"""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