from __future__ import annotations

from collections.abc import Sequence

from app.schemas import BacktestMetricsOut, BacktestPointOut, BacktestTradeOut

# Bars per year for annualising the Sharpe ratio. Unknown timeframes -> None.
BARS_PER_YEAR: dict[str, float] = {
    "1m": 525_600,
    "3m": 175_200,
    "5m": 105_120,
    "15m": 35_040,
    "30m": 17_520,
    "1h": 8_760,
    "2h": 4_380,
    "4h": 2_190,
    "6h": 1_460,
    "12h": 730,
    "1d": 365,
    "3d": 121,
    "1w": 52,
}


def max_drawdown_pct(equities: Sequence[float]) -> float:
    peak = equities[0] if equities else 0.0
    worst = 0.0
    for value in equities:
        peak = max(peak, value)
        if peak > 0:
            worst = max(worst, (peak - value) / peak)
    return worst * 100


def sharpe_ratio(equities: Sequence[float], timeframe: str) -> float | None:
    if len(equities) < 3:
        return None
    returns = [
        equities[i] / equities[i - 1] - 1 for i in range(1, len(equities)) if equities[i - 1] > 0
    ]
    if len(returns) < 2:
        return None
    mean = sum(returns) / len(returns)
    variance = sum((r - mean) ** 2 for r in returns) / (len(returns) - 1)
    std = variance**0.5
    periods = BARS_PER_YEAR.get(timeframe)
    if std == 0 or not periods:
        return None
    return mean / std * (periods**0.5)


def compute_metrics(
    curve: Sequence[BacktestPointOut],
    trades: Sequence[BacktestTradeOut],
    starting_balance: float,
    timeframe: str,
) -> BacktestMetricsOut:
    equities = [point.equity for point in curve] or [starting_balance]
    final_equity = equities[-1]
    total_return = (final_equity / starting_balance - 1) * 100 if starting_balance else 0.0
    buy_hold = (
        (curve[-1].buy_hold / starting_balance - 1) * 100 if curve and starting_balance else 0.0
    )

    returns = [trade.return_pct for trade in trades]
    gross_profit = sum(trade.pnl for trade in trades if trade.pnl > 0)
    gross_loss = -sum(trade.pnl for trade in trades if trade.pnl < 0)
    wins = sum(1 for trade in trades if trade.pnl > 0)

    return BacktestMetricsOut(
        initial_equity=starting_balance,
        final_equity=final_equity,
        total_return_pct=total_return,
        buy_hold_return_pct=buy_hold,
        max_drawdown_pct=max_drawdown_pct(equities),
        num_trades=len(trades),
        win_rate_pct=(wins / len(trades) * 100) if trades else 0.0,
        profit_factor=(gross_profit / gross_loss) if gross_loss > 0 else None,
        avg_trade_pct=(sum(returns) / len(returns)) if returns else 0.0,
        best_trade_pct=max(returns) if returns else 0.0,
        worst_trade_pct=min(returns) if returns else 0.0,
        sharpe=sharpe_ratio(equities, timeframe),
    )