"""
Performance metrics.

Sharpe ratio, Sortino ratio, max drawdown, win rate.
Standard toolbox for evaluating a trading strategy.
"""
import numpy as np


def sharpe(returns: list[float], rf: float = 0.0, periods: int = 365) -> float:
    if len(returns) < 2:
        return 0.0
    excess = np.mean(returns) - rf
    std = np.std(returns, ddof=1)
    return (excess / std) * np.sqrt(periods) if std > 0 else 0.0


def sortino(returns: list[float], rf: float = 0.0, periods: int = 365) -> float:
    if len(returns) < 2:
        return 0.0
    excess = np.mean(returns) - rf
    downside = [r for r in returns if r < 0]
    d_std = np.std(downside, ddof=1) if downside else 0.0
    return (excess / d_std) * np.sqrt(periods) if d_std > 0 else 0.0


def max_drawdown(equity: list[float]) -> float:
    if not equity:
        return 0.0
    peak = equity[0]
    worst = 0.0
    for v in equity:
        if v > peak:
            peak = v
        dd = (peak - v) / peak if peak > 0 else 0.0
        worst = max(worst, dd)
    return worst


def win_rate(trades: list[dict]) -> float:
    if not trades:
        return 0.0
    return sum(1 for t in trades if t.get("pnl", 0) > 0) / len(trades)