diff --git a/scripts/s3_02_lead_lag_multi.py b/scripts/s3_02_lead_lag_multi.py
new file mode 100644
index 0000000..a907649
--- /dev/null
+++ b/scripts/s3_02_lead_lag_multi.py
@@ -0,0 +1,290 @@
+"""S3-02: Lead-lag multi-asset squeeze.
+Quando BTC fa squeeze breakout, ETH/SOL spesso seguono.
+Usa il breakout di BTC per anticipare entrata su ETH (e viceversa).
+Testa anche correlazione inter-asset per conferma segnale.
+"""
+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 load_aligned(assets, tf):
+    """Carica e allinea dati multi-asset per timestamp."""
+    dfs = {}
+    for asset in assets:
+        try:
+            if asset == "SOL":
+                df = pd.read_parquet(f"data/raw/sol_{tf}.parquet")
+                df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+            else:
+                df = load_data(asset, tf)
+            dfs[asset] = df
+        except Exception:
+            pass
+
+    if len(dfs) < 2:
+        return None
+
+    # Allinea per timestamp
+    common_ts = set(dfs[list(dfs.keys())[0]]["timestamp"].values)
+    for df in dfs.values():
+        common_ts &= set(df["timestamp"].values)
+    common_ts = sorted(common_ts)
+
+    aligned = {}
+    for asset, df in dfs.items():
+        mask = df["timestamp"].isin(common_ts)
+        aligned[asset] = df[mask].sort_values("timestamp").reset_index(drop=True)
+
+    return aligned
+
+
+def detect_breakouts(close, high, low, volume, kcr, sq_thr=0.8, min_dur=5):
+    """Detect squeeze breakout events."""
+    events = []
+    in_sq = False
+    sq_start = 0
+    for i in range(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
+            if i - sq_start < min_dur:
+                continue
+            first_ret = (close[i] - close[i-1]) / close[i-1] if close[i-1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+            events.append({
+                "idx": i,
+                "duration": i - sq_start,
+                "direction": 1 if first_ret > 0 else -1,
+                "first_ret": first_ret,
+            })
+    return events
+
+
+print("=" * 75)
+print("  S3-02: LEAD-LAG MULTI-ASSET SQUEEZE")
+print("=" * 75)
+
+for tf in ["1h", "15m"]:
+    aligned = load_aligned(["BTC", "ETH", "SOL"], tf)
+    if aligned is None:
+        continue
+
+    n = len(aligned["BTC"])
+    ts = pd.to_datetime(aligned["BTC"]["timestamp"], unit="ms", utc=True)
+
+    print(f"\n  Timeframe: {tf}, Candles allineate: {n}")
+
+    # Calcola squeeze per ogni asset
+    asset_data = {}
+    for asset in aligned:
+        df = aligned[asset]
+        c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+        kcr = keltner_ratio(c, h, l, 14)
+        events = detect_breakouts(c, h, l, v, kcr)
+        asset_data[asset] = {"close": c, "high": h, "low": l, "vol": v, "kcr": kcr, "events": events}
+        print(f"    {asset}: {len(events)} squeeze breakouts")
+
+    # ================================================================
+    #  STRATEGIA A: Leader-follower
+    #  Quando BTC fa breakout, entra su ETH/SOL nella stessa direzione
+    # ================================================================
+    print(f"\n  --- LEADER-FOLLOWER ({tf}) ---")
+
+    for leader, follower in [("BTC", "ETH"), ("BTC", "SOL"), ("ETH", "BTC"), ("ETH", "SOL")]:
+        if leader not in asset_data or follower not in asset_data:
+            continue
+
+        leader_events = asset_data[leader]["events"]
+        fc = asset_data[follower]["close"]
+
+        for hold in [3, 6]:
+            for delay in [0, 1, 2]:
+                yearly = {}
+
+                for ev in leader_events:
+                    i = ev["idx"] + delay
+                    if i + hold >= n:
+                        continue
+
+                    # Anti-fakeout su follower
+                    entry = fc[i]
+                    exit_price = fc[min(i + hold, n - 1)]
+                    direction = ev["direction"]
+                    actual = (exit_price - entry) / entry * direction
+                    net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+                    year = ts.iloc[min(i, n-1)].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 < 30:
+                    continue
+                acc = all_w / all_t * 100
+                pnl = sum(p for d in yearly.values() for p in d["pnls"])
+                worst_y = min(yearly.items(), key=lambda x: x[1]["w"]/x[1]["t"] if x[1]["t"]>0 else 0)
+                worst_acc = worst_y[1]["w"]/worst_y[1]["t"]*100 if worst_y[1]["t"]>0 else 0
