chore: add uv.lock

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

scripts/s2_01_mean_reversion_hourly.py

@@ -0,0 +1,160 @@
+"""S2-01: Mean Reversion oraria con filtro orario.
+Idea: crypto ha bias di ritorno alla media nelle ore notturne (00-06 UTC)
+e di momentum nelle ore diurne USA (14-20 UTC).
+- Compra quando RSI < 30 in ore notturne
+- Vendi quando RSI > 70 in ore notturne
+- Hold max 4h, stop loss 1.5%
+Timeframe: 1h. Ingresso quasi giornaliero.
+"""
+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 = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def rsi(close: np.ndarray, period: int = 14) -> np.ndarray:
+    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)
+    avg_gain = np.mean(gain[:period])
+    avg_loss = np.mean(loss[:period])
+    for i in range(period, len(delta)):
+        avg_gain = (avg_gain * (period - 1) + gain[i]) / period
+        avg_loss = (avg_loss * (period - 1) + loss[i]) / period
+        if avg_loss == 0:
+            result[i + 1] = 100
+        else:
+            rs = avg_gain / avg_loss
+            result[i + 1] = 100 - 100 / (1 + rs)
+    return result
+
+
+def bollinger_pct(close: np.ndarray, window: int = 20) -> np.ndarray:
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(close)):
+        w = close[i - window : i]
+        ma = np.mean(w)
+        std = np.std(w)
+        if std > 0:
+            result[i] = (close[i] - (ma - 2 * std)) / (4 * std)
+    return result
+
+
+def run_mean_reversion(asset, tf="1h"):
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(df)
+
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    rsi_vals = rsi(close, 14)
+    bb_pct = bollinger_pct(close, 20)
+
+    split = int(n * 0.7)
+
+    configs = [
+        # (rsi_low, rsi_high, allowed_hours, hold_max, stop_pct, name)
+        (25, 75, list(range(0, 7)), 4, 0.015, "night_0-6_rsi25"),
+        (30, 70, list(range(0, 7)), 4, 0.015, "night_0-6_rsi30"),
+        (25, 75, list(range(0, 10)), 6, 0.02, "extended_0-9"),
+        (30, 70, list(range(20, 24)) + list(range(0, 6)), 4, 0.015, "late_night"),
+        (20, 80, list(range(0, 24)), 4, 0.015, "all_hours_rsi20"),
+        # Bollinger band mean reversion
+    ]
+
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — MEAN REVERSION")
+    print(f"{'#'*60}")
+
+    for rsi_low, rsi_high, allowed, hold_max, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 20), n - hold_max):
+            hour = hours[i]
+            if hour not in allowed:
+                continue
+
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 2:
+                continue
+
+            direction = None
+            if rsi_vals[i] < rsi_low and bb_pct[i] < 0.2:
+                direction = "long"
+            elif rsi_vals[i] > rsi_high and bb_pct[i] > 0.8:
+                direction = "short"
+
+            if direction is None:
+                continue
+
+            entry = close[i]
+            best_exit = i + 1
+            for j in range(i + 1, min(i + hold_max + 1, n)):
+                price = close[j]
+                if direction == "long":
+                    pnl_pct = (price - entry) / entry
+                    if pnl_pct <= -stop:
+                        best_exit = j
+                        break
+                    if pnl_pct >= stop * 1.5:
+                        best_exit = j
+                        break
+                else:
+                    pnl_pct = (entry - price) / entry
+                    if pnl_pct <= -stop:
+                        best_exit = j
+                        break
+                    if pnl_pct >= stop * 1.5:
+                        best_exit = j
+                        break
+                best_exit = j
+
+            exit_price = close[best_exit]
+            if direction == "long":
+                trade_ret = (exit_price - entry) / entry
+            else:
+                trade_ret = (entry - exit_price) / entry
+
+            net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.15 * net
+            capital = max(capital, 0)
+
+            is_correct = trade_ret > 0
+            total += 1
+            if is_correct:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_with_trades = len(daily_trades)
+        trades_per_day = total / days_with_trades if days_with_trades > 0 else 0
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:25s}: trades={total:5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd_est ~{abs(min(0, ret/3)):.0f}% €/day={dpnl:.2f} days_active={days_with_trades} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_mean_reversion(asset, "1h")
+    run_mean_reversion(asset, "15m")

scripts/s2_02_funding_rate.py

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+"""S2-02: Funding Rate Strategy.
+Quando il funding rate è molto positivo → troppi long → short il perpetual.
+Quando molto negativo → troppi short → long il perpetual.
+Si cattura sia il mean reversion del prezzo che il funding rate stesso.
+Ingresso: quando funding > 0.03% o < -0.03% (8h rate).
+"""
+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 = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def simulate_funding_strategy(asset):
+    """Simula funding rate strategy usando il proxy: overnight returns.
+    Crypto funding settlement ogni 8h → prezzo tende a correggersi dopo settlement.
+    Proxy: se ultime 8h hanno avuto forte trend, aspettati reversal dopo settlement.
+    """
+    print(f"\n{'#'*60}")
+    print(f"  {asset} — FUNDING RATE PROXY STRATEGY")
+    print(f"{'#'*60}")
+
+    df_1h = load_data(asset, "1h")
+    close = df_1h["close"].values
+    volume = df_1h["volume"].values
+    n = len(close)
+    split = int(n * 0.7)
+
+    timestamps = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    # Funding settlement su Deribit: 00:00, 08:00, 16:00 UTC
+    settlement_hours = {0, 8, 16}
+
+    configs = [
+        (0.01, 0.02, 8, 0.02, "mild_1pct"),
+        (0.015, 0.025, 8, 0.015, "moderate_1.5pct"),
+        (0.02, 0.03, 8, 0.015, "strong_2pct"),
+        (0.01, 0.015, 4, 0.01, "fast_1pct_4h"),
+        (0.02, 0.03, 12, 0.02, "slow_2pct_12h"),
+        (0.025, 0.04, 6, 0.015, "extreme_2.5pct"),
+    ]
+
+    for entry_thr, tp_mult_unused, hold_max, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 8), n - hold_max):
+            hour = hours[i]
+            if hour not in settlement_hours:
+                continue
+
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            # 8h return prima del settlement = proxy per funding pressure
+            ret_8h = (close[i] - close[i - 8]) / close[i - 8]
+
+            # Volume spike = conferma
+            vol_avg = np.mean(volume[max(0, i - 48) : i])
+            vol_recent = np.mean(volume[i - 8 : i])
+            vol_spike = vol_recent / vol_avg if vol_avg > 0 else 1
+
+            direction = None
+            if ret_8h > entry_thr and vol_spike > 1.1:
+                direction = "short"  # troppi long, attendi reversal
+            elif ret_8h < -entry_thr and vol_spike > 1.1:
+                direction = "long"  # troppi short, attendi rimbalzo
+
+            if direction is None:
+                continue
+
+            entry_price = close[i]
+            for j in range(i + 1, min(i + hold_max + 1, n)):
+                price = close[j]
+                if direction == "long":
+                    pnl_pct = (price - entry_price) / entry_price
+                else:
+                    pnl_pct = (entry_price - price) / entry_price
+
+                if pnl_pct <= -stop or pnl_pct >= stop * 2 or j == min(i + hold_max, n - 1):
+                    exit_price = price
+                    break
+            else:
+                exit_price = close[min(i + hold_max, n - 1)]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry_price) / entry_price
+            else:
+                trade_ret = (entry_price - exit_price) / entry_price
+
+            # Add funding rate income (approx 0.01% per 8h period if direction correct)
+            funding_income = 0.0001 * (hold_max / 8) if trade_ret > 0 else 0
+
+            net = (trade_ret + funding_income) * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.2 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 10:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active_days={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    simulate_funding_strategy(asset)

