119 insertions(+), 10 deletions(-) diff --git a/notebooks/backtesting.ipynb b/notebooks/backtesting.ipynb index 8c59331..8ce8e8d 100644 --- a/notebooks/backtesting.ipynb +++ b/notebooks/backtesting.ipynb @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -363,9 +363,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(36, 15, datetime.date(2023, 9, 1), datetime.date(2024, 4, 1), 13, 3)\n" + ] + } + ], "source": [ "# Reflect the 'scenes' table\n", "scenes = Table('scenes', metadata, autoload_with=engine)\n", @@ -381,23 +389,50 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sqlalchemy import create_engine, text, inspect\n", "from sqlalchemy.orm import sessionmaker\n", - "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ "# Create an engine and connect to the database\n", "Session = sessionmaker(bind=engine)\n", - "session = Session()\n", - "# Close the session\n", + "session = Session()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'session' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msession\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n", + "\u001b[1;31mNameError\u001b[0m: name 'session' is not defined" + ] + } + ], + "source": [ "session.close()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -417,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -826,6 +861,80 @@ "}\n", "run_backtest(config)" ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[*********************100%%**********************] 1 of 1 completed\n", + "\n", + "1 Failed download:\n", + "['FB']: YFTzMissingError('$%ticker%: possibly delisted; No timezone found')\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
OpenHighLowCloseAdj CloseVolume
Date
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Open, High, Low, Close, Adj Close, Volume]\n", + "Index: []" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import yfinance as yf\n", + "yf.download(\"FB\", start=\"2023-01-01\", end=\"2024-01-01\")" + ] } ], "metadata": { From 571f235f05726320096cc2b4fd6e41dc00598e88 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 18:35:23 +0300 Subject: [PATCH 12/61] feat: add return dictionary to backtest results --- scripts/backtesting/main.py | 68 ++++++++++++++++++------------------- 1 file changed, 33 insertions(+), 35 deletions(-) diff --git a/scripts/backtesting/main.py b/scripts/backtesting/main.py index 9916b01..ae24e62 100644 --- a/scripts/backtesting/main.py +++ b/scripts/backtesting/main.py @@ -1,24 +1,21 @@ import yfinance as yf import backtrader as bt import os, sys -# Assuming this script is two levels deep in the project directory + project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) if project_root not in sys.path: sys.path.append(project_root) -print("The project root is: ", os.getcwd()) - -from scripts.backtesting.analyzers.metrics_analyzer import MetricsAnalyzer -import scripts.backtesting.strategies as strategies from scripts.backtesting.util.user_input import get_user_input +from scripts.backtesting.analyzers.metrics_analyzer import MetricsAnalyzer def run_backtest(config): - initial_cash = 500 + initial_cash = config['initial_cash'] start_date = config['start_date'] end_date = config['end_date'] - ticker = config['stock_symbol'] - indicator = config['indicator_symbol'] - print("The config is: ",ticker, start_date, end_date, indicator) + ticker = config['ticker'] + indicator = config['indicator'] + # Fetch historical data try: data = yf.download(ticker, start=start_date, end=end_date) @@ -34,27 +31,27 @@ def run_backtest(config): # Add strategy based on selected indicator if indicator == 'SMA': - from strategies.sma_strategy import SMAStrategy + from scripts.backtesting.strategies.sma_strategy import SMAStrategy cerebro.addstrategy(SMAStrategy) elif indicator == 'LSTM': - from strategies.lstm_strategy import LSTMStrategy + from scripts.backtesting.strategies.lstm_strategy import LSTMStrategy cerebro.addstrategy(LSTMStrategy) elif indicator == 'MACD': - from strategies.macd_strategy import MACDStrategy + from scripts.backtesting.strategies.macd_strategy import MACDStrategy cerebro.addstrategy(MACDStrategy) elif indicator == 'RSI': - from strategies.rsi_strategy import RSIStrategy + from scripts.backtesting.strategies.rsi_strategy import RSIStrategy cerebro.addstrategy(RSIStrategy) - elif indicator == 'BB': - from strategies.bollinger_bands_strategy import BollingerBandsStrategy + elif indicator == 'Bollinger Bands': + from scripts.backtesting.strategies.bollinger_bands_strategy import BollingerBandsStrategy cerebro.addstrategy(BollingerBandsStrategy) else: print("Invalid indicator selected.") - yield "Invalid indicator selected." + return # Add analyzers - cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, annualized=True) - cerebro.addanalyzer(MetricsAnalyzer) + cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, _name='sharperatio') + cerebro.addanalyzer(MetricsAnalyzer, _name='MetricsAnalyzer') # Run backtest results = cerebro.run() @@ -63,7 +60,7 @@ def run_backtest(config): # Print results print(f"Initial Cash: {initial_cash}") print(f"Final Value: {cerebro.broker.getvalue()}") - print(f"Sharpe Ratio: {strat.analyzers.sharperatio.get_analysis()}") + print(f"Sharpe Ratio: {strat.analyzers.sharperatio.get_analysis()['sharperatio']}") metrics_analyzer = strat.analyzers.getbyname('MetricsAnalyzer') metrics = metrics_analyzer.get_analysis() @@ -72,22 +69,23 @@ def run_backtest(config): print(f"Winning Trades: {metrics['winning_trades']}") print(f"Losing Trades: {metrics['losing_trades']}") - # return { - # 'initial_cash': initial_cash, - # 'final_value': cerebro.broker.getvalue(), - # 'sharpe_ratio': strat.analyzers.sharperatio.get_analysis(), - # 'return': metrics['return'], - # 'total_trades': metrics['trades'], - # 'winning_trades': metrics['winning_trades'], - # 'losing_trades': metrics['losing_trades'] - # } + return { + 'initial_cash': initial_cash, + 'final_value': cerebro.broker.getvalue(), + 'sharpe_ratio': strat.analyzers.sharperatio.get_analysis()['sharperatio'], + 'percentage_return': metrics['return'], + 'total_trades': metrics['trades'], + 'winning_trades': metrics['winning_trades'], + 'losing_trades': metrics['losing_trades'] + } if __name__ == "__main__": - # config = { - # 'start_date': '2020-01-01', - # 'end_date': '2021-01-01', - # 'stock_symbol': 'NVDA', - # 'indicator_symbol': 'SMA' - # } - config = get_user_input() + # initial_cash, start_date, end_date, ticker, indicator = get_user_input() + config = { + 'initial_cash': 500, + 'start_date': "2024-02-01", + 'end_date': "2024-06-11", + 'ticker': "NVDA", + 'indicator': "SMA" + } run_backtest(config) \ No newline at end of file From 1e98fb4414da164a6a4548f953f53982536693b2 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 22:50:48 +0300 Subject: [PATCH 13/61] chore: update main for logger --- backend/main.py | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/backend/main.py b/backend/main.py index 25e476b..ad7b6ea 100644 --- a/backend/main.py +++ b/backend/main.py @@ -147,24 +147,28 @@ def perform_backtest(scene_id: int, db: Session = Depends(get_db)): db_scene = db.query(models.Scene).filter(models.Scene.id == scene_id).first() if db_scene is None: raise HTTPException(status_code=404, detail="Scene not found") - - # Fetch data based on the scene's date range - df = fetch_data(db_scene.start_date, db_scene.end_date) - # Perform backtest - metrics = run_backtest({ - 'period': db_scene.period, - 'indicator_name': db_scene.indicator.name - }, df) + config = { + 'initial_cash': 500, + 'start_date': db_scene.start_date.strftime('%Y-%m-%d'), + 'end_date': db_scene.end_date.strftime('%Y-%m-%d'), + 'ticker': db_scene.stock.symbol, + 'indicator': db_scene.indicator.symbol + } + + logger.info(f"Config: {config}") + + metrics = run_backtest(config=config) + + logger.info(f"Metrics: {metrics}") # Save metrics to database backtest_results = [] - for metric in metrics: - db_backtest_result = models.BacktestResult(scene_id=scene_id, **metric) - db.add(db_backtest_result) - db.commit() - db.refresh(db_backtest_result) - backtest_results.append(db_backtest_result) + db_backtest_result = models.BacktestResult(scene_id=scene_id, **metrics) + db.add(db_backtest_result) + db.commit() + db.refresh(db_backtest_result) + backtest_results.append(db_backtest_result) return backtest_results From 08bf4737c301e9e66876c6fcc131988644dfa039 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 22:51:12 +0300 Subject: [PATCH 14/61] fix: fix sharpee ratio for none value --- backend/schemas.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/schemas.py b/backend/schemas.py index 174ffc6..d86d801 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -82,7 +82,7 @@ class BacktestResultBase(BaseModel): total_trades: int winning_trades: int losing_trades: int - sharpe_ratio: float + sharpe_ratio: Optional[int] = None class BacktestResultCreate(BacktestResultBase): pass From 47653b730a34810d4b326eb53a903291663c8a1e Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Wed, 26 Jun 2024 22:51:35 +0300 Subject: [PATCH 15/61] feat: integrationof backend --- frontend/src/components/BacktestForm.js | 294 +++++++++++++++--------- notebooks/lstm.ipynb | 76 ++++-- 2 files changed, 248 insertions(+), 122 deletions(-) diff --git a/frontend/src/components/BacktestForm.js b/frontend/src/components/BacktestForm.js index 7db4259..1e783c5 100644 --- a/frontend/src/components/BacktestForm.js +++ b/frontend/src/components/BacktestForm.js @@ -1,40 +1,112 @@ -import React, { useState } from 'react'; +import React, { useState, useEffect } from 'react'; function BacktestForm() { const [parameters, setParameters] = useState({ startDate: '', endDate: '', indicator: '', + stock: '', paramsRange: '', }); - const [results, setResults] = useState(null); + const [results, setResults] = useState(null); + const [indicators, setIndicators] = useState([]); + const [stocks, setStocks] = useState([]); + const [stockDescription, setStockDescription] = useState(''); + const [indicatorDescription, setIndicatorDescription] = useState(''); + + useEffect(() => { + // Simulate fetching indicators and stocks from an API + const fetchIndicators = async () => { + const response = await fetch('http://127.0.0.1:8000/indicators/'); + const data = await response.json(); + console.log("The Response on Indicators is :: ",data); + + setIndicators(data); + }; + + const fetchStocks = async () => { + const response = await fetch('http://127.0.0.1:8000/stocks/'); + const data = await response.json(); + console.log("The Response on Stocks is :: ",data); + setStocks(data); + }; + + fetchIndicators(); + fetchStocks(); + }, []); const handleChange = (e) => { setParameters({ ...parameters, [e.target.name]: e.target.value, }); + + console.log("The changed s ::",e.target.name, "The Val =" ,e.target.value); + + if (e.target.name === 'stock') { + console.log("It is here") + const selectedStock = stocks.find(stock => stock.id === parseInt(e.target.value)); + console.log("Selected Stock :: ",selectedStock) + setStockDescription(selectedStock ? selectedStock.description : ''); + } + + if (e.target.name === 'indicator') { + const selectedIndicator = indicators.find(indicator => indicator.id === parseInt(e.target.value)); + setIndicatorDescription(selectedIndicator ? selectedIndicator.description : ''); + } }; const handleSubmit = async (e) => { e.preventDefault(); console.log(parameters); - // Simulate API call to backend - + let a = { + "period": 15, + "start_date": parameters["startDate"], + "end_date": parameters['endDate'], + "indicator_id": parameters["indicator"], // + "stock_id": parameters["stock"] // Nvidia + } + console.log("The final to be sent to API is :: ", a) + // API POST call to backend + const response = await fetch('http://127.0.0.1:8000/scenes/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(a), + }); // const response = await fetch('https://localhost'); - const response = { - "return": "10%", - "numberOfTrades": 50, - "winningTrades": 30, - "losingTrades": 20, - "maxDrawdown": "5%", - "sharpeRatio": 1.5 - } + // const response = { + // "return": "10%", + // "numberOfTrades": 50, + // "winningTrades": 30, + // "losingTrades": 20, + // "maxDrawdown": "5%", + // "sharpeRatio": 1.5 + // }; + const scene = await response.json() + console.log("The Response of backtest is :: ",scene); + + const scene_id = scene.id; + console.log("The Scene ID is :: ",scene_id); + + // const response2 = await fetch('http://127.0.0.1:8000//backtests/'+scene_id+'/'); + const response2 = await fetch('http://127.0.0.1:8000/backtests/'+scene_id+'/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(""), + }); + + const data2 = await response2.json(); + + console.log("The Response of backtest is :: ",data2); // const data = await response.json(); - const data = response; - setResults(data); + // const data = response; + setResults(data2[0]); }; return ( @@ -42,103 +114,119 @@ function BacktestForm() {

Backtest Parameters

-
- - -
+
+
+ + +
-
- - -
- -
- - -
-
- - -
-
- - -
+
+ + + {stockDescription} +
+
+ + +
+ +
+ + +
+ +
+ + + {indicatorDescription} +
+
- +
{results && ( -
-

Backtest Results

-
-

Return: {results.return}

-

Number of Trades: {results.numberOfTrades}

-

Winning Trades: {results.winningTrades}

-

Losing Trades: {results.losingTrades}

-

Max Drawdown: {results.maxDrawdown}

-

Sharpe Ratio: {results.sharpeRatio}

+
+
+

Start / End Portfolio

+

${results.initial_cash.toFixed(2)} / ${results.final_value.toFixed(2)}

+

Total Portfolio: {results.final_value.toFixed(2)}

+

Return Percentage: {(results.percentage_return * 100).toFixed(2)}%

+
+
+

Win / Loss Trade

+

{results.winning_trades} Wins / {results.losing_trades} Losses

+

Total Trade: {results.total_trades}

+

Win Trade Percentage: {((results.winning_trades / results.total_trades) * 100).toFixed(2)}%

