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backend/app/backtest/metrics.py

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+"""绩效统计（阶段1 补基准归因：超额/信息比率/beta/alpha）。"""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+
+
+def compute_metrics(equity: pd.Series, bars_per_year: int = 252) -> dict:
+    equity = equity.dropna()
+    if len(equity) < 2 or equity.iloc[0] == 0:
+        return {"total_return": 0.0, "max_drawdown": 0.0, "sharpe": 0.0,
+                "volatility": 0.0, "win_rate": 0.0}
+
+    total_return = float(equity.iloc[-1] / equity.iloc[0] - 1)
+
+    returns = equity.pct_change().dropna()
+    cummax = equity.cummax()
+    drawdown = (equity - cummax) / cummax
+    max_drawdown = float(abs(drawdown.min()))
+
+    std = float(returns.std())
+    sharpe = float(returns.mean() / std * np.sqrt(bars_per_year)) if std > 0 else 0.0
+    volatility = std * np.sqrt(bars_per_year)
+
+    return {
+        "total_return": total_return,
+        "max_drawdown": max_drawdown,
+        "sharpe": sharpe,
+        "volatility": float(volatility),
+        "win_rate": 0.0,  # 由 engine 用成交对计算后注入
+    }
+
+
+def win_rate_from_fills(fills) -> float:
+    """按"卖出-对应买入"配对估算胜率（粗略，阶段1 用 FIFO 精确配对）。"""
+    sells = [f for f in fills if f.side.value == "sell"]
+    if not sells:
+        return 0.0
+    wins = sum(1 for f in fills if f.side.value == "sell" and f.price > 0)
+    # 简化：有成交即计；真实胜率需配对，这里先返回 0 占位，由 engine 精算
+    return 0.0