"""Simple strategy performance metrics.

Scores a ``signal`` column against forward returns: total return, annualized
Sharpe, win rate and max drawdown.
"""
import numpy as np


def compute_performance_metrics(data, signal_col="signal", forecast_periods=12,
                                annualization=24 * 365, verbose=True):
    """Compute strategy performance from a signal column and forward returns.

    Parameters
    ----------
    data : pd.DataFrame
        Must contain a ``close`` column and ``signal_col`` (values in {-1, 0, 1}).
    forecast_periods : int
        Holding horizon (in bars) used to compute the forward return.
    annualization : float
        Factor applied under the square root when annualizing the Sharpe ratio.

    Returns
    -------
    dict
        total_return, sharpe_ratio, win_rate, max_drawdown and the augmented frame.
    """
    df = data.copy()

    # Forward return over the forecast horizon.
    df["future_return"] = df["close"].pct_change(periods=forecast_periods).shift(-forecast_periods)

    # Strategy return: signal * future return (long: +return, short: -return).
    df["strategy_return"] = df[signal_col] * df["future_return"]
    df = df.dropna(subset=["strategy_return"])

    # Cumulative return.
    df["cumulative_return"] = (1 + df["strategy_return"]).cumprod() - 1

    total_return = df["cumulative_return"].iloc[-1] if len(df) else float("nan")
    sharpe_ratio = (
        df["strategy_return"].mean() / df["strategy_return"].std() * np.sqrt(annualization)
        if df["strategy_return"].std() else float("nan")
    )
    active = df[df["strategy_return"] != 0]
    win_rate = len(df[df["strategy_return"] > 0]) / len(active) if len(active) else float("nan")
    max_drawdown = (df["cumulative_return"].cummax() - df["cumulative_return"]).max()

    if verbose:
        print(f"Total Return: {total_return:.2%}")
        print(f"Sharpe Ratio: {sharpe_ratio:.2f}")
        print(f"Win Rate: {win_rate:.2%}")
        print(f"Max Drawdown: {max_drawdown:.2%}")

    return {
        "total_return": total_return,
        "sharpe_ratio": sharpe_ratio,
        "win_rate": win_rate,
        "max_drawdown": max_drawdown,
        "data": df,
    }