scripts/s2_03_vol_selling.py

@@ -0,0 +1,145 @@
+"""S2-03: Volatility Selling — Straddle/Strangle corto simulato.
+La IV crypto è cronicamente sopra la realized vol → vendere premium è profittevole.
+Simulazione: vendi straddle ATM → profitto = max(0, premium - |move|).
+Premium stimato da IV storica. Ingresso giornaliero.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def realized_vol(close: np.ndarray, window: int = 24) -> np.ndarray:
+    """Annualized realized volatility rolling."""
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        rv = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+        result[i + 1] = rv
+    return result
+
+
+def implied_vol_proxy(close: np.ndarray, window: int = 48) -> np.ndarray:
+    """IV proxy: realized vol * premium factor.
+    Storicamente IV crypto ≈ 1.2-1.5x realized vol (variance risk premium).
+    """
+    rv = realized_vol(close, window)
+    # Premium factor varia: alto in panic, basso in calma
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(close)):
+        short_rv = realized_vol(close[max(0, i-12):i+1], min(12, i))[-1] if i >= 12 else rv[i]
+        if rv[i] > 0:
+            regime = short_rv / rv[i]
+            premium = 1.15 + 0.3 * max(0, regime - 1)  # più alto in regime volatile
+        else:
+            premium = 1.2
+        result[i] = rv[i] * premium
+    return result
+
+
+def bs_straddle_price(spot: float, iv: float, dte_hours: float) -> float:
+    """Black-Scholes straddle price (call + put ATM)."""
+    if dte_hours <= 0 or iv <= 0:
+        return 0
+    t = dte_hours / (24 * 365)
+    d1 = (0.5 * iv * iv * t) / (iv * np.sqrt(t))
+    call = spot * (2 * norm.cdf(d1) - 1)
+    return call * 2  # straddle = 2 * ATM call (approx for ATM)
+
+
+def run_vol_selling(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} — VOLATILITY SELLING (SHORT STRADDLE)")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv = realized_vol(close, 24)
+    iv_proxy = implied_vol_proxy(close)
+
+    configs = [
+        # (dte_hours, iv_floor, iv_rv_ratio_min, position_pct, name)
+        (24, 0.3, 1.15, 0.1, "daily_24h"),
+        (12, 0.3, 1.15, 0.08, "half_day_12h"),
+        (48, 0.3, 1.10, 0.12, "2day_48h"),
+        (24, 0.4, 1.20, 0.1, "daily_highIV"),
+        (8, 0.25, 1.10, 0.06, "ultra_short_8h"),
+        (24, 0.3, 1.30, 0.15, "daily_bigPremium"),
+    ]
+
+    for dte, iv_floor, ratio_min, pos_pct, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 50), n - dte):
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            hour = timestamps[i].dt.hour if hasattr(timestamps[i], 'dt') else timestamps.iloc[i].hour
+            if hour != 8:  # entrata alle 08 UTC ogni giorno
+                continue
+
+            current_iv = iv_proxy[i]
+            current_rv = rv[i]
+
+            if current_iv < iv_floor:
+                continue
+            if current_rv > 0 and current_iv / current_rv < ratio_min:
+                continue
+
+            spot = close[i]
+            premium = bs_straddle_price(spot, current_iv, dte)
+            premium_pct = premium / spot
+
+            # Actual move during holding period
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot)
+            actual_move_pct = actual_move / spot
+
+            # P&L: premium received - actual move (capped at max loss)
+            max_loss = spot * 0.05  # cap loss at 5% of spot
+            pnl = premium - min(actual_move, max_loss + premium)
+
+            pnl_on_capital = pnl / spot * pos_pct
+            fee_cost = FEE * 4 * pos_pct  # 4 legs: sell call, sell put, buy back
+            net_pnl = pnl_on_capital - fee_cost
+
+            capital += capital * net_pnl
+            capital = max(capital, 0)
+
+            total += 1
+            if pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_vol_selling(asset)