+
+
+

Sharpe Ratio

+

{results.sharpe_ratio}

+
+
+

Max Drawdown

+

{results.max_drawdown}

)} diff --git a/notebooks/lstm.ipynb b/notebooks/lstm.ipynb index afe0397..3207302 100644 --- a/notebooks/lstm.ipynb +++ b/notebooks/lstm.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -31,27 +31,65 @@ "text": [ "INFO:nixtla.nixtla_client:Validating inputs...\n", "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n", - "INFO:nixtla.nixtla_client:Inferred freq: h\n" + "INFO:nixtla.nixtla_client:Inferred freq: h\n", + "INFO:nixtla.nixtla_client:Attempt 1 failed...\n", + "INFO:nixtla.nixtla_client:Attempt 2 failed...\n", + "INFO:nixtla.nixtla_client:Attempt 3 failed...\n", + "INFO:nixtla.nixtla_client:Attempt 4 failed...\n", + "INFO:nixtla.nixtla_client:Attempt 5 failed...\n", + "INFO:nixtla.nixtla_client:Attempt 6 failed...\n" ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:nixtla.nixtla_client:Restricting input...\n", - "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n" + "ename": "ConnectError", + "evalue": "[Errno 11001] getaddrinfo failed", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mConnectError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:69\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[1;34m()\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 69\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:233\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[1;32m--> 233\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py:216\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_connections(closing)\n\u001b[1;32m--> 216\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[0;32m 219\u001b[0m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py:196\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 195\u001b[0m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[1;32m--> 196\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 197\u001b[0m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\n\u001b[0;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 199\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[0;32m 200\u001b[0m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m 203\u001b[0m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection.py:99\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connect_failed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 99\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mhandle_request(request)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection.py:76\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 76\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 78\u001b[0m ssl_object \u001b[38;5;241m=\u001b[39m stream\u001b[38;5;241m.\u001b[39mget_extra_info(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mssl_object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection.py:122\u001b[0m, in \u001b[0;36mHTTPConnection._connect\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconnect_tcp\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m--> 122\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_network_backend\u001b[38;5;241m.\u001b[39mconnect_tcp(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 123\u001b[0m trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m stream\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_backends\\sync.py:205\u001b[0m, in \u001b[0;36mSyncBackend.connect_tcp\u001b[1;34m(self, host, port, timeout, local_address, socket_options)\u001b[0m\n\u001b[0;32m 200\u001b[0m exc_map: ExceptionMapping \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 201\u001b[0m socket\u001b[38;5;241m.\u001b[39mtimeout: ConnectTimeout,\n\u001b[0;32m 202\u001b[0m \u001b[38;5;167;01mOSError\u001b[39;00m: ConnectError,\n\u001b[0;32m 203\u001b[0m }\n\u001b[1;32m--> 205\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[0;32m 206\u001b[0m sock \u001b[38;5;241m=\u001b[39m socket\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[0;32m 207\u001b[0m address,\n\u001b[0;32m 208\u001b[0m timeout,\n\u001b[0;32m 209\u001b[0m source_address\u001b[38;5;241m=\u001b[39msource_address,\n\u001b[0;32m 210\u001b[0m )\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\contextlib.py:153\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 153\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 155\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_exceptions.py:14\u001b[0m, in \u001b[0;36mmap_exceptions\u001b[1;34m(map)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exc, from_exc):\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m to_exc(exc) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", + "\u001b[1;31mConnectError\u001b[0m: [Errno 11001] getaddrinfo failed", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[1;31mConnectError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[5], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m nixtla_client \u001b[38;5;241m=\u001b[39m NixtlaClient(api_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnixtla-tok-31lvshOqu4BTxxhJJY9Y6oxZQHciSZYElRgwsh5Btg60GK5JG3imcfbbUhdSIOyV2HSadFlgdJTpDsJb\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# 3. Forecast the next 24 hours\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m fcst_df \u001b[38;5;241m=\u001b[39m \u001b[43mnixtla_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m24\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m80\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m90\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# 4. Plot your results (optional)\u001b[39;00m\n\u001b[0;32m 8\u001b[0m nixtla_client\u001b[38;5;241m.\u001b[39mplot(df, fcst_df, time_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m'\u001b[39m, target_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, level\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m80\u001b[39m, \u001b[38;5;241m90\u001b[39m])\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:60\u001b[0m, in \u001b[0;36mdeprecated_argument..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m argument duplicated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 59\u001b[0m kwargs[new_name] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(old_name)\n\u001b[1;32m---> 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:60\u001b[0m, in \u001b[0;36mdeprecated_argument..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m argument duplicated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 59\u001b[0m kwargs[new_name] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(old_name)\n\u001b[1;32m---> 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:1304\u001b[0m, in \u001b[0;36mNixtlaClient.forecast\u001b[1;34m(self, df, h, freq, id_col, time_col, target_col, X_df, level, quantiles, finetune_steps, finetune_loss, clean_ex_first, validate_api_key, add_history, date_features, date_features_to_one_hot, model, num_partitions)\u001b[0m\n\u001b[0;32m 1227\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Forecast your time series using TimeGPT.\u001b[39;00m\n\u001b[0;32m 1228\u001b[0m \n\u001b[0;32m 1229\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1301\u001b[0m \u001b[38;5;124;03m predictions (if level is not None).\u001b[39;00m\n\u001b[0;32m 1302\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(df, pd\u001b[38;5;241m.\u001b[39mDataFrame):\n\u001b[1;32m-> 1304\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forecast\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1305\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1306\u001b[0m \u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1307\u001b[0m \u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43mid_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1309\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1310\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43mfinetune_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43mfinetune_loss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_loss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1316\u001b[0m \u001b[43m \u001b[49m\u001b[43mclean_ex_first\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclean_ex_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1317\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_api_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_api_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1318\u001b[0m \u001b[43m \u001b[49m\u001b[43madd_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madd_history\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1319\u001b[0m \u001b[43m \u001b[49m\u001b[43mdate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1320\u001b[0m \u001b[43m \u001b[49m\u001b[43mdate_features_to_one_hot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_features_to_one_hot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1321\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1322\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_partitions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_partitions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1323\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1324\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1325\u001b[0m dist_nixtla_client \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_instantiate_distributed_nixtla_client()\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:730\u001b[0m, in \u001b[0;36mvalidate_model_parameter..wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupported_models:\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 727\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported model: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 728\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msupported models: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupported_models)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 729\u001b[0m )\n\u001b[1;32m--> 730\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:748\u001b[0m, in \u001b[0;36mpartition_by_uid..wrapper\u001b[1;34m(self, num_partitions, **kwargs)\u001b[0m\n\u001b[0;32m 746\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, num_partitions, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_partitions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m num_partitions \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 748\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs, num_partitions\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 749\u001b[0m df \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 750\u001b[0m X_df \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX_df\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:976\u001b[0m, in \u001b[0;36m_NixtlaClient._forecast\u001b[1;34m(self, df, h, freq, id_col, time_col, target_col, X_df, level, quantiles, finetune_steps, finetune_loss, clean_ex_first, validate_api_key, add_history, date_features, date_features_to_one_hot, model, num_partitions)\u001b[0m\n\u001b[0;32m 954\u001b[0m nixtla_client_model \u001b[38;5;241m=\u001b[39m _NixtlaClientModel(\n\u001b[0;32m 955\u001b[0m client\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient,\n\u001b[0;32m 956\u001b[0m h\u001b[38;5;241m=\u001b[39mh,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 971\u001b[0m max_wait_time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_wait_time,\n\u001b[0;32m 972\u001b[0m )\n\u001b[0;32m 973\u001b[0m df, X_df, uids_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_uids_to_categorical(\n\u001b[0;32m 974\u001b[0m df\u001b[38;5;241m=\u001b[39mdf, X_df\u001b[38;5;241m=\u001b[39mX_df, id_col\u001b[38;5;241m=\u001b[39mid_col\n\u001b[0;32m 975\u001b[0m )\n\u001b[1;32m--> 976\u001b[0m fcst_df \u001b[38;5;241m=\u001b[39m \u001b[43mnixtla_client_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 977\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madd_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madd_history\u001b[49m\n\u001b[0;32m 978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 979\u001b[0m fcst_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restore_uids(fcst_df, dtype\u001b[38;5;241m=\u001b[39muids_dtype, id_col\u001b[38;5;241m=\u001b[39mid_col)\n\u001b[0;32m 980\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights_x \u001b[38;5;241m=\u001b[39m nixtla_client_model\u001b[38;5;241m.\u001b[39mweights_x\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:559\u001b[0m, in \u001b[0;36m_NixtlaClientModel.forecast\u001b[1;34m(self, df, X_df, add_history)\u001b[0m\n\u001b[0;32m 557\u001b[0m main_logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPreprocessing dataframes...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 558\u001b[0m Y_df, X_df, x_cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreprocess_dataframes(df\u001b[38;5;241m=\u001b[39mdf, X_df\u001b[38;5;241m=\u001b[39mX_df)\n\u001b[1;32m--> 559\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_model_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 560\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mh \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_horizon:\n\u001b[0;32m 561\u001b[0m main_logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m 562\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe specified horizon \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m exceeds the model horizon. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 563\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis may lead to less accurate forecasts. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 564\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease consider using a smaller horizon.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 565\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:513\u001b[0m, in \u001b[0;36m_NixtlaClientModel.set_model_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 512\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_model_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 513\u001b[0m model_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_api\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 514\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 515\u001b[0m \u001b[43m \u001b[49m\u001b[43mSingleSeriesForecast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 516\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 517\u001b[0m model_params \u001b[38;5;241m=\u001b[39m model_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdetail\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 518\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_size, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_horizon \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 519\u001b[0m model_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_size\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 520\u001b[0m model_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhorizon\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 521\u001b[0m )\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:229\u001b[0m, in \u001b[0;36m_NixtlaClientModel._call_api\u001b[1;34m(self, method, request)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call_api\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, request):\n\u001b[1;32m--> 229\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_strategy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m response:\n\u001b[0;32m 231\u001b[0m response \u001b[38;5;241m=\u001b[39m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:332\u001b[0m, in \u001b[0;36mBaseRetrying.wraps..wrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m 328\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(\n\u001b[0;32m 329\u001b[0m f, functools\u001b[38;5;241m.\u001b[39mWRAPPER_ASSIGNMENTS \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__defaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__kwdefaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 330\u001b[0m )\n\u001b[0;32m 331\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs: t\u001b[38;5;241m.\u001b[39mAny, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mAny:\n\u001b[1;32m--> 332\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(f, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:469\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 467\u001b[0m retry_state \u001b[38;5;241m=\u001b[39m RetryCallState(retry_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, fn\u001b[38;5;241m=\u001b[39mfn, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m 468\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 469\u001b[0m do \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[0;32m 471\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:370\u001b[0m, in \u001b[0;36mBaseRetrying.iter\u001b[1;34m(self, retry_state)\u001b[0m\n\u001b[0;32m 368\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mactions:\n\u001b[1;32m--> 370\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43maction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:412\u001b[0m, in \u001b[0;36mBaseRetrying._post_stop_check_actions..exc_check\u001b[1;34m(rs)\u001b[0m\n\u001b[0;32m 410\u001b[0m retry_exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretry_error_cls(fut)\n\u001b[0;32m 411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreraise:\n\u001b[1;32m--> 412\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mretry_exc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 413\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m retry_exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfut\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexception\u001b[39;00m()\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:185\u001b[0m, in \u001b[0;36mRetryError.reraise\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreraise\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mNoReturn:\n\u001b[0;32m 184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_attempt\u001b[38;5;241m.\u001b[39mfailed:\n\u001b[1;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlast_attempt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\concurrent\\futures\\_base.py:438\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[0;32m 437\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m--> 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 440\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\concurrent\\futures\\_base.py:390\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m 389\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 390\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[0;32m 391\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 392\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m 393\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:472\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[0;32m 471\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 472\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 473\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m: \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n\u001b[0;32m 474\u001b[0m retry_state\u001b[38;5;241m.\u001b[39mset_exception(sys\u001b[38;5;241m.\u001b[39mexc_info()) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\client.py:2283\u001b[0m, in \u001b[0;36mNixtla.model_params\u001b[1;34m(self, request, request_options)\u001b[0m\n\u001b[0;32m 2263\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmodel_params\u001b[39m(\n\u001b[0;32m 2264\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, request: SingleSeriesForecast, request_options: typing\u001b[38;5;241m.\u001b[39mOptional[RequestOptions] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 2265\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m typing\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m 2266\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 2267\u001b[0m \u001b[38;5;124;03m Parameters:\u001b[39;00m\n\u001b[0;32m 2268\u001b[0m \u001b[38;5;124;03m - request: SingleSeriesForecast.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2281\u001b[0m \u001b[38;5;124;03m )\u001b[39;00m\n\u001b[0;32m 2282\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2283\u001b[0m _response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhttpx_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2284\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2285\u001b[0m \u001b[43m \u001b[49m\u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murljoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_base_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel_params\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2286\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2287\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_query_parameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[0;32m 2288\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2289\u001b[0m \u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2290\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_body_parameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[0;32m 2291\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 2292\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2293\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mremove_none_from_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_body_parameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2294\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2295\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2296\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_none_from_dict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2297\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 2298\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2299\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_headers\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2300\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\n\u001b[0;32m 2301\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2302\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2303\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimeout_in_seconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2304\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimeout_in_seconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[0;32m 2305\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_timeout\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2306\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2307\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_retries\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[0;32m 2308\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2309\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m _response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[0;32m 2310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pydantic_v1\u001b[38;5;241m.\u001b[39mparse_obj_as(typing\u001b[38;5;241m.\u001b[39mAny, _response\u001b[38;5;241m.\u001b[39mjson()) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\core\\http_client.py:94\u001b[0m, in \u001b[0;36mHttpClient.request\u001b[1;34m(self, max_retries, retries, *args, **kwargs)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(httpx\u001b[38;5;241m.\u001b[39mClient\u001b[38;5;241m.\u001b[39mrequest)\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[0;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: typing\u001b[38;5;241m.\u001b[39mAny, max_retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: typing\u001b[38;5;241m.\u001b[39mAny\n\u001b[0;32m 93\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m httpx\u001b[38;5;241m.\u001b[39mResponse:\n\u001b[1;32m---> 94\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhttpx_client\u001b[38;5;241m.\u001b[39mrequest(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _should_retry(response\u001b[38;5;241m=\u001b[39mresponse):\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_retries \u001b[38;5;241m>\u001b[39m retries:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:827\u001b[0m, in \u001b[0;36mClient.request\u001b[1;34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[0m\n\u001b[0;32m 812\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(message, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[0;32m 814\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_request(\n\u001b[0;32m 815\u001b[0m method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[0;32m 816\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 825\u001b[0m extensions\u001b[38;5;241m=\u001b[39mextensions,\n\u001b[0;32m 826\u001b[0m )\n\u001b[1;32m--> 827\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:914\u001b[0m, in \u001b[0;36mClient.send\u001b[1;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[0;32m 906\u001b[0m follow_redirects \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 907\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollow_redirects\n\u001b[0;32m 908\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(follow_redirects, UseClientDefault)\n\u001b[0;32m 909\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m follow_redirects\n\u001b[0;32m 910\u001b[0m )\n\u001b[0;32m 912\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[1;32m--> 914\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 915\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 916\u001b[0m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 917\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 918\u001b[0m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 919\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 921\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:942\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[1;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[0;32m 939\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[0;32m 941\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 942\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 943\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 944\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 945\u001b[0m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 946\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 947\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 948\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:979\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[1;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[0;32m 976\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequest\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 977\u001b[0m hook(request)\n\u001b[1;32m--> 979\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 980\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 981\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:1015\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 1010\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 1011\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1012\u001b[0m )\n\u001b[0;32m 1014\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[1;32m-> 1015\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1017\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[0;32m 1019\u001b[0m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;241m=\u001b[39m request\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:232\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(request\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[0;32m 220\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[0;32m 221\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 222\u001b[0m url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 230\u001b[0m extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[0;32m 231\u001b[0m )\n\u001b[1;32m--> 232\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[0;32m 233\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pool\u001b[38;5;241m.\u001b[39mhandle_request(req)\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n", + "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\contextlib.py:153\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 151\u001b[0m value \u001b[38;5;241m=\u001b[39m typ()\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 153\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 155\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value\n", + "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:86\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[1;34m()\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m 85\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(exc)\n\u001b[1;32m---> 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m mapped_exc(message) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n", + "\u001b[1;31mConnectError\u001b[0m: [Errno 11001] getaddrinfo failed" ] - }, - { - "data": { - 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", - "text/plain": [ - "
" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ From 5cf952271a12cd01b67c4cdfbd3030843a298c70 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:51:46 +0300 Subject: [PATCH 16/61] feat: add lstm strategy --- .../backtesting/strategies/lstm_strategy.py | 69 +++++++++++++++---- 1 file changed, 57 insertions(+), 12 deletions(-) diff --git a/scripts/backtesting/strategies/lstm_strategy.py b/scripts/backtesting/strategies/lstm_strategy.py index 17d47d8..d68c580 100644 --- a/scripts/backtesting/strategies/lstm_strategy.py +++ b/scripts/backtesting/strategies/lstm_strategy.py @@ -1,15 +1,60 @@ -from .base_strategy import BaseStrategy +from tensorflow.keras.models import load_model +import joblib +import numpy as np +import os, sys -class LSTMStrategy(BaseStrategy): - def __init__(self): - super().__init__() - # Load your pre-trained LSTM model here - # self.lstm_model = load_model('path_to_lstm_model') +project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) +if project_root not in sys.path: + sys.path.append(project_root) +print("Before loading", os.getcwd()) +def load_lstm_model_and_scaler(model_path='my_model.keras', scaler_path='scaler.gz'): + print("After loading", os.getcwd()) + model = load_model(model_path) + scaler = joblib.load(scaler_path) + return model, scaler - def buy_signal(self): - # Implement LSTM prediction logic here - return False # Placeholder +def make_lstm_predictions(model, scaler, df, seq_length=60): + data = df['Close'].values.reshape(-1, 1) + scaled_data = scaler.transform(data) + X_test = [] + for i in range(seq_length, len(scaled_data)): + X_test.append(scaled_data[i-seq_length:i, 0]) + X_test = np.array(X_test) + X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) + predictions = model.predict(X_test) + predictions = scaler.inverse_transform(predictions) + return predictions - def sell_signal(self): - # Implement LSTM prediction logic here - return False # Placeholder \ No newline at end of file +def run_backtest_with_lstm(df): + lstm_model, scaler = load_lstm_model_and_scaler() + predictions = make_lstm_predictions(lstm_model, scaler, df) + + initial_cash = 10000 + cash = initial_cash + position = 0 # 1 for holding stock, 0 for no stock + for i in range(len(predictions)): + predicted_price = predictions[i] + actual_price = df['Close'].values[-len(predictions) + i] + + if predicted_price > actual_price and cash > actual_price: + # Buy signal + cash -= actual_price + position += 1 + elif predicted_price < actual_price and position > 0: + # Sell signal + cash += actual_price + position -= 1 + + # Calculate metrics + final_value = cash + position * df['Close'].values[-1] + gross_profit = final_value - initial_cash + metrics = { + 'initial_cash': initial_cash, + 'final_value': final_value, # Add any transaction costs if applicable + 'number_of_trades': position, + 'winning_trades': position if gross_profit > 0 else 0, + 'losing_trades': position if gross_profit <= 0 else 0, + 'max_drawdown': -1000, # Calculate this properly based on your strategy + 'sharpe_ratio': 1.5 # Calculate this properly based on your strategy + } + return metrics \ No newline at end of file From 48dc5e146ebf7638049e8a2b2b270602782b1630 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:53:00 +0300 Subject: [PATCH 17/61] fix: update sharpee from int to float --- backend/schemas.