import itertools, random
import pandas as pd
from backtest.engine import run_backtest, WINDOW
from backtest.metrics import compute_metrics

def _score(m, min_trades):
    if m["trades"] < min_trades:
        return float("-inf")
    return m["return_pct"] / max(m["max_dd_pct"], 1.0)    # rendement / drawdown

def walk_forward(df, strat_name, base_params, grid, tf, symbol, spec, risk_cfg, bt, wf, log=print):
    keys = list(grid)
    combos = [dict(base_params, **dict(zip(keys, v))) for v in itertools.product(*grid.values())]
    if len(combos) > wf["max_combos"]:
        combos = random.Random(1).sample(combos, wf["max_combos"])
    n, tr_n, te_n = len(df), wf["train_bars"], wf["test_bars"]
    windows, oos_trades, oos_eq = [], [], []
    balance, start, k = bt["initial_balance"], 0, 0
    while start + tr_n + te_n <= n:
        k += 1
        a, b, e = start, start + tr_n, start + tr_n + te_n
        train = df.iloc[a:b].reset_index(drop=True)
        best, best_s, best_m = None, float("-inf"), None
        for p in combos:
            r = run_backtest(train, strat_name, p, tf, symbol, spec, risk_cfg, bt)
            m = compute_metrics(r["trades"], r["equity"], r["initial"])
            s = _score(m, wf["min_trades"])
            if s > best_s:
                best, best_s, best_m = p, s, m
        if best is None:
            log(f"Fenêtre {k}: aucune combinaison valide (trop peu de trades), ignorée")
            start += te_n
            continue
        test = df.iloc[b - WINDOW:e].reset_index(drop=True)          # warm-up + test
        r = run_backtest(test, strat_name, best, tf, symbol, spec, risk_cfg, bt, initial=balance, start_idx=WINDOW)
        m = compute_metrics(r["trades"], r["equity"], r["initial"])
        balance = m["final"]
        oos_trades.append(r["trades"]); oos_eq.append(r["equity"])
        windows.append({"k": k, "train_from": str(df.time.iloc[a])[:10], "test_from": str(df.time.iloc[b])[:10],
                        "test_to": str(df.time.iloc[e - 1])[:10], "params": {x: best[x] for x in keys},
                        "is_return": best_m["return_pct"], "is_dd": best_m["max_dd_pct"],
                        "oos_return": m["return_pct"], "oos_dd": m["max_dd_pct"], "oos_trades": m["trades"]})
        log(f"Fenêtre {k}: IS {best_m['return_pct']:+.1f}% -> OOS {m['return_pct']:+.1f}% | {windows[-1]['params']}")
        start += te_n
    trades = pd.concat(oos_trades, ignore_index=True) if oos_trades else pd.DataFrame()
    eq = pd.concat(oos_eq) if oos_eq else pd.Series(dtype=float)
    metrics = compute_metrics(trades, eq, bt["initial_balance"]) if len(eq) else None
    wfe = None
    if windows:
        is_avg = sum(w["is_return"] for w in windows) / len(windows)
        oos_avg = sum(w["oos_return"] for w in windows) / len(windows)
        wfe = (oos_avg / is_avg * 100) if is_avg > 0 else None   # >50% = plutôt robuste
    return {"windows": windows, "trades": trades, "equity": eq, "metrics": metrics, "wfe": wfe}