scripts/s2_04_momentum_microstructure.py

@@ -0,0 +1,159 @@
+"""S2-04: Momentum microstructure su 5m.
+Approccio: cattura micro-trend intraday.
+- Identifica breakout da consolidamento su 5m
+- Conferma con volume e acceleration
+- Hold breve (15-30 min), stop stretto
+- Target: molti piccoli guadagni, alta frequenza
+"""
+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 = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def ema(arr: np.ndarray, period: int) -> np.ndarray:
+    result = np.full(len(arr), np.nan)
+    k = 2 / (period + 1)
+    result[period - 1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        result[i] = arr[i] * k + result[i - 1] * (1 - k)
+    return result
+
+
+def atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, period: int = 14) -> np.ndarray:
+    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]
+    return ema(tr, period)
+
+
+def run_momentum(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} 5m — MOMENTUM MICROSTRUCTURE")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "5m")
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(close)
+    split = int(n * 0.7)
+
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    ema_fast = ema(close, 8)
+    ema_slow = ema(close, 21)
+    ema_trend = ema(close, 55)
+    atr_vals = atr(high, low, close, 14)
+
+    configs = [
+        # (consolidation_bars, breakout_atr_mult, hold_bars, stop_atr, tp_atr, min_vol_mult, name)
+        (12, 1.5, 3, 1.0, 2.0, 1.3, "tight_12bar"),
+        (12, 1.5, 6, 1.5, 2.5, 1.2, "medium_12bar"),
+        (24, 2.0, 6, 1.5, 3.0, 1.5, "wide_24bar"),
+        (6, 1.2, 3, 1.0, 1.5, 1.1, "fast_6bar"),
+        (12, 1.5, 3, 0.8, 2.0, 1.3, "tight_stop"),
+        (18, 1.8, 4, 1.2, 2.5, 1.4, "balanced_18bar"),
+    ]
+
+    for consol_bars, brk_mult, hold_bars, stop_m, tp_m, vol_mult, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 60), n - hold_bars):
+            if np.isnan(ema_fast[i]) or np.isnan(ema_slow[i]) or np.isnan(atr_vals[i]) or atr_vals[i] == 0:
+                continue
+
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 5:
+                continue
+
+            # Consolidation: range delle ultime N barre < 1.5 ATR
+            consol_range = np.max(high[i - consol_bars : i]) - np.min(low[i - consol_bars : i])
+            if consol_range > 1.5 * atr_vals[i]:
+                continue
+
+            # Breakout: current bar breaks consolidation range
+            consol_high = np.max(high[i - consol_bars : i])
+            consol_low = np.min(low[i - consol_bars : i])
+
+            breakout_up = close[i] > consol_high + atr_vals[i] * (brk_mult - 1)
+            breakout_down = close[i] < consol_low - atr_vals[i] * (brk_mult - 1)
+
+            if not (breakout_up or breakout_down):
+                continue
+
+            # Volume confirmation
+            vol_avg = np.mean(volume[max(0, i - 24) : i])
+            if vol_avg > 0 and volume[i] < vol_avg * vol_mult:
+                continue
+
+            # Trend filter: only trade in direction of trend
+            if breakout_up and close[i] < ema_trend[i]:
+                continue
+            if breakout_down and close[i] > ema_trend[i]:
+                continue
+
+            direction = "long" if breakout_up else "short"
+            entry = close[i]
+            stop_price = entry - atr_vals[i] * stop_m if direction == "long" else entry + atr_vals[i] * stop_m
+            tp_price = entry + atr_vals[i] * tp_m if direction == "long" else entry - atr_vals[i] * tp_m
+
+            exit_price = close[min(i + hold_bars, n - 1)]
+            for j in range(i + 1, min(i + hold_bars + 1, n)):
+                if direction == "long":
+                    if low[j] <= stop_price:
+                        exit_price = stop_price
+                        break
+                    if high[j] >= tp_price:
+                        exit_price = tp_price
+                        break
+                else:
+                    if high[j] >= stop_price:
+                        exit_price = stop_price
+                        break
+                    if low[j] <= tp_price:
+                        exit_price = tp_price
+                        break
+                exit_price = close[j]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry) / entry
+            else:
+                trade_ret = (entry - exit_price) / entry
+
+            net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.1 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 30:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / (24 * 12)
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 55 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} t/day={total/days_active:.1f} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_momentum(asset)

scripts/s2_05_gap_fade.py

@@ -0,0 +1,132 @@
+"""S2-05: Gap fade + overnight reversal.
+Crypto non ha gap di apertura classici, ma ha "gap di sessione":
+- Asia open (00 UTC): tende a continuare il trend USA precedente
+- EU open (07 UTC): spesso corregge eccessi notturni
+- USA open (13-14 UTC): alta volatilità, breakout o reversal
+
+Strategia: fai fade dell'overextension al cambio sessione.
+Se il prezzo ha fatto >1.5% nella sessione precedente, aspettati reversal.
+"""
+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 = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def run_gap_fade(asset, tf="1h"):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — GAP FADE / SESSION REVERSAL")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    session_opens = {
+        "asia": 0,
+        "eu": 7,
+        "usa": 14,
+    }
+
+    configs = [
+        # (session_name, lookback_hours, entry_thr, hold_hours, stop_pct, name)
+        ("eu", 7, 0.015, 4, 0.012, "eu_fade_1.5pct"),
+        ("eu", 7, 0.02, 4, 0.015, "eu_fade_2pct"),
+        ("eu", 7, 0.01, 6, 0.01, "eu_fade_1pct_6h"),
+        ("usa", 7, 0.015, 4, 0.012, "usa_fade_1.5pct"),
+        ("usa", 7, 0.02, 4, 0.015, "usa_fade_2pct"),
+        ("asia", 8, 0.02, 6, 0.015, "asia_fade_2pct"),
+        ("eu", 7, 0.025, 3, 0.015, "eu_fade_2.5pct_fast"),
+        ("usa", 6, 0.015, 3, 0.01, "usa_fade_fast"),
+    ]
+
+    for session, lookback, entry_thr, hold, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        session_hour = session_opens[session]
+
+        for i in range(max(split, lookback + 1), n - hold):
+            if hours[i] != session_hour:
+                continue
+
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            prev_ret = (close[i] - close[i - lookback]) / close[i - lookback]
+
+            direction = None
+            if prev_ret > entry_thr:
+                direction = "short"  # fade the rally
+            elif prev_ret < -entry_thr:
+                direction = "long"  # fade the dump
+
+            if direction is None:
+                continue
+
+            entry = close[i]
+            exit_price = close[min(i + hold, n - 1)]
+
+            for j in range(i + 1, min(i + hold + 1, n)):
+                if direction == "long":
+                    if (close[j] - entry) / entry >= stop * 2:
+                        exit_price = close[j]
+                        break
+                    if (entry - close[j]) / entry >= stop:
+                        exit_price = close[j]
+                        break
+                else:
+                    if (entry - close[j]) / entry >= stop * 2:
+                        exit_price = close[j]
+                        break
+                    if (close[j] - entry) / entry >= stop:
+                        exit_price = close[j]
+                        break
+                exit_price = close[j]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry) / entry
+            else:
+                trade_ret = (entry - exit_price) / entry
+
+            net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.2 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 15:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 58 and ann >= 30 else ""
+        print(f"  {name:25s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_gap_fade(asset)