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/schemas.py b/backend/schemas.py index d86d801..351b505 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -82,7 +82,7 @@ class BacktestResultBase(BaseModel): total_trades: int winning_trades: int losing_trades: int - sharpe_ratio: Optional[int] = None + sharpe_ratio: Optional[float] = None class BacktestResultCreate(BacktestResultBase): pass From e5fee37c476647646585ed11b006d82f9047712c Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:53:16 +0300 Subject: [PATCH 18/61] chore: update readme --- README.md | 132 +++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 125 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 6e3a831..d5f6ea3 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,14 @@ -# Scalable Crypto Backtesting Infrastructure +# Scalable Stock and Crypto Backtesting Infrastructure + +## Table of Contents + +- [Installation](#installation) +- [Usage](#usage) +- [Project Structure](#project-structure) +- [Endpoints](#endpoints) +- [Kafka Integration](#kafka-integration) +- [LSTM Integration](#lstm-integration) +- [License](#license) ## Overview @@ -56,7 +66,20 @@ pip install -r requirements.txt ``` ## Project Structure ```bash -├── data/ +├── backend/ +│ ├── main.py +│ ├── models.py +│ ├── schemas.py +│ ├── database.py +│ ├── auth.py +│ ├── requirements.txt +│ ├── README.md +│ └── scripts +│ ├── backtesting +│ ├── init_data.py +│ ├── kafka_config.py +│ │ ├── main.py +│ └── init_db.py ├── scripts/ │ └── download_data.py ├── tests/ @@ -66,13 +89,108 @@ pip install -r requirements.txt ├── requirements.txt └── README.md ``` + +### Set Up Kafka + +If you do not have Kafka installed, you can run it using Docker: + +docker run -d --name zookeeper -p 2181:2181 zookeeper:3.4.9 +docker run -d --name kafka -p 9092:9092 --link zookeeper wurstmeister/kafka:latest + +Alternatively, follow the [Kafka Quickstart](https://kafka.apache.org/quickstart) guide to set up Kafka. + +### Configure Environment Variables + +Create a `.env` file in the root directory and add the following configurations: + +- KAFKA_BROKER_URL=localhost:9092 +- SCENE_TOPIC=scene_parameters +- RESULT_TOPIC=backtest_results + +### Initialize the Database + +python -m scripts.init_db + +## Backend Usage + +### Run the FastAPI Application + +uvicorn main:app --reload + +### Sending Requests + +Use a tool like Postman, Thunder Client, or Curl to interact with the API. + +### Endpoints + +### Health Check + +**GET /health** + +### Create Indicator + +**POST /indicators/** + +```sh +Body +{ + "name": "Simple Moving Average", + "symbol": "SMA", + "description": "A simple moving average indicator" +} +``` + +### Read Indicators + +**GET /indicators/** + +### Create Stock + +**POST /stocks/** + +```sh +Body +{ + "name": "Apple Inc.", + "symbol": "AAPL", + "description": "Apple Inc. stock" +} +``` + +### Read Stocks + +**GET /stocks/** + +### Create Scene + +**POST /scenes/** + +```sh +Body +{ + "period": 20, + "indicator_id": 1, + "stock_id": 1, + "start_date": "2023-01-01", + "end_date": "2023-12-31" +} +``` + +### Read Scenes + +**GET /scenes/** + +### Perform Backtest + +**POST /backtests/{scene_id}** + +### Read Backtest Results + +**GET /backtest_results/** + ## Contributors - Abubeker Shamil -- Addisu Alemu - Michael George -- Sheila Murugi - - ## License This project is licensed under the MIT License - see the LICENSE file for details. @@ -85,4 +203,4 @@ This project is licensed under the MIT License - see the LICENSE file for detail - Rehmet **References:** -backtrader, Freqtrade, Vectorbt, Kafka, Airflow, MLflow, CML +backtrader, Freqtrade, Vectorbt, Kafka, Airflow, MLflow, CML \ No newline at end of file From 1cd03f83ddf9fb7e759ac9410be2202a1178406d Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:54:38 +0300 Subject: [PATCH 19/61] feat: inegrate mlflow to backtest runs --- scripts/backtesting/main.py | 220 +++++++++++++++++++++++++----------- 1 file changed, 152 insertions(+), 68 deletions(-) diff --git a/scripts/backtesting/main.py b/scripts/backtesting/main.py index ae24e62..e3be56f 100644 --- a/scripts/backtesting/main.py +++ b/scripts/backtesting/main.py @@ -8,76 +8,160 @@ from scripts.backtesting.util.user_input import get_user_input from scripts.backtesting.analyzers.metrics_analyzer import MetricsAnalyzer +import mlflow +import mlflow.pyfunc def run_backtest(config): - initial_cash = config['initial_cash'] - start_date = config['start_date'] - end_date = config['end_date'] - ticker = config['ticker'] - indicator = config['indicator'] - - # Fetch historical data - try: - data = yf.download(ticker, start=start_date, end=end_date) - data_feed = bt.feeds.PandasData(dataname=data) - except Exception as e: - print(f"Error fetching data: {e}") - return - - # Initialize Cerebro engine - cerebro = bt.Cerebro() - cerebro.adddata(data_feed) - cerebro.broker.setcash(initial_cash) - - # Add strategy based on selected indicator - if indicator == 'SMA': - from scripts.backtesting.strategies.sma_strategy import SMAStrategy - cerebro.addstrategy(SMAStrategy) - elif indicator == 'LSTM': - from scripts.backtesting.strategies.lstm_strategy import LSTMStrategy - cerebro.addstrategy(LSTMStrategy) - elif indicator == 'MACD': - from scripts.backtesting.strategies.macd_strategy import MACDStrategy - cerebro.addstrategy(MACDStrategy) - elif indicator == 'RSI': - from scripts.backtesting.strategies.rsi_strategy import RSIStrategy - cerebro.addstrategy(RSIStrategy) - elif indicator == 'Bollinger Bands': - from scripts.backtesting.strategies.bollinger_bands_strategy import BollingerBandsStrategy - cerebro.addstrategy(BollingerBandsStrategy) - else: - print("Invalid indicator selected.") - return - - # Add analyzers - cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, _name='sharperatio') - cerebro.addanalyzer(MetricsAnalyzer, _name='MetricsAnalyzer') - - # Run backtest - results = cerebro.run() - strat = results[0] - - # Print results - print(f"Initial Cash: {initial_cash}") - print(f"Final Value: {cerebro.broker.getvalue()}") - print(f"Sharpe Ratio: {strat.analyzers.sharperatio.get_analysis()['sharperatio']}") - - metrics_analyzer = strat.analyzers.getbyname('MetricsAnalyzer') - metrics = metrics_analyzer.get_analysis() - print(f"Return: {metrics['return']}") - print(f"Total Trades: {metrics['trades']}") - print(f"Winning Trades: {metrics['winning_trades']}") - print(f"Losing Trades: {metrics['losing_trades']}") - - return { - 'initial_cash': initial_cash, - 'final_value': cerebro.broker.getvalue(), - 'sharpe_ratio': strat.analyzers.sharperatio.get_analysis()['sharperatio'], - 'percentage_return': metrics['return'], - 'total_trades': metrics['trades'], - 'winning_trades': metrics['winning_trades'], - 'losing_trades': metrics['losing_trades'] - } + with mlflow.start_run(): + initial_cash = config['initial_cash'] + start_date = config['start_date'] + end_date = config['end_date'] + ticker = config['ticker'] + indicator = config['indicator'] + + # Fetch historical data + try: + data = yf.download(ticker, start=start_date, end=end_date) + data_feed = bt.feeds.PandasData(dataname=data) + except Exception as e: + print(f"Error fetching data: {e}") + return + + # Initialize Cerebro engine + cerebro = bt.Cerebro() + cerebro.adddata(data_feed) + cerebro.broker.setcash(initial_cash) + + # Add strategy based on selected indicator + if indicator == 'SMA': + from scripts.backtesting.strategies.sma_strategy import SMAStrategy + cerebro.addstrategy(SMAStrategy) + elif indicator == 'LSTM': + from scripts.backtesting.strategies.lstm_strategy import run_backtest_with_lstm + return run_backtest_with_lstm(df=data) + elif indicator == 'MACD': + from scripts.backtesting.strategies.macd_strategy import MACDStrategy + cerebro.addstrategy(MACDStrategy) + elif indicator == 'RSI': + from scripts.backtesting.strategies.rsi_strategy import RSIStrategy + cerebro.addstrategy(RSIStrategy) + elif indicator == 'BB': + from scripts.backtesting.strategies.bollinger_bands_strategy import BollingerBandsStrategy + cerebro.addstrategy(BollingerBandsStrategy) + else: + print("Invalid indicator selected.") + return + + # Add analyzers + cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, _name='sharperatio') + cerebro.addanalyzer(MetricsAnalyzer, _name='MetricsAnalyzer') + + # Run backtest + results = cerebro.run() + strat = results[0] + + # Log results to MLflow + mlflow.log_param("initial_cash", initial_cash) + mlflow.log_param("start_date", start_date) + mlflow.log_param("end_date", end_date) + mlflow.log_param("ticker", ticker) + mlflow.log_param("indicator", indicator) + + final_value = cerebro.broker.getvalue() + sharpe_ratio = strat.analyzers.sharperatio.get_analysis()['sharperatio'] + metrics_analyzer = strat.analyzers.getbyname('MetricsAnalyzer') + metrics = metrics_analyzer.get_analysis() + percentage_return = metrics['return'] + total_trades = metrics['trades'] + winning_trades = metrics['winning_trades'] + losing_trades = metrics['losing_trades'] + + mlflow.log_metric("final_value", final_value) + mlflow.log_metric("sharpe_ratio", sharpe_ratio) + mlflow.log_metric("percentage_return", percentage_return) + mlflow.log_metric("total_trades", total_trades) + mlflow.log_metric("winning_trades", winning_trades) + mlflow.log_metric("losing_trades", losing_trades) + + return { + 'initial_cash': initial_cash, + 'final_value': final_value, + 'sharpe_ratio': sharpe_ratio, + 'percentage_return': percentage_return, + 'total_trades': total_trades, + 'winning_trades': winning_trades, + 'losing_trades': losing_trades + } + +# def run_backtest(config): +# initial_cash = config['initial_cash'] +# start_date = config['start_date'] +# end_date = config['end_date'] +# ticker = config['ticker'] +# indicator = config['indicator'] + +# # Fetch historical data +# try: +# data = yf.download(ticker, start=start_date, end=end_date) +# data_feed = bt.feeds.PandasData(dataname=data) +# except Exception as e: +# print(f"Error fetching data: {e}") +# return + +# # Initialize Cerebro engine +# cerebro = bt.Cerebro() +# cerebro.adddata(data_feed) +# cerebro.broker.setcash(initial_cash) + +# # Add strategy based on selected indicator +# if indicator == 'SMA': +# from scripts.backtesting.strategies.sma_strategy import SMAStrategy +# cerebro.addstrategy(SMAStrategy) +# elif indicator == 'LSTM': +# from scripts.backtesting.strategies.lstm_strategy import LSTMStrategy +# cerebro.addstrategy(LSTMStrategy) +# elif indicator == 'MACD': +# from scripts.backtesting.strategies.macd_strategy import MACDStrategy +# cerebro.addstrategy(MACDStrategy) +# elif indicator == 'RSI': +# from scripts.backtesting.strategies.rsi_strategy import RSIStrategy +# cerebro.addstrategy(RSIStrategy) +# elif indicator == 'BB': +# from scripts.backtesting.strategies.bollinger_bands_strategy import BollingerBandsStrategy +# cerebro.addstrategy(BollingerBandsStrategy) +# else: +# print("Invalid indicator selected.") +# return + +# # Add analyzers +# cerebro.addanalyzer(bt.analyzers.SharpeRatio, riskfreerate=0.0, _name='sharperatio') +# cerebro.addanalyzer(MetricsAnalyzer, _name='MetricsAnalyzer') + +# # Run backtest +# results = cerebro.run() +# strat = results[0] + +# # Print results +# print(f"Initial Cash: {initial_cash}") +# print(f"Final Value: {cerebro.broker.getvalue()}") +# print(f"Sharpe Ratio: {strat.analyzers.sharperatio.get_analysis()['sharperatio']}") + +# metrics_analyzer = strat.analyzers.getbyname('MetricsAnalyzer') +# metrics = metrics_analyzer.get_analysis() +# print(f"Return: {metrics['return']}") +# print(f"Total Trades: {metrics['trades']}") +# print(f"Winning Trades: {metrics['winning_trades']}") +# print(f"Losing Trades: {metrics['losing_trades']}") + +# return { +# 'initial_cash': initial_cash, +# 'final_value': cerebro.broker.getvalue(), +# 'sharpe_ratio': strat.analyzers.sharperatio.get_analysis()['sharperatio'], +# 'percentage_return': metrics['return'], +# 'total_trades': metrics['trades'], +# 'winning_trades': metrics['winning_trades'], +# 'losing_trades': metrics['losing_trades'] +# } if __name__ == "__main__": # initial_cash, start_date, end_date, ticker, indicator = get_user_input() From 2625c9d5e108b1afdce2b5988655c7f1e06728b3 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:55:08 +0300 Subject: [PATCH 20/61] feat: add sharpee and max draws --- .../backtesting/strategies/lstm_strategy.py | 39 +++++++++++++++---- 1 file changed, 31 insertions(+), 8 deletions(-) diff --git a/scripts/backtesting/strategies/lstm_strategy.py b/scripts/backtesting/strategies/lstm_strategy.py index d68c580..47a3c58 100644 --- a/scripts/backtesting/strategies/lstm_strategy.py +++ b/scripts/backtesting/strategies/lstm_strategy.py @@ -6,9 +6,8 @@ project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../')) if project_root not in sys.path: sys.path.append(project_root) -print("Before loading", os.getcwd()) + def load_lstm_model_and_scaler(model_path='my_model.keras', scaler_path='scaler.gz'): - print("After loading", os.getcwd()) model = load_model(model_path) scaler = joblib.load(scaler_path) return model, scaler @@ -25,13 +24,32 @@ def make_lstm_predictions(model, scaler, df, seq_length=60): predictions = scaler.inverse_transform(predictions) return predictions -def run_backtest_with_lstm(df): +def calculate_max_drawdown(portfolio_values): + # Calculate drawdown + peak = portfolio_values[0] + max_drawdown = 0 + for value in portfolio_values: + if value > peak: + peak = value + drawdown = (peak - value) / peak + if drawdown > max_drawdown: + max_drawdown = drawdown + return max_drawdown + +import numpy as np + +def calculate_sharpe_ratio(returns, risk_free_rate=0): + excess_returns = returns - risk_free_rate + return np.mean(excess_returns) / np.std(excess_returns) + +def run_backtest_with_lstm(scene_parameters, df): lstm_model, scaler = load_lstm_model_and_scaler() predictions = make_lstm_predictions(lstm_model, scaler, df) initial_cash = 10000 cash = initial_cash position = 0 # 1 for holding stock, 0 for no stock + portfolio_values = [cash] for i in range(len(predictions)): predicted_price = predictions[i] actual_price = df['Close'].values[-len(predictions) + i] @@ -44,17 +62,22 @@ def run_backtest_with_lstm(df): # Sell signal cash += actual_price position -= 1 + portfolio_values.append(cash + position * df['Close'].values[-1]) # Calculate metrics final_value = cash + position * df['Close'].values[-1] gross_profit = final_value - initial_cash + returns = np.diff(portfolio_values) / portfolio_values[:-1] + sharpe_ratio = calculate_sharpe_ratio(returns) + max_drawdown = calculate_max_drawdown(portfolio_values) metrics = { 'initial_cash': initial_cash, 'final_value': final_value, # Add any transaction costs if applicable - 'number_of_trades': position, - 'winning_trades': position if gross_profit > 0 else 0, - 'losing_trades': position if gross_profit <= 0 else 0, - 'max_drawdown': -1000, # Calculate this properly based on your strategy - 'sharpe_ratio': 1.5 # Calculate this properly based on your strategy + 'total_trades': len(predictions), # Example, adjust as needed + 'winning_trades': sum(1 for r in returns if r > 0), + 'losing_trades': sum(1 for r in returns if r <= 0), + 'percentage_return': returns, + 'max_drawdown': max_drawdown, + 'sharpe_ratio': sharpe_ratio } return metrics \ No newline at end of file From 5eb9917fe6283a8abdbd46053bfc48ac0e2ae49e Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:55:24 +0300 Subject: [PATCH 21/61] test: experiment --- notebooks/kafka.ipynb | 22 +- notebooks/lstm.ipynb | 891 ++++++++++++++++++++++++++++++++++++++---- 2 files changed, 821 insertions(+), 92 deletions(-) diff --git a/notebooks/kafka.ipynb b/notebooks/kafka.ipynb index 3c2e1aa..d857dc1 100644 --- a/notebooks/kafka.ipynb +++ b/notebooks/kafka.ipynb @@ -43,17 +43,17 @@ "metadata": {}, "outputs": [ { - "ename": "NoBrokersAvailable", - "evalue": "NoBrokersAvailable", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mNoBrokersAvailable\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[1], line 7\u001b[0m\n\u001b[0;32m 4\u001b[0m broker \u001b[38;5;241m=\u001b[39m 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"output_type": "stream", + "text": [ + "Topics in Kafka cluster:\n", + "docker-connect-status\n", + "_schemas\n", + "scene_parameters\n", + "__consumer_offsets\n", + "_confluent-ksql-ksqldb-server__command_topic\n", + "docker-connect-offsets\n", + "docker-connect-configs\n" ] } ], diff --git a/notebooks/lstm.ipynb b/notebooks/lstm.ipynb index 3207302..996bcea 100644 --- a/notebooks/lstm.ipynb +++ b/notebooks/lstm.ipynb @@ -2,9 +2,21 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'nixtla'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m 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Instantiate the NixtlaClient\n", + "nixtla_client = NixtlaClient(api_key = 'nixtla-tok-31lvshOqu4BTxxhJJY9Y6oxZQHciSZYElRgwsh5Btg60GK5JG3imcfbbUhdSIOyV2HSadFlgdJTpDsJb')\n", + "\n", + "# 3. Forecast the next 24 hours\n", + "fcst_df = nixtla_client.forecast(df, h=24, level=[80, 90])\n", + "\n", + "# 4. Plot your results (optional)\n", + "nixtla_client.plot(df, fcst_df, time_col='ds', target_col='y', level=[80, 90])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 3. Detect Anomalies \n", + "anomalies_df = nixtla_client.detect_anomalies(df, time_col='ds', target_col='y', freq='D')\n", + "\n", + "# 4. Plot your results (optional)\n", + "nixtla_client.plot(df, anomalies_df,time_col='ds', target_col='y')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install statsforecast mlforecast neuralforecast hierarchicalforecast\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from statsforecast import StatsForecast\n", + "from statsforecast.models import AutoARIMA\n", + "\n", + "# Prepare your data (assuming a pandas DataFrame)\n", + "# df should have columns: 'unique_id', 'ds', 'y'\n", + "df = scene_data\n", + "\n", + "# Initialize the forecaster\n", + "sf = StatsForecast(\n", + " models=[AutoARIMA(season_length=12)],\n", + " freq='M'\n", + ")\n", + "\n", + "# Fit the model\n", + "sf.fit(df)\n", + "\n", + "# Make predictions\n", + "predictions = sf.predict(h=12, level=[95])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, 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MarkupSafe, markdown, idna, grpcio, google-pasta, gast, charset-normalizer, certifi, astunparse, absl-py, werkzeug, requests, opt-einsum, ml-dtypes, markdown-it-py, h5py, tensorboard, rich, keras, tensorflow\n", + "Successfully installed MarkupSafe-2.1.5 absl-py-2.1.0 astunparse-1.6.3 certifi-2024.6.2 charset-normalizer-3.3.2 flatbuffers-24.3.25 gast-0.5.5 google-pasta-0.2.0 grpcio-1.64.1 h5py-3.11.0 idna-3.7 keras-3.4.1 libclang-18.1.1 markdown-3.6 markdown-it-py-3.0.0 mdurl-0.1.2 ml-dtypes-0.3.2 namex-0.0.8 numpy-1.26.4 opt-einsum-3.3.0 optree-0.11.0 protobuf-4.25.3 requests-2.32.3 rich-13.7.1 tensorboard-2.16.2 tensorboard-data-server-0.7.2 tensorflow-2.16.1 termcolor-2.4.0 urllib3-2.2.2 werkzeug-3.0.3 wrapt-1.16.0\n" + ] + } + ], + "source": [ + "!pip install tensorflow\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install scikit-learn -q" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def create_sequences(data, seq_length):\n", + " sequences = []\n", + " for i in range(len(data) - seq_length):\n", + " sequences.append(data[i:i + seq_length])\n", + " return np.array(sequences)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import LSTM, Dense, Dropout\n", + "\n", + "def build_lstm_model(input_shape):\n", + " model = Sequential()\n", + " model.add(LSTM(50, return_sequences=True, input_shape=input_shape))\n", + " model.add(Dropout(0.2))\n", + " model.add(LSTM(50, return_sequences=False))\n", + " model.add(Dropout(0.2))\n", + " model.add(Dense(1))\n", + " \n", + " model.compile(optimizer='adam', loss='mean_squared_error')\n", + " return model\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def train_lstm_model(model, X_train, y_train, epochs=50, batch_size=32):\n", + " model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=0.2)\n", + " return model\n" + ] + }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ + { + "data": { + "text/plain": [ + "'c:\\\\Users\\\\user\\\\Downloads\\\\ten_academy\\\\week9\\\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "\n", + "def prepare_lstm_data(df, target_column, seq_length):\n", + " data = df[target_column].values.reshape(-1, 1)\n", + " scaler = MinMaxScaler(feature_range=(0, 1))\n", + " scaled_data = scaler.fit_transform(data)\n", + " \n", + " X = []\n", + " y = []\n", + " for i in range(seq_length, len(scaled_data)):\n", + " X.append(scaled_data[i-seq_length:i, 0])\n", + " y.append(scaled_data[i, 0])\n", + " \n", + " # Convert lists to numpy arrays\n", + " X, y = np.array(X), np.array(y)\n", + " \n", + " # Debugging statements\n", + " print(f\"Length of scaled_data: {len(scaled_data)}\")\n", + " print(f\"Length of X: {len(X)}\")\n", + " print(f\"Shape of X before reshape: {X.shape}\")\n", + " \n", + " # Check if X is not empty and has the correct shape\n", + " if X.size == 0:\n", + " raise ValueError(\"The sequence length is too large for the dataset. Reduce the sequence length.\")\n", + " \n", + " # Ensure X has the correct number of dimensions\n", + " if len(X.shape) != 2:\n", + " raise ValueError(f\"Expected 2D array for X but got {X.shape}\")\n", + " \n", + " X = np.reshape(X, (X.shape[0], X.shape[1], 1))\n", + " return X, y, scaler\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of scaled_data: 31\n", + "Length of X: 11\n", + "Shape of X before reshape: (11, 20)\n", + "Shape of X after reshape: (11, 20, 1)\n" + ] + } + ], + "source": [ + "\n", + "# Example usage\n", + "df = pd.read_csv('data/binance_btc_usdt_candlestick.csv', index_col='timestamp', parse_dates=True)\n", + "X, y, scaler = prepare_lstm_data(df, 'close', seq_length=20)\n", + "print(f\"Shape of X after reshape: {X.shape}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of scaled_data: 31\n", + "Length of X: 11\n", + "Shape of X before reshape: (11, 20)\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:nixtla.nixtla_client:Validating inputs...\n", - "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n", - "INFO:nixtla.nixtla_client:Inferred freq: h\n", - "INFO:nixtla.nixtla_client:Attempt 1 failed...\n", - "INFO:nixtla.nixtla_client:Attempt 2 failed...\n", - "INFO:nixtla.nixtla_client:Attempt 3 failed...\n", - "INFO:nixtla.nixtla_client:Attempt 4 failed...\n", - "INFO:nixtla.nixtla_client:Attempt 5 failed...\n", - "INFO:nixtla.nixtla_client:Attempt 6 failed...\n" + "c:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" ] }, { - "ename": "ConnectError", - "evalue": "[Errno 11001] getaddrinfo failed", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mConnectError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:69\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[1;34m()\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 69\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:233\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 232\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[1;32m--> 233\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_pool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreq\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py:216\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 215\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_close_connections(closing)\n\u001b[1;32m--> 216\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# Return the response. Note that in this case we still have to manage\u001b[39;00m\n\u001b[0;32m 219\u001b[0m \u001b[38;5;66;03m# the point at which the response is closed.\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection_pool.py:196\u001b[0m, in \u001b[0;36mConnectionPool.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 194\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 195\u001b[0m \u001b[38;5;66;03m# Send the request on the assigned connection.\u001b[39;00m\n\u001b[1;32m--> 196\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 197\u001b[0m \u001b[43m \u001b[49m\u001b[43mpool_request\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\n\u001b[0;32m 198\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 199\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ConnectionNotAvailable:\n\u001b[0;32m 200\u001b[0m \u001b[38;5;66;03m# In some cases a connection may initially be available to\u001b[39;00m\n\u001b[0;32m 201\u001b[0m \u001b[38;5;66;03m# handle a request, but then become unavailable.\u001b[39;00m\n\u001b[0;32m 202\u001b[0m \u001b[38;5;66;03m#\u001b[39;00m\n\u001b[0;32m 203\u001b[0m \u001b[38;5;66;03m# In this case we clear the connection and try again.\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection.py:99\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 98\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connect_failed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 99\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m 101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection\u001b[38;5;241m.\u001b[39mhandle_request(request)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection.py:76\u001b[0m, in \u001b[0;36mHTTPConnection.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_connection \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m---> 76\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 78\u001b[0m ssl_object \u001b[38;5;241m=\u001b[39m stream\u001b[38;5;241m.\u001b[39mget_extra_info(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mssl_object\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_sync\\connection.py:122\u001b[0m, in \u001b[0;36mHTTPConnection._connect\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m Trace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconnect_tcp\u001b[39m\u001b[38;5;124m\"\u001b[39m, logger, request, kwargs) \u001b[38;5;28;01mas\u001b[39;00m trace:\n\u001b[1;32m--> 122\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_network_backend\u001b[38;5;241m.\u001b[39mconnect_tcp(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 123\u001b[0m trace\u001b[38;5;241m.\u001b[39mreturn_value \u001b[38;5;241m=\u001b[39m stream\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_backends\\sync.py:205\u001b[0m, in \u001b[0;36mSyncBackend.connect_tcp\u001b[1;34m(self, host, port, timeout, local_address, socket_options)\u001b[0m\n\u001b[0;32m 200\u001b[0m exc_map: ExceptionMapping \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 201\u001b[0m socket\u001b[38;5;241m.\u001b[39mtimeout: ConnectTimeout,\n\u001b[0;32m 202\u001b[0m \u001b[38;5;167;01mOSError\u001b[39;00m: ConnectError,\n\u001b[0;32m 203\u001b[0m }\n\u001b[1;32m--> 205\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_exceptions(exc_map):\n\u001b[0;32m 206\u001b[0m sock \u001b[38;5;241m=\u001b[39m socket\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[0;32m 207\u001b[0m address,\n\u001b[0;32m 208\u001b[0m timeout,\n\u001b[0;32m 209\u001b[0m source_address\u001b[38;5;241m=\u001b[39msource_address,\n\u001b[0;32m 210\u001b[0m )\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\contextlib.py:153\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 153\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 155\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpcore\\_exceptions.py:14\u001b[0m, in \u001b[0;36mmap_exceptions\u001b[1;34m(map)\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(exc, from_exc):\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m to_exc(exc) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n\u001b[0;32m 15\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n", - "\u001b[1;31mConnectError\u001b[0m: [Errno 11001] getaddrinfo failed", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mConnectError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 5\u001b[0m\n\u001b[0;32m 2\u001b[0m nixtla_client \u001b[38;5;241m=\u001b[39m NixtlaClient(api_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnixtla-tok-31lvshOqu4BTxxhJJY9Y6oxZQHciSZYElRgwsh5Btg60GK5JG3imcfbbUhdSIOyV2HSadFlgdJTpDsJb\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 4\u001b[0m \u001b[38;5;66;03m# 3. Forecast the next 24 hours\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m fcst_df \u001b[38;5;241m=\u001b[39m \u001b[43mnixtla_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m24\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m80\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m90\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# 4. Plot your results (optional)\u001b[39;00m\n\u001b[0;32m 8\u001b[0m nixtla_client\u001b[38;5;241m.\u001b[39mplot(df, fcst_df, time_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m'\u001b[39m, target_col\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, level\u001b[38;5;241m=\u001b[39m[\u001b[38;5;241m80\u001b[39m, \u001b[38;5;241m90\u001b[39m])\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:60\u001b[0m, in \u001b[0;36mdeprecated_argument..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m argument duplicated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 59\u001b[0m kwargs[new_name] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(old_name)\n\u001b[1;32m---> 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:60\u001b[0m, in \u001b[0;36mdeprecated_argument..decorator..wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m argument duplicated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 59\u001b[0m kwargs[new_name] \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(old_name)\n\u001b[1;32m---> 60\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:1304\u001b[0m, in \u001b[0;36mNixtlaClient.forecast\u001b[1;34m(self, df, h, freq, id_col, time_col, target_col, X_df, level, quantiles, finetune_steps, finetune_loss, clean_ex_first, validate_api_key, add_history, date_features, date_features_to_one_hot, model, num_partitions)\u001b[0m\n\u001b[0;32m 1227\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Forecast your time series using TimeGPT.\u001b[39;00m\n\u001b[0;32m 1228\u001b[0m \n\u001b[0;32m 1229\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1301\u001b[0m \u001b[38;5;124;03m predictions (if level is not None).\u001b[39;00m\n\u001b[0;32m 1302\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1303\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(df, pd\u001b[38;5;241m.\u001b[39mDataFrame):\n\u001b[1;32m-> 1304\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forecast\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1305\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1306\u001b[0m \u001b[43m \u001b[49m\u001b[43mh\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mh\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1307\u001b[0m \u001b[43m \u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1308\u001b[0m \u001b[43m \u001b[49m\u001b[43mid_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mid_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1309\u001b[0m \u001b[43m \u001b[49m\u001b[43mtime_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtime_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1310\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget_col\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtarget_col\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1311\u001b[0m \u001b[43m \u001b[49m\u001b[43mX_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_df\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1312\u001b[0m \u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1313\u001b[0m \u001b[43m \u001b[49m\u001b[43mquantiles\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquantiles\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1314\u001b[0m \u001b[43m \u001b[49m\u001b[43mfinetune_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1315\u001b[0m \u001b[43m \u001b[49m\u001b[43mfinetune_loss\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfinetune_loss\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1316\u001b[0m \u001b[43m \u001b[49m\u001b[43mclean_ex_first\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclean_ex_first\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1317\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidate_api_key\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidate_api_key\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1318\u001b[0m \u001b[43m \u001b[49m\u001b[43madd_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madd_history\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1319\u001b[0m \u001b[43m \u001b[49m\u001b[43mdate_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1320\u001b[0m \u001b[43m \u001b[49m\u001b[43mdate_features_to_one_hot\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdate_features_to_one_hot\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1321\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1322\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_partitions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_partitions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1323\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1324\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1325\u001b[0m dist_nixtla_client \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_instantiate_distributed_nixtla_client()\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:730\u001b[0m, in \u001b[0;36mvalidate_model_parameter..wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 725\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m model \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupported_models:\n\u001b[0;32m 726\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 727\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munsupported model: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 728\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msupported models: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msupported_models)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 729\u001b[0m )\n\u001b[1;32m--> 730\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:748\u001b[0m, in \u001b[0;36mpartition_by_uid..wrapper\u001b[1;34m(self, num_partitions, **kwargs)\u001b[0m\n\u001b[0;32m 746\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, num_partitions, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 747\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_partitions \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m num_partitions \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m--> 748\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs, num_partitions\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 749\u001b[0m df \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 750\u001b[0m X_df \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX_df\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:976\u001b[0m, in \u001b[0;36m_NixtlaClient._forecast\u001b[1;34m(self, df, h, freq, id_col, time_col, target_col, X_df, level, quantiles, finetune_steps, finetune_loss, clean_ex_first, validate_api_key, add_history, date_features, date_features_to_one_hot, model, num_partitions)\u001b[0m\n\u001b[0;32m 954\u001b[0m nixtla_client_model \u001b[38;5;241m=\u001b[39m _NixtlaClientModel(\n\u001b[0;32m 955\u001b[0m client\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclient,\n\u001b[0;32m 956\u001b[0m h\u001b[38;5;241m=\u001b[39mh,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 971\u001b[0m max_wait_time\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_wait_time,\n\u001b[0;32m 972\u001b[0m )\n\u001b[0;32m 973\u001b[0m df, X_df, uids_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_uids_to_categorical(\n\u001b[0;32m 974\u001b[0m df\u001b[38;5;241m=\u001b[39mdf, X_df\u001b[38;5;241m=\u001b[39mX_df, id_col\u001b[38;5;241m=\u001b[39mid_col\n\u001b[0;32m 975\u001b[0m )\n\u001b[1;32m--> 976\u001b[0m fcst_df \u001b[38;5;241m=\u001b[39m \u001b[43mnixtla_client_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforecast\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 977\u001b[0m \u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_df\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mX_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43madd_history\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madd_history\u001b[49m\n\u001b[0;32m 978\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 979\u001b[0m fcst_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restore_uids(fcst_df, dtype\u001b[38;5;241m=\u001b[39muids_dtype, id_col\u001b[38;5;241m=\u001b[39mid_col)\n\u001b[0;32m 980\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mweights_x \u001b[38;5;241m=\u001b[39m nixtla_client_model\u001b[38;5;241m.\u001b[39mweights_x\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:559\u001b[0m, in \u001b[0;36m_NixtlaClientModel.forecast\u001b[1;34m(self, df, X_df, add_history)\u001b[0m\n\u001b[0;32m 557\u001b[0m main_logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPreprocessing dataframes...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 558\u001b[0m Y_df, X_df, x_cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpreprocess_dataframes(df\u001b[38;5;241m=\u001b[39mdf, X_df\u001b[38;5;241m=\u001b[39mX_df)\n\u001b[1;32m--> 559\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_model_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 560\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mh \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_horizon:\n\u001b[0;32m 561\u001b[0m main_logger\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[0;32m 562\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mThe specified horizon \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m exceeds the model horizon. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m 563\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis may lead to less accurate forecasts. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 564\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease consider using a smaller horizon.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 565\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:513\u001b[0m, in \u001b[0;36m_NixtlaClientModel.set_model_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 512\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_model_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 513\u001b[0m model_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_api\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 514\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel_params\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 515\u001b[0m \u001b[43m \u001b[49m\u001b[43mSingleSeriesForecast\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfreq\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 516\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 517\u001b[0m model_params \u001b[38;5;241m=\u001b[39m model_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdetail\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 518\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minput_size, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel_horizon \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 519\u001b[0m model_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minput_size\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 520\u001b[0m model_params[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhorizon\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 521\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\nixtla_client.py:229\u001b[0m, in \u001b[0;36m_NixtlaClientModel._call_api\u001b[1;34m(self, method, request)\u001b[0m\n\u001b[0;32m 228\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_call_api\u001b[39m(\u001b[38;5;28mself\u001b[39m, method, request):\n\u001b[1;32m--> 229\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_retry_strategy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmethod\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m response:\n\u001b[0;32m 231\u001b[0m response \u001b[38;5;241m=\u001b[39m response[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:332\u001b[0m, in \u001b[0;36mBaseRetrying.wraps..wrapped_f\u001b[1;34m(*args, **kw)\u001b[0m\n\u001b[0;32m 328\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(\n\u001b[0;32m 329\u001b[0m f, functools\u001b[38;5;241m.\u001b[39mWRAPPER_ASSIGNMENTS \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__defaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__kwdefaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 330\u001b[0m )\n\u001b[0;32m 331\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs: t\u001b[38;5;241m.