import numpy as np
import pandas as pd


def total_return(equity: pd.Series) -> float:
    return equity.iloc[-1] / equity.iloc[0] - 1


def annualized_return(equity: pd.Series, periods: int = 252) -> float:
    n = len(equity)
    return (1 + total_return(equity)) ** (periods / n) - 1


def annualized_volatility(returns: pd.Series, periods: int = 252) -> float:
    return returns.std() * np.sqrt(periods)


def sharpe_ratio(returns: pd.Series, risk_free_rate: float = 0.0, periods: int = 252) -> float:
    excess = returns - risk_free_rate / periods
    std = excess.std()
    if std == 0:
        return 0.0
    return (excess.mean() / std) * np.sqrt(periods)


def sortino_ratio(returns: pd.Series, risk_free_rate: float = 0.0, periods: int = 252) -> float:
    excess = returns - risk_free_rate / periods
    downside_std = excess[excess < 0].std()
    if downside_std == 0:
        return 0.0
    return (excess.mean() / downside_std) * np.sqrt(periods)


def max_drawdown(equity: pd.Series) -> float:
    rolling_peak = equity.cummax()
    drawdown = (equity - rolling_peak) / rolling_peak
    return drawdown.min()


def calmar_ratio(equity: pd.Series, periods: int = 252) -> float:
    ann_ret = annualized_return(equity, periods)
    mdd = abs(max_drawdown(equity))
    if mdd == 0:
        return np.inf
    return ann_ret / mdd


def win_rate(returns: pd.Series) -> float:
    active = returns[returns != 0]
    if len(active) == 0:
        return 0.0
    return (active > 0).sum() / len(active)


def summary(equity: pd.Series, name: str = "Strategy") -> dict:
    returns = equity.pct_change().dropna()
    metrics = {
        "Total Return":          f"{total_return(equity):.2%}",
        "Annualized Return":     f"{annualized_return(equity):.2%}",
        "Annualized Volatility": f"{annualized_volatility(returns):.2%}",
        "Sharpe Ratio":          f"{sharpe_ratio(returns):.2f}",
        "Sortino Ratio":         f"{sortino_ratio(returns):.2f}",
        "Max Drawdown":          f"{max_drawdown(equity):.2%}",
        "Calmar Ratio":          f"{calmar_ratio(equity):.2f}",
        "Win Rate":              f"{win_rate(returns):.2%}",
    }
    print(f"\n{'='*45}")
    print(f"  {name}")
    print(f"{'='*45}")
    for k, v in metrics.items():
        print(f"  {k:<26} {v:>10}")
    return metrics