diff --git a/scripts/s3_03_ultimate_squeeze.py b/scripts/s3_03_ultimate_squeeze.py
new file mode 100644
index 0000000..0478451
--- /dev/null
+++ b/scripts/s3_03_ultimate_squeeze.py
@@ -0,0 +1,256 @@
+"""S3-03: Ultimate Squeeze — combina TUTTI i filtri migliori.
+Filtri che funzionano (testati singolarmente):
+- Anti-fakeout (+1% acc)
+- Long squeeze duration (+1% acc)
+- Cross-asset squeeze simultaneo (+0.5%)
+- Timing 4-16 UTC (+0.5%)
+- Correlation ETH-BTC alta per ETH trades (+1%)
+- Volume confirmation al breakout
+
+Nuovi filtri da testare:
+- Volume delta: up_volume - down_volume al breakout
+- Momentum confirmation: breakout nella direzione del trend 1h
+- Volatility regime: skip in regime estremo (RV > 100%)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_RT = 0.002
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc, wh, wl = close[i-window:i], high[i-window:i], 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 = (ma+1.5*atr)-(ma-1.5*atr)
+        bb = (ma+2*bb_std)-(ma-2*bb_std)
+        if kc > 0: r[i] = bb/kc
+    return r
+
+
+def ema(arr, period):
+    r = np.full(len(arr), np.nan)
+    k = 2/(period+1)
+    r[period-1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        r[i] = arr[i]*k + r[i-1]*(1-k)
+    return r
+
+
+def rv_ann(close, window):
+    lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i+1] = np.std(lr[i-window:i]) * np.sqrt(24*365)
+    return r
+
+
+def run_ultimate(primary, tf="15m"):
+    secondary = "ETH" if primary == "BTC" else "BTC"
+
+    df = load_data(primary, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(df)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    df2 = load_data(secondary, tf)
+    c2, ts2 = df2["close"].values, df2["timestamp"].values
+
+    kcr = keltner_ratio(c, h, l, 14)
+    kcr2 = keltner_ratio(c2, df2["high"].values, df2["low"].values, 14)
+
+    ema_50 = ema(c, 50)
+    rv_48 = rv_ann(c, 48)
+
+    # Rolling correlation
+    ret1 = np.diff(np.log(np.where(c == 0, 1e-10, c)))
+    ret2 = np.diff(np.log(np.where(c2[:len(c)] == 0, 1e-10, c2[:len(c)])))
+    min_len = min(len(ret1), len(ret2))
+    ret1 = ret1[:min_len]
+    ret2 = ret2[:min_len]
+    corr = np.full(n, np.nan)
+    for i in range(48, min_len):
+        cv = np.corrcoef(ret1[i-48:i], ret2[i-48:i])[0,1]
+        corr[i+1] = cv if np.isfinite(cv) else 0
+
+    # Detect squeezes
+    events = []
+    in_sq = False
+    sq_start = 0
+    for i in range(15, n):
+        if np.isnan(kcr[i]): continue
+        is_sq = kcr[i] < 0.8
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            dur = i - sq_start
+            if dur < 5 or i + 6 >= n:
+                continue
+            events.append({"idx": i, "dur": dur, "sq_start": sq_start})
+
+    print(f"\n{'#'*70}")
+    print(f"  {primary} {tf} — ULTIMATE SQUEEZE ({len(events)} squeeze events)")
+    print(f"{'#'*70}")
+
+    filters_map = {
+        "antifake": lambda ev, i: not _antifake(c, h, l, i),
+        "long_sq": lambda ev, i: ev["dur"] >= 10,
+        "timing": lambda ev, i: 4 <= ts.iloc[i].hour <= 16,
+        "cross": lambda ev, i: _cross_squeeze(kcr2, i, ts, ts2),
+        "corr_high": lambda ev, i: not np.isnan(corr[i]) and abs(corr[i]) >= 0.6,
+        "vol_confirm": lambda ev, i: _vol_confirm(v, i, ev["sq_start"]),
+        "trend_align": lambda ev, i: _trend_align(c, ema_50, i),
+        "low_rv": lambda ev, i: not np.isnan(rv_48[i]) and rv_48[i] < 1.5,
+    }
+
+    def _antifake(c, h, l, i):
+        if i + 1 >= len(c): return False
+        br = h[i] - l[i]
+        if br <= 0: return False
+        if c[i] > c[i-1]:
+            return (h[i] - c[i]) / br > 0.6
+        return (c[i] - l[i]) / br > 0.6
+
+    def _cross_squeeze(kcr2, i, ts1, ts2_arr):
+        i2 = np.searchsorted(ts2_arr, ts.values[i].astype("int64") // 10**6)
+        i2 = min(i2, len(kcr2)-1)
+        return any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85 for j in range(max(0,i2-10), i2+1))
+
+    def _vol_confirm(v, i, sq_start):
+        avg = np.mean(v[sq_start:i])
+        return avg > 0 and v[i] > avg * 1.3
+
+    def _trend_align(c, ema_val, i):
+        if np.isnan(ema_val[i]): return True
+        first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+        if first_ret > 0:
+            return c[i] > ema_val[i]
+        return c[i] < ema_val[i]
+
+    # Test combinazioni incrementali
+    combos = [
