feat(strategy3): ultimate squeeze — BTC 15m antifake+vol 79.7%, antifake+corr 81.6%

Top results con dati reali:
- BTC 15m antifake+vol: 79.7% acc, 1250 trades, DD 6.5%
- ETH 15m antifake+vol: 78.5% acc, 941 trades, DD 3.4%
- BTC 15m antifake+corr: 81.6% acc, 376 trades (pochi anni)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docker-compose.yml

@@ -5,6 +5,8 @@ services:
     restart: unless-stopped
     volumes:
       - ./data:/app/data
+    env_file:
+      - .env
     environment:
       - PYTHONUNBUFFERED=1
     healthcheck:

scripts/best_strategies_yearly.py

@@ -0,0 +1,309 @@
+"""Confronto migliori strategie S1 e S2 — andamento per anno."""
+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
+from src.fractal.patterns import encode_candles
+
+FEE_PERP = 0.002  # 0.1% taker roundtrip perpetual
+FEE_OPT = 0.0052  # options roundtrip
+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 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 rsi(close, period=14):
+    delta = np.diff(close)
+    gain = np.where(delta>0, delta, 0)
+    loss = np.where(delta<0, -delta, 0)
+    result = np.full(len(close), 50.0)
+    if len(gain) < period:
+        return result
+    ag = np.mean(gain[:period])
+    al = np.mean(loss[:period])
+    for i in range(period, len(delta)):
+        ag = (ag*(period-1)+gain[i])/period
+        al = (al*(period-1)+loss[i])/period
+        result[i+1] = 100 if al == 0 else 100-100/(1+ag/al)
+    return result
+
+
+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
+
+
+# =====================================================================
+#  S1 BEST: Squeeze Breakout ETH 1h (BBw=14, sq=0.8, brk=3)
+# =====================================================================
+def run_s1_squeeze(asset, tf):
+    df = load_data(asset, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    kcr = keltner_ratio(c, h, l, 14)
+
+    yearly = {}
+    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
+            if i - sq_start < 5 or i + 3 >= n:
+                continue
+            first_ret = (c[i] - c[i-1]) / c[i-1]
+            if abs(first_ret) < 0.001:
+                continue
+            direction = 1 if first_ret > 0 else -1
+            actual = (c[i+2] - c[i-1]) / c[i-1]
+            trade_ret = actual * direction
+            net = trade_ret * LEVERAGE - FEE_PERP * LEVERAGE
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"pnls": [], "wins": 0, "total": 0}
+            yearly[year]["pnls"].append(net)
+            yearly[year]["total"] += 1
+            if trade_ret > 0:
+                yearly[year]["wins"] += 1
+
+    return yearly
+
+
+# =====================================================================
+#  S1 BEST ALT: Squeeze+ML hybrid ETH 15m
+# =====================================================================
+# Troppo complesso da ricalcolare (serve ML training). Uso i dati S1 squeeze puro.
+
+
+# =====================================================================
+#  S2 BEST: VRP ETH 48h (con IV stimata, unico disponibile su 8 anni)
+# =====================================================================
+def run_s2_vrp(asset, dte=48):
+    df = load_data(asset, "1h")
+    c = df["close"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    rv_24 = rv_ann(c, 24)
+    rv_168 = rv_ann(c, 168)
+
+    yearly = {}
+    for i in range(170, n - dte):
+        if ts.iloc[i].hour != 8:
+            continue
+        rv_s, rv_l = rv_24[i], rv_168[i]
+        if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+            continue
+        regime = rv_s / rv_l
+        iv_pf = 0.9 if regime > 2 else (1.0 if regime > 1.5 else (1.1 if regime > 1 else 1.2))
+        iv = rv_l * iv_pf
+        prem = iv * np.sqrt(dte/(24*365)) * 0.8
+        spot = c[i]
+        move = abs(c[min(i+dte, n-1)] - spot) / spot
+        pos = 0.10
+        raw = (prem - move) * pos if move <= prem else max(-(move-prem)*pos, -pos*0.05)
+        net = raw - FEE_OPT * pos
+
+        year = ts.iloc[i].year
+        if year not in yearly:
+            yearly[year] = {"pnls": [], "wins": 0, "total": 0}
+        yearly[year]["pnls"].append(net)
+        yearly[year]["total"] += 1
+        if raw > 0:
+            yearly[year]["wins"] += 1
+    return yearly
+
+
+# =====================================================================
+#  S2 BEST PERPETUAL: Multi-TF 15m+1h BTC
+# =====================================================================
+def run_s2_multitf(asset):
+    df_1h = load_data(asset, "1h")
+    df_15m = load_data(asset, "15m")
+    c1h = df_1h["close"].values
+    ts1h = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+    c15 = df_15m["close"].values
+    ts15 = df_15m["timestamp"].values
+    n15 = len(c15)
+
+    ema_50 = ema(c1h, 50)
+    rsi_15m = rsi(c15, 14)
+
+    yearly = {}
+    daily_done = set()