\u001b[39mAny, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mAny:\n\u001b[1;32m--> 332\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(f, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw)\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:469\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 467\u001b[0m retry_state \u001b[38;5;241m=\u001b[39m RetryCallState(retry_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, fn\u001b[38;5;241m=\u001b[39mfn, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m 468\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 469\u001b[0m do \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[0;32m 471\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:370\u001b[0m, in \u001b[0;36mBaseRetrying.iter\u001b[1;34m(self, retry_state)\u001b[0m\n\u001b[0;32m 368\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 369\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mactions:\n\u001b[1;32m--> 370\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43maction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 371\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:412\u001b[0m, in \u001b[0;36mBaseRetrying._post_stop_check_actions..exc_check\u001b[1;34m(rs)\u001b[0m\n\u001b[0;32m 410\u001b[0m retry_exc \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mretry_error_cls(fut)\n\u001b[0;32m 411\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreraise:\n\u001b[1;32m--> 412\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[43mretry_exc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 413\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m retry_exc \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfut\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexception\u001b[39;00m()\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:185\u001b[0m, in \u001b[0;36mRetryError.reraise\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mreraise\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mNoReturn:\n\u001b[0;32m 184\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_attempt\u001b[38;5;241m.\u001b[39mfailed:\n\u001b[1;32m--> 185\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlast_attempt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\concurrent\\futures\\_base.py:438\u001b[0m, in \u001b[0;36mFuture.result\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 436\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[0;32m 437\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[1;32m--> 438\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 440\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[0;32m 442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\concurrent\\futures\\_base.py:390\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[0;32m 389\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 390\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[0;32m 391\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 392\u001b[0m \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[0;32m 393\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\tenacity\\__init__.py:472\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[1;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 470\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[0;32m 471\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 472\u001b[0m result \u001b[38;5;241m=\u001b[39m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 473\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m: \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n\u001b[0;32m 474\u001b[0m retry_state\u001b[38;5;241m.\u001b[39mset_exception(sys\u001b[38;5;241m.\u001b[39mexc_info()) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\client.py:2283\u001b[0m, in \u001b[0;36mNixtla.model_params\u001b[1;34m(self, request, request_options)\u001b[0m\n\u001b[0;32m 2263\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmodel_params\u001b[39m(\n\u001b[0;32m 2264\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m, request: SingleSeriesForecast, request_options: typing\u001b[38;5;241m.\u001b[39mOptional[RequestOptions] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 2265\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m typing\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m 2266\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 2267\u001b[0m \u001b[38;5;124;03m Parameters:\u001b[39;00m\n\u001b[0;32m 2268\u001b[0m \u001b[38;5;124;03m - request: SingleSeriesForecast.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 2281\u001b[0m \u001b[38;5;124;03m )\u001b[39;00m\n\u001b[0;32m 2282\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2283\u001b[0m _response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhttpx_client\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2284\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPOST\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2285\u001b[0m \u001b[43m \u001b[49m\u001b[43murllib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murljoin\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_base_url\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmodel_params\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2286\u001b[0m \u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2287\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_query_parameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[0;32m 2288\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2289\u001b[0m \u001b[43m \u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2290\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_body_parameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[0;32m 2291\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 2292\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2293\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mremove_none_from_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_body_parameters\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2294\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2295\u001b[0m \u001b[43m \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjsonable_encoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2296\u001b[0m \u001b[43m \u001b[49m\u001b[43mremove_none_from_dict\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2297\u001b[0m \u001b[43m \u001b[49m\u001b[43m{\u001b[49m\n\u001b[0;32m 2298\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_headers\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2299\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43madditional_headers\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2300\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\n\u001b[0;32m 2301\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2302\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2303\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimeout_in_seconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2304\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtimeout_in_seconds\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\n\u001b[0;32m 2305\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_client_wrapper\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_timeout\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2306\u001b[0m \u001b[43m \u001b[49m\u001b[43mretries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2307\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrequest_options\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mmax_retries\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mrequest_options\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# type: ignore\u001b[39;49;00m\n\u001b[0;32m 2308\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2309\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;241m200\u001b[39m \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m _response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m300\u001b[39m:\n\u001b[0;32m 2310\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pydantic_v1\u001b[38;5;241m.\u001b[39mparse_obj_as(typing\u001b[38;5;241m.\u001b[39mAny, _response\u001b[38;5;241m.\u001b[39mjson()) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\nixtla\\core\\http_client.py:94\u001b[0m, in \u001b[0;36mHttpClient.request\u001b[1;34m(self, max_retries, retries, *args, **kwargs)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(httpx\u001b[38;5;241m.\u001b[39mClient\u001b[38;5;241m.\u001b[39mrequest)\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[0;32m 92\u001b[0m \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs: typing\u001b[38;5;241m.\u001b[39mAny, max_retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, retries: \u001b[38;5;28mint\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: typing\u001b[38;5;241m.\u001b[39mAny\n\u001b[0;32m 93\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m httpx\u001b[38;5;241m.\u001b[39mResponse:\n\u001b[1;32m---> 94\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhttpx_client\u001b[38;5;241m.\u001b[39mrequest(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 95\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _should_retry(response\u001b[38;5;241m=\u001b[39mresponse):\n\u001b[0;32m 96\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m max_retries \u001b[38;5;241m>\u001b[39m retries:\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:827\u001b[0m, in \u001b[0;36mClient.request\u001b[1;34m(self, method, url, content, data, files, json, params, headers, cookies, auth, follow_redirects, timeout, extensions)\u001b[0m\n\u001b[0;32m 812\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(message, \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[0;32m 814\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_request(\n\u001b[0;32m 815\u001b[0m method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[0;32m 816\u001b[0m url\u001b[38;5;241m=\u001b[39murl,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 825\u001b[0m extensions\u001b[38;5;241m=\u001b[39mextensions,\n\u001b[0;32m 826\u001b[0m )\n\u001b[1;32m--> 827\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:914\u001b[0m, in \u001b[0;36mClient.send\u001b[1;34m(self, request, stream, auth, follow_redirects)\u001b[0m\n\u001b[0;32m 906\u001b[0m follow_redirects \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m 907\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfollow_redirects\n\u001b[0;32m 908\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(follow_redirects, UseClientDefault)\n\u001b[0;32m 909\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m follow_redirects\n\u001b[0;32m 910\u001b[0m )\n\u001b[0;32m 912\u001b[0m auth \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_request_auth(request, auth)\n\u001b[1;32m--> 914\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_auth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 915\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 916\u001b[0m \u001b[43m \u001b[49m\u001b[43mauth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mauth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 917\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 918\u001b[0m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 919\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 920\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 921\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:942\u001b[0m, in \u001b[0;36mClient._send_handling_auth\u001b[1;34m(self, request, auth, follow_redirects, history)\u001b[0m\n\u001b[0;32m 939\u001b[0m request \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(auth_flow)\n\u001b[0;32m 941\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m--> 942\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_handling_redirects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 943\u001b[0m \u001b[43m \u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 944\u001b[0m \u001b[43m \u001b[49m\u001b[43mfollow_redirects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfollow_redirects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 945\u001b[0m \u001b[43m \u001b[49m\u001b[43mhistory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhistory\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 946\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 947\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 948\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:979\u001b[0m, in \u001b[0;36mClient._send_handling_redirects\u001b[1;34m(self, request, follow_redirects, history)\u001b[0m\n\u001b[0;32m 976\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrequest\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m 977\u001b[0m hook(request)\n\u001b[1;32m--> 979\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_send_single_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 980\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 981\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m hook \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_event_hooks[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresponse\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_client.py:1015\u001b[0m, in \u001b[0;36mClient._send_single_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 1010\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m 1011\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAttempted to send an async request with a sync Client instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 1012\u001b[0m )\n\u001b[0;32m 1014\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m request_context(request\u001b[38;5;241m=\u001b[39mrequest):\n\u001b[1;32m-> 1015\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mtransport\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhandle_request\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1017\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(response\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[0;32m 1019\u001b[0m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;241m=\u001b[39m request\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:232\u001b[0m, in \u001b[0;36mHTTPTransport.handle_request\u001b[1;34m(self, request)\u001b[0m\n\u001b[0;32m 218\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(request\u001b[38;5;241m.\u001b[39mstream, SyncByteStream)\n\u001b[0;32m 220\u001b[0m req \u001b[38;5;241m=\u001b[39m httpcore\u001b[38;5;241m.\u001b[39mRequest(\n\u001b[0;32m 221\u001b[0m method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m 222\u001b[0m url\u001b[38;5;241m=\u001b[39mhttpcore\u001b[38;5;241m.\u001b[39mURL(\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 230\u001b[0m extensions\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mextensions,\n\u001b[0;32m 231\u001b[0m )\n\u001b[1;32m--> 232\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m map_httpcore_exceptions():\n\u001b[0;32m 233\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pool\u001b[38;5;241m.\u001b[39mhandle_request(req)\n\u001b[0;32m 235\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(resp\u001b[38;5;241m.\u001b[39mstream, typing\u001b[38;5;241m.\u001b[39mIterable)\n", - "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python310\\lib\\contextlib.py:153\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__exit__\u001b[1;34m(self, typ, value, traceback)\u001b[0m\n\u001b[0;32m 151\u001b[0m value \u001b[38;5;241m=\u001b[39m typ()\n\u001b[0;32m 152\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 153\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mthrow\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtyp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraceback\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m exc:\n\u001b[0;32m 155\u001b[0m \u001b[38;5;66;03m# Suppress StopIteration *unless* it's the same exception that\u001b[39;00m\n\u001b[0;32m 156\u001b[0m \u001b[38;5;66;03m# was passed to throw(). This prevents a StopIteration\u001b[39;00m\n\u001b[0;32m 157\u001b[0m \u001b[38;5;66;03m# raised inside the \"with\" statement from being suppressed.\u001b[39;00m\n\u001b[0;32m 158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m exc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m value\n", - "File \u001b[1;32mc:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\httpx\\_transports\\default.py:86\u001b[0m, in \u001b[0;36mmap_httpcore_exceptions\u001b[1;34m()\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m\n\u001b[0;32m 85\u001b[0m message \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(exc)\n\u001b[1;32m---> 86\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m mapped_exc(message) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mexc\u001b[39;00m\n", - "\u001b[1;31mConnectError\u001b[0m: [Errno 11001] getaddrinfo failed" + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 9s/step - loss: 0.0414 - val_loss: 0.0969\n", + "Epoch 2/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0291 - val_loss: 0.0734\n", + "Epoch 3/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0227 - val_loss: 0.0538\n", + "Epoch 4/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0270 - val_loss: 0.0434\n", + "Epoch 5/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.0283 - val_loss: 0.0425\n", + "Epoch 6/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0229 - val_loss: 0.0456\n", + "Epoch 7/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0260 - val_loss: 0.0521\n", + "Epoch 8/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 222ms/step - loss: 0.0222 - val_loss: 0.0586\n", + "Epoch 9/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0212 - val_loss: 0.0650\n", + "Epoch 10/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 244ms/step - loss: 0.0192 - val_loss: 0.0704\n", + "Epoch 11/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - loss: 0.0178 - val_loss: 0.0729\n", + "Epoch 12/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 239ms/step - loss: 0.0224 - val_loss: 0.0734\n", + "Epoch 13/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step - loss: 0.0231 - val_loss: 0.0727\n", + "Epoch 14/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.0255 - val_loss: 0.0703\n", + "Epoch 15/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.0214 - val_loss: 0.0673\n", + "Epoch 16/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0225 - val_loss: 0.0637\n", + "Epoch 17/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 0.0183 - val_loss: 0.0602\n", + "Epoch 18/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 196ms/step - loss: 0.0270 - val_loss: 0.0570\n", + "Epoch 19/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0269 - val_loss: 0.0545\n", + "Epoch 20/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0210 - val_loss: 0.0532\n", + "Epoch 21/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0194 - val_loss: 0.0528\n", + "Epoch 22/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0194 - val_loss: 0.0525\n", + "Epoch 23/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0154 - val_loss: 0.0520\n", + "Epoch 24/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 182ms/step - loss: 0.0212 - val_loss: 0.0512\n", + "Epoch 25/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 262ms/step - loss: 0.0211 - val_loss: 0.0501\n", + "Epoch 26/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.0242 - val_loss: 0.0494\n", + "Epoch 27/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0201 - val_loss: 0.0483\n", + "Epoch 28/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.0183 - val_loss: 0.0476\n", + "Epoch 29/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0172 - val_loss: 0.0474\n", + "Epoch 30/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0198 - val_loss: 0.0469\n", + "Epoch 31/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 185ms/step - loss: 0.0203 - val_loss: 0.0468\n", + "Epoch 32/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0208 - val_loss: 0.0466\n", + "Epoch 33/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0191 - val_loss: 0.0457\n", + "Epoch 34/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0191 - val_loss: 0.0441\n", + "Epoch 35/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0173 - val_loss: 0.0422\n", + "Epoch 36/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0216 - val_loss: 0.0409\n", + "Epoch 37/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0143 - val_loss: 0.0400\n", + "Epoch 38/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 216ms/step - loss: 0.0167 - val_loss: 0.0381\n", + "Epoch 39/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 306ms/step - loss: 0.0210 - val_loss: 0.0362\n", + "Epoch 40/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 253ms/step - loss: 0.0212 - val_loss: 0.0354\n", + "Epoch 41/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0189 - val_loss: 0.0354\n", + "Epoch 42/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 211ms/step - loss: 0.0171 - val_loss: 0.0354\n", + "Epoch 43/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 201ms/step - loss: 0.0161 - val_loss: 0.0356\n", + "Epoch 44/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0218 - val_loss: 0.0362\n", + "Epoch 45/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0162 - val_loss: 0.0361\n", + "Epoch 46/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0160 - val_loss: 0.0357\n", + "Epoch 47/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0149 - val_loss: 0.0352\n", + "Epoch 48/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0168 - val_loss: 0.0331\n", + "Epoch 49/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0192 - val_loss: 0.0305\n", + "Epoch 50/50\n", + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 219ms/step - loss: 0.0205 - val_loss: 0.0272\n" ] + }, + { + "data": { + "text/plain": [ + "['scaler.gz']" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# 1. Instantiate the NixtlaClient\n", - "nixtla_client = NixtlaClient(api_key = 'nixtla-tok-31lvshOqu4BTxxhJJY9Y6oxZQHciSZYElRgwsh5Btg60GK5JG3imcfbbUhdSIOyV2HSadFlgdJTpDsJb')\n", + "df = pd.read_csv('data/binance_btc_usdt_candlestick.csv', index_col='timestamp', parse_dates=True)\n", + "X, y, scaler = prepare_lstm_data(df, 'close', seq_length=20)\n", + "input_shape = (X.shape[1], 1)\n", "\n", - "# 3. Forecast the next 24 hours\n", - "fcst_df = nixtla_client.forecast(df, h=24, level=[80, 90])\n", + "lstm_model = build_lstm_model(input_shape)\n", + "lstm_model = train_lstm_model(lstm_model, X, y, epochs=50, batch_size=32)\n", "\n", - "# 4. Plot your results (optional)\n", - "nixtla_client.plot(df, fcst_df, time_col='ds', target_col='y', level=[80, 90])" + "# Save the model and scaler\n", + "lstm_model.save('my_model.keras')\n", + "import joblib\n", + "joblib.dump(scaler, 'scaler.gz')\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.models import load_model\n", + "import joblib\n", + "\n", + "def load_lstm_model_and_scaler(model_path='my_model.keras', scaler_path='scaler.gz'):\n", + " model = load_model(model_path)\n", + " scaler = joblib.load(scaler_path)\n", + " return model, scaler\n", + "\n", + "def make_lstm_predictions(model, scaler, df, seq_length=20):\n", + " data = df['close'].values.reshape(-1, 1)\n", + " scaled_data = scaler.transform(data)\n", + " \n", + " X_test = []\n", + " for i in range(seq_length, len(scaled_data)):\n", + " X_test.append(scaled_data[i-seq_length:i, 0])\n", + " \n", + " X_test = np.array(X_test)\n", + " X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n", + " \n", + " predictions = model.predict(X_test)\n", + " predictions = scaler.inverse_transform(predictions)\n", + " return predictions\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 842ms/step\n" + ] + } + ], + "source": [ + "lstm_model, scaler = load_lstm_model_and_scaler()\n", + "predictions = make_lstm_predictions(lstm_model, scaler, df)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[32737.451],\n", + " [32878.387],\n", + " [33015.863],\n", + " [33109.344],\n", + " [33171.016],\n", + " [33254.03 ],\n", + " [33315.6 ],\n", + " [33331.934],\n", + " [33315.68 ],\n", + " [33231.465],\n", + " [33149.55 ]], dtype=float32)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predictions" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "INFO:nixtla.nixtla_client:Validating inputs...\n", - "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n", - "c:\\Users\\user\\Downloads\\ten_academy\\week9\\Scalable_Backtesting_Infrastructure_for_Crypto_Trading\\backtest\\lib\\site-packages\\utilsforecast\\preprocessing.py:188: UserWarning: Some values were lost during filling, please make sure that all your times meet the specified frequency.\n", - "For example if you have 'W-TUE' as your frequency, make sure that all your times are actually Tuesdays.\n", - " warnings.warn(\n", - "INFO:nixtla.nixtla_client:Calling Anomaly Detector Endpoint...\n" + "[*********************100%%**********************] 1 of 1 completed\n" ] - }, + } + ], + "source": [ + "import yfinance as yf\n", + "import pandas as pd\n", + "\n", + "# def download_data(ticker, start_date, end_date):\n", + "data = yf.download(\"NVDA\", start=\"2023-11-01\", end=\"2024-05-01\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ { "data": { - "image/png": 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OpenHighLowCloseAdj CloseVolume
Date
2023-11-0140.88399942.38100140.86899942.32500142.315823437593000
2023-11-0243.32799943.88399942.89400143.50600143.496559409172000
2023-11-0344.02000045.30899843.72300045.00500144.995239424610000
2023-11-0645.28500045.93500144.89899845.75099945.741074400733000
2023-11-0745.71900246.21799945.15800145.95500245.945034343165000
\n", + "
" + ], "text/plain": [ - "
" + " Open High Low Close Adj Close Volume\n", + "Date \n", + "2023-11-01 40.883999 42.381001 40.868999 42.325001 42.315823 437593000\n", + "2023-11-02 43.327999 43.883999 42.894001 43.506001 43.496559 409172000\n", + "2023-11-03 44.020000 45.308998 43.723000 45.005001 44.995239 424610000\n", + "2023-11-06 45.285000 45.935001 44.898998 45.750999 45.741074 400733000\n", + "2023-11-07 45.719002 46.217999 45.158001 45.955002 45.945034 343165000" ] }, - "execution_count": 7, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# 3. Detect Anomalies \n", - "anomalies_df = nixtla_client.detect_anomalies(df, time_col='ds', target_col='y', freq='D')\n", + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Date\n", + "2023-11-01 42.325001\n", + "2023-11-02 43.506001\n", + "2023-11-03 45.005001\n", + "2023-11-06 45.750999\n", + "2023-11-07 45.955002\n", + " ... \n", + "2024-04-24 79.677002\n", + "2024-04-25 82.632004\n", + "2024-04-26 87.735001\n", + "2024-04-29 87.757004\n", + "2024-04-30 86.402000\n", + "Name: Close, Length: 124, dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data['Close']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "# Example backtesting logic using predictions\n", + "initial_cash = 10000\n", + "cash = initial_cash\n", + "position = 0 # 1 for holding stock, 0 for no stock\n", + "for i in range(len(predictions)):\n", + " predicted_price = predictions[i]\n", + " actual_price = df['close'].values[-len(predictions) + i]\n", + " \n", + " if predicted_price > actual_price and cash > actual_price:\n", + " # Buy signal\n", + " cash -= actual_price\n", + " position += 1\n", + " elif predicted_price < actual_price and position > 0:\n", + " # Sell signal\n", + " cash += actual_price\n", + " position -= 1\n", "\n", - "# 4. Plot your results (optional)\n", - "nixtla_client.plot(df, anomalies_df,time_col='ds', target_col='y')" + "# Calculate metrics\n", + "final_value = cash + position * df['close'].values[-1]\n", + "gross_profit = final_value - initial_cash\n", + "metrics = {\n", + " 'gross_profit': gross_profit,\n", + "- 'net_profit': gross_profit, # Add any transaction costs if applicable-\n", + " 'number_of_trades': position,\n", + " 'winning_trades': position if gross_profit > 0 else 0,\n", + " 'losing_trades': position if gross_profit <= 0 else 0,\n", + " 'max_drawdown': -1000, # Calculate this properly based on your strategy\n", + " 'sharpe_ratio': 1.5 # Calculate this properly based on your strategy\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([33149.55], dtype=float32)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "predicted_price" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "33504.69" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "actual_price" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'gross_profit': 0.0,\n", + " 'net_profit': 0.0,\n", + " 'number_of_trades': 0,\n", + " 'winning_trades': 0,\n", + " 'losing_trades': 0,\n", + " 'max_drawdown': -1000,\n", + " 'sharpe_ratio': 1.5}" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics" ] } ], From 4d58ea8529c600f388ff674421b9d3c59b3bfe22 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 09:55:47 +0300 Subject: [PATCH 22/61] mlflow: datas of mlflow --- .../10907cb5bf4b4682a3e3263cd492417d/meta.yaml | 15 +++++++++++++++ .../tags/mlflow.runName | 1 + 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new file mode 100644 index 0000000..7a2e1bc --- /dev/null +++ b/mlruns/0/10907cb5bf4b4682a3e3263cd492417d/meta.yaml @@ -0,0 +1,15 @@ +artifact_uri: mlflow-artifacts:/0/10907cb5bf4b4682a3e3263cd492417d/artifacts +end_time: 1719470197620 +entry_point_name: '' +experiment_id: '0' +lifecycle_stage: active +run_id: 10907cb5bf4b4682a3e3263cd492417d +run_name: silent-yak-815 +run_uuid: 10907cb5bf4b4682a3e3263cd492417d +source_name: '' +source_type: 4 +source_version: '' +start_time: 1719470193459 +status: 4 +tags: [] +user_id: user diff --git a/mlruns/0/10907cb5bf4b4682a3e3263cd492417d/tags/mlflow.runName b/mlruns/0/10907cb5bf4b4682a3e3263cd492417d/tags/mlflow.runName new file mode 100644 index 0000000..76d0fe7 --- /dev/null +++ b/mlruns/0/10907cb5bf4b4682a3e3263cd492417d/tags/mlflow.runName @@ -0,0 +1 @@ +silent-yak-815 \ No newline at end of file diff --git a/mlruns/0/10907cb5bf4b4682a3e3263cd492417d/tags/mlflow.source.name b/mlruns/0/10907cb5bf4b4682a3e3263cd492417d/tags/mlflow.source.name 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c73674e37cc0dbaa28e1cb0022ec8608cb5e949f Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 10:10:52 +0300 Subject: [PATCH 23/61] chore: add mlruns to gitifnore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 8943095..ecb2aff 100644 --- a/.gitignore +++ b/.gitignore @@ -120,7 +120,7 @@ celerybeat.pid # SageMath parsed files *.sage.py - +mlruns/ # Environments .env .venv From 6e95e4ab50c908947cc3b719d989abd9aab3d656 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 10:11:15 +0300 Subject: [PATCH 24/61] feat: add max draw down to model column --- backend/models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/backend/models.py b/backend/models.py index 1490c62..38c9446 100644 --- a/backend/models.py +++ b/backend/models.py @@ -47,6 +47,7 @@ class BacktestResult(Base): initial_cash = Column(Float, nullable=False) final_value = Column(Float, nullable=False) percentage_return = Column(Float) + max_drawdown = Column(Float) total_trades = Column(Integer) winning_trades = Column(Integer) losing_trades = Column(Integer) From 24914a6dda7c8c7b6f223f7118557100a1c8e14a Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 10:11:34 +0300 Subject: [PATCH 25/61] feat: add max drawdown to schema --- backend/schemas.py | 1 + 1 file changed, 1 insertion(+) diff --git a/backend/schemas.py b/backend/schemas.py index 351b505..9411591 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -83,6 +83,7 @@ class BacktestResultBase(BaseModel): winning_trades: int losing_trades: int sharpe_ratio: Optional[float] = None + max_drawdown: Optional[float] = None class BacktestResultCreate(BacktestResultBase): pass From 53d969c8ff9f68dd8c2ca5585f822098b475eac3 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 10:11:58 +0300 Subject: [PATCH 26/61] feat: add error handling and features --- frontend/src/components/BacktestForm.js | 128 +++++++++++++----------- 1 file changed, 67 insertions(+), 61 deletions(-) diff --git a/frontend/src/components/BacktestForm.js b/frontend/src/components/BacktestForm.js index 1e783c5..e0121ee 100644 --- a/frontend/src/components/BacktestForm.js +++ b/frontend/src/components/BacktestForm.js @@ -14,22 +14,30 @@ function BacktestForm() { const [stocks, setStocks] = useState([]); const [stockDescription, setStockDescription] = useState(''); const [indicatorDescription, setIndicatorDescription] = useState(''); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); useEffect(() => { - // Simulate fetching indicators and stocks from an API const fetchIndicators = async () => { - const response = await fetch('http://127.0.0.1:8000/indicators/'); - const data = await response.json(); - console.log("The Response on Indicators is :: ",data); - - setIndicators(data); + try { + const response = await fetch('http://127.0.0.1:8000/indicators/'); + if (!response.ok) throw new Error('Error fetching indicators'); + const data = await response.json(); + setIndicators(data); + } catch (err) { + setError(err.message); + } }; const fetchStocks = async () => { - const response = await fetch('http://127.0.0.1:8000/stocks/'); - const data = await response.json(); - console.log("The Response on Stocks is :: ",data); - setStocks(data); + try { + const response = await fetch('http://127.0.0.1:8000/stocks/'); + if (!response.ok) throw new Error('Error fetching stocks'); + const data = await response.json(); + setStocks(data); + } catch (err) { + setError(err.message); + } }; fetchIndicators(); @@ -42,12 +50,8 @@ function BacktestForm() { [e.target.name]: e.target.value, }); - console.log("The changed s ::",e.target.name, "The Val =" ,e.target.value); - if (e.target.name === 'stock') { - console.log("It is here") const selectedStock = stocks.find(stock => stock.id === parseInt(e.target.value)); - console.log("Selected Stock :: ",selectedStock) setStockDescription(selectedStock ? selectedStock.description : ''); } @@ -59,54 +63,48 @@ function BacktestForm() { const handleSubmit = async (e) => { e.preventDefault(); - console.log(parameters); - let a = { + setLoading(true); + setError(null); + + let payload = { "period": 15, "start_date": parameters["startDate"], "end_date": parameters['endDate'], - "indicator_id": parameters["indicator"], // - "stock_id": parameters["stock"] // Nvidia - } - console.log("The final to be sent to API is :: ", a) - // API POST call to backend - const response = await fetch('http://127.0.0.1:8000/scenes/', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(a), - }); - // const response = await fetch('https://localhost'); - // const response = { - // "return": "10%", - // "numberOfTrades": 50, - // "winningTrades": 30, - // "losingTrades": 20, - // "maxDrawdown": "5%", - // "sharpeRatio": 1.5 - // }; - const scene = await response.json() - console.log("The Response of backtest is :: ",scene); - - const scene_id = scene.id; - console.log("The Scene ID is :: ",scene_id); - - // const response2 = await fetch('http://127.0.0.1:8000//backtests/'+scene_id+'/'); - const response2 = await fetch('http://127.0.0.1:8000/backtests/'+scene_id+'/', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify(""), - }); - - const data2 = await response2.json(); + "indicator_id": parameters["indicator"], + "stock_id": parameters["stock"] + }; - console.log("The Response of backtest is :: ",data2); - - // const data = await response.json(); - // const data = response; - setResults(data2[0]); + try { + const response = await fetch('http://127.0.0.1:8000/scenes/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(payload), + }); + + if (!response.ok) throw new Error('Error creating scene'); + + const scene = await response.json(); + const scene_id = scene.id; + + const response2 = await fetch(`http://127.0.0.1:8000/backtests/${scene_id}/`, { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(""), + }); + + if (!response2.ok) throw new Error('Error running backtest'); + + const data2 = await response2.json(); + setResults(data2[0]); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } }; return ( @@ -125,7 +123,7 @@ function BacktestForm() { value={parameters.paramsRange} onChange={handleChange} className="input input-bordered" - placeholder="e.g., 1000, 2000,10,000" + placeholder="e.g., 1000, 2000, 10,000" required />

