Merge 15b9f90ae2 into 10c136f49c
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Fried-MK
GitHub
commit
23e41671e0
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import backtrader as bt
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import pandas as pd
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import yfinance as yf
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import json
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import os
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import shutil
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from datetime import datetime, timedelta
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from tradingagents.graph.trading_graph import TradingAgentsGraph
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from tradingagents.default_config import DEFAULT_CONFIG
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from dotenv import load_dotenv
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# Load environment variables
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load_dotenv()
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class TradingAgentsStrategy(bt.Strategy):
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"""Strategy that uses TradingAgents for decision making"""
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def __init__(self, trading_agent, ticker, backtest_config):
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self.trading_agent = trading_agent
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self.ticker = ticker
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self.backtest_config = backtest_config
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self.decisions = {}
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self.trade_count = 0
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self.data_checks = {}
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def next(self):
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# Get current date
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current_date = self.datas[0].datetime.date(0)
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date_str = current_date.strftime("%Y-%m-%d")
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# Verify data range to avoid look-ahead bias
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self.verify_data_range(date_str)
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# Get decision from TradingAgents
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if date_str not in self.decisions:
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print(f"Processing date: {date_str}")
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try:
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_, decision = self.trading_agent.propagate(self.ticker, date_str)
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self.decisions[date_str] = decision
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print(f"Decision: {decision}")
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except Exception as e:
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print(f"Error getting decision for {date_str}: {e}")
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self.decisions[date_str] = "HOLD"
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decision = self.decisions[date_str]
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# Execute trade based on decision
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if decision == "BUY" and not self.position:
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# Buy with 100% of available cash
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size = int(self.broker.getcash() / self.data.close[0])
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if size > 0:
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self.buy(size=size)
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self.trade_count += 1
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print(f"BUY {self.ticker} on {date_str} at ${self.data.close[0]:.2f}")
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elif decision == "SELL" and self.position:
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# Sell all positions
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self.sell(size=self.position.size)
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self.trade_count += 1
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print(f"SELL {self.ticker} on {date_str} at ${self.data.close[0]:.2f}")
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def verify_data_range(self, date_str):
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"""Verify that data range is correct to avoid look-ahead bias"""
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current_date = datetime.strptime(date_str, "%Y-%m-%d")
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# Check if we already verified this date
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if date_str in self.data_checks:
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return
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# Verify data feed doesn't contain future data
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data_end_date = self.datas[0].datetime.date(-1)
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if data_end_date > current_date:
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print(f"⚠️ Warning: Data feed contains future data beyond {date_str}")
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self.data_checks[date_str] = True
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def clean_cache():
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"""Clean cache to avoid look-ahead bias"""
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print("\n=== Cleaning cache to avoid look-ahead bias ===")
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# Clean yfinance cache
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yfinance_cache = "yfinance_cache"
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if os.path.exists(yfinance_cache):
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shutil.rmtree(yfinance_cache)
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print(f"✓ Cleaned yfinance cache: {yfinance_cache}")
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# Clean dataflows cache
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dataflows_cache = "dataflows/data_cache"
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if os.path.exists(dataflows_cache):
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shutil.rmtree(dataflows_cache)
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print(f"✓ Cleaned dataflows cache: {dataflows_cache}")
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# Clean backtest results (optional)
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# backtest_results = "backtest_results"
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# if os.path.exists(backtest_results):
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# shutil.rmtree(backtest_results)
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# print(f"✓ Cleaned backtest results: {backtest_results}")
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def run_backtest(ticker, start_date, end_date, initial_cash=100000, clean_cache_flag=True):
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"""Run backtest for a given ticker and date range"""
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# Clean cache to avoid look-ahead bias
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if clean_cache_flag:
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clean_cache()
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# Create Cerebro engine
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cerebro = bt.Cerebro()
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# Set initial cash
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cerebro.broker.setcash(initial_cash)
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# Add strategy
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config = DEFAULT_CONFIG.copy()
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config["llm_provider"] = "openrouter"
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config["deep_think_llm"] = "deepseek/deepseek-chat"
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config["quick_think_llm"] = "openai/gpt-4o-mini"
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config["max_debate_rounds"] = 2
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# Verify date range
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start_dt = datetime.strptime(start_date, "%Y-%m-%d")
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end_dt = datetime.strptime(end_date, "%Y-%m-%d")
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if start_dt >= end_dt:
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raise ValueError("Start date must be before end date")
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if end_dt > datetime.now():
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raise ValueError("End date cannot be in the future")
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trading_agent = TradingAgentsGraph(debug=False, config=config)
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backtest_config = {
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"ticker": ticker,
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"start_date": start_date,
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"end_date": end_date,
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"initial_cash": initial_cash,
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"clean_cache": clean_cache_flag
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}
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cerebro.addstrategy(TradingAgentsStrategy, trading_agent=trading_agent, ticker=ticker, backtest_config=backtest_config)
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# Get historical data from yfinance
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print("\n=== Fetching historical data ===")