scripts/s2_06_iron_condor.py

@@ -0,0 +1,164 @@
+"""S2-06: Iron Condor simulato + Variance Risk Premium harvesting.
+Vendi un range: se il prezzo sta dentro il range a scadenza → profitto.
+Più sofisticato del vol selling puro:
+- Calcolo IV vs RV (variance risk premium)
+- Selezione larghezza condor in base a IV/RV ratio
+- Dynamic position sizing: più capital quando IV/RV ratio è alto
+- Ingresso giornaliero, scadenze 24h e 48h
+- Include: tail risk protection (chiudi se move > 2 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 = 0.001
+INITIAL = 1000
+
+
+def realized_vol_ann(close: np.ndarray, window: int) -> np.ndarray:
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+    return result
+
+
+def run_iron_condor(asset, tf="1h"):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — IRON CONDOR / VARIANCE PREMIUM")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = realized_vol_ann(close, 24)
+    rv_48 = realized_vol_ann(close, 48)
+    rv_168 = realized_vol_ann(close, 168)  # 1 week
+
+    IV_PREMIUM = 1.25  # IV typically 1.2-1.3x RV in crypto
+
+    configs = [
+        # (dte_hours, condor_width_mult, max_loss_pct, vrp_min, pos_pct, name)
+        (24, 1.0, 0.03, 1.10, 0.15, "24h_1x_std"),
+        (24, 1.5, 0.04, 1.10, 0.12, "24h_1.5x_safe"),
+        (24, 0.8, 0.025, 1.15, 0.18, "24h_0.8x_aggr"),
+        (48, 1.0, 0.035, 1.10, 0.15, "48h_1x_std"),
+        (48, 1.5, 0.05, 1.10, 0.12, "48h_1.5x_safe"),
+        (48, 0.7, 0.025, 1.20, 0.20, "48h_0.7x_highVRP"),
+        (72, 1.2, 0.04, 1.10, 0.12, "72h_1.2x"),
+        (24, 1.0, 0.03, 1.30, 0.20, "24h_veryHighVRP"),
+        (24, 1.2, 0.035, 1.10, 0.15, "24h_1.2x_balanced"),
+    ]
+
+    for dte, width_mult, max_loss, vrp_min, pos_pct, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+        max_dd = 0
+        peak = capital
+
+        for i in range(max(split, 170), n - dte):
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            hour = timestamps.iloc[i].hour
+            if hour != 8:
+                continue
+
+            rv_short = rv_24[i]
+            rv_long = rv_168[i]
+
+            if rv_short <= 0 or rv_long <= 0:
+                continue
+
+            iv_est = rv_long * IV_PREMIUM
+            vrp_ratio = iv_est / rv_short
+
+            if vrp_ratio < vrp_min:
+                continue
+
+            spot = close[i]
+            t_years = dte / (24 * 365)
+
+            # Condor range: spot ± width * daily_std * sqrt(t)
+            daily_std = rv_short / np.sqrt(365)
+            range_width = width_mult * daily_std * np.sqrt(dte / 24) * spot
+
+            upper_strike = spot + range_width
+            lower_strike = spot - range_width
+
+            # Premium collected (simplified BS for condor)
+            # Premium ≈ IV * sqrt(t) * (width factor)
+            premium_pct = iv_est * np.sqrt(t_years) * 0.4 * (1 / width_mult)
+
+            # Check if price stays in range
+            exit_idx = min(i + dte, n - 1)
+            price_path = close[i : exit_idx + 1]
+            max_move = max(np.max(price_path) - spot, spot - np.min(price_path))
+            final_price = close[exit_idx]
+
+            in_range = lower_strike <= final_price <= upper_strike
+            breached_hard = max_move > spot * max_loss
+
+            if breached_hard:
+                pnl_pct = -max_loss * pos_pct
+            elif in_range:
+                pnl_pct = premium_pct * pos_pct
+            else:
+                # Partial loss: exceeded range but not catastrophic
+                excess = max(0, final_price - upper_strike, lower_strike - final_price)
+                loss = min(excess / spot, max_loss)
+                pnl_pct = (premium_pct - loss) * pos_pct
+
+            fee_cost = FEE * 2 * pos_pct
+            net_pnl = pnl_pct - fee_cost
+
+            capital += capital * net_pnl
+            capital = max(capital, 0)
+
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak if peak > 0 else 0
+            max_dd = max(max_dd, dd)
+
+            total += 1
+            if net_pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅✅" if acc >= 70 and ann >= 50 else "✅" if acc >= 65 and ann >= 30 else ""
+        print(f"  {name:22s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_iron_condor(asset)
+
+# === COMBINAZIONE: Iron Condor + Funding + Gap Fade ===
+print(f"\n{'#'*60}")
+print(f"  COMBINAZIONE: MULTI-STRATEGY PORTFOLIO")
+print(f"{'#'*60}")
+
+# Simula portafoglio: 50% iron condor ETH, 25% iron condor BTC, 25% gap fade ETH
+print("  (Dettagli nel prossimo script con backtest combinato)")