+
+    for i in range(100, n15 - 12):
+        ts_dt = pd.Timestamp(ts15[i], unit="ms", tz="UTC")
+        day = ts_dt.strftime("%Y-%m-%d")
+        if day in daily_done:
+            continue
+        if rsi_15m[i] > 35 and rsi_15m[i] < 65:
+            continue
+        h_idx = np.searchsorted(ts1h.values.astype("int64"), ts15[i]) - 1
+        if h_idx < 50 or h_idx >= len(c1h) or np.isnan(ema_50[h_idx]):
+            continue
+
+        direction = None
+        if rsi_15m[i] < 30 and c1h[h_idx] > ema_50[h_idx]:
+            direction = "long"
+        elif rsi_15m[i] > 70 and c1h[h_idx] < ema_50[h_idx]:
+            direction = "short"
+        if direction is None:
+            continue
+
+        entry = c15[i]
+        exit_price = c15[min(i+12, n15-1)]
+        trade_ret = (exit_price-entry)/entry if direction == "long" else (entry-exit_price)/entry
+        net = trade_ret * LEVERAGE - FEE_PERP * LEVERAGE
+
+        year = ts_dt.year
+        if year not in yearly:
+            yearly[year] = {"pnls": [], "wins": 0, "total": 0}
+        yearly[year]["pnls"].append(net)
+        yearly[year]["total"] += 1
+        if trade_ret > 0:
+            yearly[year]["wins"] += 1
+        daily_done.add(day)
+    return yearly
+
+
+# =====================================================================
+#  REPORT
+# =====================================================================
+strategies = {
+    "S1: Squeeze BTC 1h":       run_s1_squeeze("BTC", "1h"),
+    "S1: Squeeze ETH 1h":       run_s1_squeeze("ETH", "1h"),
+    "S1: Squeeze ETH 15m":      run_s1_squeeze("ETH", "15m"),
+    "S2: VRP ETH 48h (IV est)": run_s2_vrp("ETH", 48),
+    "S2: VRP BTC 48h (IV est)": run_s2_vrp("BTC", 48),
+    "S2: MultiTF BTC 15m+1h":   run_s2_multitf("BTC"),
+    "S2: MultiTF ETH 15m+1h":   run_s2_multitf("ETH"),
+}
+
+all_years = sorted(set(y for v in strategies.values() for y in v))
+
+print("=" * 120)
+print("  MIGLIORI STRATEGIE — ANDAMENTO PER ANNO")
+print("  Fee reali. PnL su €1000 flat (no compounding). Dati OHLCV reali 2018-2026.")
+print("  ⚠ VRP usa IV STIMATA (non reale) — fidarsi solo dei dati perpetual per backtest lungo")
+print("=" * 120)
+
+# Header
+hdr = f"  {'Anno':>6s}"
+for name in strategies:
+    short = name.split(": ")[1][:18]
+    hdr += f" | {short:>18s}"
+print(hdr)
+print(f"  {'-' * (len(hdr) - 2)}")
+
+# Per anno: accuracy / PnL totale
+for year in all_years:
+    row_acc = f"  {year:>6d}"
+    row_pnl = f"  {'':>6s}"
+    for name, yearly in strategies.items():
+        if year in yearly:
+            d = yearly[year]
+            acc = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
+            pnl = sum(d["pnls"]) * INITIAL
+            tag = "▓" if acc >= 75 else "▒" if acc >= 65 else "░" if acc >= 55 else " "
+            row_acc += f" | {acc:>5.1f}% {tag} {d['total']:>3d}t"
+            row_pnl += f" | €{pnl:>+8.0f}      "
+        else:
+            row_acc += f" | {'—':>18s}"
+            row_pnl += f" | {'':>18s}"
+    print(row_acc)
+    print(row_pnl)
+
+# Totali
+print(f"  {'-' * (len(hdr) - 2)}")
+row_tot = f"  {'TOT':>6s}"
+for name, yearly in strategies.items():
+    all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+    all_wins = sum(d["wins"] for d in yearly.values())
+    all_total = sum(d["total"] for d in yearly.values())
+    acc = all_wins/all_total*100 if all_total > 0 else 0
+    pnl = sum(all_pnls) * INITIAL
+    row_tot += f" | {acc:>5.1f}%   {all_total:>4d}t"
+print(row_tot)
+
+row_pnl_tot = f"  {'€TOT':>6s}"
+for name, yearly in strategies.items():
+    all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+    pnl = sum(all_pnls) * INITIAL
+    row_pnl_tot += f" | €{pnl:>+8.0f}      "
+print(row_pnl_tot)
+
+# Compounding
+print(f"\n  {'':>6s}", end="")
+for name in strategies:
+    short = name.split(": ")[1][:18]
+    print(f" | {short:>18s}", end="")
+print()
+
+row_comp = f"  {'COMP':>6s}"
+for name, yearly in strategies.items():
+    cap = float(INITIAL)
+    for year in sorted(yearly):
+        for pnl in yearly[year]["pnls"]:
+            cap += cap * pnl
+            cap = max(cap, 10)
+    row_comp += f" | €{cap:>12,.0f}    "
+print(row_comp)
+
+# Drawdown
+row_dd = f"  {'MAXDD':>6s}"
+for name, yearly in strategies.items():
+    cap = float(INITIAL)
+    peak = cap
+    mdd = 0
+    for year in sorted(yearly):
+        for pnl in yearly[year]["pnls"]:
+            cap += cap * pnl
+            cap = max(cap, 10)
+            if cap > peak: peak = cap
+            dd = (peak - cap) / peak
+            mdd = max(mdd, dd)
+    row_dd += f" | {mdd*100:>12.1f}%   "
+print(row_dd)
+
+# Legenda
+print(f"\n  Legenda: ▓ ≥75% acc  ▒ ≥65% acc  ░ ≥55% acc")
+print(f"  ⚠ S2 VRP: IV stimata (rv_long × 1.0-1.2), NON dati reali opzioni")
+print(f"  S1 Squeeze e S2 MultiTF: dati OHLCV reali al 100%")

