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