scripts/s2_07_vol_premium_refined.py

@@ -0,0 +1,252 @@
+"""S2-07: Variance Risk Premium harvesting — versione raffinata.
+Ottimizzazione del vol selling con:
+1. IV/RV ratio dinamico per entry timing
+2. Tail risk cutoff (chiudi se move > N sigma)
+3. Position sizing proporzionale al premium
+4. Combinazione con directional bias (da gap fade)
+5. Multi-asset portfolio (ETH + BTC)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def realized_vol(close, window=24):
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+    return result
+
+
+def run_vrp(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} 1h — VARIANCE RISK PREMIUM REFINED")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = realized_vol(close, 24)
+    rv_48 = realized_vol(close, 48)
+    rv_168 = realized_vol(close, 168)
+
+    configs = [
+        # (dte_h, iv_mult, cutoff_sigma, pos_base, entry_hour, dynamic_sizing, name)
+        (24, 1.20, 2.5, 0.10, 8, False, "24h_base"),
+        (24, 1.25, 2.5, 0.12, 8, False, "24h_highPrem"),
+        (24, 1.20, 2.0, 0.10, 8, False, "24h_tightCut"),
+        (24, 1.20, 3.0, 0.12, 8, False, "24h_wideCut"),
+        (48, 1.20, 2.5, 0.12, 8, False, "48h_base"),
+        (48, 1.25, 2.5, 0.15, 8, False, "48h_highPrem"),
+        (48, 1.30, 2.5, 0.15, 8, False, "48h_vhighPrem"),
+        (48, 1.20, 3.0, 0.15, 8, False, "48h_wideCut"),
+        (24, 1.20, 2.5, 0.10, 8, True, "24h_dynSize"),
+        (48, 1.20, 2.5, 0.12, 8, True, "48h_dynSize"),
+        (24, 1.20, 2.5, 0.10, 0, False, "24h_midnight"),
+        (24, 1.20, 2.5, 0.10, 16, False, "24h_afternoon"),
+        (36, 1.22, 2.5, 0.12, 8, False, "36h_medium"),
+        (24, 1.15, 2.5, 0.08, 8, False, "24h_lowPrem_safe"),
+        (48, 1.20, 2.0, 0.10, 8, True, "48h_tight_dyn"),
+    ]
+
+    for dte, iv_mult, cutoff, pos_base, entry_h, dyn_size, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+        peak_capital = capital
+        max_dd = 0
+
+        for i in range(max(split, 170), n - dte):
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            if timestamps.iloc[i].hour != entry_h:
+                continue
+
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if rv_s <= 0.05 or rv_l <= 0.05:
+                continue
+
+            iv_est = rv_l * iv_mult
+            vrp = iv_est - rv_s
+
+            if vrp <= 0:
+                continue
+
+            spot = close[i]
+            t = dte / (24 * 365)
+            daily_std = rv_s / np.sqrt(365)
+
+            # Premium = IV * sqrt(t) * spot * factor
+            premium = iv_est * np.sqrt(t) * spot * 0.4
+            premium_pct = premium / spot
+
+            # Expected move based on IV
+            expected_move = iv_est * np.sqrt(t) * spot
+
+            # Cutoff: close if actual move > cutoff * expected_move
+            max_allowed_move = expected_move * cutoff
+
+            # Dynamic sizing: more when VRP is high
+            if dyn_size:
+                vrp_ratio = vrp / rv_s
+                pos_pct = min(pos_base * (1 + vrp_ratio), pos_base * 2)
+            else:
+                pos_pct = pos_base
+
+            # Check actual path
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot)
+
+            # Early exit: check if intra-period move exceeds cutoff
+            breached = False
+            for j in range(i + 1, exit_idx + 1):
+                intra_move = abs(close[j] - spot)
+                if intra_move > max_allowed_move:
+                    breached = True
+                    exit_idx = j
+                    actual_move = intra_move
+                    break
+
+            if breached:
+                loss = min(actual_move / spot, 0.05) * pos_pct
+                pnl = -loss
+            else:
+                profit = premium_pct * pos_pct
+                partial_loss = max(0, actual_move / spot - premium_pct) * pos_pct * 0.5
+                pnl = profit - partial_loss
+
+            fee_cost = FEE * 2 * pos_pct
+            net = pnl - fee_cost
+
+            capital += capital * net
+            capital = max(capital, 0)
+
+            if capital > peak_capital:
+                peak_capital = capital
+            dd = (peak_capital - capital) / peak_capital if peak_capital > 0 else 0
+            max_dd = max(max_dd, dd)
+
+            total += 1
+            if pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅✅" if acc >= 70 and ann >= 50 else "✅" if acc >= 65 and ann >= 30 else ""
+        print(f"  {name:22s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+    return daily_trades
+
+
+# Run both assets
+results = {}
+for asset in ["ETH", "BTC"]:
+    results[asset] = run_vrp(asset)
+
+# Multi-asset portfolio simulation
+print(f"\n{'#'*60}")
+print(f"  MULTI-ASSET PORTFOLIO: ETH + BTC")
+print(f"{'#'*60}")
+
+df_eth = load_data("ETH", "1h")
+df_btc = load_data("BTC", "1h")
+close_eth = df_eth["close"].values
+close_btc = df_btc["close"].values
+n = min(len(close_eth), len(close_btc))
+split = int(n * 0.7)
+ts = pd.to_datetime(df_eth["timestamp"].values[:n], unit="ms", utc=True)
+
+rv_eth = realized_vol(close_eth[:n], 168)
+rv_btc = realized_vol(close_btc[:n], 168)
+
+capital = float(INITIAL)
+total = 0
+correct = 0
+peak = capital
+max_dd = 0
+daily_trades = {}
+
+for i in range(max(split, 170), n - 48):
+    day = ts[i].strftime("%Y-%m-%d")
+    if daily_trades.get(day, 0) >= 1:
+        continue
+    if ts[i].hour != 8:
+        continue
+
+    for asset_close, rv_arr, name in [(close_eth[:n], rv_eth, "ETH"), (close_btc[:n], rv_btc, "BTC")]:
+        rv = rv_arr[i]
+        if rv <= 0.05:
+            continue
+        iv = rv * 1.22
+        spot = asset_close[i]
+        t = 48 / (24 * 365)
+        premium_pct = iv * np.sqrt(t) * 0.4
+        expected_move = iv * np.sqrt(t) * spot
+        max_move = expected_move * 2.5
+
+        exit_idx = min(i + 48, n - 1)
+        actual_move = abs(asset_close[exit_idx] - spot)
+
+        breached = False
+        for j in range(i + 1, exit_idx + 1):
+            if abs(asset_close[j] - spot) > max_move:
+                breached = True
+                actual_move = abs(asset_close[j] - spot)
+                break
+
+        pos_pct = 0.07  # 7% per asset = 14% total
+        if breached:
+            pnl = -min(actual_move / spot, 0.05) * pos_pct
+        else:
+            profit = premium_pct * pos_pct
+            partial = max(0, actual_move / spot - premium_pct) * pos_pct * 0.5
+            pnl = profit - partial
+
+        capital += capital * (pnl - FEE * 2 * pos_pct)
+        capital = max(capital, 0)
+        total += 1
+        if pnl > 0:
+            correct += 1
+
+    if capital > peak:
+        peak = capital
+    dd = (peak - capital) / peak if peak > 0 else 0
+    max_dd = max(max_dd, dd)
+    daily_trades[day] = daily_trades.get(day, 0) + 1
+
+if total > 0:
+    acc = correct / total * 100
+    ret = (capital - INITIAL) / INITIAL * 100
+    test_days = (n - split) / 24
+    test_years = test_days / 365.25
+    ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+    dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+    print(f"\n  ETH+BTC 48h portfolio: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={len(daily_trades)}")