scripts/s3_01_squeeze_improved.py

@@ -0,0 +1,317 @@
+"""S3-01: Squeeze Migliorato — test per-anno, dati reali.
+Miglioramenti rispetto al squeeze base:
+1. Cross-asset: squeeze su BTC + ETH contemporaneo = segnale più forte
+2. Timing orario: accuracy per fascia oraria
+3. Squeeze duration weighted: squeeze lunghi → breakout più forti
+4. Dual-timeframe: squeeze su 1h confermato da 15m
+5. Anti-fakeout: skip se candela post-breakout ritraccia >50%
+6. Dynamic exit: trailing stop basato su ATR
+"""
+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 atr_calc(high, low, close, period=14):
+    tr = np.maximum(high-low, np.maximum(np.abs(high-np.roll(close,1)), np.abs(low-np.roll(close,1))))
+    tr[0] = high[0]-low[0]
+    r = np.full(len(close), np.nan)
+    r[period-1] = np.mean(tr[:period])
+    k = 2/(period+1)
+    for i in range(period, len(close)):
+        r[i] = tr[i]*k + r[i-1]*(1-k)
+    return r
+
+
+def detect_squeezes(close, high, low, volume, kcr, sq_thr=0.8, min_dur=5):
+    """Ritorna lista di squeeze events con metadata."""
+    events = []
+    in_sq = False
+    sq_start = 0
+    n = len(close)
+
+    for i in range(1, n):
+        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
+            dur = i - sq_start
+            if dur < min_dur:
+                continue
+            avg_vol = np.mean(volume[sq_start:i])
+            # Range durante squeeze
+            sq_range = (np.max(high[sq_start:i]) - np.min(low[sq_start:i])) / close[sq_start] if close[sq_start] > 0 else 0
+            events.append({
+                "release_idx": i,
+                "duration": dur,
+                "avg_vol": avg_vol,
+                "squeeze_range": sq_range,
+                "kcr_at_release": kcr[i],
+            })
+    return events
+
+
+def run_improved_squeeze(primary_asset, tf="1h"):
+    # Carica asset primario
+    df = load_data(primary_asset, 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)
+    ts_ms = df["timestamp"].values
+
+    kcr = keltner_ratio(c, h, l, 14)
+    atr_14 = atr_calc(h, l, c, 14)
+    events = detect_squeezes(c, h, l, v, kcr)
+
+    # Carica asset secondario per cross-check
+    secondary = "BTC" if primary_asset == "ETH" else "ETH"
+    df2 = load_data(secondary, tf)
+    c2, h2, l2 = df2["close"].values, df2["high"].values, df2["low"].values
+    ts2_ms = df2["timestamp"].values
+    kcr2 = keltner_ratio(c2, h2, l2, 14)
+
+    # Mappa ts2 → indici per allineare
+    def find_idx2(ts_val):
+        idx = np.searchsorted(ts2_ms, ts_val)
+        return min(idx, len(c2)-1)
+
+    # Carica 15m per dual-TF
+    if tf == "1h":
+        df_15m = load_data(primary_asset, "15m")
+        c15 = df_15m["close"].values
+        h15 = df_15m["high"].values
+        l15 = df_15m["low"].values
+        ts15 = df_15m["timestamp"].values
+        kcr_15m = keltner_ratio(c15, h15, l15, 14)
+    else:
+        kcr_15m = None
+        ts15 = None
+
+    # ================================================================
+    #  CONFIGURAZIONI
+    # ================================================================
+    configs = [
+        # (name, use_cross, use_timing, use_duration, use_dual_tf, use_antifake, use_trailing, hold, stop_atr)
+        ("BASE",                False, False, False, False, False, False, 3, 0),
+        ("cross_asset",         True,  False, False, False, False, False, 3, 0),
+        ("timing_filter",       False, True,  False, False, False, False, 3, 0),
+        ("long_squeeze",        False, False, True,  False, False, False, 3, 0),
+        ("dual_tf",             False, False, False, True,  False, False, 3, 0),
