"""Performance statistics for wavelet strategy backtests.

Stats dataclass mirrors what an allocator would ask for:
  CAGR, Sharpe, Sortino, Max DD, Calmar, hit rate, turnover, correlation.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import pandas as pd


@dataclass
class Stats:
    name: str
    years: float
    total_return: float
    cagr: float
    volatility: float
    sharpe: float
    sortino: float
    max_drawdown: float
    calmar: float
    hit_rate: float
    annual_turnover: float
    time_in_market: float
    avg_gross_leverage: float
    correlation_to_spy: Optional[float] = None
    extra: dict = field(default_factory=dict)

    def as_dict(self) -> dict:
        return {
            "Name": self.name,
            "Years": f"{self.years:.1f}",
            "Total Return": f"{self.total_return:.1%}",
            "CAGR": f"{self.cagr:.2%}",
            "Volatility": f"{self.volatility:.2%}",
            "Sharpe": f"{self.sharpe:.3f}",
            "Sortino": f"{self.sortino:.3f}",
            "Max Drawdown": f"{self.max_drawdown:.2%}",
            "Calmar": f"{self.calmar:.3f}",
            "Hit Rate": f"{self.hit_rate:.1%}",
            "Annual Turnover": f"{self.annual_turnover:.1f}×",
            "Time in Market": f"{self.time_in_market:.1%}",
            "Avg Gross Leverage": f"{self.avg_gross_leverage:.2f}×",
            "Corr to SPY": f"{self.correlation_to_spy:.2f}" if self.correlation_to_spy is not None else "—",
        }


def perf(
    returns: pd.Series,
    positions: pd.Series,
    name: str = "Strategy",
    benchmark: Optional[pd.Series] = None,
    cost_bps: float = 1.0,
    ann_factor: int = 252,
) -> Stats:
    """Compute full performance statistics from a daily returns series.

    Args:
        returns:    daily P&L series (after costs), aligned with calendar
        positions:  daily position series (gross leverage proxy)
        name:       strategy label
        benchmark:  optional benchmark return series for correlation
        cost_bps:   round-trip cost in basis points (informational only; should
                    already be baked into `returns`)
        ann_factor: trading days per year (default 252)

    Returns:
        Stats dataclass with all metrics.
    """
    rets = returns.dropna()
    if len(rets) == 0:
        return Stats(
            name=name, years=0, total_return=0, cagr=0, volatility=0,
            sharpe=0, sortino=0, max_drawdown=0, calmar=0,
            hit_rate=0, annual_turnover=0, time_in_market=0, avg_gross_leverage=0,
        )

    years = len(rets) / ann_factor

    total_return = (1 + rets).prod() - 1
    cagr = (1 + total_return) ** (1 / years) - 1 if years > 0 else 0.0

    vol = rets.std() * np.sqrt(ann_factor)

    sharpe = (rets.mean() * ann_factor) / vol if vol > 0 else 0.0

    downside = rets[rets < 0]
    downside_vol = downside.std() * np.sqrt(ann_factor) if len(downside) > 0 else 1e-9
    sortino = (rets.mean() * ann_factor) / downside_vol if downside_vol > 0 else 0.0

    cumulative = (1 + rets).cumprod()
    rolling_max = cumulative.cummax()
    drawdowns = cumulative / rolling_max - 1
    max_dd = float(drawdowns.min())

    calmar = cagr / abs(max_dd) if max_dd != 0 else 0.0

    hit_rate = float((rets > 0).mean())

    pos_aligned = positions.reindex(rets.index).fillna(0)

    turnover = pos_aligned.diff().abs().sum() / years if years > 0 else 0.0

    time_in_market = float((pos_aligned.abs() > 0.01).mean())

    avg_gross_leverage = float(pos_aligned.abs().mean())

    corr = None
    if benchmark is not None:
        bm = benchmark.reindex(rets.index).dropna()
        common = rets.reindex(bm.index).dropna()
        bm_aligned = bm.reindex(common.index).dropna()
        common = common.reindex(bm_aligned.index)
        if len(common) > 30:
            corr = float(common.corr(bm_aligned))

    return Stats(
        name=name,
        years=years,
        total_return=total_return,
        cagr=cagr,
        volatility=vol,
        sharpe=sharpe,
        sortino=sortino,
        max_drawdown=max_dd,
        calmar=calmar,
        hit_rate=hit_rate,
        annual_turnover=turnover,
        time_in_market=time_in_market,
        avg_gross_leverage=avg_gross_leverage,
        correlation_to_spy=corr,
    )