@@ -202,11 +200,19 @@ function BacktestForm() {
- +
- {results && ( + {error && ( +
+ {error} +
+ )} + + {results && !loading && (

Start / End Portfolio

From 8c7e4acf3b32675a07d36cd2ebe5d3e34a9e3d6d Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 10:12:16 +0300 Subject: [PATCH 27/61] test: experimentation --- notebooks/backtesting.ipynb | 30 +++++++++++++++++++----------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/notebooks/backtesting.ipynb b/notebooks/backtesting.ipynb index 8ce8e8d..82e8045 100644 --- a/notebooks/backtesting.ipynb +++ b/notebooks/backtesting.ipynb @@ -340,7 +340,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +353,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -370,7 +370,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "(36, 15, datetime.date(2023, 9, 1), datetime.date(2024, 4, 1), 13, 3)\n" + "(1, 15, datetime.date(2023, 9, 1), datetime.date(2024, 4, 1), 13, 3)\n", + "(2, 15, datetime.date(2024, 1, 19), datetime.date(2024, 6, 26), 7, 3)\n", + "(6, 15, datetime.date(2023, 10, 20), datetime.date(2024, 6, 26), 8, 3)\n", + "(13, 15, datetime.date(2023, 11, 10), datetime.date(2024, 6, 26), 3, 3)\n", + "(14, 15, datetime.date(2023, 11, 10), datetime.date(2024, 6, 26), 6, 3)\n", + "(15, 15, datetime.date(2024, 5, 26), datetime.date(2024, 6, 26), 4, 3)\n", + "(16, 15, datetime.date(2024, 3, 7), datetime.date(2024, 6, 26), 13, 3)\n", + "(19, 15, datetime.date(2024, 1, 3), datetime.date(2024, 6, 26), 9, 5)\n", + "(20, 15, datetime.date(2023, 2, 9), datetime.date(2024, 6, 25), 13, 3)\n", + "(22, 15, datetime.date(2023, 12, 27), datetime.date(2024, 6, 25), 13, 3)\n" ] } ], @@ -389,18 +398,17 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from sqlalchemy import create_engine, text, inspect\n", - "from sqlalchemy.orm import sessionmaker\n", - "\n" + "from sqlalchemy.orm import sessionmaker" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -432,18 +440,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "\n", "# Execute the DROP TABLE command with CASCADE\n", "# drop all ['stocks', 'scenes', 'indicators', 'backtest_results', 'users']\n", - "session.execute(text(\"DROP TABLE backtest_results CASCADE\"))\n", + "# session.execute(text(\"DROP TABLE backtest_results CASCADE\"))\n", "# session.execute(text(\"DROP TABLE stocks CASCADE\"))\n", "# session.execute(text(\"DROP TABLE indicators CASCADE\"))\n", "# session.execute(text(\"DROP TABLE scenes CASCADE\"))\n", - "# session.execute(text(\"DROP TABLE users CASCADE\"))\n", + "session.execute(text(\"DROP TABLE users CASCADE\"))\n", "session.commit()\n", "\n", "# Close the session\n", @@ -452,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 10, "metadata": {}, "outputs": [ { From 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zC@}B=9Zpd5Ky}Ok>u_1IR7YODx~vL{;LB2lv9ZCa7#@=D>m=cDDIWzo=K7>DrN_4D zbinfdO4AnmI{;F-+jVLg)3&c&Q<@E*hV3;pqjDrV7EFq-@0*y1vXC%0?85zEx2-$$ z>zJFz8es!U?7ak-f$3{Bh%1Nno_vLj^aHpfJl32^UZQWNwCe@<2=fiv8`rY;8xq=R z*iSI!`sZ8h%XDrD?+2vw@fwKmmFmYa|A!{p z1`PJs?w2pGL&z#B>UAY0n8>M`P;H|~T+pO3n+Ga>0^u~uVzbfCW{YJCq%3$PHpyhF zYAGo*9xnll;b&CVWnGzgM~j{xa(nm7YNdpv;HqX9UKmId6urexJ|&bv8*2rypic>y zKgl|$_P3j755_N%$uq@`w6^cz}71*yjt1_0LW2{Gsy+IsLIEzRi{Wbz}lr0-ErCMi*0^rmdpet*LeKvsZqEiVL+ozht P>1#W-1qTpY4xIcqla$2V literal 0 HcmV?d00001 From 4756d3793d29c16e9543ce51da0aa1b3d8f4299a Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 10:12:43 +0300 Subject: [PATCH 29/61] lstm: experimentation --- scripts/backtesting/strategies/lstm_strategy.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/backtesting/strategies/lstm_strategy.py b/scripts/backtesting/strategies/lstm_strategy.py index 47a3c58..dc05233 100644 --- a/scripts/backtesting/strategies/lstm_strategy.py +++ b/scripts/backtesting/strategies/lstm_strategy.py @@ -42,7 +42,7 @@ def calculate_sharpe_ratio(returns, risk_free_rate=0): excess_returns = returns - risk_free_rate return np.mean(excess_returns) / np.std(excess_returns) -def run_backtest_with_lstm(scene_parameters, df): +def run_backtest_with_lstm(df): lstm_model, scaler = load_lstm_model_and_scaler() predictions = make_lstm_predictions(lstm_model, scaler, df) From 295a46a5e63f4a96bb338d780a53557ddc8149b1 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:34:50 +0300 Subject: [PATCH 30/61] feat: update frontend --- frontend/src/App.js | 7 ++- frontend/src/components/Login.js | 46 ++++++++++++++--- frontend/src/components/Navbar.js | 13 ++++- frontend/src/components/Signup.js | 82 +++++++++++++++++++++++++------ 4 files changed, 123 insertions(+), 25 deletions(-) diff --git a/frontend/src/App.js b/frontend/src/App.js index 57c325f..556d831 100644 --- a/frontend/src/App.js +++ b/frontend/src/App.js @@ -5,6 +5,11 @@ import BacktestForm from './components/BacktestForm'; import Signup from './components/Signup'; import Login from './components/Login'; +const PrivateRoute = ({ element: Element, ...rest }) => { + const token = localStorage.getItem('token'); + return token ? : ; +}; + function App() { return ( @@ -15,7 +20,7 @@ function App() { } /> } /> } /> - } /> + } />
diff --git a/frontend/src/components/Login.js b/frontend/src/components/Login.js index 7209930..aed28a9 100644 --- a/frontend/src/components/Login.js +++ b/frontend/src/components/Login.js @@ -1,7 +1,7 @@ -import React from 'react'; +import React, { useState } from 'react'; import { useFormik } from 'formik'; import * as Yup from 'yup'; -import { Link } from 'react-router-dom'; +import { Link, useNavigate } from 'react-router-dom'; const validationSchema = Yup.object({ username: Yup.string() @@ -16,15 +16,42 @@ const validationSchema = Yup.object({ }); function Login() { + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const navigate = useNavigate(); + const formik = useFormik({ initialValues: { username: '', password: '', }, validationSchema, - onSubmit: (values) => { - console.log(values); - // Handle login + onSubmit: async (values) => { + setLoading(true); + setError(null); + + const formData = new FormData(); + formData.append('username', values.username); + formData.append('password', values.password); + + try { + const response = await fetch('http://127.0.0.1:8000/auth/token', { + method: 'POST', + body: formData, + }); + + if (!response.ok) { + throw new Error('Invalid username or password'); + } + + const data = await response.json(); + localStorage.setItem('token', data.token); + navigate('/backtest'); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } }, }); @@ -68,11 +95,16 @@ function Login() { ) : null}
-
+ {error && ( +
+ {error} +
+ )}

Don't have an account? Sign up here

diff --git a/frontend/src/components/Navbar.js b/frontend/src/components/Navbar.js index dd71653..a85cb14 100644 --- a/frontend/src/components/Navbar.js +++ b/frontend/src/components/Navbar.js @@ -1,10 +1,16 @@ import React from 'react'; -import { Link, useLocation } from 'react-router-dom'; +import { Link, useLocation, useNavigate } from 'react-router-dom'; function Navbar() { const location = useLocation(); + const navigate = useNavigate(); const currentPath = location.pathname; + const handleLogout = () => { + localStorage.removeItem('token'); + navigate('/login'); + }; + return (
@@ -13,7 +19,10 @@ function Navbar() {
{currentPath === '/login' && Sign Up} {currentPath === '/signup' && Login} - {currentPath === '/backtest' && Logout} + {currentPath === '/backtest' && Profile} + {(currentPath === '/backtest' || currentPath === '/profile') && ( + + )}
); diff --git a/frontend/src/components/Signup.js b/frontend/src/components/Signup.js index 8377641..80dc530 100644 --- a/frontend/src/components/Signup.js +++ b/frontend/src/components/Signup.js @@ -1,11 +1,14 @@ -import React from 'react'; +import React, { useState } from 'react'; import { useFormik } from 'formik'; import * as Yup from 'yup'; -import { Link } from 'react-router-dom'; +import { Link, useNavigate } from 'react-router-dom'; const validationSchema = Yup.object({ - username: Yup.string() - .min(3, 'Username must be at least 3 characters') + full_name: Yup.string() + .min(3, 'Full name must be at least 3 characters') + .required('Required'), + email: Yup.string() + .email('Invalid email address') .required('Required'), password: Yup.string() .min(6, 'Password must be at least 6 characters') @@ -16,15 +19,42 @@ const validationSchema = Yup.object({ }); function Signup() { + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const navigate = useNavigate(); + const formik = useFormik({ initialValues: { - username: '', + full_name: '', + email: '', password: '', }, validationSchema, - onSubmit: (values) => { - console.log(values); - // Handle signin + onSubmit: async (values) => { + setLoading(true); + setError(null); + + try { + const response = await fetch('http://127.0.0.1:8000/auth/users/', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify(values), + }); + + if (!response.ok) { + const res = await response.json() + console.log("response is signup :: ", res) + throw new Error(res['detail']); + } + + navigate('/login'); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } }, }); @@ -35,19 +65,36 @@ function Signup() {
- {formik.touched.username && formik.errors.username ? ( -
{formik.errors.username}
+ {formik.touched.full_name && formik.errors.full_name ? ( +
{formik.errors.full_name}
+ ) : null} +
+
+ + + {formik.touched.email && formik.errors.email ? ( +
{formik.errors.email}
) : null}
@@ -68,11 +115,16 @@ function Signup() { ) : null}
-
+ {error && ( +
+ {error} +
+ )}

Already have an account? Login here

From a6096e0d3067e04032d4e788009701b0318826b0 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:35:08 +0300 Subject: [PATCH 31/61] chore: add logging --- backend/auth.py | 1 + 1 file changed, 1 insertion(+) diff --git a/backend/auth.py b/backend/auth.py index bbfa9bc..b609905 100644 --- a/backend/auth.py +++ b/backend/auth.py @@ -68,6 +68,7 @@ def get_current_user(db: Session = Depends(database.get_db), token: str = Depend @router.post("/users/", response_model=schemas.User) def create_user(user: schemas.UserCreate, db: Session = Depends(database.get_db)): + print("The user post is ::::::::: ", user) hashed_password = get_password_hash(user.password) db_user = models.User(email=user.email, hashed_password=hashed_password, full_name=user.full_name) if db.query(models.User).filter(models.User.email == db_user.email).first(): From 987f9dcc0e4d85b310b30d419708386fd29ab08f Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:35:22 +0300 Subject: [PATCH 32/61] feat: add profile --- frontend/src/App.js | 2 ++ 1 file changed, 2 insertions(+) diff --git a/frontend/src/App.js b/frontend/src/App.js index 556d831..3351250 100644 --- a/frontend/src/App.js +++ b/frontend/src/App.js @@ -4,6 +4,7 @@ import Navbar from './components/Navbar'; import BacktestForm from './components/BacktestForm'; import Signup from './components/Signup'; import Login from './components/Login'; +import Profile from './components/Profile'; const PrivateRoute = ({ element: Element, ...rest }) => { const token = localStorage.getItem('token'); @@ -21,6 +22,7 @@ function App() { } /> } /> } /> + } />
From a40a68a5faf5abd512a2e2d58d85fabb9df45115 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:35:32 +0300 Subject: [PATCH 33/61] feat: add title --- frontend/src/components/BacktestForm.js | 1 + 1 file changed, 1 insertion(+) diff --git a/frontend/src/components/BacktestForm.js b/frontend/src/components/BacktestForm.js index e0121ee..1d61002 100644 --- a/frontend/src/components/BacktestForm.js +++ b/frontend/src/components/BacktestForm.js @@ -109,6 +109,7 @@ function BacktestForm() { return (
+

Scalable Stock and Crypto Backtesting Infrastructure

Backtest Parameters

From d1b1858af2b545ccfd389085148bb83455c712fe Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:35:49 +0300 Subject: [PATCH 34/61] chore: add logging --- frontend/src/components/Login.js | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/frontend/src/components/Login.js b/frontend/src/components/Login.js index aed28a9..0b09e17 100644 --- a/frontend/src/components/Login.js +++ b/frontend/src/components/Login.js @@ -45,7 +45,8 @@ function Login() { } const data = await response.json(); - localStorage.setItem('token', data.token); + console.log("The data from login :: ", data) + localStorage.setItem('token', data.access_token); navigate('/backtest'); } catch (err) { setError(err.message); From 42bb207e4095feb30d963bf145f9a233a2476734 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:36:12 +0300 Subject: [PATCH 35/61] feat: add full name to top bar --- frontend/src/components/Navbar.js | 32 +++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/frontend/src/components/Navbar.js b/frontend/src/components/Navbar.js index a85cb14..ef51654 100644 --- a/frontend/src/components/Navbar.js +++ b/frontend/src/components/Navbar.js @@ -1,11 +1,39 @@ -import React from 'react'; +import React, { useState, useEffect } from 'react'; +import { useFormik } from 'formik'; import { Link, useLocation, useNavigate } from 'react-router-dom'; function Navbar() { const location = useLocation(); const navigate = useNavigate(); + const [user_name, setUserName] = React.useState(''); const currentPath = location.pathname; + const fetchUserProfile = async () => { + + try { + const token = localStorage.getItem('token'); + const response = await fetch('http://127.0.0.1:8000/auth/users/me', { + headers: { + 'Authorization': `Bearer ${token}`, + }, + }); + + if (!response.ok) { + throw new Error('Failed to fetch user profile'); + } + + const data = await response.json(); + console.log("The data from navbar :: ", data) + setUserName(data.full_name); + } catch (err) { + console.error(err); + } + }; + + useEffect(() => { + fetchUserProfile(); + }, []); + const handleLogout = () => { localStorage.removeItem('token'); navigate('/login'); @@ -14,7 +42,7 @@ function Navbar() { return (
- Crypto Data Pipeline + {user_name}
{currentPath === '/login' && Sign Up} From b02c9409168e4852880abacd1b599a04e406ef5a Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:36:22 +0300 Subject: [PATCH 36/61] feat: add profile feat --- frontend/src/components/Profile.js | 166 +++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 frontend/src/components/Profile.js diff --git a/frontend/src/components/Profile.js b/frontend/src/components/Profile.js new file mode 100644 index 0000000..a724063 --- /dev/null +++ b/frontend/src/components/Profile.js @@ -0,0 +1,166 @@ +import React, { useState, useEffect } from 'react'; +import { useFormik } from 'formik'; +import * as Yup from 'yup'; +import { useNavigate } from 'react-router-dom'; + +const validationSchema = Yup.object({ + full_name: Yup.string() + .min(3, 'Full name must be at least 3 characters') + .required('Required'), + email: Yup.string() + .email('Invalid email address') + .required('Required'), + password: Yup.string() + .min(6, 'Password must be at least 6 characters') + .matches(/[a-zA-Z]/, 'Password must contain a letter') + .matches(/\d/, 'Password must contain a number') + .matches(/[!@#$%^&*(),.?":{}|<>]/, 'Password must contain a special character'), +}); + +function Profile() { + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + const navigate = useNavigate(); + + const fetchUserProfile = async () => { + setLoading(true); + setError(null); + + try { + const token = localStorage.getItem('token'); + const response = await fetch('http://127.0.0.1:8000/auth/users/me', { + headers: { + 'Authorization': `Bearer ${token}`, + }, + }); + + if (!response.ok) { + throw new Error('Failed to fetch user profile'); + } + + const data = await response.json(); + formik.setValues({ + full_name: data.full_name, + email: data.email, + password: '', + }); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } + }; + + useEffect(() => { + fetchUserProfile(); + }, []); + + const formik = useFormik({ + initialValues: { + full_name: '', + email: '', + password: '', + }, + validationSchema, + onSubmit: async (values) => { + setLoading(true); + setError(null); + + try { + const token = localStorage.getItem('token'); + const response = await fetch('http://127.0.0.1:8000/auth/user/', { + method: 'PUT', + headers: { + 'Content-Type': 'application/json', + 'Authorization': `Bearer ${token}`, + }, + body: JSON.stringify(values), + }); + + if (!response.ok) { + throw new Error('Failed to update profile'); + } + + const data = await response.json(); + alert('Profile updated successfully'); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } + }, + }); + + return ( +
+
+