+        ("anti_fakeout",        False, False, False, False, True,  False, 3, 0),
+        ("trailing_stop",       False, False, False, False, False, True,  6, 1.5),
+        ("cross+timing",        True,  True,  False, False, False, False, 3, 0),
+        ("cross+long+timing",   True,  True,  True,  False, False, False, 3, 0),
+        ("cross+dual_tf",       True,  False, False, True,  False, False, 3, 0),
+        ("ALL_FILTERS",         True,  True,  True,  True,  True,  False, 3, 0),
+        ("ALL+trailing",        True,  True,  True,  True,  True,  True,  6, 1.5),
+        ("cross+antifake",      True,  False, False, False, True,  False, 3, 0),
+        ("timing+antifake",     False, True,  False, False, True,  False, 3, 0),
+        ("cross+timing+antifk", True,  True,  False, False, True,  False, 3, 0),
+        ("cross+timing+trail",  True,  True,  False, False, False, True,  6, 1.5),
+    ]
+
+    print(f"\n{'#'*75}")
+    print(f"  {primary_asset} {tf} — SQUEEZE MIGLIORATO")
+    print(f"{'#'*75}")
+
+    results = []
+
+    for name, f_cross, f_timing, f_dur, f_dual, f_antifake, f_trail, hold, stop_atr_m in configs:
+        yearly = {}
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+
+        for ev in events:
+            i = ev["release_idx"]
+            if i + hold + 2 >= n:
+                continue
+
+            # --- FILTRI ---
+            skip = False
+
+            # Cross-asset: secondary deve anche essere in squeeze recente o breakout
+            if f_cross:
+                i2 = find_idx2(ts_ms[i])
+                if i2 >= 5:
+                    sec_in_squeeze = any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85 for j in range(max(0,i2-10), i2+1))
+                    if not sec_in_squeeze:
+                        skip = True
+
+            # Timing: solo certe ore (testato: 6-14 UTC migliori)
+            if f_timing:
+                hour = ts.iloc[i].hour
+                if hour < 4 or hour > 16:
+                    skip = True
+
+            # Duration: solo squeeze > 10 barre
+            if f_dur:
+                if ev["duration"] < 10:
+                    skip = True
+
+            # Dual-TF: squeeze anche su 15m
+            if f_dual and kcr_15m is not None and ts15 is not None:
+                i15 = np.searchsorted(ts15, ts_ms[i])
+                if i15 >= 5:
+                    sq_15m = any(not np.isnan(kcr_15m[j]) and kcr_15m[j] < 0.85 for j in range(max(0,i15-20), i15+1))
+                    if not sq_15m:
+                        skip = True
+
+            # Anti-fakeout: prima candela post-breakout non deve ritracciare >50%
+            if f_antifake and i + 1 < n:
+                breakout_bar_range = h[i] - l[i]
+                if breakout_bar_range > 0:
+                    if c[i] > c[i-1]:  # breakout up
+                        retrace = (h[i] - c[i]) / breakout_bar_range
+                    else:  # breakout down
+                        retrace = (c[i] - l[i]) / breakout_bar_range
+                    if retrace > 0.6:
+                        skip = True
+
+            if skip:
+                continue
+
+            # --- DIREZIONE ---
+            first_ret = (c[i] - c[i-1]) / c[i-1]
+            if abs(first_ret) < 0.001:
+                continue
+            direction = 1 if first_ret > 0 else -1
+
+            # --- EXIT ---
+            entry = c[i-1]
+            if f_trail and not np.isnan(atr_14[i]):
+                # Trailing stop
+                trail_dist = atr_14[i] * stop_atr_m
+                best_price = entry
+                exit_price = c[min(i+hold, n-1)]
+                for j in range(i, min(i+hold+1, n)):
+                    if direction == 1:
+                        best_price = max(best_price, h[j])
+                        if l[j] <= best_price - trail_dist:
+                            exit_price = best_price - trail_dist
+                            break
+                    else:
+                        best_price = min(best_price, l[j])
+                        if h[j] >= best_price + trail_dist:
+                            exit_price = best_price + trail_dist
+                            break
+                    exit_price = c[j]
+            else:
+                exit_price = c[min(i+hold-1, 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] = {"wins": 0, "total": 0, "pnls": []}
+            yearly[year]["total"] += 1
+            if actual > 0:
+                yearly[year]["wins"] += 1
+            yearly[year]["pnls"].append(net * INITIAL)
+
+        all_t = sum(d["total"] for d in yearly.values())
+        all_w = sum(d["wins"] for d in yearly.values())
+        if all_t < 30:
+            continue
+
+        acc = all_w / all_t * 100
+        all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+        tot_pnl = sum(all_pnls)
+
+        # Worst year
+        worst_y_acc = 100
+        worst_y = ""
+        for y, d in yearly.items():
+            ya = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
+            if ya < worst_y_acc:
+                worst_y_acc = ya
+                worst_y = str(y)
+
+        results.append({
+            "name": name, "trades": all_t, "acc": acc, "pnl": tot_pnl,
+            "max_dd": max_dd*100, "capital": capital,
+            "worst": f"{worst_y}({worst_y_acc:.0f}%)",
+            "yearly": yearly,
+        })
+
+    # Sort by accuracy
+    results.sort(key=lambda x: x["acc"], reverse=True)
+
+    print(f"\n  {'Name':.<26s} {'Trades':>7s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>6s} {'Capital':>10s} {'Worst':>12s}")
+    print(f"  {'-'*80}")
+    for r in results:
+        tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 76 else ""
+        print(f"  {r['name']:.<26s} {r['trades']:>7d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['max_dd']:>5.1f}% €{r['capital']:>9,.0f} {r['worst']:>12s} {tag}")
+
+    # Dettaglio per anno del migliore
+    if results:
+        best = results[0]
+        print(f"\n  MIGLIORE: {best['name']} → {best['acc']:.1f}% acc")
+        print(f"  {'Anno':>6s} {'Trades':>7s} {'Acc':>6s} {'PnL€':>9s}")
+        for y in sorted(best["yearly"]):
+            d = best["yearly"][y]
+            ya = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
+            yp = sum(d["pnls"])
+            tag = " ← CRASH" if y in [2020,2021,2022] else ""
+            print(f"  {y:>6d} {d['total']:>7d} {ya:>5.1f}% €{yp:>+8.0f}{tag}")
+
+    return results
+
+
+# Run su entrambi gli asset e timeframe
+all_results = {}
+for asset in ["ETH", "BTC"]:
+    for tf in ["1h", "15m"]:
+        key = f"{asset}_{tf}"
+        all_results[key] = run_improved_squeeze(asset, tf)
+
+# Classifica globale
+print(f"\n\n{'='*75}")
+print(f"  CLASSIFICA GLOBALE — TOP 15")
+print(f"{'='*75}")
+
+global_list = []
+for key, results in all_results.items():
+    for r in results:
+        global_list.append({**r, "asset_tf": key})
+
+global_list.sort(key=lambda x: x["acc"], reverse=True)
+print(f"\n  {'Asset_TF':.<12s} {'Name':.<26s} {'Trades':>6s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>5s} {'Worst':>12s}")
+for r in global_list[:15]:
+    tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 76 else ""
+    print(f"  {r['asset_tf']:.<12s} {r['name']:.<26s} {r['trades']:>6d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['max_dd']:>4.1f}% {r['worst']:>12s} {tag}")

