from __future__ import annotations
from dataclasses import dataclass
import numpy as np
import pandas as pd


@dataclass
class Perf:
    total_return: float
    max_drawdown: float
    sharpe_ratio: float
    win_rate: float
    num_trades: int
    final_equity: float
    initial_equity: float
    num_stop_losses: int
    total_fees: float
    total_slippage_usd: float
    avg_slippage_bps: float


def compute_metrics(equity_curve: pd.Series, trades: list[dict]) -> Perf:
    ret = equity_curve.pct_change().fillna(0.0)
    total_return = equity_curve.iat[-1] / equity_curve.iat[0] - 1.0
    cummax = equity_curve.cummax()
    dd = (equity_curve / cummax - 1.0).min()
    max_drawdown = dd

    if ret.std(ddof=0) > 0:
        sharpe = (ret.mean() / ret.std(ddof=0)) * np.sqrt(252 * 24 * 60)  # minute bars -> annualized
    else:
        sharpe = 0.0

    closes = [t for t in trades if t.get("side") == "SELL"]
    wins = [t for t in closes if t.get("pnl", 0.0) > 0]
    win_rate = (len(wins) / len(closes)) if closes else 0.0

    fees = sum(t.get("fee", 0.0) for t in trades)
    slip = sum(t.get("slippage", 0.0) for t in trades)
    slippage_bps = [t.get("slippage_bps", 0.0) for t in trades if "slippage_bps" in t]

    return Perf(
        total_return=total_return,
        max_drawdown=max_drawdown,
        sharpe_ratio=sharpe,
        win_rate=win_rate,
        num_trades=len(closes),
        final_equity=float(equity_curve.iat[-1]),
        initial_equity=float(equity_curve.iat[0]),
        num_stop_losses=sum(1 for t in closes if t.get("reason") == "stop"),
        total_fees=fees,
        total_slippage_usd=slip,
        avg_slippage_bps=float(np.mean(slippage_bps)) if slippage_bps else 0.0,
    )