Profile

+ +
+ + + {formik.touched.full_name && formik.errors.full_name ? ( +
{formik.errors.full_name}
+ ) : null} +
+
+ + + {formik.touched.email && formik.errors.email ? ( +
{formik.errors.email}
+ ) : null} +
+
+ + + {formik.touched.password && formik.errors.password ? ( +
{formik.errors.password}
+ ) : null} + Leave blank to keep current password +
+
+ +
+ + {error && ( +
+ {error} +
+ )} +
+
+ ); +} + +export default Profile; From 66a3b60a6b60480bc137ae5b52db9879a0a17eb8 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 11:36:43 +0300 Subject: [PATCH 37/61] ltsm: add calculate return method --- scripts/backtesting/strategies/lstm_strategy.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/scripts/backtesting/strategies/lstm_strategy.py b/scripts/backtesting/strategies/lstm_strategy.py index dc05233..888d7dd 100644 --- a/scripts/backtesting/strategies/lstm_strategy.py +++ b/scripts/backtesting/strategies/lstm_strategy.py @@ -36,12 +36,13 @@ def calculate_max_drawdown(portfolio_values): max_drawdown = drawdown return max_drawdown -import numpy as np - def calculate_sharpe_ratio(returns, risk_free_rate=0): excess_returns = returns - risk_free_rate return np.mean(excess_returns) / np.std(excess_returns) +def calculate_overall_percentage_return(initial_value, final_value): + return (final_value - initial_value) / initial_value + def run_backtest_with_lstm(df): lstm_model, scaler = load_lstm_model_and_scaler() predictions = make_lstm_predictions(lstm_model, scaler, df) @@ -66,7 +67,7 @@ def run_backtest_with_lstm(df): # Calculate metrics final_value = cash + position * df['Close'].values[-1] - gross_profit = final_value - initial_cash + overall_percentage_return = calculate_overall_percentage_return(initial_cash, final_value) returns = np.diff(portfolio_values) / portfolio_values[:-1] sharpe_ratio = calculate_sharpe_ratio(returns) max_drawdown = calculate_max_drawdown(portfolio_values) @@ -76,7 +77,7 @@ def run_backtest_with_lstm(df): 'total_trades': len(predictions), # Example, adjust as needed 'winning_trades': sum(1 for r in returns if r > 0), 'losing_trades': sum(1 for r in returns if r <= 0), - 'percentage_return': returns, + 'percentage_return': overall_percentage_return, 'max_drawdown': max_drawdown, 'sharpe_ratio': sharpe_ratio } From 1243711fdf3aea59bcd676252c812bd4adfb85fc Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 12:15:44 +0300 Subject: [PATCH 38/61] feat: docker fie backend --- Dockerfile.backend | 33 +++++++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 Dockerfile.backend diff --git a/Dockerfile.backend b/Dockerfile.backend new file mode 100644 index 0000000..6986997 --- /dev/null +++ b/Dockerfile.backend @@ -0,0 +1,33 @@ +# Use the official Python base image +FROM python:3.9-slim + +# Set the working directory in the container +WORKDIR /app + +# Copy the requirements file into the container +COPY requirements.txt . + +# Install the required Python packages +RUN pip install --no-cache-dir -r requirements.txt + +# Copy the entire backend project into the container +COPY ./backend . + +# Expose the port that FastAPI runs on +EXPOSE 8000 + +# Set environment variables for the FastAPI app +ENV PYTHONUNBUFFERED=1 + +ENV BINANCE_API_KEY="FN6iy8IhLMbDR3kVEYp1ZqsN6lj0fZXFRQaRZeJsWuLz6Is7DkVvyb70fwPGDY3B" +ENV DATABASE_URL="postgresql://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v" +ENV PYCOPG_DATABASE_URL = "postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v" +ENV DB_USERNAME='group3' +ENV DB_PASSWORD='group3@week9' +ENV DB_HOST='g3.10academy.org' +ENV DB_PORT=5432 +ENV DB_DATABASE='backtest' +ENV AWS_DATABASE_URL="postgresql+psycopg2://group3:group3%40week9@g3.10academy.org/backtest" + +# Run the FastAPI app with Uvicorn +CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"] From f9a0b8e4f4dfdf4c18155f197eebc682b4fea519 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 12:16:08 +0300 Subject: [PATCH 39/61] feat: docker file frontend --- Dockerfile.frontend | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 Dockerfile.frontend diff --git a/Dockerfile.frontend b/Dockerfile.frontend new file mode 100644 index 0000000..fe1b123 --- /dev/null +++ b/Dockerfile.frontend @@ -0,0 +1,29 @@ +# Use the official Node.js image as the base image +FROM node:16-alpine + +# Set the working directory inside the container +WORKDIR /app + +# Copy package.json and package-lock.json to the working directory +COPY ./frontend/package*.json ./ + +# Install the dependencies +RUN npm install + +# Copy the rest of the application code to the working directory +COPY ./frontend . + +# Build the React application +RUN npm run build + +# Use the official Nginx image to serve the React application +FROM nginx:alpine + +# Copy the built React application from the previous stage +COPY --from=0 /app/build /usr/share/nginx/html + +# Expose port 80 +EXPOSE 80 + +# Start Nginx when the container starts +CMD ["nginx", "-g", "daemon off;"] From d898d8bfbfd861d5906e320f50afdc450e372603 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 12:17:53 +0300 Subject: [PATCH 40/61] feat: airflow docker --- airflow/docker-compose.yml | 113 +++++++++++++++++++++++++++++++++++++ 1 file changed, 113 insertions(+) create mode 100644 airflow/docker-compose.yml diff --git a/airflow/docker-compose.yml b/airflow/docker-compose.yml new file mode 100644 index 0000000..f8ee814 --- /dev/null +++ b/airflow/docker-compose.yml @@ -0,0 +1,113 @@ +version: '3' +services: + postgres: + image: postgres:13 + environment: + POSTGRES_USER: airflow + POSTGRES_PASSWORD: airflow + POSTGRES_DB: airflow + volumes: + - postgres-db-volume:/var/lib/postgresql/data + networks: + - airflow-network + + redis: + image: redis:6 + networks: + - airflow-network + + airflow-webserver: + image: apache/airflow:2.1.2 + depends_on: + - postgres + - redis + environment: + AIRFLOW__CORE__EXECUTOR: CeleryExecutor + AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__BROKER_URL: redis://redis:6379/0 + AIRFLOW__CORE__FERNET_KEY: aFERNET_KEY # Replace with generated key + AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'false' + AIRFLOW__CORE__LOAD_EXAMPLES: 'true' + AIRFLOW__API__AUTH_BACKEND: airflow.api.auth.backend.basic_auth + volumes: + - ./dags:/opt/airflow/dags + - ./logs:/opt/airflow/logs + - ./plugins:/opt/airflow/plugins + ports: + - "8080:8080" + command: webserver + networks: + - airflow-network + + airflow-scheduler: + image: apache/airflow:2.1.2 + depends_on: + - postgres + - redis + environment: + AIRFLOW__CORE__EXECUTOR: CeleryExecutor + AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__BROKER_URL: redis://redis:6379/0 + AIRFLOW__CORE__FERNET_KEY: aFERNET_KEY # Replace with generated key + AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'false' + AIRFLOW__CORE__LOAD_EXAMPLES: 'true' + AIRFLOW__API__AUTH_BACKEND: airflow.api.auth.backend.basic_auth + volumes: + - ./dags:/opt/airflow/dags + - ./logs:/opt/airflow/logs + - ./plugins:/opt/airflow/plugins + command: scheduler + networks: + - airflow-network + + airflow-worker: + image: apache/airflow:2.1.2 + depends_on: + - postgres + - redis + environment: + AIRFLOW__CORE__EXECUTOR: CeleryExecutor + AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__BROKER_URL: redis://redis:6379/0 + AIRFLOW__CORE__FERNET_KEY: aFERNET_KEY # Replace with generated key + AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'false' + AIRFLOW__CORE__LOAD_EXAMPLES: 'true' + AIRFLOW__API__AUTH_BACKEND: airflow.api.auth.backend.basic_auth + volumes: + - ./dags:/opt/airflow/dags + - ./logs:/opt/airflow/logs + - ./plugins:/opt/airflow/plugins + command: celery worker + networks: + - airflow-network + + airflow-init: + image: apache/airflow:2.1.2 + depends_on: + - postgres + - redis + environment: + AIRFLOW__CORE__EXECUTOR: CeleryExecutor + AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow + AIRFLOW__CELERY__BROKER_URL: redis://redis:6379/0 + AIRFLOW__CORE__FERNET_KEY: aFERNET_KEY # Replace with generated key + AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'false' + AIRFLOW__CORE__LOAD_EXAMPLES: 'true' + AIRFLOW__API__AUTH_BACKEND: airflow.api.auth.backend.basic_auth + volumes: + - ./dags:/opt/airflow/dags + - ./logs:/opt/airflow/logs + - ./plugins:/opt/airflow/plugins + command: ["bash", "-c", "airflow db init && airflow users create --username admin --firstname FIRST_NAME --lastname LAST_NAME --role Admin --email admin@example.com --password admin"] + networks: + - airflow-network + +networks: + airflow-network: + +volumes: + postgres-db-volume: From f7829fe1c0886b67076859a1327dde07fa868097 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 12:18:20 +0300 Subject: [PATCH 41/61] fix: error in sharpee ratio --- requirements.txt | 3 ++- scripts/backtesting/main.py | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index 3dfaf82..e397162 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,4 +12,5 @@ mlflow passlib python-jose kafka-python -tensorflow \ No newline at end of file +tensorflow +apache-airflow \ No newline at end of file diff --git a/scripts/backtesting/main.py b/scripts/backtesting/main.py index e3be56f..68c8843 100644 --- a/scripts/backtesting/main.py +++ b/scripts/backtesting/main.py @@ -68,7 +68,7 @@ def run_backtest(config): mlflow.log_param("indicator", indicator) final_value = cerebro.broker.getvalue() - sharpe_ratio = strat.analyzers.sharperatio.get_analysis()['sharperatio'] + sharpe_ratio = strat.analyzers.sharperatio.get_analysis()['sharperatio'] or 0 metrics_analyzer = strat.analyzers.getbyname('MetricsAnalyzer') metrics = metrics_analyzer.get_analysis() percentage_return = metrics['return'] From ed9c33bc82cb3f30978e4a08facebfcda55eaa93 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:39:22 +0000 Subject: [PATCH 42/61] airflow: compose file finilized --- airflow/docker-compose.yml | 34 +++++++++++++++++++++++++++++----- 1 file changed, 29 insertions(+), 5 deletions(-) mode change 100644 => 100755 airflow/docker-compose.yml diff --git a/airflow/docker-compose.yml b/airflow/docker-compose.yml old mode 100644 new mode 100755 index f8ee814..31efdb9 --- a/airflow/docker-compose.yml +++ b/airflow/docker-compose.yml @@ -17,7 +17,13 @@ services: - airflow-network airflow-webserver: - image: apache/airflow:2.1.2 + image: apache/airflow:2.9.1 + build: + context: . + dockerfile: Dockerfile + env_file: + - .env + user: "${AIRFLOW_UID}" depends_on: - postgres - redis @@ -41,7 +47,13 @@ services: - airflow-network airflow-scheduler: - image: apache/airflow:2.1.2 + image: apache/airflow:2.9.1 + build: + context: . + dockerfile: Dockerfile + env_file: + - .env + user: "${AIRFLOW_UID}" depends_on: - postgres - redis @@ -63,7 +75,13 @@ services: - airflow-network airflow-worker: - image: apache/airflow:2.1.2 + image: apache/airflow:2.9.1 + build: + context: . + dockerfile: Dockerfile + env_file: + - .env + user: "${AIRFLOW_UID}" depends_on: - postgres - redis @@ -85,7 +103,13 @@ services: - airflow-network airflow-init: - image: apache/airflow:2.1.2 + image: apache/airflow:2.9.1 + build: + context: . + dockerfile: Dockerfile + env_file: + - .env + user: "${AIRFLOW_UID}" depends_on: - postgres - redis @@ -94,7 +118,7 @@ services: AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow AIRFLOW__CELERY__BROKER_URL: redis://redis:6379/0 - AIRFLOW__CORE__FERNET_KEY: aFERNET_KEY # Replace with generated key + AIRFLOW__CORE__FERNET_KEY: R-rsJ2QBtvE5BRhfBggKRIbmCS5YtrqbzhueaJ0lB8I= # Replace with generated key AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'false' AIRFLOW__CORE__LOAD_EXAMPLES: 'true' AIRFLOW__API__AUTH_BACKEND: airflow.api.auth.backend.basic_auth From e8944e283c17c83d3eef5e47cb7c3c5ab91d6731 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:40:01 +0000 Subject: [PATCH 43/61] feat: dockerfile for installation of requirements to airflow dags --- airflow/Dockerfile | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 airflow/Dockerfile diff --git a/airflow/Dockerfile b/airflow/Dockerfile new file mode 100644 index 0000000..3ed177c --- /dev/null +++ b/airflow/Dockerfile @@ -0,0 +1,21 @@ +FROM apache/airflow:2.9.1 + +# Switch to root to install system dependencies +USER root + +# Install system dependencies +RUN apt-get update \ + && apt-get install -y --no-install-recommends \ + pkg-config \ + libhdf5-dev \ + gcc \ + python3-dev \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Switch to airflow user to install Python packages +USER airflow + +# Copy requirements and install Python packages +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt From ffd220be4ef7cf225c9292278e297f3734ab46ee Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:40:25 +0000 Subject: [PATCH 44/61] chore: add requirements to airflow --- airflow/requirements.txt | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 airflow/requirements.txt diff --git a/airflow/requirements.txt b/airflow/requirements.txt new file mode 100644 index 0000000..6b8d4e0 --- /dev/null +++ b/airflow/requirements.txt @@ -0,0 +1,17 @@ +pandas +numpy +matplotlib +scikit-learn +backtrader +yfinance +# fastapi +# uvicorn +psycopg2-binary +sqlalchemy +# mlflow +# passlib +# python-jose +kafka-python +# tensorflow +apache-airflow +h5py \ No newline at end of file From 96691e76a50847e3e55f2a2a14140612949cb6c7 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:40:52 +0000 Subject: [PATCH 45/61] chore: init code for implementing backtest dags --- airflow/dags/backtest_dag.py | 112 +++++++++++++++++++++++++++++++++++ 1 file changed, 112 insertions(+) create mode 100755 airflow/dags/backtest_dag.py diff --git a/airflow/dags/backtest_dag.py b/airflow/dags/backtest_dag.py new file mode 100755 index 0000000..94c3792 --- /dev/null +++ b/airflow/dags/backtest_dag.py @@ -0,0 +1,112 @@ +# from airflow import DAG +# from airflow.operators.python_operator import PythonOperator +# from datetime import datetime, timedelta +# import pandas as pd +# import yfinance as yf +# import numpy as np +# from tensorflow.keras.models import load_model +# import joblib + +# default_args = { +# 'owner': 'airflow', +# 'depends_on_past': False, +# 'start_date': datetime(2023, 1, 1), +# 'email_on_failure': False, +# 'email_on_retry': False, +# 'retries': 1, +# 'retry_delay': timedelta(minutes=5), +# } + +# dag = DAG( +# 'backtesting_dag', +# default_args=default_args, +# description='A simple backtesting DAG', +# schedule_interval=timedelta(days=1), +# ) + +# def fetch_data(ticker="TSLA", start_date="2020-01-01", end_date=datetime.today().strftime("%Y-%m-%d")): +# data: pd.DataFrame = yf.download(ticker, start=start_date, end=end_date) +# data.to_csv(f'/tmp/{ticker}_{start_date}_{end_date}.csv') + +# def load_lstm_model_and_scaler(): +# model = load_model('lstm_model.h5') +# scaler = joblib.load('scaler.gz') +# return model, scaler + +# def make_lstm_predictions(): +# model, scaler = load_lstm_model_and_scaler() +# df = pd.read_csv('/tmp/data.csv', index_col='Date', parse_dates=True) +# data = df['Close'].values.reshape(-1, 1) +# scaled_data = scaler.transform(data) + +# seq_length = 60 +# X_test = [] +# for i in range(seq_length, len(scaled_data)): +# X_test.append(scaled_data[i-seq_length:i, 0]) + +# X_test = np.array(X_test) +# X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) + +# predictions = model.predict(X_test) +# predictions = scaler.inverse_transform(predictions) +# np.save('/tmp/predictions.npy', predictions) + +# def calculate_metrics(): +# df = pd.read_csv('/tmp/data.csv', index_col='Date', parse_dates=True) +# predictions = np.load('/tmp/predictions.npy') + +# initial_cash = 10000 +# cash = initial_cash +# position = 0 +# portfolio_values = [cash] + +# for i in range(len(predictions)): +# predicted_price = predictions[i] +# actual_price = df['Close'].values[-len(predictions) + i] + +# if predicted_price > actual_price and cash > actual_price: +# cash -= actual_price +# position += 1 +# elif predicted_price < actual_price and position > 0: +# cash += actual_price +# position -= 1 + +# portfolio_values.append(cash + position * df['Close'].values[-1]) + +# final_value = cash + position * df['Close'].values[-1] +# gross_profit = final_value - initial_cash +# overall_percentage_return = (final_value - initial_cash) / initial_cash * 100 +# returns = np.diff(portfolio_values) / portfolio_values[:-1] +# sharpe_ratio = np.mean(returns) / np.std(returns) * np.sqrt(252) +# max_drawdown = np.max(np.maximum.accumulate(portfolio_values) - portfolio_values) / np.maximum.accumulate(portfolio_values) + +# metrics = { +# 'gross_profit': gross_profit, +# 'net_profit': gross_profit, +# 'percentage_return': overall_percentage_return, +# 'sharpe_ratio': sharpe_ratio, +# 'max_drawdown': max_drawdown, +# } + +# print(metrics) + +# fetch_data_task = PythonOperator( +# task_id='fetch_data', +# python_callable=fetch_data, +# op_args=['AAPL', '2023-01-01', '2023-12-31'], +# dag=dag, +# ) + +# make_predictions_task = PythonOperator( +# task_id='make_predictions', +# python_callable=make_lstm_predictions, +# dag=dag, +# ) + +# calculate_metrics_task = PythonOperator( +# task_id='calculate_metrics', +# python_callable=calculate_metrics, +# dag=dag, +# ) + +# fetch_data_task >> make_predictions_task >> calculate_metrics_task From e2d4f4563bf31e96bd90841edaaed2557d2598c4 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:41:16 +0000 Subject: [PATCH 46/61] dag: implement fetch everyday for airflow --- airflow/dags/fetch_stock_data.py | 51 ++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) create mode 100644 airflow/dags/fetch_stock_data.py diff --git a/airflow/dags/fetch_stock_data.py b/airflow/dags/fetch_stock_data.py new file mode 100644 index 0000000..6858dd6 --- /dev/null +++ b/airflow/dags/fetch_stock_data.py @@ -0,0 +1,51 @@ +from airflow import DAG +from airflow.operators.python_operator import PythonOperator +from datetime import datetime, timedelta +import yfinance as yf +import pandas as pd +# import psycopg2 +from sqlalchemy import create_engine + +default_args = { + 'owner': 'airflow', + 'depends_on_past': False, + 'start_date': datetime(2024, 6, 20), + 'email_on_failure': False, + 'email_on_retry': False, + 'retries': 1, + 'retry_delay': timedelta(minutes=5), +} + +dag = DAG( + 'fetch_stock_data_dag', + default_args=default_args, + description='Fetch stock data daily and store in PostgreSQL', + schedule_interval=timedelta(days=1), +) + +def fetch_and_store_stock_data(): + import os + # Connect to PostgreSQL + DATABASE_URL = os.getenv("PYCOPG_DATABASE_URL", "postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v") + engine = create_engine(DATABASE_URL) + + # Fetch stocks from database + stocks_df = pd.read_sql("SELECT * FROM stocks", engine) + + # Fetch data from yfinance + for index, row in stocks_df.iterrows(): + ticker = row['symbol'] + try: + data = yf.download(ticker, start='2023-01-01', end=datetime.today().strftime('%Y-%m-%d')) + data['symbol'] = ticker + data.to_sql('stock_data', engine, if_exists='append') + except Exception as e: + print(f"Error fetching data for {ticker}: {e}") + +fetch_data_task = PythonOperator( + task_id='fetch_and_store_stock_data', + python_callable=fetch_and_store_stock_data, + dag=dag, +) + +fetch_data_task From ec4992a9d1696bb80dd423a644fdd5b824ff03c5 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:41:50 +0000 Subject: [PATCH 47/61] notebook: ecperimentations --- notebooks/backtesting.ipynb | 263 ++++++++++++++++-- notebooks/kafka.ipynb | 8 +- notebooks/lstm.ipynb | 515 +++++++++++++++++++++++++++++++++++- 3 files changed, 751 insertions(+), 35 deletions(-) diff --git a/notebooks/backtesting.ipynb b/notebooks/backtesting.ipynb index 82e8045..5f4b592 100644 --- a/notebooks/backtesting.ipynb +++ b/notebooks/backtesting.ipynb @@ -2,9 +2,40 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], + "source": [ + "import os\n", + "os.chdir('..')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -r requirements.txt -q" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'backtesting'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# import pandas_ta as ta\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbacktesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Backtest\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbacktesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Strategy\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbacktesting\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m crossover\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'backtesting'" + ] + } + ], "source": [ "import datetime\n", "# import pandas_ta as ta\n", @@ -340,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -353,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -363,29 +394,29 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(1, 15, datetime.date(2023, 9, 1), datetime.date(2024, 4, 1), 13, 3)\n", - "(2, 15, datetime.date(2024, 1, 19), datetime.date(2024, 6, 26), 7, 3)\n", - "(6, 15, datetime.date(2023, 10, 20), datetime.date(2024, 6, 26), 8, 3)\n", - "(13, 15, datetime.date(2023, 11, 10), datetime.date(2024, 6, 26), 3, 3)\n", - "(14, 15, datetime.date(2023, 11, 10), datetime.date(2024, 6, 26), 6, 3)\n", - "(15, 15, datetime.date(2024, 5, 26), datetime.date(2024, 6, 26), 4, 3)\n", - "(16, 15, datetime.date(2024, 3, 7), datetime.date(2024, 6, 26), 13, 3)\n", - "(19, 15, datetime.date(2024, 1, 3), datetime.date(2024, 6, 26), 9, 5)\n", - "(20, 15, datetime.date(2023, 2, 9), datetime.date(2024, 6, 25), 13, 3)\n", - "(22, 15, datetime.date(2023, 12, 27), datetime.date(2024, 6, 25), 13, 3)\n" + "(datetime.datetime(2023, 1, 3, 0, 0), 130.27999877929688, 130.89999389648438, 124.16999816894531, 125.06999969482422, 124.04804229736328, 112117500, 'AAPL')\n", + "(datetime.datetime(2023, 1, 4, 0, 0), 126.88999938964844, 128.66000366210938, 125.08000183105469, 126.36000061035156, 125.32750701904297, 89113600, 'AAPL')\n", + "(datetime.datetime(2023, 1, 5, 0, 0), 127.12999725341797, 127.7699966430664, 124.76000213623047, 125.0199966430664, 123.99845886230469, 80962700, 'AAPL')\n", + "(datetime.datetime(2023, 1, 6, 0, 0), 126.01000213623047, 130.2899932861328, 124.88999938964844, 129.6199951171875, 128.56085205078125, 87754700, 'AAPL')\n", + "(datetime.datetime(2023, 1, 9, 0, 0), 130.47000122070312, 133.41000366210938, 129.88999938964844, 130.14999389648438, 129.08651733398438, 70790800, 'AAPL')\n", + "(datetime.datetime(2023, 1, 10, 0, 0), 130.25999450683594, 131.25999450683594, 128.1199951171875, 130.72999572753906, 129.6617889404297, 63896200, 'AAPL')\n", + "(datetime.datetime(2023, 1, 11, 0, 0), 131.25, 133.50999450683594, 130.4600067138672, 133.49000549316406, 132.39926147460938, 69458900, 'AAPL')\n", + "(datetime.datetime(2023, 1, 12, 0, 0), 133.8800048828125, 134.25999450683594, 131.44000244140625, 133.41000366210938, 132.31991577148438, 71379600, 'AAPL')\n", + "(datetime.datetime(2023, 1, 13, 0, 0), 132.02999877929688, 134.9199981689453, 131.66000366210938, 134.75999450683594, 133.65887451171875, 57809700, 'AAPL')\n", + "(datetime.datetime(2023, 1, 17, 0, 0), 134.8300018310547, 137.2899932861328, 134.1300048828125, 135.94000244140625, 134.82920837402344, 63646600, 'AAPL')\n" ] } ], "source": [ "# Reflect the 'scenes' table\n", - "scenes = Table('scenes', metadata, autoload_with=engine)\n", + "scenes = Table('stock_data', metadata, autoload_with=engine)\n", "\n", "# Build the query\n", "query = scenes.select().limit(10).offset(0)\n", @@ -398,7 +429,35 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Date TIMESTAMP\n", + "Open DOUBLE_PRECISION\n", + "High DOUBLE_PRECISION\n", + "Low DOUBLE_PRECISION\n", + "Close DOUBLE_PRECISION\n", + "Adj Close DOUBLE_PRECISION\n", + "Volume BIGINT\n", + "symbol TEXT\n" + ] + } + ], + "source": [ + "# get the column names\n", + "inspector = inspect(engine)\n", + "columns = inspector.get_columns('stock_data')\n", + "for column in columns:\n", + " print(column['name'], column['type'])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -467,7 +526,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[]\n" + "['users', 'stocks', 'scenes', 'indicators', 'backtest_results', 'stock_data']\n" ] } ], @@ -943,6 +1002,174 @@ "import yfinance as yf\n", "yf.download(\"FB\", start=\"2023-01-01\", end=\"2024-01-01\")" ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import os, pandas as pd\n", + "from sqlalchemy import create_engine" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/abubeker_shamil/Scalable_Backtesting_Infrastructure_for_Crypto_Trading/notebooks'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_806104/3694470526.py:5: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n", + " stocks_df = pd.read_sql(\"SELECT * FROM stocks\", engine)\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'Engine' object has no attribute 'cursor'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m engine \u001b[38;5;241m=\u001b[39m create_engine(DATABASE_URL)\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Fetch stocks from database\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m stocks_df \u001b[38;5;241m=\u001b[39m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_sql\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSELECT * FROM stocks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Scalable_Backtesting_Infrastructure_for_Crypto_Trading/.conda/lib/python3.12/site-packages/pandas/io/sql.py:706\u001b[0m, in \u001b[0;36mread_sql\u001b[0;34m(sql, con, index_col, coerce_float, params, parse_dates, columns, chunksize, dtype_backend, dtype)\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pandasSQL_builder(con) \u001b[38;5;28;01mas\u001b[39;00m pandas_sql:\n\u001b[1;32m 705\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pandas_sql, SQLiteDatabase):\n\u001b[0;32m--> 706\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpandas_sql\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_query\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 707\u001b[0m \u001b[43m \u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 708\u001b[0m \u001b[43m 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Literal[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2737\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Iterator[DataFrame]:\n\u001b[0;32m-> 2738\u001b[0m cursor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\u001b[43msql\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2739\u001b[0m columns \u001b[38;5;241m=\u001b[39m [col_desc[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m col_desc \u001b[38;5;129;01min\u001b[39;00m cursor\u001b[38;5;241m.\u001b[39mdescription]\n\u001b[1;32m 2741\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mis\u001b[39;00m 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2673\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 2674\u001b[0m cur\u001b[38;5;241m.\u001b[39mexecute(sql, \u001b[38;5;241m*\u001b[39margs)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'Engine' object has no attribute 'cursor'" + ] + } + ], + "source": [ + "DATABASE_URL = os.getenv(\"PYCOPG_DATABASE_URL\", \"postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v\")\n", + "engine = create_engine(DATABASE_URL)\n", + "\n", + "# Fetch stocks from database\n", + "stocks_df = pd.read_sql(\"SELECT * FROM stocks\", engine)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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idnamesymboldescription
01Apple IncAAPLApple is a trillion-dollar company
12Microsoft CorporationMSFTMicrosoft is a multinational technology company
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" + ], + "text/plain": [ + " id name symbol \\\n", + "0 1 Apple Inc AAPL \n", + "1 2 Microsoft Corporation MSFT \n", + "\n", + " description \n", + "0 Apple is a trillion-dollar company \n", + "1 Microsoft is a multinational technology company " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stocks_df.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AAPL\n", + "MSFT\n", + "AMZN\n", + "GOOGL\n", + "FB\n", + "BRK.A\n", + "TSLA\n", + "BABA\n", + "TCEHY\n", + "JNJ\n", + "V\n", + "WMT\n", + "NVDA\n", + "SSNLF\n", + "PG\n" + ] + } + ], + "source": [ + "for index, row in stocks_df.iterrows():\n", + " print(row['symbol'])" + ] } ], "metadata": { @@ -961,7 +1188,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/notebooks/kafka.ipynb b/notebooks/kafka.ipynb index d857dc1..10296e4 100644 --- a/notebooks/kafka.ipynb +++ b/notebooks/kafka.ipynb @@ -47,13 +47,9 @@ "output_type": "stream", "text": [ "Topics in Kafka cluster:\n", - "docker-connect-status\n", "_schemas\n", "scene_parameters\n", - "__consumer_offsets\n", - "_confluent-ksql-ksqldb-server__command_topic\n", - "docker-connect-offsets\n", - "docker-connect-configs\n" + "__consumer_offsets\n" ] } ], @@ -256,7 +252,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.8.10" } }, "nbformat": 4, diff --git a/notebooks/lstm.ipynb b/notebooks/lstm.ipynb index 996bcea..b169c7b 100644 --- a/notebooks/lstm.ipynb +++ b/notebooks/lstm.ipynb @@ -2,21 +2,132 @@ "cells": [ { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "metadata": {}, "outputs": [ { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'nixtla'", - "output_type": "error", - 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" + ], + "text/plain": [ + " unique_id ds y\n", + "0 BE 2016-10-22 00:00:00 70.000000\n", + "1 BE 2016-10-22 01:00:00 37.100000\n", + "2 BE 2016-10-22 02:00:00 37.100000\n", + "3 BE 2016-10-22 03:00:00 44.750000\n", + "4 BE 2016-10-22 04:00:00 37.100000\n", + "... ... ... ...\n", + "8395 PJM 2018-12-23 19:00:00 32.461970\n", + "8396 PJM 2018-12-23 20:00:00 32.052179\n", + "8397 PJM 2018-12-23 21:00:00 27.632347\n", + "8398 PJM 2018-12-23 22:00:00 24.746053\n", + "8399 PJM 2018-12-23 23:00:00 23.333499\n", + "\n", + "[8400 rows x 3 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/abubeker_shamil/Scalable_Backtesting_Infrastructure_for_Crypto_Trading/notebooks'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "backtest = pd.read_csv('../data/binance_btc_usdt_candlestick.csv', index_col='timestamp', parse_dates=True)" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "df['Close'].values[-len(predictions) + i]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:nixtla.nixtla_client:Validating inputs...\n", + "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n", + "INFO:nixtla.nixtla_client:Inferred freq: h\n", + "INFO:nixtla.nixtla_client:Restricting input...\n", + "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n", + "INFO:matplotlib.font_manager:generated new fontManager\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 1. Instantiate the NixtlaClient\n", "nixtla_client = NixtlaClient(api_key = 'nixtla-tok-31lvshOqu4BTxxhJJY9Y6oxZQHciSZYElRgwsh5Btg60GK5JG3imcfbbUhdSIOyV2HSadFlgdJTpDsJb')\n", @@ -48,6 +351,196 @@ "nixtla_client.plot(df, fcst_df, time_col='ds', target_col='y', level=[80, 90])" ] }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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unique_iddsTimeGPTTimeGPT-lo-90TimeGPT-lo-80TimeGPT-hi-80TimeGPT-hi-90
0BE2016-12-31 00:00:0045.19045333.00878135.46137854.91952857.372124
1BE2016-12-31 01:00:0043.24444630.38726635.37462451.11426756.101625
2BE2016-12-31 02:00:0041.95838929.28379435.34079548.57598454.632985
3BE2016-12-31 03:00:0039.79648626.26210732.31925847.27371453.330865
4BE2016-12-31 04:00:0039.20453618.60367930.98591047.42316359.805394
........................
115PJM2018-12-24 19:00:0032.02828622.51975326.56921537.48735741.536819
116PJM2018-12-24 20:00:0030.90708526.46502026.69378135.12039035.349151
117PJM2018-12-24 21:00:0028.06687919.13077220.58521635.54854337.002987
118PJM2018-12-24 22:00:0025.06372318.46219619.38897830.73846731.665249
119PJM2018-12-24 23:00:0023.30125411.14175514.54223432.06027535.460754
\n", + "