scripts/s2_08_vrp_honest.py

@@ -0,0 +1,245 @@
+"""S2-08: VRP Honest Test.
+Problemi del test precedente:
+1. IV stimata con moltiplicatore fisso → troppo ottimista
+2. Nessun stress test su crash
+3. Nessun costo di margin
+4. Walk-forward mancante
+
+Fix:
+- IV calcolata come rolling ratio IV/RV da dati DVOL reali (90 giorni)
+  e applicata storicamente con variabilità
+- Stress test esplicito su periodi di crisi
+- Margin requirement: 5% del notional bloccato
+- Walk-forward: retrain IV/RV ratio ogni 30 giorni
+- Fee realistiche: 0.05% maker + 0.05% taker per gamba = 0.2% roundtrip straddle
+- Slippage: 0.1% per esecuzione
+"""
+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
+
+# Costi REALISTICI Deribit options
+FEE_PER_LEG = 0.0003  # 0.03% per leg (Deribit option fee)
+SLIPPAGE = 0.001  # 0.1% bid-ask spread per leg
+TOTAL_COST_ROUNDTRIP = (FEE_PER_LEG + SLIPPAGE) * 4  # 4 legs: sell call, sell put, buy back both
+MARGIN_REQUIREMENT = 0.05  # 5% del notional bloccato come margine
+INITIAL = 1000
+
+
+def realized_vol_ann(close, window):
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), np.nan)
+    for i in range(window, len(log_ret)):
+        result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+    return result
+
+
+def iv_estimate_realistic(rv_short, rv_long, regime_vol):
+    """Stima IV realistica basata su regime.
+    In calma: IV ≈ 1.1-1.2x RV
+    In stress: IV ≈ 0.8-1.0x RV (perché RV è già esplosa ma IV non tiene il passo)
+    Post-crash: IV ≈ 1.5-2.0x RV (IV elevata, RV sta scendendo)
+    """
+    if rv_short <= 0 or rv_long <= 0:
+        return rv_long * 1.1 if rv_long > 0 else 0.5
+
+    # Regime detection
+    regime_ratio = rv_short / rv_long
+
+    if regime_ratio > 2.0:
+        # CRASH in corso: RV short term esplosa, IV non scala altrettanto
+        premium = 0.85 + np.random.normal(0, 0.05)
+    elif regime_ratio > 1.3:
+        # Alta volatilità: premium compresso
+        premium = 1.0 + np.random.normal(0, 0.05)
+    elif regime_ratio < 0.7:
+        # Post-crash calma: IV ancora alta, RV scesa
+        premium = 1.3 + np.random.normal(0, 0.1)
+    else:
+        # Normale: premium standard
+        premium = 1.15 + np.random.normal(0, 0.08)
+
+    premium = max(0.7, min(premium, 1.8))  # clamp
+    return rv_long * premium
+
+
+def straddle_premium_pct(iv, dte_hours):
+    """Premium straddle ATM in % del spot. Approssimazione BS."""
+    if iv <= 0 or dte_hours <= 0:
+        return 0
+    t = dte_hours / (24 * 365)
+    # ATM straddle ≈ spot * iv * sqrt(t) * 0.8 (approssimazione standard)
+    return iv * np.sqrt(t) * 0.8
+
+
+def run_vrp_honest(asset, dte_hours=24, n_simulations=5):
+    print(f"\n{'='*65}")
+    print(f"  {asset} — VRP HONEST TEST (DTE={dte_hours}h)")
+    print(f"  Fees: {TOTAL_COST_ROUNDTRIP*100:.2f}% roundtrip, Slippage incluso")
+    print(f"  Margin: {MARGIN_REQUIREMENT*100}% del notional")
+    print(f"{'='*65}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = realized_vol_ann(close, 24)
+    rv_72 = realized_vol_ann(close, 72)
+    rv_168 = realized_vol_ann(close, 168)
+
+    # Identifica periodi di crisi per report separato
+    crisis_periods = {
+        "COVID crash (Mar 2020)": ("2020-03-01", "2020-04-01"),
+        "May 2021 crash": ("2021-05-01", "2021-06-01"),
+        "Luna/3AC (Jun 2022)": ("2022-06-01", "2022-07-15"),
+        "FTX collapse (Nov 2022)": ("2022-11-01", "2022-12-15"),
+    }
+
+    all_sim_results = []
+
+    for sim in range(n_simulations):
+        np.random.seed(42 + sim)
+        capital = float(INITIAL)
+        total = 0
+        correct = 0
+        peak = capital
+        max_dd = 0
+        daily_trades = {}
+        crisis_pnl = {k: 0.0 for k in crisis_periods}
+
+        for i in range(max(split, 170), n - dte_hours):
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+            if timestamps.iloc[i].hour != 8:
+                continue
+
+            rv_s = rv_24[i]
+            rv_m = rv_72[i]
+            rv_l = rv_168[i]
+
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s <= 0.05 or rv_l <= 0.05:
+                continue
+
+            # IV realistica con variabilità
+            iv = iv_estimate_realistic(rv_s, rv_l, rv_m)
+
+            # Premium straddle
+            prem_pct = straddle_premium_pct(iv, dte_hours)
+
+            if prem_pct <= TOTAL_COST_ROUNDTRIP:
+                continue  # non vale la pena, costi > premium
+
+            spot = close[i]
+
+            # Position size: limitata dal margine
+            margin_per_unit = spot * MARGIN_REQUIREMENT
+            max_notional = capital / margin_per_unit * spot
+            pos_pct = min(0.15, capital / (spot * MARGIN_REQUIREMENT * 10))  # conservativo
+
+            # Actual path
+            exit_idx = min(i + dte_hours, n - 1)
+            actual_move_pct = abs(close[exit_idx] - spot) / spot
+
+            # Intra-period max move (per stress check)
+            path = close[i : exit_idx + 1]
+            max_adverse_pct = max(np.max(path) - spot, spot - np.min(path)) / spot
+
+            # P&L straddle short
+            if actual_move_pct <= prem_pct:
+                # In profitto: premium - actual move
+                raw_pnl_pct = (prem_pct - actual_move_pct) * pos_pct
+            else:
+                # In perdita: move > premium
+                loss = actual_move_pct - prem_pct
+                # Cap loss at 3x premium (risk management)
+                loss = min(loss, prem_pct * 3)
+                raw_pnl_pct = -loss * pos_pct
+
+            # Costi
+            cost = TOTAL_COST_ROUNDTRIP * pos_pct
+            net_pnl_pct = raw_pnl_pct - cost
+
+            capital += capital * net_pnl_pct
+            capital = max(capital, 10)  # floor
+
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+            total += 1
+            if raw_pnl_pct > 0:
+                correct += 1
+
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+            # Track crisis PnL
+            for crisis_name, (c_start, c_end) in crisis_periods.items():
+                if c_start <= day <= c_end:
+                    crisis_pnl[crisis_name] += capital * net_pnl_pct
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+
+        all_sim_results.append({
+            "sim": sim,
+            "trades": total,
+            "accuracy": acc,
+            "return": ret,
+            "annualized": ann,
+            "max_dd": max_dd * 100,
+            "daily_pnl": dpnl,
+            "final_capital": capital,
+            "days_active": len(daily_trades),
+            "crisis_pnl": crisis_pnl,
+        })
+
+    if not all_sim_results:
+        print("  No results!")
+        return
+
+    # Aggregate across simulations
+    accs = [r["accuracy"] for r in all_sim_results]
+    anns = [r["annualized"] for r in all_sim_results]
+    dds = [r["max_dd"] for r in all_sim_results]
+    dpnls = [r["daily_pnl"] for r in all_sim_results]
+    rets = [r["return"] for r in all_sim_results]
+
+    print(f"\n  {'Metric':<20s} {'Mean':>10s} {'Min':>10s} {'Max':>10s}")
+    print(f"  {'-'*50}")
+    print(f"  {'Accuracy':.<20s} {np.mean(accs):>9.1f}% {np.min(accs):>9.1f}% {np.max(accs):>9.1f}%")
+    print(f"  {'Annualized':.<20s} {np.mean(anns):>9.1f}% {np.min(anns):>9.1f}% {np.max(anns):>9.1f}%")
+    print(f"  {'Max Drawdown':.<20s} {np.mean(dds):>9.1f}% {np.min(dds):>9.1f}% {np.max(dds):>9.1f}%")
+    print(f"  {'€/day':.<20s} {np.mean(dpnls):>9.2f}€ {np.min(dpnls):>9.2f}€ {np.max(dpnls):>9.2f}€")
+    print(f"  {'Total return':.<20s} {np.mean(rets):>9.1f}% {np.min(rets):>9.1f}% {np.max(rets):>9.1f}%")
+    print(f"  {'Trades':.<20s} {all_sim_results[0]['trades']:>10d}")
+    print(f"  {'Days active':.<20s} {all_sim_results[0]['days_active']:>10d}")
+
+    # Crisis performance
+    print(f"\n  STRESS TEST — Performance durante crisi:")
+    for crisis_name in crisis_periods:
+        crisis_vals = [r["crisis_pnl"][crisis_name] for r in all_sim_results]
+        avg_crisis = np.mean(crisis_vals)
+        print(f"    {crisis_name:30s}: avg PnL = €{avg_crisis:+.2f}")
+
+    return all_sim_results
+
+
+# Run con diversi DTE
+for asset in ["ETH", "BTC"]:
+    for dte in [24, 48]:
+        run_vrp_honest(asset, dte, n_simulations=10)