+                tag = "✅" if acc >= 76 else ""
+                print(f"    {leader}→{follower} d={delay} h={hold}: trades={all_t:5d} acc={acc:.1f}% pnl=€{pnl:+.0f} worst={worst_y[0]}({worst_acc:.0f}%) {tag}")
+
+    # ================================================================
+    #  STRATEGIA B: Consensus multi-asset
+    #  Trade solo quando 2+ asset hanno squeeze breakout nello stesso momento
+    # ================================================================
+    print(f"\n  --- CONSENSUS MULTI-ASSET ({tf}) ---")
+
+    # Build event map: timestamp → list of (asset, direction)
+    event_map = {}
+    for asset, data in asset_data.items():
+        for ev in data["events"]:
+            idx = ev["idx"]
+            if idx not in event_map:
+                event_map[idx] = []
+            event_map[idx].append((asset, ev["direction"]))
+
+    for target in ["BTC", "ETH", "SOL"]:
+        if target not in asset_data:
+            continue
+        tc = asset_data[target]["close"]
+
+        for min_consensus in [2, 3]:
+            for window_bars in [1, 3, 5]:
+                yearly = {}
+                daily_done = set()
+
+                for idx in sorted(event_map.keys()):
+                    if idx + 6 >= n:
+                        continue
+
+                    day = ts.iloc[idx].strftime("%Y-%m-%d")
+                    if day in daily_done:
+                        continue
+
+                    # Count consensus within window
+                    nearby_events = []
+                    for j in range(max(0, idx - window_bars), idx + window_bars + 1):
+                        if j in event_map:
+                            nearby_events.extend(event_map[j])
+
+                    # Unique assets
+                    unique_assets = set(a for a, d in nearby_events)
+                    if len(unique_assets) < min_consensus:
+                        continue
+
+                    # Majority direction
+                    dirs = [d for a, d in nearby_events]
+                    majority = 1 if sum(dirs) > 0 else -1
+
+                    entry = tc[idx]
+                    exit_price = tc[min(idx + 3, n - 1)]
+                    actual = (exit_price - entry) / entry * majority
+                    net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+                    year = ts.iloc[idx].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)
+                    daily_done.add(day)
+
+                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"])
+                tag = "✅" if acc >= 76 else ""
+                print(f"    {target} consensus>={min_consensus} w={window_bars}: trades={all_t:4d} acc={acc:.1f}% pnl=€{pnl:+.0f} {tag}")
+
+    # ================================================================
+    #  STRATEGIA C: Correlation-weighted squeeze
+    #  Peso il segnale squeeze in base alla correlazione rolling con BTC
+    # ================================================================
+    print(f"\n  --- CORRELATION-WEIGHTED ({tf}) ---")
+
+    for target in ["ETH", "SOL"]:
+        if target not in asset_data:
+            continue
+        tc = asset_data[target]["close"]
+        btc_c = asset_data["BTC"]["close"]
+
+        # Rolling correlation
+        corr_window = 48  # 48 bars
+        rolling_corr = np.full(n, np.nan)
+        ret_t = np.diff(np.log(np.where(tc == 0, 1e-10, tc)))
+        ret_b = np.diff(np.log(np.where(btc_c == 0, 1e-10, btc_c)))
+        for i in range(corr_window, len(ret_t)):
+            c_val = np.corrcoef(ret_t[i-corr_window:i], ret_b[i-corr_window:i])[0, 1]
+            rolling_corr[i + 1] = c_val if np.isfinite(c_val) else 0
+
+        events = asset_data[target]["events"]
+
+        for corr_thr in [0.5, 0.6, 0.7, 0.8]:
+            yearly = {}
+            for ev in events:
+                i = ev["idx"]
+                if i + 3 >= n or np.isnan(rolling_corr[i]):
+                    continue
+
+                # Solo quando correlazione con BTC è alta
+                if abs(rolling_corr[i]) < corr_thr:
+                    continue
+
+                entry = tc[i - 1]
+                exit_price = tc[min(i + 2, n - 1)]
+                actual = (exit_price - entry) / entry * ev["direction"]
+                net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+                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"])
+            tag = "✅" if acc >= 76 else ""
+            print(f"    {target} corr>={corr_thr}: trades={all_t:4d} acc={acc:.1f}% pnl=€{pnl:+.0f} {tag}")