"""Causal replay backtester — the RL verifier's beating heart.

Ports tradewatch's paper_ledger.py accounting into a vectorised, deterministic,
LEAK-PROOF backtest. A strategy is a pure function:

    def strategy(features_up_to_t: dict[str, float], position: float) -> float
        # returns target position in [-1, 1]  (fraction of equity; <0 = short)

The engine calls it bar-by-bar, passing ONLY features at indices <= t. The strategy
can never see the future because future bars are not in the dict it receives. Fills
happen at the NEXT bar's open-equivalent (close[t] used as decision price, close[t+1]
as fill realisation) with slippage + fees, so a decision at t cannot be filled at t's
hindsight-perfect price.

Metrics: Sharpe (primary), CAGR, max drawdown, equity/cash exposure, turnover, win rate.
Benchmarks: buy-and-hold, MA-crossover, z-score mean-reversion.
"""

from __future__ import annotations

from dataclasses import dataclass, field

import numpy as np

from .features import feature_frame, sma, zscore


@dataclass
class BacktestResult:
    equity_curve: np.ndarray
    returns: np.ndarray
    sharpe: float
    cagr: float
    max_drawdown: float
    turnover: float
    win_rate: float
    final_equity: float
    avg_exposure: float
    n_trades: int
    error: str | None = None
    meta: dict = field(default_factory=dict)

    def to_dict(self) -> dict:
        return {
            "sharpe": round(self.sharpe, 4),
            "cagr": round(self.cagr, 4),
            "max_drawdown": round(self.max_drawdown, 4),
            "turnover": round(self.turnover, 4),
            "win_rate": round(self.win_rate, 4),
            "final_equity": round(self.final_equity, 4),
            "avg_exposure": round(self.avg_exposure, 4),
            "n_trades": self.n_trades,
            "error": self.error,
        }


# Periods-per-year for annualisation (daily bars by default; 24*365 for hourly).
def _ann_factor(bars_per_year: float) -> float:
    return float(np.sqrt(bars_per_year))


def _metrics(equity: np.ndarray, exposures: np.ndarray, bars_per_year: float,
             error: str | None = None) -> BacktestResult:
    equity = np.asarray(equity, dtype=float)
    if len(equity) < 2 or np.any(~np.isfinite(equity)) or np.any(equity <= 0):
        return BacktestResult(equity, np.zeros(1), 0.0, 0.0, 1.0, 0.0, 0.0,
                              float(equity[-1]) if len(equity) else 0.0, 0.0, 0,
                              error=error or "degenerate equity")
    rets = np.diff(equity) / equity[:-1]
    sd = rets.std()
    sharpe = float(rets.mean() / sd * _ann_factor(bars_per_year)) if sd > 1e-12 else 0.0
    n = len(equity)
    years = n / bars_per_year
    cagr = float((equity[-1] / equity[0]) ** (1 / years) - 1) if years > 0 and equity[0] > 0 else 0.0
    peak = np.maximum.accumulate(equity)
    max_dd = float(np.max((peak - equity) / peak)) if len(peak) else 0.0
    exp = np.asarray(exposures, dtype=float)
    turnover = float(np.sum(np.abs(np.diff(np.insert(exp, 0, 0.0)))))
    n_trades = int(np.sum(np.abs(np.diff(np.insert(exp, 0, 0.0))) > 1e-9))
    win_rate = float(np.mean(rets > 0)) if len(rets) else 0.0
    return BacktestResult(equity, rets, sharpe, cagr, max_dd, turnover, win_rate,
                          float(equity[-1]), float(np.mean(np.abs(exp))), n_trades, error=error)


def run_backtest(
    close: np.ndarray,
    strategy_fn,
    *,
    fee_bps: float = 30.0,        # 0.30% per unit turnover (DEX-realistic)
    slippage_bps: float = 20.0,   # 0.20% adverse slippage on fills
    bars_per_year: float = 365.0,
    allow_short: bool = True,
    warmup: int = 50,             # bars reserved for feature warmup (no trading)
) -> BacktestResult:
    """Step the strategy causally over `close`, return performance metrics.

    Fill model: decision made on info up to t, position realised against return
    from t -> t+1, minus fees/slippage proportional to position change.
    """
    close = np.asarray(close, dtype=float)
    if len(close) < warmup + 5:
        return _metrics(np.array([1.0, 1.0]), np.zeros(1), bars_per_year, error="too few bars")

    feats = feature_frame(close)
    rets = np.zeros(len(close))
    rets[1:] = np.diff(close) / np.where(close[:-1] == 0, np.nan, close[:-1])
    rets = np.nan_to_num(rets)

    equity = [1.0]
    exposures = []
    pos = 0.0
    cost_rate = (fee_bps + slippage_bps) / 1e4

    for t in range(warmup, len(close) - 1):
        # features visible at t (scalar snapshot, causal by construction)
        view = {k: (float(v[t]) if np.isfinite(v[t]) else 0.0) for k, v in feats.items()}
        try:
            target = float(strategy_fn(view, pos))
        except Exception as e:  # noqa: BLE001 - strategy bugs => penalised, not crash
            return _metrics(np.array(equity + [equity[-1]]), np.array(exposures + [pos]),
                            bars_per_year, error=f"strategy raised: {type(e).__name__}: {e}")
        if not np.isfinite(target):
            target = 0.0
        lo = -1.0 if allow_short else 0.0
        target = max(lo, min(1.0, target))

        trade_cost = abs(target - pos) * cost_rate
        # equity evolves with NEXT bar's realised return at the chosen exposure
        pnl = target * rets[t + 1]
        new_equity = equity[-1] * (1.0 + pnl - trade_cost)
        equity.append(new_equity)
        exposures.append(target)
        pos = target

    return _metrics(np.array(equity), np.array(exposures), bars_per_year)


# ── Benchmarks (the anti-overfit baselines a strategy must beat) ──────────────

def _buy_and_hold(view, pos):
    return 1.0


def _ma_crossover(view, pos):
    return 1.0 if view.get("sma_10", 0) > view.get("sma_20", 0) else 0.0


def _zscore_meanrev(view, pos):
    z = view.get("zscore_20", 0.0)
    if z < -1.5:
        return 1.0
    if z > 1.5:
        return -1.0
    return pos  # hold


def benchmarks(close: np.ndarray, **kw) -> dict[str, BacktestResult]:
    return {
        "buy_and_hold": run_backtest(close, _buy_and_hold, **kw),
        "ma_crossover": run_backtest(close, _ma_crossover, **kw),
        "zscore_meanrev": run_backtest(close, _zscore_meanrev, **kw),
    }