scripts/s2_09_vrp_per_year.py

@@ -0,0 +1,181 @@
+"""S2-09: VRP test per-anno — verità nuda.
+Test su OGNI anno separatamente per vedere performance durante crash.
+Niente compounding — PnL medio per trade in punti percentuali.
+Costi realistici Deribit options.
+"""
+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_ROUNDTRIP = 0.0052  # 0.52% roundtrip (4 legs × 0.13%)
+INITIAL = 1000
+
+
+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 straddle_prem(iv, dte_h):
+    if iv <= 0 or dte_h <= 0:
+        return 0
+    return iv * np.sqrt(dte_h / (24 * 365)) * 0.8
+
+
+def run_per_year(asset, dte=24):
+    print(f"\n{'='*70}")
+    print(f"  {asset} — VRP PER ANNO (DTE={dte}h, NO compounding)")
+    print(f"  Fee roundtrip: {FEE_ROUNDTRIP*100:.2f}%")
+    print(f"{'='*70}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = rv_ann(close, 24)
+    rv_168 = rv_ann(close, 168)
+
+    # IV/RV premium: conservative estimate per regime
+    # Storicamene crypto VRP ≈ 15-30% (IV/RV ≈ 1.15-1.30)
+    # Ma durante crash VRP va NEGATIVO (RV > IV)
+
+    years = sorted(set(ts.dt.year))
+
+    print(f"\n  {'Year':>6s} {'Trades':>7s} {'Wins':>5s} {'Acc%':>6s} {'AvgPnL%':>9s} {'TotPnL€':>9s} {'Worst%':>8s} {'MaxMove%':>9s}")
+    print(f"  {'-'*70}")
+
+    all_pnls = []
+    yearly_stats = []
+
+    for year in years:
+        year_mask = ts.dt.year == year
+        year_indices = np.where(year_mask.values)[0]
+
+        if len(year_indices) < 200:
+            continue
+
+        trades_pnl = []
+        trades_detail = []
+
+        for i in year_indices:
+            if i < 170 or i + dte >= n:
+                continue
+            if ts.iloc[i].hour != 8:
+                continue
+
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+                continue
+
+            # IV estimate: regime-dependent
+            regime = rv_s / rv_l if rv_l > 0 else 1.0
+
+            if regime > 2.0:
+                # CRASH: RV esplosa, IV probabilmente = RV o meno
+                iv_premium_factor = 0.9
+            elif regime > 1.5:
+                iv_premium_factor = 1.0
+            elif regime > 1.0:
+                iv_premium_factor = 1.1
+            else:
+                # Calm: VRP positivo
+                iv_premium_factor = 1.2
+
+            iv = rv_l * iv_premium_factor
+            prem = straddle_prem(iv, dte)
+
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot) / spot
+
+            # P&L (senza compounding — flat € su €1000)
+            pos_size = INITIAL * 0.10  # 10% fisso, no leverage
+            if actual_move <= prem:
+                raw_pnl = (prem - actual_move) * pos_size
+            else:
+                raw_pnl = -(actual_move - prem) * pos_size
+                raw_pnl = max(raw_pnl, -pos_size * 0.05)  # cap loss
+
+            cost = FEE_ROUNDTRIP * pos_size
+            net_pnl = raw_pnl - cost
+
+            trades_pnl.append(net_pnl)
+            trades_detail.append({
+                "prem": prem,
+                "move": actual_move,
+                "regime": regime,
+                "rv_s": rv_s,
+                "iv": iv,
+            })
+            all_pnls.append(net_pnl)
+
+        if not trades_pnl:
+            continue
+
+        wins = sum(1 for p in trades_pnl if p > 0)
+        acc = wins / len(trades_pnl) * 100
+        avg_pnl = np.mean(trades_pnl)
+        tot_pnl = np.sum(trades_pnl)
+        worst = np.min(trades_pnl)
+        max_move = max(t["move"] for t in trades_detail) * 100
+
+        tag = ""
+        if year in [2020, 2021, 2022]:
+            tag = " ← CRASH YEAR"
+        if acc >= 70 and avg_pnl > 0:
+            tag += " ✅"
+
+        print(f"  {year:>6d} {len(trades_pnl):>7d} {wins:>5d} {acc:>5.1f}% {avg_pnl:>+8.2f}€ {tot_pnl:>+8.0f}€ {worst:>+7.2f}€ {max_move:>8.1f}% {tag}")
+
+        yearly_stats.append({
+            "year": year, "trades": len(trades_pnl), "acc": acc,
+            "avg_pnl": avg_pnl, "tot_pnl": tot_pnl, "worst": worst,
+        })
+
+    # Summary
+    if all_pnls:
+        total_trades = len(all_pnls)
+        total_wins = sum(1 for p in all_pnls if p > 0)
+        print(f"\n  {'TOTALE':>6s} {total_trades:>7d} {total_wins:>5d} {total_wins/total_trades*100:>5.1f}% {np.mean(all_pnls):>+8.2f}€ {np.sum(all_pnls):>+8.0f}€ {np.min(all_pnls):>+7.2f}€")
+
+        # Con compounding realistico
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+        for pnl in all_pnls:
+            capital += pnl * (capital / INITIAL)  # scala con capitale
+            capital = max(capital, 10)
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+        years_total = (yearly_stats[-1]["year"] - yearly_stats[0]["year"] + 1)
+        ann = ((capital / INITIAL) ** (1 / years_total) - 1) * 100 if capital > 0 else -100
+        daily_avg = (capital - INITIAL) / (total_trades)  # approx 1 trade/day
+
+        print(f"\n  CON COMPOUNDING:")
+        print(f"    Capitale finale: €{capital:,.0f}")
+        print(f"    ROI annualizzato: {ann:+.1f}%")
+        print(f"    Max Drawdown: {max_dd*100:.1f}%")
+        print(f"    €/trade medio: €{daily_avg:.2f}")
+
+        # Worst year
+        worst_year = min(yearly_stats, key=lambda x: x["tot_pnl"])
+        best_year = max(yearly_stats, key=lambda x: x["tot_pnl"])
+        print(f"\n    Anno peggiore: {worst_year['year']} → {worst_year['tot_pnl']:+.0f}€ ({worst_year['acc']:.0f}% acc)")
+        print(f"    Anno migliore: {best_year['year']} → {best_year['tot_pnl']:+.0f}€ ({best_year['acc']:.0f}% acc)")
+
+
+for asset in ["ETH", "BTC"]:
+    for dte in [24, 48]:
+        run_per_year(asset, dte)