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data = yf.download(ticker, start=start_date, end=end_date)
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# Verify data quality
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if data.empty:
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raise ValueError(f"No data found for {ticker} between {start_date} and {end_date}")
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print(f"✓ Data fetched: {len(data)} trading days")
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print(f"✓ Date range: {data.index.min().date()} to {data.index.max().date()}")
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# Convert to backtrader data feed
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data_feed = bt.feeds.PandasData(
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dataname=data,
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datetime=0,
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high=1,
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low=2,
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open=3,
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close=4,
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volume=5,
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openinterest=-1
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)
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# Add data feed to cerebro
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cerebro.adddata(data_feed, name=ticker)
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# Add analyzers
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cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name='sharpe')
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cerebro.addanalyzer(bt.analyzers.DrawDown, _name='drawdown')
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cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name='trades')
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cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='annual')
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cerebro.addanalyzer(bt.analyzers.Returns, _name='returns')
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cerebro.addanalyzer(bt.analyzers.PositionsValue, _name='positions')
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# Run backtest
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print(f"\n=== Starting Backtest for {ticker} ===")
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print(f"Date range: {start_date} to {end_date}")
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print(f"Initial cash: ${initial_cash:.2f}")
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print(f"LLM Provider: {config['llm_provider']}")
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print(f"Models: Deep={config['deep_think_llm']}, Quick={config['quick_think_llm']}")
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results = cerebro.run()
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# Get results
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strategy = results[0]
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final_value = cerebro.broker.getvalue()
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total_return = ((final_value - initial_cash) / initial_cash) * 100
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# Get analyzer results
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sharpe = strategy.analyzers.sharpe.get_analysis()
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drawdown = strategy.analyzers.drawdown.get_analysis()
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trades = strategy.analyzers.trades.get_analysis()
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annual = strategy.analyzers.annual.get_analysis()
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returns = strategy.analyzers.returns.get_analysis()
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# Calculate additional metrics
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total_trades = trades.get('total', {}).get('total', 0)
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won_trades = trades.get('won', {}).get('total', 0)
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win_rate = won_trades / max(total_trades, 1) * 100
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# Print results
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print(f"\n=== Backtest Results ===")
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print(f"Final portfolio value: ${final_value:.2f}")
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print(f"Total return: {total_return:.2f}%")
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print(f"Daily return: {returns.get('rnorm', 0) * 100:.4f}%")
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print(f"Sharpe Ratio: {sharpe.get('sharperatio', 'N/A'):.2f}")
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print(f"Max Drawdown: {drawdown.get('max', {}).get('drawdown', 'N/A'):.2f}%")
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print(f"Total trades: {total_trades}")
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print(f"Win rate: {win_rate:.2f}%")
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print(f"Average trade duration: {trades.get('len', {}).get('average', 'N/A'):.1f} days")
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# Save results
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save_results(ticker, start_date, end_date, {
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"initial_cash": initial_cash,
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"final_value": final_value,
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"total_return": total_return,
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"daily_return": returns.get('rnorm', 0),
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"sharpe_ratio": sharpe.get('sharperatio', None),
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"max_drawdown": drawdown.get('max', {}).get('drawdown', None),
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"total_trades": total_trades,
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"won_trades": won_trades,
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"win_rate": win_rate,
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"average_trade_duration": trades.get('len', {}).get('average', None),
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"decisions": strategy.decisions,
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"config": backtest_config
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})
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# Plot results
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print("\n=== Generating backtest chart ===")
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cerebro.plot(style='candlestick')
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def save_results(ticker, start_date, end_date, results):
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"""Save backtest results to file"""
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results_dir = f"backtest_results/{ticker}/"
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os.makedirs(results_dir, exist_ok=True)
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filename = f"backtest_{start_date}_{end_date}.json"
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filepath = os.path.join(results_dir, filename)
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with open(filepath, "w", encoding="utf-8") as f:
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json.dump(results, f, indent=4, default=str)
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print(f"Results saved to: {filepath}")
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def run_multiple_backtests(ticker_list, start_date, end_date, initial_cash=100000):
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"""Run backtests for multiple tickers"""
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all_results = {}
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for ticker in ticker_list:
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print(f"\n{'='*60}")
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print(f"Running backtest for {ticker}")
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print(f"{'='*60}")
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try:
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# Run backtest without cleaning cache for subsequent tickers
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clean_cache_flag = (ticker == ticker_list[0])
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run_backtest(ticker, start_date, end_date, initial_cash, clean_cache_flag)
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except Exception as e:
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print(f"Error running backtest for {ticker}: {e}")
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all_results[ticker] = {"error": str(e)}
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return all_results
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if __name__ == "__main__":
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# Define parameters
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ticker = "NVDA"
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start_date = "2024-01-01"
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end_date = "2024-03-29"
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initial_cash = 100000
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# Run backtest
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run_backtest(ticker, start_date, end_date, initial_cash)
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# Example: Run multiple backtests
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# tickers = ["NVDA", "AAPL", "MSFT"]
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# run_multiple_backtests(tickers, start_date, end_date, initial_cash)
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