fixed long-short evaluation
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Zichen Liu
parent
e3952edf91
commit
55e0287ace
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@ -30,72 +30,81 @@ class TradingAgentsBacktester:
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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 using the same return calculation logic as rule-based strategies.
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Process:
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1. Collect signals (actions: 1=BUY, 0=HOLD, -1=SELL) for all dates
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2. Convert actions to positions (0=flat, 1=long) using same logic as baselines
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3. Calculate returns as: strategy_return = position.shift(1) * market_return
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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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# Restrict to window
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df = data.loc[start_date:end_date].copy()
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decisions: List[Dict] = []
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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 all signals/decisions
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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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# Get decision from TradingAgents graph
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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: Convert actions to positions (same logic as baseline strategies)
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position = self._actions_to_position(signals)
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# Step 3: Calculate returns using standardized logic
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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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market_ret = close.pct_change().fillna(0.0)
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exposure = position.shift(1).fillna(0.0) # Yesterday's position determines today's exposure
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strat_ret = (exposure * market_ret).astype(float)
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cumret = (1.0 + strat_ret).cumprod()
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portval = self.initial_capital * cumret
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# Build portfolio DataFrame with same structure as baseline strategies
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portfolio = pd.DataFrame(index=df.index)
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portfolio["action"] = signals # 1=BUY, 0=HOLD, -1=SELL
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portfolio["position"] = position # 1=long, 0=flat
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portfolio["close"] = close
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if "Volume" in df.columns:
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vol = df["Volume"]
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if isinstance(vol, pd.DataFrame) and vol.shape[1] == 1:
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vol = vol.iloc[:, 0]
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if isinstance(vol, pd.Series):
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portfolio["Volume"] = vol
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portfolio["market_return"] = market_ret
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portfolio["strategy_return"] = strat_ret
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portfolio["cumulative_return"] = cumret
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portfolio["portfolio_value"] = portval
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portfolio["trade_delta"] = portfolio["position"].diff().fillna(0.0) # +1=buy, -1=sell
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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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@ -135,13 +144,37 @@ class TradingAgentsBacktester:
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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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# Use output_dir if provided, otherwise use default
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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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@ -151,6 +184,7 @@ class TradingAgentsBacktester:
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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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@ -151,7 +151,8 @@ def run_evaluation(
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print(f" Debate Rounds: {cfg['max_debate_rounds']}")
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graph = TradingAgentsGraph(
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selected_analysts=["market", "social", "news", "fundamentals"],
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selected_analysts=["news"],
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# selected_analysts=["market", "social", "news", "fundamentals"],
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debug=False,
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config=cfg
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)
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