+        ("BASE", []),
+        ("antifake", ["antifake"]),
+        ("long_sq", ["long_sq"]),
+        ("antifake+long", ["antifake", "long_sq"]),
+        ("antifake+timing", ["antifake", "timing"]),
+        ("antifake+cross", ["antifake", "cross"]),
+        ("antifake+corr", ["antifake", "corr_high"]),
+        ("antifake+vol", ["antifake", "vol_confirm"]),
+        ("antifake+trend", ["antifake", "trend_align"]),
+        ("af+long+timing", ["antifake", "long_sq", "timing"]),
+        ("af+long+cross", ["antifake", "long_sq", "cross"]),
+        ("af+long+corr", ["antifake", "long_sq", "corr_high"]),
+        ("af+long+trend", ["antifake", "long_sq", "trend_align"]),
+        ("af+long+cross+time", ["antifake", "long_sq", "cross", "timing"]),
+        ("af+long+corr+time", ["antifake", "long_sq", "corr_high", "timing"]),
+        ("af+long+corr+trend", ["antifake", "long_sq", "corr_high", "trend_align"]),
+        ("ALL_NO_VOL", ["antifake", "long_sq", "cross", "timing", "corr_high", "trend_align", "low_rv"]),
+        ("ALL", ["antifake", "long_sq", "cross", "timing", "corr_high", "vol_confirm", "trend_align", "low_rv"]),
+        ("BEST_5", ["antifake", "long_sq", "corr_high", "trend_align", "low_rv"]),
+    ]
+
+    results = []
+    for combo_name, filter_names in combos:
+        yearly = {}
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+
+        for ev in events:
+            i = ev["idx"]
+            first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+
+            skip = False
+            for fn in filter_names:
+                if fn in filters_map and not filters_map[fn](ev, i):
+                    skip = True
+                    break
+            if skip:
+                continue
+
+            direction = 1 if first_ret > 0 else -1
+            entry = c[i-1]
+            exit_price = c[min(i+2, n-1)]
+            actual = (exit_price - entry) / entry * direction
+            net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+            if capital > peak: peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"w": 0, "t": 0, "pnls": []}
+            yearly[year]["t"] += 1
+            if actual > 0: yearly[year]["w"] += 1
+            yearly[year]["pnls"].append(net * INITIAL)
+
+        all_t = sum(d["t"] for d in yearly.values())
+        all_w = sum(d["w"] for d in yearly.values())
+        if all_t < 20: continue
+
+        acc = all_w / all_t * 100
+        pnl = sum(p for d in yearly.values() for p in d["pnls"])
+        worst = min(yearly.items(), key=lambda x: x[1]["w"]/x[1]["t"] if x[1]["t"]>0 else 0)
+        wa = worst[1]["w"]/worst[1]["t"]*100 if worst[1]["t"]>0 else 0
+
+        results.append({
+            "name": combo_name, "trades": all_t, "acc": acc, "pnl": pnl,
+            "dd": max_dd*100, "capital": capital, "worst": f"{worst[0]}({wa:.0f}%)",
+            "yearly": yearly,
+        })
+
+    results.sort(key=lambda x: x["acc"], reverse=True)
+
+    print(f"\n  {'Name':.<28s} {'Trades':>6s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>5s} {'Worst':>12s}")
+    print(f"  {'-'*70}")
+    for r in results[:20]:
+        tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 78 else ""
+        print(f"  {r['name']:.<28s} {r['trades']:>6d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['dd']:>4.1f}% {r['worst']:>12s} {tag}")
+
+    # Dettaglio migliore
+    if results:
+        best = results[0]
+        print(f"\n  MIGLIORE: {best['name']} → {best['acc']:.1f}% acc, DD {best['dd']:.1f}%")
+        for y in sorted(best["yearly"]):
+            d = best["yearly"][y]
+            ya = d["w"]/d["t"]*100 if d["t"]>0 else 0
+            tag = " ← CRASH" if y in [2020,2021,2022] else ""
+            print(f"    {y}: {d['t']:4d}t {ya:5.1f}% €{sum(d['pnls']):+.0f}{tag}")
+
+    return results
+
+
+all_r = []
+for asset in ["BTC", "ETH"]:
+    for tf in ["15m", "1h"]:
+        r = run_ultimate(asset, tf)
+        for x in r:
+            all_r.append({**x, "key": f"{asset}_{tf}"})
+
+all_r.sort(key=lambda x: x["acc"], reverse=True)
+print(f"\n\n{'='*70}")
+print(f"  TOP 10 GLOBALE")
+print(f"{'='*70}")
+for r in all_r[:10]:
+    tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 78 else ""
+    print(f"  {r['key']:.<10s} {r['name']:.<28s} {r['trades']:>5d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} DD {r['dd']:.1f}% {r['worst']:>12s} {tag}")