diff --git a/scripts/13_squeeze_ml_hybrid.py b/scripts/13_squeeze_ml_hybrid.py
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+++ b/scripts/13_squeeze_ml_hybrid.py
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+"""Strategia 13: Squeeze + ML ibrida.
+Squeeze breakout come PRE-FILTRO (quando tradare),
+ML come CONFERMA DIREZIONALE (quale direzione).
+
+Pipeline:
+1. Rileva squeeze release (Bollinger esce da Keltner)
+2. Estrai features frattali/strutturali dalla finestra
+3. ML predice direzione con confidenza
+4. Trade SOLO se squeeze + ML concordano
+
+Obiettivo: accuracy squeeze (>80%) + volume trade ML.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import StandardScaler
+from src.data.downloader import load_data
+from src.fractal.patterns import encode_candles
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def keltner_ratio(close, high, low, window=20):
+    n = len(close)
+    result = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i-window:i]
+        wh = high[i-window:i]
+        wl = low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc, 1)), np.abs(wl - np.roll(wc, 1))))
+        atr = np.mean(tr[1:])
+        kc_r = (ma + 1.5*atr) - (ma - 1.5*atr)
+        bb_r = (ma + 2*bb_std) - (ma - 2*bb_std)
+        if kc_r > 0:
+            result[i] = bb_r / kc_r
+    return result
+
+
+def detect_squeeze_releases(close, high, low, volume, bb_w, sq_thr, min_duration=5):
+    kcr = keltner_ratio(close, high, low, bb_w)
+    events = []
+    in_sq = False
+    sq_start = 0
+
+    for i in range(bb_w + 1, len(close)):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            duration = i - sq_start
+            if duration < min_duration:
+                continue
+
+            avg_vol = np.mean(volume[sq_start:i])
+            events.append({
+                "idx": i,
+                "squeeze_start": sq_start,
+                "duration": duration,
+                "avg_vol_squeeze": avg_vol,
+                "kcr_at_release": kcr[i],
+            })
+    return events
+
+
+def build_features_at(df, i, squeeze_info):
+    """Features per il punto di squeeze release."""
+    if i < 100:
+        return None
+
+    o = df["open"].values
+    h = df["high"].values
+    l = df["low"].values
+    c = df["close"].values
+    v = df["volume"].values
+
+    feats = []
+
+    # Structural features multi-window
+    for w in [12, 24, 48]:
+        win_c = c[i-w:i]
+        win_o = o[i-w:i]
+        win_h = h[i-w:i]
+        win_l = l[i-w:i]
+        win_v = v[i-w:i]
+
+        mn, mx = win_l.min(), max(win_h.max(), win_c.max())
+        rng = mx - mn if mx - mn > 0 else 1e-10
+
+        total = win_h - win_l
+        total = np.where(total == 0, 1e-10, total)
+        body = np.abs(win_c - win_o) / total
+        direction = np.sign(win_c - win_o)
+
+        log_c = np.log(np.where(win_c == 0, 1e-10, win_c))
+        rets = np.diff(log_c)
+
+        v_mean = np.mean(win_v)
+
+        feats.extend([
+            np.mean(rets) if len(rets) > 0 else 0,
+            np.std(rets) if len(rets) > 0 else 0,
+            np.sum(rets) if len(rets) > 0 else 0,
+            float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
+            float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
+            np.mean(body),
+            np.std(body),
+            np.mean(direction),
+            np.mean(direction[-min(3, w):]),
+            (win_c[-1] - mn) / rng,
+            win_v[-1] / v_mean if v_mean > 0 else 1,
+            np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
+        ])
+
+    # Squeeze-specific features
+    sq = squeeze_info
+    feats.extend([
+        sq["duration"],
+        sq["duration"] / 24,  # durata in giorni
+        sq["kcr_at_release"],
+        v[i-1] / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
+        np.mean(v[i:min(i+3, len(v))]) / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
+    ])
+
+    # Price position
+    h48 = np.max(h[max(0, i-48):i])
+    l48 = np.min(l[max(0, i-48):i])
+    r48 = h48 - l48
+    feats.append((c[i-1] - l48) / r48 if r48 > 0 else 0.5)
+
+    # ATR normalized
+    tr = np.maximum(h[i-14:i] - l[i-14:i],
+                    np.maximum(np.abs(h[i-14:i] - np.roll(c[i-14:i], 1)),
+                               np.abs(l[i-14:i] - np.roll(c[i-14:i], 1))))
+    atr = np.mean(tr[1:])
+    feats.append(atr / c[i-1] if c[i-1] > 0 else 0)
+
+    # First bar momentum (la barra di breakout)
+    first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+    feats.append(first_ret)
+
+    return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
+
+
+def run_hybrid(asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct):
+    print(f"\n{'='*65}")
+    print(f"  {asset} {tf} — HYBRID Squeeze+ML (BBw={bb_w}, sq={sq_thr}, brk={brk_bars})")
+    print(f"  Leverage: {leverage}x, Position: {pos_pct*100:.0f}%")
+    print(f"{'='*65}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(df)
+
+    events = detect_squeeze_releases(close, high, low, volume, bb_w, sq_thr)
+    print(f"  Squeeze releases totali: {len(events)}")
+
+    # Build dataset: solo ai punti di squeeze
+    X_all, y_all, ev_all = [], [], []
+    for ev in events:
+        i = ev["idx"]
+        if i + brk_bars >= n or i < 100:
+            continue
+        feats = build_features_at(df, i, ev)
+        if feats is None:
+            continue
+        actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+        X_all.append(feats)
+        y_all.append(1 if actual_ret > 0 else 0)
+        ev_all.append(ev)
+
+    if len(X_all) < 50:
+        print("  Troppi pochi campioni.")