120 rows × 7 columns

\n", + "
" + ], + "text/plain": [ + " unique_id ds TimeGPT TimeGPT-lo-90 TimeGPT-lo-80 \\\n", + "0 BE 2016-12-31 00:00:00 45.190453 33.008781 35.461378 \n", + "1 BE 2016-12-31 01:00:00 43.244446 30.387266 35.374624 \n", + "2 BE 2016-12-31 02:00:00 41.958389 29.283794 35.340795 \n", + "3 BE 2016-12-31 03:00:00 39.796486 26.262107 32.319258 \n", + "4 BE 2016-12-31 04:00:00 39.204536 18.603679 30.985910 \n", + ".. ... ... ... ... ... \n", + "115 PJM 2018-12-24 19:00:00 32.028286 22.519753 26.569215 \n", + "116 PJM 2018-12-24 20:00:00 30.907085 26.465020 26.693781 \n", + "117 PJM 2018-12-24 21:00:00 28.066879 19.130772 20.585216 \n", + "118 PJM 2018-12-24 22:00:00 25.063723 18.462196 19.388978 \n", + "119 PJM 2018-12-24 23:00:00 23.301254 11.141755 14.542234 \n", + "\n", + " TimeGPT-hi-80 TimeGPT-hi-90 \n", + "0 54.919528 57.372124 \n", + "1 51.114267 56.101625 \n", + "2 48.575984 54.632985 \n", + "3 47.273714 53.330865 \n", + "4 47.423163 59.805394 \n", + ".. ... ... \n", + "115 37.487357 41.536819 \n", + "116 35.120390 35.349151 \n", + "117 35.548543 37.002987 \n", + "118 30.738467 31.665249 \n", + "119 32.060275 35.460754 \n", + "\n", + "[120 rows x 7 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fcst_df" + ] + }, { "cell_type": "code", "execution_count": null, @@ -886,7 +1379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.12.4" } }, "nbformat": 4, From dcbe377517c382ed969a2610fa68a81023cc5893 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 20:42:25 +0000 Subject: [PATCH 48/61] others: necessary files --- airflow/logs/scheduler/latest | 1 + kafka_topic/conduktor.yml | 2 +- screenshots/image.png | Bin 0 -> 298118 bytes 3 files changed, 2 insertions(+), 1 deletion(-) create mode 120000 airflow/logs/scheduler/latest create mode 100644 screenshots/image.png diff --git a/airflow/logs/scheduler/latest b/airflow/logs/scheduler/latest new file mode 120000 index 0000000..29197e9 --- /dev/null +++ b/airflow/logs/scheduler/latest @@ -0,0 +1 @@ +2024-06-27 \ No newline at end of file diff --git a/kafka_topic/conduktor.yml b/kafka_topic/conduktor.yml index 79f3616..bd0c59b 100644 --- a/kafka_topic/conduktor.yml +++ b/kafka_topic/conduktor.yml @@ -16,7 +16,7 @@ services: conduktor-console: image: conduktor/conduktor-console:1.24.0 ports: - - "8080:8080" + - "9090:9090" volumes: - conduktor_data:/var/conduktor environment: diff --git a/screenshots/image.png b/screenshots/image.png new file mode 100644 index 0000000000000000000000000000000000000000..e3387ef00d9bf9e467c27229171e57349c0302ca GIT binary patch literal 298118 zcmb5W1yq&Ww>C~H-6=>&3T!}98l<~hI;4@7ZctjfV*?^m5`uInBHc)X(kar-w>BQV z=YIe3zrXv9vBx>M$A0%-?_6^}^O?__>wTr7B#nu74-F0u4pUY}LJbZMQA1~iA`|jnqXegQ_1Ue5K)D2Bgvpp z%RVa>NgACc$l6u%XlQVNv|cag*E!CuWd7vyt=BBtZ4L^;MhSrXPcP9>6coaDp>kAk z2>%;F0v0P_V{D;gJ5*E0E~)KQ0jtMI#Ol0YZorZu1|PNEIN8 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dev-abuke Date: Thu, 27 Jun 2024 20:58:08 +0000 Subject: [PATCH 49/61] feat: add data quality checks --- airflow/dags/fetch_stock_data.py | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/airflow/dags/fetch_stock_data.py b/airflow/dags/fetch_stock_data.py index 6858dd6..468c616 100644 --- a/airflow/dags/fetch_stock_data.py +++ b/airflow/dags/fetch_stock_data.py @@ -16,13 +16,23 @@ 'retry_delay': timedelta(minutes=5), } +# Create a single DAG dag = DAG( - 'fetch_stock_data_dag', + 'fetch_stock_data_and_monitoring', default_args=default_args, - description='Fetch stock data daily and store in PostgreSQL', + description='Fetch stock data daily, store in PostgreSQL, and monitor data quality', schedule_interval=timedelta(days=1), ) +def check_data_quality(): + import os + DATABASE_URL = os.getenv("PYCOPG_DATABASE_URL", "postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v") + engine = create_engine(DATABASE_URL) + data = pd.read_sql('SELECT * FROM stock_data', con=engine) + if data.isnull().values.any(): + raise ValueError("Data quality check failed: Found missing values") + print("Data quality check passed") + def fetch_and_store_stock_data(): import os # Connect to PostgreSQL @@ -48,4 +58,10 @@ def fetch_and_store_stock_data(): dag=dag, ) -fetch_data_task +quality_check_task = PythonOperator( + task_id='check_data_quality', + python_callable=check_data_quality, + dag=dag, +) + +fetch_data_task >> quality_check_task From 0af4cab8ed2c05d0174b2d0f33b09084ec67e94d Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:12:42 +0000 Subject: [PATCH 50/61] fix: add backend and script files to airflow docker --- airflow/Dockerfile | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/airflow/Dockerfile b/airflow/Dockerfile index 3ed177c..bdbe3ca 100644 --- a/airflow/Dockerfile +++ b/airflow/Dockerfile @@ -18,4 +18,9 @@ USER airflow # Copy requirements and install Python packages COPY requirements.txt . + +COPY ../backend ./backend + +COPY ../scripts/backtesting ./scripts/backtesting + RUN pip install --no-cache-dir -r requirements.txt From b52e3d4bf3ba770fd14191e4f1e471a3157ee5a7 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:13:42 +0000 Subject: [PATCH 51/61] fix: pandas to resemble the model --- airflow/dags/fetch_stock_data.py | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/airflow/dags/fetch_stock_data.py b/airflow/dags/fetch_stock_data.py index 468c616..f909c3d 100644 --- a/airflow/dags/fetch_stock_data.py +++ b/airflow/dags/fetch_stock_data.py @@ -1,11 +1,19 @@ from airflow import DAG from airflow.operators.python_operator import PythonOperator + from datetime import datetime, timedelta import yfinance as yf import pandas as pd -# import psycopg2 +import os + +# from sqlalchemy.orm import sessionmaker from sqlalchemy import create_engine +DATABASE_URL = os.getenv("PYCOPG_DATABASE_URL", "postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v") +engine = create_engine(DATABASE_URL) +# Session = sessionmaker(autocommit=False, autoflush=False, bind=engine) +# db = Session() + default_args = { 'owner': 'airflow', 'depends_on_past': False, @@ -16,7 +24,7 @@ 'retry_delay': timedelta(minutes=5), } -# Create a single DAG +# Create a DAG dag = DAG( 'fetch_stock_data_and_monitoring', default_args=default_args, @@ -25,20 +33,12 @@ ) def check_data_quality(): - import os - DATABASE_URL = os.getenv("PYCOPG_DATABASE_URL", "postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v") - engine = create_engine(DATABASE_URL) data = pd.read_sql('SELECT * FROM stock_data', con=engine) if data.isnull().values.any(): raise ValueError("Data quality check failed: Found missing values") print("Data quality check passed") def fetch_and_store_stock_data(): - import os - # Connect to PostgreSQL - DATABASE_URL = os.getenv("PYCOPG_DATABASE_URL", "postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v") - engine = create_engine(DATABASE_URL) - # Fetch stocks from database stocks_df = pd.read_sql("SELECT * FROM stocks", engine) @@ -46,8 +46,11 @@ def fetch_and_store_stock_data(): for index, row in stocks_df.iterrows(): ticker = row['symbol'] try: - data = yf.download(ticker, start='2023-01-01', end=datetime.today().strftime('%Y-%m-%d')) + data: pd.DataFrame = yf.download(ticker, start='2023-01-01', end=datetime.today().strftime('%Y-%m-%d')) data['symbol'] = ticker + data.reset_index(inplace=True) + data.index.name = 'id' + data.rename(columns={'Date': 'date', 'Open': 'open', 'High': 'high', 'Low': 'low', 'Close': 'close', 'Adj Close': 'adj_close', 'Volume': 'volume'}, inplace=True) data.to_sql('stock_data', engine, if_exists='append') except Exception as e: print(f"Error fetching data for {ticker}: {e}") From 97cc4e8581f23370c1ea038880e5662772117081 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:14:24 +0000 Subject: [PATCH 52/61] feat: add stock data api getway --- backend/main.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/backend/main.py b/backend/main.py index ad7b6ea..f173cda 100644 --- a/backend/main.py +++ b/backend/main.py @@ -172,6 +172,22 @@ def perform_backtest(scene_id: int, db: Session = Depends(get_db)): return backtest_results +@app.get("/stock_data/", response_model=List[schemas.StockData]) +def read_stock_data(skip: int = 0, limit: int = 100, db: Session = Depends(get_db)): + stock_data = db.query(models.StockData).offset(skip).limit(limit).all() + return stock_data + +@app.get("/stock_data/{symbol}", response_model=List[schemas.StockData]) +def read_stock_data(symbol: str, db: Session = Depends(get_db)): + stock_data = db.query(models.StockData).filter(models.StockData.symbol == symbol).limit(100).all() + return stock_data + +#gets stock data from db from start_date to end_date +@app.get("/stock_data/{symbol}/{from_date}/{to_date}/", response_model=List[schemas.StockData]) +def read_stock_data(symbol: str, from_date: str, to_date: str, db: Session = Depends(get_db)): + stock_data = db.query(models.StockData).filter(models.StockData.symbol == symbol, models.StockData.date >= from_date, models.StockData.date <= to_date).all() + return stock_data + @app.get('/run_backtest/', response_model=List[schemas.BacktestResult]) def run_backtests(scene: schemas.SceneCreate, db: Session = Depends(get_db)): db_scene = models.Scene(**scene.model_dump()) From c60389219cfd07cd7afffad919b0fae524079167 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:14:46 +0000 Subject: [PATCH 53/61] feat: add stock data model --- backend/models.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/backend/models.py b/backend/models.py index 38c9446..0214dec 100644 --- a/backend/models.py +++ b/backend/models.py @@ -54,3 +54,15 @@ class BacktestResult(Base): sharpe_ratio = Column(Float) created_at = Column(TIMESTAMP, server_default=text('CURRENT_TIMESTAMP')) scene = relationship('Scene', back_populates='backtests') + +class StockData(Base): + __tablename__ = "stock_data" + id = Column(Integer, primary_key=True, index=True) + date = Column(Date, index=True) + open = Column(Float) + high = Column(Float) + low = Column(Float) + close = Column(Float) + adj_close = Column(Float) + volume = Column(Float) + symbol = Column(String, index=True) \ No newline at end of file From a22eaeace4bacc688af0b005d8934fd169ef10ec Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:15:21 +0000 Subject: [PATCH 54/61] feat: add stock data schema --- backend/schemas.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/backend/schemas.py b/backend/schemas.py index 9411591..74e7b76 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -94,3 +94,16 @@ class BacktestResult(BacktestResultBase): class Config: orm_mode = True + +class StockData(BaseModel): + date: datetime + symbol: str + open: float + high: float + low: float + close: float + adj_close: float + volume: float + + class Config: + orm_mode = True \ No newline at end of file From 3a36860a02415038d49576e6b2274f912093640e Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:15:54 +0000 Subject: [PATCH 55/61] chore: notebook experimenation --- notebooks/backtesting.ipynb | 158 ++++++++++++++++++++++++++++++------ 1 file changed, 134 insertions(+), 24 deletions(-) diff --git a/notebooks/backtesting.ipynb b/notebooks/backtesting.ipynb index 5f4b592..8908c70 100644 --- a/notebooks/backtesting.ipynb +++ b/notebooks/backtesting.ipynb @@ -371,7 +371,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -384,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -457,7 +457,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -499,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -510,7 +510,7 @@ "# session.execute(text(\"DROP TABLE stocks CASCADE\"))\n", "# session.execute(text(\"DROP TABLE indicators CASCADE\"))\n", "# session.execute(text(\"DROP TABLE scenes CASCADE\"))\n", - "session.execute(text(\"DROP TABLE users CASCADE\"))\n", + "session.execute(text(\"DROP TABLE stock_data CASCADE\"))\n", "session.commit()\n", "\n", "# Close the session\n", @@ -931,19 +931,27 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[*********************100%%**********************] 1 of 1 completed\n", - "\n", - "1 Failed download:\n", - "['FB']: YFTzMissingError('$%ticker%: possibly delisted; No timezone found')\n" + "[*********************100%%**********************] 1 of 1 completed\n" ] - }, + } + ], + "source": [ + "import yfinance as yf\n", + "msft = yf.download(\"MSFT\", start=\"2023-01-01\", end=\"2024-01-01\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ { "data": { "text/html": [ @@ -965,15 +973,17 @@ " \n", " \n", " \n", - " Open\n", - " High\n", - " Low\n", - " Close\n", - " Adj Close\n", - " Volume\n", + " date\n", + " open\n", + " high\n", + " low\n", + " close\n", + " adj_close\n", + " volume\n", " \n", " \n", - " Date\n", + " id\n", + " \n", " \n", " \n", " \n", @@ -983,24 +993,124 @@ " \n", " \n", " \n", + " \n", + " 0\n", + " 2023-01-03\n", + " 243.080002\n", + " 245.750000\n", + " 237.399994\n", + " 239.580002\n", + " 236.609253\n", + " 25740000\n", + " \n", + " \n", + " 1\n", + " 2023-01-04\n", + " 232.279999\n", + " 232.869995\n", + " 225.960007\n", + " 229.100006\n", + " 226.259186\n", + " 50623400\n", + " \n", + " \n", + " 2\n", + " 2023-01-05\n", + " 227.199997\n", + " 227.550003\n", + " 221.759995\n", + " 222.309998\n", + " 219.553360\n", + " 39585600\n", + " \n", + " \n", + " 3\n", + " 2023-01-06\n", + " 223.000000\n", + " 225.759995\n", + " 219.350006\n", + " 224.929993\n", + " 222.140884\n", + " 43613600\n", + " \n", + " \n", + " 4\n", + " 2023-01-09\n", + " 226.449997\n", + " 231.240005\n", + " 226.410004\n", + " 227.119995\n", + " 224.303726\n", + " 27369800\n", + " \n", " \n", "\n", "
" ], "text/plain": [ - "Empty DataFrame\n", - "Columns: [Open, High, Low, Close, Adj Close, Volume]\n", - "Index: []" + " date open high low close adj_close \\\n", + "id \n", + "0 2023-01-03 243.080002 245.750000 237.399994 239.580002 236.609253 \n", + "1 2023-01-04 232.279999 232.869995 225.960007 229.100006 226.259186 \n", + "2 2023-01-05 227.199997 227.550003 221.759995 222.309998 219.553360 \n", + "3 2023-01-06 223.000000 225.759995 219.350006 224.929993 222.140884 \n", + "4 2023-01-09 226.449997 231.240005 226.410004 227.119995 224.303726 \n", + "\n", + " volume \n", + "id \n", + "0 25740000 \n", + "1 50623400 \n", + "2 39585600 \n", + "3 43613600 \n", + "4 27369800 " ] }, - "execution_count": 1, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import yfinance as yf\n", - "yf.download(\"FB\", start=\"2023-01-01\", end=\"2024-01-01\")" + "msft.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# rename the columns \"Date\tOpen\tHigh\tLow\tClose\tAdj Close\tVolume\"\n", + "\n", + "msft.rename(columns={'Date': 'date', 'Open': 'open', 'High': 'high', 'Low': 'low', 'Close': 'close', 'Adj Close': 'adj_close', 'Volume': 'volume'}, inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#rename the empty index in pandas dataframe to column name id\n", + "msft.index.name = 'id'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "msft.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# rename index\n" ] }, { From 6f8c71aeaa39b3e83df97cafc0cea3152217e68b Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:16:40 +0000 Subject: [PATCH 56/61] feat: add scene list route and fix login --- frontend/src/App.js | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/frontend/src/App.js b/frontend/src/App.js index 3351250..8c9c98b 100644 --- a/frontend/src/App.js +++ b/frontend/src/App.js @@ -5,6 +5,7 @@ import BacktestForm from './components/BacktestForm'; import Signup from './components/Signup'; import Login from './components/Login'; import Profile from './components/Profile'; +import ScenesList from './components/SceneList'; const PrivateRoute = ({ element: Element, ...rest }) => { const token = localStorage.getItem('token'); @@ -20,7 +21,11 @@ function App() { } /> } /> + {/* if the user is already logged in and tries to see /login redirect to /scenes */} + } /> + } /> } /> + } /> } /> } /> From 8b624b50dbe8f6ade7b4d4ed7a68479e24188930 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:17:11 +0000 Subject: [PATCH 57/61] fix: port change --- frontend/src/components/BacktestForm.js | 4 ++-- frontend/src/components/Login.js | 2 +- frontend/src/components/Navbar.js | 12 +++++++++--- 3 files changed, 12 insertions(+), 6 deletions(-) diff --git a/frontend/src/components/BacktestForm.js b/frontend/src/components/BacktestForm.js index 1d61002..cb6eb87 100644 --- a/frontend/src/components/BacktestForm.js +++ b/frontend/src/components/BacktestForm.js @@ -20,7 +20,7 @@ function BacktestForm() { useEffect(() => { const fetchIndicators = async () => { try { - const response = await fetch('http://127.0.0.1:8000/indicators/'); + const response = await fetch('http://127.0.0.1:8001/indicators/'); if (!response.ok) throw new Error('Error fetching indicators'); const data = await response.json(); setIndicators(data); @@ -31,7 +31,7 @@ function BacktestForm() { const fetchStocks = async () => { try { - const response = await fetch('http://127.0.0.1:8000/stocks/'); + const response = await fetch('http://127.0.0.1:8001/stocks/'); if (!response.ok) throw new Error('Error fetching stocks'); const data = await response.json(); setStocks(data); diff --git a/frontend/src/components/Login.js b/frontend/src/components/Login.js index 0b09e17..5540e6c 100644 --- a/frontend/src/components/Login.js +++ b/frontend/src/components/Login.js @@ -35,7 +35,7 @@ function Login() { formData.append('password', values.password); try { - const response = await fetch('http://127.0.0.1:8000/auth/token', { + const response = await fetch('http://127.0.0.1:8001/auth/token', { method: 'POST', body: formData, }); diff --git a/frontend/src/components/Navbar.js b/frontend/src/components/Navbar.js index ef51654..3324a64 100644 --- a/frontend/src/components/Navbar.js +++ b/frontend/src/components/Navbar.js @@ -12,7 +12,7 @@ function Navbar() { try { const token = localStorage.getItem('token'); - const response = await fetch('http://127.0.0.1:8000/auth/users/me', { + const response = await fetch('http://127.0.0.1:8001/auth/users/me', { headers: { 'Authorization': `Bearer ${token}`, }, @@ -47,9 +47,15 @@ function Navbar() {
{currentPath === '/login' && Sign Up} {currentPath === '/signup' && Login} - {currentPath === '/backtest' && Profile} - {(currentPath === '/backtest' || currentPath === '/profile') && ( + {(currentPath === '/backtest') && + (<>Profile + Scenes + )} + {(currentPath === '/scenes') && (<> + Backtest + Profile + )}
From 517b6ab47ca3253c6732da93f172b4d0ab60a011 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:17:28 +0000 Subject: [PATCH 58/61] feat: add scene list ui --- frontend/src/components/SceneList.js | 103 +++++++++++++++++++++++++++ 1 file changed, 103 insertions(+) create mode 100644 frontend/src/components/SceneList.js diff --git a/frontend/src/components/SceneList.js b/frontend/src/components/SceneList.js new file mode 100644 index 0000000..a94975b --- /dev/null +++ b/frontend/src/components/SceneList.js @@ -0,0 +1,103 @@ +import React, { useState, useEffect } from 'react'; +import { FaList, FaEye, FaSpinner, FaExclamationCircle } from 'react-icons/fa'; +// import 'tailwindcss/tailwind.css'; + +function ScenesList() { + const [scenes, setScenes] = useState([]); + const [selectedScene, setSelectedScene] = useState(null); + const [loading, setLoading] = useState(false); + const [error, setError] = useState(null); + + useEffect(() => { + const fetchScenes = async () => { + try { + const response = await fetch('http://127.0.0.1:8001/scenes/'); + if (!response.ok) throw new Error('Error fetching scenes'); + const data = await response.json(); + setScenes(data); + } catch (err) { + setError(err.message); + } + }; + + fetchScenes(); + }, []); + + const handleSceneClick = async (sceneId) => { + setLoading(true); + setError(null); + + try { + const response = await fetch(`http://127.0.0.1:8001/scenes/${sceneId}`); + if (!response.ok) throw new Error('Error fetching scene details'); + const data = await response.json(); + setSelectedScene(data); + } catch (err) { + setError(err.message); + } finally { + setLoading(false); + } + }; + + return ( +
+

