---

if LSTM_AVAILABLE:
        ens_score = 0.40 * oos_xgb + 0.40 * oos_lgb + 0.20 * oos_lstm
        print(f"\n  {DIM('Ensemble: XGB 40% + LGB 40% + LSTM 20%')}")
    else:
        ens_score = 0.50 * oos_xgb + 0.50 * oos_lgb
        print(f"\n  {DIM('Ensemble: XGB 50% + LGB 50%  (LSTM skipped)')}")

    # ── Threshold search + vectorised backtest ───────────────────────────────
    print(f"\n  {CYAN('⚙')}  [5/5] Threshold search + vectorised backtest...")

    best_r = None
    for long_thresh in [0.05, 0.08, 0.10, 0.12, 0.15, 0.18, 0.20, 0.25]:
        for short_thresh in [-0.05, -0.08, -0.10, -0.12, -0.15, -0.18, -0.20, -0.25]:
            sig = np.zeros(n, np.int8)
            sig[oos_mask & (ens_score >  long_thresh)]  =  1
            sig[oos_mask & (ens_score <  short_thresh)] = -1

            n_trades = int(np.sum(np.abs(np.diff(sig.astype(int)))) // 2)
            if n_trades < 20: continue  # must have at least 20 trades

            changes = np.where(np.diff(sig, prepend=sig[0]) != 0)[0]
            cap     = capital
            equity  = np.full(n, capital, dtype=np.float64)
            wins = losses = 0
            gw = gl = 0.0

            for i in range(len(changes) - 1):
                eb = changes[i]; xb = changes[i + 1]
                d  = int(sig[eb])
                if d == 0:
                    equity[eb:xb] = cap
                    continue
                ep  = c[eb]; xp = c[xb]
                sz  = (cap * 0.25) / ep
                pnl = d * (xp - ep) * sz * (1 - FEE) ** 2
                cap = max(cap + pnl, 1.0)
                equity[eb:xb] = cap
                if pnl > 0: wins  += 1; gw += pnl
                else:       losses += 1; gl += abs(pnl)
            equity[changes[-1]:] = cap

            total_ret = (cap - capital) / capital * 100
            peak      = np.maximum.accumulate(equity)
            dd        = (equity - peak) / peak * 100
            max_dd    = float(np.min(dd))
            n_days    = len(equity) // 1440
            if n_days > 2:
                dl     = np.array([
                    np.diff(np.log(equity[i*1440:(i+1)*1440] + 1e-10)).sum()
                    for i in range(n_days)
                ])
                sharpe = float(dl.mean() / (dl.std() + 1e-10) * np.sqrt(365))
            else:
                sharpe = 0.0
            tt  = wins + losses
            wr  = wins / tt * 100 if tt > 0 else 0.0
            cal = total_ret / abs(max_dd) if abs(max_dd) > 0.01 else 0.0
            pf  = gw / (gl + 1e-10)

            r = dict(
                sharpe=round(sharpe, 3), ret=round(total_ret, 2),
                max_dd=round(max_dd, 2), calmar=round(cal, 3),
                pf=round(pf, 3), trades=tt, win_rate=round(wr, 1),
                final=round(cap, 2), equity=equity, signals=sig,
                long_thresh=long_thresh, short_thresh=short_thresh,
            )

            if best_r is None or r['sharpe'] > best_r['sharpe']:
                best_r = r
                col = GREEN if r['sharpe'] >= 2.0 else (YELLOW if r['sharpe'] >= 1.0 else RED)
                sh_str = f"{r['sharpe']:+.3f}"
                print(
                    f"  {col('↑')} Sharpe={col(sh_str)}  "
                    f"Ret={r['ret']:+.1f}%  MaxDD={r['max_dd']:.1f}%  "
                    f"Trades={r['trades']:,}  WR={r['win_rate']:.0f}%  "
                    f"Calmar={r['calmar']:.2f}  "
                    f"[L>{long_thresh} S<{short_thresh}]"
                )

    if best_r is None:
        print(f"  {RED('✗')} No valid threshold found. Try more data or looser filters.")
        return {}

    # ── Save best params ─────────────────────────────────────────────────────
    CONFIG_DIR.mkdir(exist_ok=True)
    params = {