+        return None
+
+    X = np.array(X_all)
+    y = np.array(y_all)
+
+    print(f"  Samples: {len(X)}, Up: {np.mean(y)*100:.1f}%")
+
+    # Walk-forward
+    TRAIN_SIZE = max(int(len(X) * 0.5), 50)
+    STEP_SIZE = max(int(len(X) * 0.1), 10)
+
+    results = {}
+
+    for ml_thr in [0.50, 0.55, 0.60, 0.65, 0.70]:
+        capital = float(INITIAL)
+        equity = [capital]
+        trades_list = []
+        correct = 0
+        total = 0
+
+        start = 0
+        while start + TRAIN_SIZE + STEP_SIZE <= len(X):
+            train_end = start + TRAIN_SIZE
+            test_end = min(train_end + STEP_SIZE, len(X))
+
+            X_tr = X[start:train_end]
+            y_tr = y[start:train_end]
+            X_te = X[train_end:test_end]
+            y_te = y[train_end:test_end]
+
+            if len(np.unique(y_tr)) < 2:
+                start += STEP_SIZE
+                continue
+
+            scaler = StandardScaler()
+            X_tr_s = scaler.fit_transform(X_tr)
+            X_te_s = scaler.transform(X_te)
+
+            model = GradientBoostingClassifier(
+                n_estimators=150, max_depth=4, min_samples_leaf=10,
+                learning_rate=0.05, subsample=0.8, random_state=42,
+            )
+            model.fit(X_tr_s, y_tr)
+
+            up_idx = list(model.classes_).index(1) if 1 in model.classes_ else -1
+            if up_idx < 0:
+                start += STEP_SIZE
+                continue
+
+            for j in range(len(X_te)):
+                proba = model.predict_proba(X_te_s[j:j+1])[0]
+                p_up = proba[up_idx]
+
+                ev = ev_all[train_end + j]
+                i = ev["idx"]
+                actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+
+                # ML decide direction
+                direction = None
+                if p_up >= ml_thr:
+                    direction = "long"
+                elif p_up <= (1 - ml_thr):
+                    direction = "short"
+
+                if direction is None:
+                    continue
+
+                is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
+                total += 1
+                if is_correct:
+                    correct += 1
+
+                trade_ret = actual_ret if direction == "long" else -actual_ret
+                net = trade_ret * leverage - FEE * 2 * leverage
+                pnl = capital * pos_pct * net
+                capital += pnl
+                capital = max(capital, 0)
+                equity.append(capital)
+
+                trades_list.append({
+                    "idx": i,
+                    "direction": direction,
+                    "p_up": p_up,
+                    "actual_ret": actual_ret,
+                    "correct": is_correct,
+                    "pnl": pnl,
+                })
+
+            start += STEP_SIZE
+
+        if total == 0:
+            continue
+
+        acc = correct / total * 100
+
+        # Max drawdown
+        peak = equity[0]
+        max_dd = 0
+        for v in equity:
+            if v > peak:
+                peak = v
+            dd = (peak - v) / peak if peak > 0 else 0
+            max_dd = max(max_dd, dd)
+
+        # Annualized
+        first_ev = ev_all[TRAIN_SIZE] if TRAIN_SIZE < len(ev_all) else ev_all[0]
+        last_ev = ev_all[-1]
+        test_candles = last_ev["idx"] - first_ev["idx"]
+        if tf == "1h":
+            test_days = test_candles / 24
+        elif tf == "15m":
+            test_days = test_candles / (24 * 4)
+        else:
+            test_days = test_candles / 24
+        test_years = test_days / 365.25 if test_days > 0 else 1
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        daily_pnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        trades_yr = total / test_years if test_years > 0 else 0
+
+        tag = ""
+        if acc >= 80:
+            tag = " ✅✅"
+        elif acc >= 70:
+            tag = " ✅"
+
