285 lines
11 KiB
Python
285 lines
11 KiB
Python
"""
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Backtesting engine for TradingAgents and baseline strategies.
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Both TradingAgents and rule-based strategies use identical return calculation logic:
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1. Generate signals/actions: 1 (BUY), 0 (HOLD), -1 (SELL)
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2. Convert actions to positions: 1 (long), 0 (flat)
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3. Calculate returns: strategy_return = position.shift(1) * market_return
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This ensures apples-to-apples comparison across all strategies.
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, List
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from pathlib import Path
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import json
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STD_FIELDS = {"Open", "High", "Low", "Close", "Adj Close", "Volume"}
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class TradingAgentsBacktester:
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"""Backtest engine for TradingAgents framework."""
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def __init__(self, trading_agents_graph, initial_capital=100000, output_dir=None):
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self.graph = trading_agents_graph
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self.initial_capital = float(initial_capital)
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self.name = "TradingAgents"
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self.output_dir = output_dir
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def backtest(self, ticker: str, start_date: str, end_date: str, data: pd.DataFrame) -> pd.DataFrame:
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"""
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Backtest TradingAgents with realistic single-asset account simulation.
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Supports long, short, and flat positions with 1× leverage on shorts.
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"""
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df = data.loc[start_date:end_date].copy()
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decisions = []
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signals = pd.Series(0, index=df.index, dtype=float)
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print(f"\nRunning TradingAgents backtest on {ticker} from {start_date} to {end_date}")
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print(f"Total trading days: {len(df)}")
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print("-" * 80)
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# === Step 1: Collect signals ===
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for i, (date, row) in enumerate(df.iterrows()):
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date_str = date.strftime("%Y-%m-%d")
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price = float(row["Close"])
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try:
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print(f"\n[{i+1}/{len(df)}] {date_str} ... ", end="")
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final_state, decision = self.graph.propagate(ticker, date_str)
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print(f"Decision: {decision}")
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signal = self._parse_decision(decision)
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decisions.append({"date": date_str, "decision": decision, "signal": signal, "price": price})
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except Exception as e:
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print(f"Error: {e}")
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signal = 0
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decisions.append({"date": date_str, "decision": "ERROR", "signal": 0, "price": price, "error": str(e)})
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signals.loc[date] = signal
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# === Step 2: Run realistic cash+shares backtest ===
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close = pd.to_numeric(df["Close"], errors="coerce")
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cash = self.initial_capital
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shares = 0.0
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prev_value = cash
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records = []
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for i, (date, price) in enumerate(close.items()):
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action = signals.iloc[i]
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# 先计算上一个交易日的组合价值
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portfolio_value = cash + shares * price
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# === 若方向改变，先平仓 ===
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if (shares > 0 and action <= 0) or (shares < 0 and action >= 0):
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cash += shares * price # 卖出现有股票或回补空头
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shares = 0.0
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# === 建仓逻辑 ===
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if action == 1 and shares == 0:
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# 做多
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shares = cash / price
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cash = 0.0
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elif action == -1 and shares == 0:
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# 做空（1倍杠杆）
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shares = -cash / price
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cash = 2 * cash # 保证金 + 卖出所得
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# === 更新组合价值 ===
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new_value = cash + shares * price
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daily_return = (new_value / prev_value) - 1 if prev_value != 0 else 0.0
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prev_value = new_value
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records.append({
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"date": date.strftime("%Y-%m-%d"),
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"action": action,
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"shares": shares,
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"close_price": price,
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"cash": cash,
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"portfolio_value": new_value,
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"strategy_return": daily_return,
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})
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# === Step 3: 转为 DataFrame 并计算累计收益 ===
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portfolio = pd.DataFrame(records).set_index("date")
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portfolio["cumulative_return"] = (1 + portfolio["strategy_return"]).cumprod()
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portfolio["ticker"] = ticker
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self.latest_portfolio = portfolio
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self._save_decisions_log(ticker, decisions, start_date, end_date)
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return portfolio
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@staticmethod
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def _actions_to_position(actions: pd.Series) -> pd.Series:
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"""
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Convert action series to a long-only position series in {0,1}.
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Same logic as baseline strategies for consistency.
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"""
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a = actions.astype(float).fillna(0.0).clip(-1, 1).values
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pos = np.zeros_like(a, dtype=float)
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for i in range(len(a)):
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if i == 0:
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pos[i] = 1.0 if a[i] > 0 else 0.0
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else:
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if a[i] > 0: # buy → long
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pos[i] = 1.0
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elif a[i] < 0: # sell → flat
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pos[i] = 0.0
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else: # hold → keep previous
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pos[i] = pos[i-1]
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return pd.Series(pos, index=actions.index, name="position")
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def _parse_decision(self, decision: str) -> int:
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"""
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Parse decision to signal.
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We interpret:
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- contains 'BUY' or 'LONG' -> 1
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- contains 'SELL' or 'EXIT' -> -1 (we use -1 as 'close to cash' here)
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- otherwise HOLD -> 0
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"""
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d = str(decision).upper()
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if "BUY" in d or "LONG" in d:
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return 1
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if "SELL" in d or "EXIT" in d or "CLOSE" in d:
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return -1
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return 0
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def _save_decisions_log(self, ticker: str, decisions: List[Dict], start_date: str, end_date: str):
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"""
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Save detailed TradingAgents decisions and portfolio state to JSON.
