diff --git a/evaluation_long_short/backtest.py b/evaluation_long_short/backtest.py index 77347c07..b453c7bd 100644 --- a/evaluation_long_short/backtest.py +++ b/evaluation_long_short/backtest.py @@ -30,72 +30,81 @@ class TradingAgentsBacktester: def backtest(self, ticker: str, start_date: str, end_date: str, data: pd.DataFrame) -> pd.DataFrame: """ - Backtest TradingAgents using the same return calculation logic as rule-based strategies. - - Process: - 1. Collect signals (actions: 1=BUY, 0=HOLD, -1=SELL) for all dates - 2. Convert actions to positions (0=flat, 1=long) using same logic as baselines - 3. Calculate returns as: strategy_return = position.shift(1) * market_return + Backtest TradingAgents with realistic single-asset account simulation. + Supports long, short, and flat positions with 1× leverage on shorts. """ - # Restrict to window df = data.loc[start_date:end_date].copy() - - decisions: List[Dict] = [] + decisions = [] signals = pd.Series(0, index=df.index, dtype=float) print(f"\nRunning TradingAgents backtest on {ticker} from {start_date} to {end_date}") print(f"Total trading days: {len(df)}") print("-" * 80) - # Step 1: Collect all signals/decisions + # === Step 1: Collect signals === for i, (date, row) in enumerate(df.iterrows()): date_str = date.strftime("%Y-%m-%d") price = float(row["Close"]) - - # Get decision from TradingAgents graph try: print(f"\n[{i+1}/{len(df)}] {date_str} ... ", end="") final_state, decision = self.graph.propagate(ticker, date_str) print(f"Decision: {decision}") signal = self._parse_decision(decision) decisions.append({"date": date_str, "decision": decision, "signal": signal, "price": price}) - except Exception as e: print(f"Error: {e}") signal = 0 decisions.append({"date": date_str, "decision": "ERROR", "signal": 0, "price": price, "error": str(e)}) - signals.loc[date] = signal - # Step 2: Convert actions to positions (same logic as baseline strategies) - position = self._actions_to_position(signals) - - # Step 3: Calculate returns using standardized logic + # === Step 2: Run realistic cash+shares backtest === close = pd.to_numeric(df["Close"], errors="coerce") - market_ret = close.pct_change().fillna(0.0) - exposure = position.shift(1).fillna(0.0) # Yesterday's position determines today's exposure - strat_ret = (exposure * market_ret).astype(float) - - cumret = (1.0 + strat_ret).cumprod() - portval = self.initial_capital * cumret - - # Build portfolio DataFrame with same structure as baseline strategies - portfolio = pd.DataFrame(index=df.index) - portfolio["action"] = signals # 1=BUY, 0=HOLD, -1=SELL - portfolio["position"] = position # 1=long, 0=flat - portfolio["close"] = close - if "Volume" in df.columns: - vol = df["Volume"] - if isinstance(vol, pd.DataFrame) and vol.shape[1] == 1: - vol = vol.iloc[:, 0] - if isinstance(vol, pd.Series): - portfolio["Volume"] = vol - portfolio["market_return"] = market_ret - portfolio["strategy_return"] = strat_ret - portfolio["cumulative_return"] = cumret - portfolio["portfolio_value"] = portval - portfolio["trade_delta"] = portfolio["position"].diff().fillna(0.0) # +1=buy, -1=sell + cash = self.initial_capital + shares = 0.0 + prev_value = cash + records = [] + for i, (date, price) in enumerate(close.items()): + action = signals.iloc[i] + + # 先计算上一个交易日的组合价值 + portfolio_value = cash + shares * price + + # === 若方向改变，先平仓 === + if (shares > 0 and action <= 0) or (shares < 0 and action >= 0): + cash += shares * price # 卖出现有股票或回补空头 + shares = 0.0 + + # === 建仓逻辑 === + if action == 1 and shares == 0: + # 做多 + shares = cash / price + cash = 0.0 + elif action == -1 and shares == 0: + # 做空（1倍杠杆） + shares = -cash / price + cash = 2 * cash # 保证金 + 卖出所得 + + # === 更新组合价值 === + new_value = cash + shares * price + daily_return = (new_value / prev_value) - 1 if prev_value != 0 else 0.0 + prev_value = new_value + + records.append({ + "date": date.strftime("%Y-%m-%d"), + "action": action, + "shares": shares, + "close_price": price, + "cash": cash, + "portfolio_value": new_value, + "strategy_return": daily_return, + }) + + # === Step 3: 转为 DataFrame 并计算累计收益 === + portfolio = pd.DataFrame(records).set_index("date") + portfolio["cumulative_return"] = (1 + portfolio["strategy_return"]).cumprod() + portfolio["ticker"] = ticker + self.latest_portfolio = portfolio self._save_decisions_log(ticker, decisions, start_date, end_date) return portfolio @@ -135,13 +144,37 @@ class TradingAgentsBacktester: return 0 def _save_decisions_log(self, ticker: str, decisions: List[Dict], start_date: str, end_date: str): - # Use output_dir if provided, otherwise use default + """ + Save detailed TradingAgents decisions and portfolio state to JSON. + Adds shares, cash, and cumulative return (cr) from the latest backtest results. + """ if self.output_dir: out = Path(self.output_dir) / ticker / "TradingAgents" else: out = Path(f"eval_results/{ticker}/TradingAgents") out.mkdir(parents=True, exist_ok=True) fp = out / f"decisions_{start_date}_to_{end_date}.json" + + # Try to include computed portfolio metrics if available + try: + # Attempt to load the latest portfolio CSV/DF from memory + if hasattr(self, "latest_portfolio"): + port = self.latest_portfolio + port = port.reset_index() + port_dict = {d["date"]: d for d in port.to_dict(orient="records")} + # Merge portfolio stats into each decision record + for d in decisions: + date = d["date"] + if date in port_dict: + d.update({ + "shares": port_dict[date].get("shares"), + "cash": port_dict[date].get("cash"), + "portfolio_value": port_dict[date].get("portfolio_value"), + "cumulative_return": port_dict[date].get("cumulative_return"), + }) + except Exception as e: + print(f"Warning: could not merge portfolio stats into log ({e})") + with open(fp, "w") as f: json.dump({ "strategy": "TradingAgents", @@ -151,6 +184,7 @@ class TradingAgentsBacktester: "total_days": len(decisions), "decisions": decisions }, f, indent=2) + print(f" ✓ Saved TradingAgents detailed decisions to: {fp}") diff --git a/evaluation_long_short/run_evaluation.py b/evaluation_long_short/run_evaluation.py index 63ea077e..b101806d 100644 --- a/evaluation_long_short/run_evaluation.py +++ b/evaluation_long_short/run_evaluation.py @@ -151,7 +151,8 @@ def run_evaluation( print(f" Debate Rounds: {cfg['max_debate_rounds']}") graph = TradingAgentsGraph( - selected_analysts=["market", "social", "news", "fundamentals"], + selected_analysts=["news"], + # selected_analysts=["market", "social", "news", "fundamentals"], debug=False, config=cfg )