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}")

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}")

src/live/paper_trader.py

@@ -10,6 +10,7 @@ import pandas as pd
 
 from src.live.cerbero_client import CerberoClient
 from src.live.signal_engine import SignalEngine
+from src.live.telegram_notifier import notify_event
 
 LOG_DIR = Path(__file__).resolve().parents[2] / "data" / "paper_trades"
 INSTRUMENT = "ETH_USDC-PERPETUAL"
@@ -52,6 +53,7 @@ class PaperTrader:
         with open(self.log_path, "a") as f:
             f.write(json.dumps(entry) + "\n")
         print(f"  [{entry['timestamp'][:19]}] {event}: {json.dumps(data or {})}")
+        notify_event(event, data)
 
     def save_status(self):
         status = {

src/live/telegram_notifier.py

@@ -0,0 +1,39 @@
+"""Notifiche Telegram per il paper trader."""
+from __future__ import annotations
+
+import os
+import urllib.request
+import urllib.parse
+import json
+
+BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "")
+
+NOTIFY_EVENTS = {
+    "SIGNAL", "OPENED", "CLOSED", "OPEN_FAILED", "CLOSE_FAILED",
+    "ERROR", "STARTUP", "SHUTDOWN", "TRAINING_FAILED",
+}
+
+
+def send_telegram(text: str) -> bool:
+    if not BOT_TOKEN or not CHAT_ID:
+        return False
+    try:
+        url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
+        data = urllib.parse.urlencode({"chat_id": CHAT_ID, "text": text, "parse_mode": "HTML"}).encode()
+        urllib.request.urlopen(url, data, timeout=10)
+        return True
+    except Exception:
+        return False
+
+
+def notify_event(event: str, data: dict | None = None):
+    if event not in NOTIFY_EVENTS:
+        return
+    lines = [f"📊 <b>{event}</b>"]
+    if data:
+        for k, v in data.items():
+            if k in ("signal",):
+                continue
+            lines.append(f"  {k}: {v}")
+    send_telegram("\n".join(lines))

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