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Adds shares, cash, and cumulative return (cr) from the latest backtest results.
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"""
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if self.output_dir:
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out = Path(self.output_dir) / ticker / "TradingAgents"
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else:
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out = Path(f"eval_results/{ticker}/TradingAgents")
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out.mkdir(parents=True, exist_ok=True)
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fp = out / f"decisions_{start_date}_to_{end_date}.json"
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# Try to include computed portfolio metrics if available
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try:
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# Attempt to load the latest portfolio CSV/DF from memory
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if hasattr(self, "latest_portfolio"):
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port = self.latest_portfolio
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port = port.reset_index()
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port_dict = {d["date"]: d for d in port.to_dict(orient="records")}
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# Merge portfolio stats into each decision record
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for d in decisions:
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date = d["date"]
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if date in port_dict:
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d.update({
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"shares": port_dict[date].get("shares"),
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"cash": port_dict[date].get("cash"),
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"portfolio_value": port_dict[date].get("portfolio_value"),
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"cumulative_return": port_dict[date].get("cumulative_return"),
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})
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except Exception as e:
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print(f"Warning: could not merge portfolio stats into log ({e})")
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with open(fp, "w") as f:
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json.dump({
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"strategy": "TradingAgents",
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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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"total_days": len(decisions),
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"decisions": decisions
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}, f, indent=2)
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print(f" ✓ Saved TradingAgents detailed decisions to: {fp}")
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class BacktestEngine:
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"""Engine to run and compare multiple strategies."""
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def __init__(self, data: pd.DataFrame, initial_capital: float = 100000):
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self.data = data
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self.initial_capital = float(initial_capital)
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self.results: Dict[str, pd.DataFrame] = {}
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def run_strategy(self, strategy, start_date: str = None, end_date: str = None, label = None) -> pd.DataFrame:
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data_filtered = self.data.loc[start_date:end_date] if (start_date and end_date) else self.data
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print(f"\nRunning {strategy.name}...")
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portfolio = strategy.backtest(data_filtered)
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self.results[label or strategy.name] = portfolio
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return portfolio
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def run_all_strategies(self, strategies: Dict, start_date: str = None, end_date: str = None):
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for name, strategy in strategies.items():
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try:
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self.run_strategy(strategy, start_date, end_date)
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print(f"✓ {name} completed")
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except Exception as e:
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print(f"✗ {name} failed: {e}")
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def get_results(self) -> Dict[str, pd.DataFrame]:
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return self.results
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def load_stock_data(ticker: str, start_date: str, end_date: str) -> pd.DataFrame:
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try:
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import yfinance as yf
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# Normalize accidental ('A','A','P','L') / ['A','A','P','L']
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if isinstance(ticker, (list, tuple)) and all(isinstance(c, str) and len(c) == 1 for c in ticker):
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ticker = "".join(ticker)
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if not isinstance(ticker, str):
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raise ValueError("Pass a single ticker symbol as a string, e.g., 'AAPL'.")
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df = yf.download(ticker, start=start_date, end=end_date, progress=False)
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if df.empty:
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raise ValueError(f"No data found for {ticker}")
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return df
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except Exception as e:
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print(f"Error loading data: {e}")
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raise
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def standardize_single_ticker(df: pd.DataFrame, ticker: str | None = None) -> pd.DataFrame:
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"""Return a single-ticker OHLCV DataFrame with simple columns.
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Works with yfinance single or multi-ticker outputs.
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"""
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df = df.copy()
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# If columns are MultiIndex (common with multi-ticker yfinance)
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if isinstance(df.columns, pd.MultiIndex):
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# Figure out which level is the field (Open/High/...) and which is ticker
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lvl0 = set(map(str, df.columns.get_level_values(0)))
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lvl1 = set(map(str, df.columns.get_level_values(1)))
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if len(STD_FIELDS & lvl0) > 0:
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field_level, ticker_level = 0, 1
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elif len(STD_FIELDS & lvl1) > 0:
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field_level, ticker_level = 1, 0
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else:
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raise ValueError("Cannot detect OHLCV field level in MultiIndex columns.")
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available = list(pd.Index(df.columns.get_level_values(ticker_level)).unique())
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# Normalize weird ticker inputs like ('A','A','P','L') -> 'AAPL'
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if isinstance(ticker, (list, tuple)) and all(isinstance(c, str) and len(c) == 1 for c in ticker):
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ticker = "".join(ticker)
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if ticker is None:
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if len(available) != 1:
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raise ValueError(f"Multi-ticker DataFrame. Pick one with ticker=..., available={available}")
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ticker = available[0]
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if str(ticker) not in map(str, available):
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raise ValueError(f"Ticker {ticker!r} not in columns. Available: {available}")
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# Slice to that ticker and drop the ticker level
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df = df.xs(ticker, axis=1, level=ticker_level)
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# Map Adj Close -> Close if Close missing
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if "Close" not in df.columns and "Adj Close" in df.columns:
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df = df.rename(columns={"Adj Close": "Close"})
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# Final sanity
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req = ["Open", "High", "Low", "Close"]
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missing = [c for c in req if c not in df.columns]
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if missing:
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raise ValueError(f"Data missing columns: {missing}")
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# Ensure 'Close' is a Series (not 1-col DataFrame)
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close = df["Close"]
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if isinstance(close, pd.DataFrame) and close.shape[1] == 1:
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df["Close"] = close.iloc[:, 0]
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return df |