+        print(f"  ml_thr={ml_thr:.2f}: trades={total:4d} acc={acc:.1f}%{tag} ret={(capital-INITIAL)/INITIAL*100:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% sharpe= trades/yr={trades_yr:.0f} €/day={daily_pnl:.2f}")
+
+        results[ml_thr] = {
+            "trades": total, "accuracy": acc, "capital": capital,
+            "annualized": ann, "max_dd": max_dd * 100, "daily_pnl": daily_pnl,
+            "trades_yr": trades_yr,
+        }
+
+    # Modalità "squeeze puro" come baseline
+    capital_sq = float(INITIAL)
+    correct_sq = 0
+    total_sq = 0
+    split = int(len(X) * 0.5)
+
+    for k in range(split, len(X)):
+        ev = ev_all[k]
+        i = ev["idx"]
+        if i + brk_bars >= n:
+            continue
+        first_ret = (close[i] - close[i-1]) / close[i-1]
+        if abs(first_ret) < 0.001:
+            continue
+        direction = 1 if first_ret > 0 else -1
+        actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+        is_correct = (direction == 1 and actual_ret > 0) or (direction == -1 and actual_ret < 0)
+        total_sq += 1
+        if is_correct:
+            correct_sq += 1
+        trade_ret = actual_ret * direction
+        net = trade_ret * leverage - FEE * 2 * leverage
+        capital_sq += capital_sq * pos_pct * net
+        capital_sq = max(capital_sq, 0)
+
+    if total_sq > 0:
+        acc_sq = correct_sq / total_sq * 100
+        print(f"  BASELINE squeeze puro: trades={total_sq:4d} acc={acc_sq:.1f}% ret={(capital_sq-INITIAL)/INITIAL*100:+.1f}%")
+
+    return results
+
+
+# ===== MAIN: test su multiple configurazioni =====
+print("=" * 70)
+print("  STRATEGIA 13: SQUEEZE + ML IBRIDA")
+print("=" * 70)
+
+configs = [
+    # (asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct)
+    ("ETH", "1h", 20, 0.8, 3, 3, 0.2),
+    ("ETH", "1h", 30, 0.9, 3, 3, 0.2),
+    ("ETH", "1h", 14, 0.8, 3, 3, 0.2),
+    ("ETH", "1h", 20, 0.9, 3, 3, 0.2),
+    ("BTC", "1h", 14, 0.8, 3, 3, 0.2),
+    ("BTC", "1h", 20, 0.8, 3, 3, 0.2),
+    ("BTC", "1h", 14, 0.9, 6, 3, 0.2),
+    ("ETH", "15m", 14, 0.8, 3, 3, 0.15),
+    ("ETH", "15m", 20, 0.9, 3, 3, 0.15),
+    ("BTC", "15m", 14, 0.9, 3, 3, 0.15),
+    # Aggressive
+    ("ETH", "1h", 20, 0.8, 3, 5, 0.3),
+    ("ETH", "1h", 30, 0.9, 3, 5, 0.3),
+]
+
+all_results = []
+for cfg in configs:
+    r = run_hybrid(*cfg)
+    if r:
+        for thr, data in r.items():
+            all_results.append({
+                "config": f"{cfg[0]} {cfg[1]} BBw={cfg[2]} sq={cfg[3]} brk={cfg[4]} lev={cfg[5]} pos={cfg[6]}",
+                "ml_thr": thr,
+                **data,
+            })
+
+# Sort by accuracy
+print("\n\n" + "=" * 70)
+print("  CLASSIFICA PER ACCURACY (top 20)")
+print("=" * 70)
+sorted_acc = sorted(all_results, key=lambda x: x["accuracy"], reverse=True)
+for r in sorted_acc[:20]:
+    tag = "✅✅" if r["accuracy"] >= 80 else "✅" if r["accuracy"] >= 70 else ""
+    print(f"  {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% {tag:4s} trades={r['trades']:4d} ret={(r['capital']-INITIAL)/INITIAL*100:+.1f}% ann={r['annualized']:+.1f}% dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
+
+print("\n" + "=" * 70)
+print("  CLASSIFICA PER ROI ANNUO (top 20, min 20 trades)")
+print("=" * 70)
+sorted_roi = sorted([r for r in all_results if r["trades"] >= 20], key=lambda x: x["annualized"], reverse=True)
+for r in sorted_roi[:20]:
+    tag = "✅✅" if r["accuracy"] >= 80 else "✅" if r["accuracy"] >= 70 else ""
+    print(f"  {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% {tag:4s} ann={r['annualized']:+.1f}% trades={r['trades']:4d} dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
+
+print("\n" + "=" * 70)
+print("  SWEET SPOT: acc>=75% AND ann>=20% AND trades>=15")
+print("=" * 70)
+sweet = [r for r in all_results if r["accuracy"] >= 75 and r["annualized"] >= 20 and r["trades"] >= 15]
+sweet.sort(key=lambda x: x["accuracy"] * x["annualized"], reverse=True)
+for r in sweet:
+    print(f"  {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% ann={r['annualized']:+.1f}% trades={r['trades']:4d} dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")