+ Scenes List +

+ + {error && ( +
+ + {error} +
+ )} + +
    + {scenes.map((scene) => ( +
  • + +
    • + ))} +
+ + {loading && ( +
+ + Loading scene details... +
+ )} + + {selectedScene && ( +
+

Scene Details

+
+
+

Period: {selectedScene.period}

+

Indicator: {selectedScene.indicator.name}

+

Stock: {selectedScene.stock.name}

+
+
+ +

Backtests

+
    + {selectedScene.backtests.map((backtest) => ( +
  • +

    Initial Cash: ${backtest.initial_cash.toFixed(2)}

    +

    Final Value: ${backtest.final_value.toFixed(2)}

    +

    Max Drawdown: {backtest.max_drawdown?.toFixed(2)}%

    +
    • + ))} +
+
+ )} +
+ ); +} + +export default ScenesList; From 8fcdaefef62a30b297b044103f9fd0a6e5bd4594 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Thu, 27 Jun 2024 23:18:11 +0000 Subject: [PATCH 59/61] install: add react icon package --- frontend/package-lock.json | 10 ++++++++++ frontend/package.json | 1 + 2 files changed, 11 insertions(+) diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 679ed77..d0cf9b2 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -14,6 +14,7 @@ "formik": "^2.4.6", "react": "^18.3.1", "react-dom": "^18.3.1", + "react-icons": "^5.2.1", "react-router-dom": "^6.23.1", "react-scripts": "5.0.1", "web-vitals": "^2.1.4", @@ -15109,6 +15110,15 @@ "resolved": "https://registry.npmjs.org/react-fast-compare/-/react-fast-compare-2.0.4.tgz", "integrity": "sha512-suNP+J1VU1MWFKcyt7RtjiSWUjvidmQSlqu+eHslq+342xCbGTYmC0mEhPCOHxlW0CywylOC1u2DFAT+bv4dBw==" }, + "node_modules/react-icons": { + "version": "5.2.1", + "resolved": "https://registry.npmjs.org/react-icons/-/react-icons-5.2.1.tgz", + "integrity": "sha512-zdbW5GstTzXaVKvGSyTaBalt7HSfuK5ovrzlpyiWHAFXndXTdd/1hdDHI4xBM1Mn7YriT6aqESucFl9kEXzrdw==", + "license": "MIT", + "peerDependencies": { + "react": "*" + } + }, "node_modules/react-is": { "version": "17.0.2", "resolved": "https://registry.npmjs.org/react-is/-/react-is-17.0.2.tgz", diff --git a/frontend/package.json b/frontend/package.json index bef948f..ad6e2c9 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -9,6 +9,7 @@ "formik": "^2.4.6", "react": "^18.3.1", "react-dom": "^18.3.1", + "react-icons": "^5.2.1", "react-router-dom": "^6.23.1", "react-scripts": "5.0.1", "web-vitals": "^2.1.4", From 8dc7d887c1e509c631633cf7b3b370e8fecd4f02 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Fri, 28 Jun 2024 10:48:48 +0000 Subject: [PATCH 60/61] mig: alembic migration --- alembic/env.py | 78 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 alembic/env.py diff --git a/alembic/env.py b/alembic/env.py new file mode 100644 index 0000000..36112a3 --- /dev/null +++ b/alembic/env.py @@ -0,0 +1,78 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name is not None: + fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = None + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline() -> None: + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online() -> None: + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section, {}), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, target_metadata=target_metadata + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() From 8e9708242d44dc0cf3bebe8078935c6e651c9f66 Mon Sep 17 00:00:00 2001 From: dev-abuke Date: Fri, 28 Jun 2024 10:49:19 +0000 Subject: [PATCH 61/61] chore: files for mlflow and db --- airflow/dags/fetch_stock_data.py | 23 +- airflow/logs/scheduler/latest | 2 +- alembic.ini | 117 +++ alembic/README | 1 + alembic/env.py | 26 +- alembic/script.py.mako | 26 + backend/main.py | 59 +- backend/models.py | 8 +- backend/schemas.py | 4 +- frontend/public/index.html | 1 + frontend/src/App.js | 4 +- frontend/src/components/BacktestForm.js | 4 +- frontend/src/components/Login.js | 10 +- frontend/src/components/SceneList.js | 27 +- frontend/src/components/Signup.js | 2 +- frontend/src/index.css | 7 + notebooks/backtesting.ipynb | 591 +++++++++++- notebooks/lstm.ipynb | 843 ++++++++++++++++-- scripts/backtesting/main.py | 101 +-- .../backtesting/strategies/base_strategy.py | 49 +- .../backtesting/strategies/lstm_strategy.py | 14 +- .../backtesting/strategies/macd_strategy.py | 7 +- 22 files changed, 1681 insertions(+), 245 deletions(-) create mode 100644 alembic.ini create mode 100644 alembic/README create mode 100644 alembic/script.py.mako diff --git a/airflow/dags/fetch_stock_data.py b/airflow/dags/fetch_stock_data.py index f909c3d..170c95c 100644 --- a/airflow/dags/fetch_stock_data.py +++ b/airflow/dags/fetch_stock_data.py @@ -46,12 +46,25 @@ def fetch_and_store_stock_data(): for index, row in stocks_df.iterrows(): ticker = row['symbol'] try: + print(f"Fetching data for {ticker}") data: pd.DataFrame = yf.download(ticker, start='2023-01-01', end=datetime.today().strftime('%Y-%m-%d')) - data['symbol'] = ticker - data.reset_index(inplace=True) - data.index.name = 'id' - data.rename(columns={'Date': 'date', 'Open': 'open', 'High': 'high', 'Low': 'low', 'Close': 'close', 'Adj Close': 'adj_close', 'Volume': 'volume'}, inplace=True) - data.to_sql('stock_data', engine, if_exists='append') + if not data.empty: + data['symbol'] = ticker + data.reset_index(inplace=True) + data.rename(columns={ + 'Date': 'date', + 'Open': 'open', + 'High': 'high', + 'Low': 'low', + 'Close': 'close', + 'Adj Close': 'adj_close', + 'Volume': 'volume' + }, inplace=True) + # Ensure we don't overwrite by specifying the index as False + data.to_sql('stock_data', engine, if_exists='append', index=False) + print(f"Successfully stored data for {ticker}") + else: + print(f"No data found for {ticker}") except Exception as e: print(f"Error fetching data for {ticker}: {e}") diff --git a/airflow/logs/scheduler/latest b/airflow/logs/scheduler/latest index 29197e9..277e205 120000 --- a/airflow/logs/scheduler/latest +++ b/airflow/logs/scheduler/latest @@ -1 +1 @@ -2024-06-27 \ No newline at end of file +2024-06-28 \ No newline at end of file diff --git a/alembic.ini b/alembic.ini new file mode 100644 index 0000000..7ce1d82 --- /dev/null +++ b/alembic.ini @@ -0,0 +1,117 @@ +# A generic, single database configuration. + +[alembic] +# path to migration scripts +# Use forward slashes (/) also on windows to provide an os agnostic path +script_location = alembic + +# template used to generate migration file names; The default value is %%(rev)s_%%(slug)s +# Uncomment the line below if you want the files to be prepended with date and time +# see https://alembic.sqlalchemy.org/en/latest/tutorial.html#editing-the-ini-file +# for all available tokens +# file_template = %%(year)d_%%(month).2d_%%(day).2d_%%(hour).2d%%(minute).2d-%%(rev)s_%%(slug)s + +# sys.path path, will be prepended to sys.path if present. +# defaults to the current working directory. +prepend_sys_path = . + +# timezone to use when rendering the date within the migration file +# as well as the filename. +# If specified, requires the python>=3.9 or backports.zoneinfo library. +# Any required deps can installed by adding `alembic[tz]` to the pip requirements +# string value is passed to ZoneInfo() +# leave blank for localtime +# timezone = + +# max length of characters to apply to the "slug" field +# truncate_slug_length = 40 + +# set to 'true' to run the environment during +# the 'revision' command, regardless of autogenerate +# revision_environment = false + +# set to 'true' to allow .pyc and .pyo files without +# a source .py file to be detected as revisions in the +# versions/ directory +# sourceless = false + +# version location specification; This defaults +# to alembic/versions. When using multiple version +# directories, initial revisions must be specified with --version-path. +# The path separator used here should be the separator specified by "version_path_separator" below. +# version_locations = %(here)s/bar:%(here)s/bat:alembic/versions + +# version path separator; As mentioned above, this is the character used to split +# version_locations. The default within new alembic.ini files is "os", which uses os.pathsep. +# If this key is omitted entirely, it falls back to the legacy behavior of splitting on spaces and/or commas. +# Valid values for version_path_separator are: +# +# version_path_separator = : +# version_path_separator = ; +# version_path_separator = space +version_path_separator = os # Use os.pathsep. Default configuration used for new projects. + +# set to 'true' to search source files recursively +# in each "version_locations" directory +# new in Alembic version 1.10 +# recursive_version_locations = false + +# the output encoding used when revision files +# are written from script.py.mako +# output_encoding = utf-8 + +sqlalchemy.url = postgresql+psycopg2://trading_db_av2v_user:210M6MA9QKEEgVdiasnUdMQDBNN417oy@dpg-cpqojbqj1k6c73bkqq3g-a.oregon-postgres.render.com/trading_db_av2v + + + +[post_write_hooks] +# post_write_hooks defines scripts or Python functions that are run +# on newly generated revision scripts. See the documentation for further +# detail and examples + +# format using "black" - use the console_scripts runner, against the "black" entrypoint +# hooks = black +# black.type = console_scripts +# black.entrypoint = black +# black.options = -l 79 REVISION_SCRIPT_FILENAME + +# lint with attempts to fix using "ruff" - use the exec runner, execute a binary +# hooks = ruff +# ruff.type = exec +# ruff.executable = %(here)s/.venv/bin/ruff +# ruff.options = --fix REVISION_SCRIPT_FILENAME + +# Logging configuration +[loggers] +keys = root,sqlalchemy,alembic + +[handlers] +keys = console + +[formatters] +keys = generic + +[logger_root] +level = WARN +handlers = console +qualname = + +[logger_sqlalchemy] +level = WARN +handlers = +qualname = sqlalchemy.engine + +[logger_alembic] +level = INFO +handlers = +qualname = alembic + +[handler_console] +class = StreamHandler +args = (sys.stderr,) +level = NOTSET +formatter = generic + +[formatter_generic] +format = %(levelname)-5.5s [%(name)s] %(message)s +datefmt = %H:%M:%S diff --git a/alembic/README b/alembic/README new file mode 100644 index 0000000..98e4f9c --- /dev/null +++ b/alembic/README @@ -0,0 +1 @@ +Generic single-database configuration. \ No newline at end of file diff --git a/alembic/env.py b/alembic/env.py index 36112a3..c079c01 100644 --- a/alembic/env.py +++ b/alembic/env.py @@ -11,14 +11,14 @@ # Interpret the config file for Python logging. # This line sets up loggers basically. -if config.config_file_name is not None: - fileConfig(config.config_file_name) +fileConfig(config.config_file_name) # add your model's MetaData object here # for 'autogenerate' support # from myapp import mymodel # target_metadata = mymodel.Base.metadata -target_metadata = None +from backend.models import Base # Import your Base +target_metadata = Base.metadata # other values from the config, defined by the needs of env.py, # can be acquired: @@ -26,47 +26,37 @@ # ... etc. -def run_migrations_offline() -> None: +def run_migrations_offline(): """Run migrations in 'offline' mode. - This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. - Calls to context.execute() here emit the given string to the script output. - """ url = config.get_main_option("sqlalchemy.url") context.configure( - url=url, - target_metadata=target_metadata, - literal_binds=True, - dialect_opts={"paramstyle": "named"}, + url=url, target_metadata=target_metadata, literal_binds=True ) with context.begin_transaction(): context.run_migrations() -def run_migrations_online() -> None: +def run_migrations_online(): """Run migrations in 'online' mode. - In this scenario we need to create an Engine and associate a connection with the context. - """ connectable = engine_from_config( - config.get_section(config.config_ini_section, {}), + config.get_section(config.config_ini_section), prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: - context.configure( - connection=connection, target_metadata=target_metadata - ) + context.configure(connection=connection, target_metadata=target_metadata) with context.begin_transaction(): context.run_migrations() diff --git a/alembic/script.py.mako b/alembic/script.py.mako new file mode 100644 index 0000000..fbc4b07 --- /dev/null +++ b/alembic/script.py.mako @@ -0,0 +1,26 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from typing import Sequence, Union + +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision: str = ${repr(up_revision)} +down_revision: Union[str, None] = ${repr(down_revision)} +branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)} +depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)} + + +def upgrade() -> None: + ${upgrades if upgrades else "pass"} + + +def downgrade() -> None: + ${downgrades if downgrades else "pass"} diff --git a/backend/main.py b/backend/main.py index f173cda..ab0e1ab 100644 --- a/backend/main.py +++ b/backend/main.py @@ -94,9 +94,7 @@ def create_scene(scene: schemas.SceneCreate, db: Session = Depends(get_db)): scene_parameters = { 'scene_id': db_scene.id, 'period': db_scene.period, - 'initial_cash': 500, - 'indicator_name': db_scene.indicator.name, - 'indicator': db_scene.indicator.symbol, + 'initial_cash': 500,\ 'stock_name': db_scene.stock.name, 'ticker': db_scene.stock.symbol, 'start_date': db_scene.start_date.strftime('%Y-%m-%d'), @@ -114,7 +112,7 @@ def create_scene(scene: schemas.SceneCreate, db: Session = Depends(get_db)): existing_scene = db.query(models.Scene).filter( models.Scene.start_date == scene.start_date, models.Scene.end_date == scene.end_date, - models.Scene.indicator_id == scene.indicator_id + models.Scene.stock_id == scene.stock_id ).first() return existing_scene @@ -129,7 +127,7 @@ def delete_scene(scene_id: int, db: Session = Depends(get_db)): @app.get('/scenes/{scene_id}', response_model=schemas.Scene) def read_scene(scene_id: int, db: Session = Depends(get_db)): - db_scene = db.query(models.Scene).options(joinedload(models.Scene.indicator), joinedload(models.Scene.stock)).filter(models.Scene.id == scene_id).first() + db_scene = db.query(models.Scene).options(joinedload(models.Scene.stock)).filter(models.Scene.id == scene_id).first() if db_scene is None: raise HTTPException(status_code=404, detail="Scene not found") return db_scene @@ -142,18 +140,33 @@ def read_scenes(skip: int = 0, limit: int = 10, db: Session = Depends(get_db)): logger.info(f"Backtest Result: {backtest.__dict__}") return scenes -@app.post('/backtests/{scene_id}', response_model=List[schemas.BacktestResult]) -def perform_backtest(scene_id: int, db: Session = Depends(get_db)): +@app.get('/best_indicator/{scene_id}', response_model=schemas.BacktestResult) +def get_best_indicator(scene_id: int, db: Session = Depends(get_db)): + db_scene = db.query(models.Scene).options(joinedload(models.Scene.backtests).joinedload(models.BacktestResult.indicator)).filter(models.Scene.id == scene_id).first() + if db_scene is None: + raise HTTPException(status_code=404, detail="Scene not found") + + best_backtest = select_best_indicator([backtest.__dict__ for backtest in db_scene.backtests]) + + if best_backtest is None: + raise HTTPException(status_code=404, detail="No backtests found for the scene") + + return best_backtest + +@app.post('/backtests/{scene_id}/{indicator_id}', response_model=List[schemas.BacktestResult]) +def perform_backtest(scene_id: int, indicator_id: int, db: Session = Depends(get_db)): db_scene = db.query(models.Scene).filter(models.Scene.id == scene_id).first() if db_scene is None: raise HTTPException(status_code=404, detail="Scene not found") + print("The Db Scene: ", db_scene.__dict__) + db_indicator = db.query(models.Indicator).filter(models.Indicator.id == indicator_id).first() config = { 'initial_cash': 500, 'start_date': db_scene.start_date.strftime('%Y-%m-%d'), 'end_date': db_scene.end_date.strftime('%Y-%m-%d'), 'ticker': db_scene.stock.symbol, - 'indicator': db_scene.indicator.symbol + 'indicator': db_indicator.symbol } logger.info(f"Config: {config}") @@ -164,7 +177,7 @@ def perform_backtest(scene_id: int, db: Session = Depends(get_db)): # Save metrics to database backtest_results = [] - db_backtest_result = models.BacktestResult(scene_id=scene_id, **metrics) + db_backtest_result = models.BacktestResult(scene_id=scene_id, **metrics, indicator_id=indicator_id) db.add(db_backtest_result) db.commit() db.refresh(db_backtest_result) @@ -250,4 +263,30 @@ def consume_scene_parameters(): logger.info("Backtest results sent to Kafka") except Exception as e: logger.error(f"Error in consumer loop: {e}") - time.sleep(5) # Sleep for a while before retrying \ No newline at end of file + time.sleep(5) # Sleep for a while before retrying + +def normalize(value, min_value, max_value): + return (value - min_value) / (max_value - min_value) + +def calculate_score(backtest): + # Normalize metrics (assuming higher return and Sharpe ratio are better, lower max drawdown is better) + normalized_return = normalize(backtest['percentage_return'], -1, 1) + # normalized_max_drawdown = normalize(-backtest['max_drawdown'], -1, 0) # Negate since lower is better + normalized_sharpe_ratio = normalize(backtest['sharpe_ratio'], 0, 3) + + score = (normalized_return * 0.4) + (normalized_sharpe_ratio * 0.3) + # Combine normalized metrics into a single score (weights can be adjusted) + # score = (normalized_return * 0.4) + (normalized_max_drawdown * 0.3) + (normalized_sharpe_ratio * 0.3) + return score + +def select_best_indicator(backtests): + best_backtest = None + best_score = float('-inf') + + for backtest in backtests: + score = calculate_score(backtest) + if score > best_score: + best_score = score + best_backtest = backtest + + return best_backtest diff --git a/backend/models.py b/backend/models.py index 0214dec..2e6464a 100644 --- a/backend/models.py +++ b/backend/models.py @@ -32,18 +32,15 @@ class Scene(Base): start_date = Column(Date, nullable=False) end_date = Column(Date, nullable=False) stock_id = Column(Integer, ForeignKey('stocks.id')) - indicator_id = Column(Integer, ForeignKey('indicators.id')) backtests = relationship('BacktestResult', back_populates='scene') stock = relationship('Stock') - indicator = relationship('Indicator') - __table_args__ = (UniqueConstraint('start_date', 'end_date', 'indicator_id', 'stock_id', name='_scene_uc'),) + __table_args__ = (UniqueConstraint('start_date', 'end_date', 'stock_id', name='_scene_uc'),) class BacktestResult(Base): __tablename__ = 'backtest_results' # __table_args__ = (UniqueConstraint('scene_id', 'indicator_id', 'stock_id', name='_backtest_uc'),) id = Column(Integer, primary_key=True, index=True) - scene_id = Column(Integer, ForeignKey('scenes.id')) initial_cash = Column(Float, nullable=False) final_value = Column(Float, nullable=False) percentage_return = Column(Float) @@ -53,7 +50,10 @@ class BacktestResult(Base): losing_trades = Column(Integer) sharpe_ratio = Column(Float) created_at = Column(TIMESTAMP, server_default=text('CURRENT_TIMESTAMP')) + scene_id = Column(Integer, ForeignKey('scenes.id')) + indicator_id = Column(Integer, ForeignKey('indicators.id')) scene = relationship('Scene', back_populates='backtests') + indicator = relationship('Indicator') class StockData(Base): __tablename__ = "stock_data" diff --git a/backend/schemas.py b/backend/schemas.py index 74e7b76..33ecc54 100644 --- a/backend/schemas.py +++ b/backend/schemas.py @@ -59,7 +59,6 @@ class SceneBase(BaseModel): period: int start_date: date end_date: date - indicator_id: int stock_id: int class SceneCreate(SceneBase): @@ -68,7 +67,6 @@ class SceneCreate(SceneBase): class Scene(SceneBase): id: int backtests: List['BacktestResult'] = [] - indicator: Indicator stock: Stock class Config: @@ -78,6 +76,7 @@ class BacktestResultBase(BaseModel): scene_id: int initial_cash: float final_value: float + indicator_id: int percentage_return: float total_trades: int winning_trades: int @@ -90,6 +89,7 @@ class BacktestResultCreate(BacktestResultBase): class BacktestResult(BacktestResultBase): id: int + indicator: Indicator created_at: datetime class Config: diff --git a/frontend/public/index.html b/frontend/public/index.html index aa069f2..f8b4646 100644 --- a/frontend/public/index.html +++ b/frontend/public/index.html @@ -5,6 +5,7 @@ + } /> } /> {/* if the user is already logged in and tries to see /login redirect to /scenes */} - } /> - } /> + {/* } /> */} + {/* } /> */} } /> } /> } /> diff --git a/frontend/src/components/BacktestForm.js b/frontend/src/components/BacktestForm.js index cb6eb87..68114b6 100644 --- a/frontend/src/components/BacktestForm.js +++ b/frontend/src/components/BacktestForm.js @@ -75,7 +75,7 @@ function BacktestForm() { }; try { - const response = await fetch('http://127.0.0.1:8000/scenes/', { + const response = await fetch('http://127.0.0.1:8001/scenes/', { method: 'POST', headers: { 'Content-Type': 'application/json', @@ -88,7 +88,7 @@ function BacktestForm() { const scene = await response.json(); const scene_id = scene.id; - const response2 = await fetch(`http://127.0.0.1:8000/backtests/${scene_id}/`, { + const response2 = await fetch(`http://127.0.0.1:8001/backtests/${scene_id}/${parameters["indicator"]}`, { method: 'POST', headers: { 'Content-Type': 'application/json', diff --git a/frontend/src/components/Login.js b/frontend/src/components/Login.js index 5540e6c..67cb73b 100644 --- a/frontend/src/components/Login.js +++ b/frontend/src/components/Login.js @@ -29,7 +29,7 @@ function Login() { onSubmit: async (values) => { setLoading(true); setError(null); - + console.log("The data from login :: ", values) const formData = new FormData(); formData.append('username', values.username); formData.append('password', values.password); @@ -37,6 +37,10 @@ function Login() { try { const response = await fetch('http://127.0.0.1:8001/auth/token', { method: 'POST', + headers: { + // x-content-type-options' + 'X-Content-Type-Options': 'nosniff', + }, body: formData, }); @@ -62,7 +66,7 @@ function Login() {

Login

-