scripts/s2_10_vrp_filtered.py

@@ -0,0 +1,297 @@
+"""S2-10: VRP + filtri multipli per alzare accuracy.
+Filtri testati:
+1. NO vol sell se squeeze attivo (compressione → breakout imminente, NON vendere vol)
+2. NO vol sell se RV short-term > RV long-term (regime esplosivo)
+3. NO vol sell se move delle ultime 4h > 2% (momentum in corso)
+4. NO vol sell se volume spike > 2x media (evento in corso)
+5. COMBINAZIONI dei filtri sopra
+Test per-anno, NO compounding per PnL medio, compounding a fine report.
+"""
+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_ROUNDTRIP = 0.0052
+INITIAL = 1000
+
+
+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 keltner_ratio(close, high, low, window=14):
+    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 straddle_prem(iv, dte_h):
+    if iv <= 0 or dte_h <= 0:
+        return 0
+    return iv * np.sqrt(dte_h / (24 * 365)) * 0.8
+
+
+def run_filtered(asset, dte=48):
+    print(f"\n{'='*75}")
+    print(f"  {asset} — VRP + FILTRI (DTE={dte}h)")
+    print(f"{'='*75}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(close)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = rv_ann(close, 24)
+    rv_168 = rv_ann(close, 168)
+    kcr = keltner_ratio(close, high, low, 14)
+
+    # Pre-calcolo filtri
+    vol_avg_48 = np.full(n, np.nan)
+    for i in range(48, n):
+        vol_avg_48[i] = np.mean(volume[i - 48 : i])
+
+    ret_4h = np.full(n, 0.0)
+    for i in range(4, n):
+        ret_4h[i] = abs(close[i] - close[i - 4]) / close[i - 4]
+
+    filter_configs = [
+        # (name, use_squeeze, use_regime, use_momentum, use_volume, squeeze_thr, regime_thr, mom_thr, vol_thr)
+        ("BASELINE (no filter)",   False, False, False, False, 0, 0, 0, 0),
+        ("squeeze_only",           True,  False, False, False, 0.85, 0, 0, 0),
+        ("regime_only",            False, True,  False, False, 0, 1.3, 0, 0),
+        ("momentum_only",          False, False, True,  False, 0, 0, 0.02, 0),
+        ("volume_only",            False, False, False, True,  0, 0, 0, 2.0),
+        ("squeeze+regime",         True,  True,  False, False, 0.85, 1.3, 0, 0),
+        ("squeeze+momentum",       True,  False, True,  False, 0.85, 0, 0.02, 0),
+        ("squeeze+volume",         True,  False, False, True,  0.85, 0, 0, 2.0),
+        ("regime+momentum",        False, True,  True,  False, 0, 1.3, 0.02, 0),
+        ("ALL_FILTERS",            True,  True,  True,  True,  0.85, 1.3, 0.02, 2.0),
+        ("squeeze+regime+mom",     True,  True,  True,  False, 0.85, 1.3, 0.02, 0),
+        ("squeeze_tight",          True,  False, False, False, 0.80, 0, 0, 0),
+        ("squeeze_tight+regime",   True,  True,  False, False, 0.80, 1.3, 0, 0),
+        ("regime_strict",          False, True,  False, False, 0, 1.2, 0, 0),
+        ("mom_strict",             False, False, True,  False, 0, 0, 0.015, 0),
+        ("squeeze_tight+mom_strict", True, False, True, False, 0.80, 0, 0.015, 0),
+        ("ALL_TIGHT",              True,  True,  True,  True,  0.80, 1.2, 0.015, 1.8),
+    ]
+
+    results_table = []
+
+    for name, f_sq, f_reg, f_mom, f_vol, sq_thr, reg_thr, mom_thr, vol_thr in filter_configs:
+        all_pnls = []
+        yearly = {}
+
+        for i in range(170, n - dte):
+            if ts.iloc[i].hour != 8:
+                continue
+
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+                continue
+
+            # === FILTRI ===
+            skip = False
+
+            if f_sq and not np.isnan(kcr[i]):
+                in_squeeze = kcr[i] < sq_thr
+                # Controlla se squeeze nelle ultime 5 barre
+                recent_squeeze = any(not np.isnan(kcr[j]) and kcr[j] < sq_thr for j in range(max(0, i - 5), i + 1))
+                if recent_squeeze:
+                    skip = True
+
+            if f_reg and rv_l > 0:
+                if rv_s / rv_l > reg_thr:
+                    skip = True
+
+            if f_mom:
+                if ret_4h[i] > mom_thr:
+                    skip = True
+
+            if f_vol and not np.isnan(vol_avg_48[i]) and vol_avg_48[i] > 0:
+                if volume[i] > vol_avg_48[i] * vol_thr:
+                    skip = True
+
+            if skip:
+                continue
+
+            # === TRADE ===
+            regime = rv_s / rv_l if rv_l > 0 else 1.0
+            if regime > 2.0:
+                iv_pf = 0.9
+            elif regime > 1.5:
+                iv_pf = 1.0
+            elif regime > 1.0:
+                iv_pf = 1.1
+            else:
+                iv_pf = 1.2
+            iv = rv_l * iv_pf
+
+            prem = straddle_prem(iv, dte)
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot) / spot
+
+            pos_size = INITIAL * 0.10
+            if actual_move <= prem:
+                raw = (prem - actual_move) * pos_size
+            else:
+                raw = -(actual_move - prem) * pos_size
+                raw = max(raw, -pos_size * 0.05)
+
+            net = raw - FEE_ROUNDTRIP * pos_size
+            all_pnls.append(net)
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = []
+            yearly[year].append(net)
+
+        if len(all_pnls) < 50:
+            continue
+
+        wins = sum(1 for p in all_pnls if p > 0)
+        acc = wins / len(all_pnls) * 100
+        avg_pnl = np.mean(all_pnls)
+        tot_pnl = np.sum(all_pnls)
+        worst_trade = np.min(all_pnls)
+        avg_win = np.mean([p for p in all_pnls if p > 0]) if wins > 0 else 0
+        avg_loss = np.mean([p for p in all_pnls if p <= 0]) if wins < len(all_pnls) else 0
+
+        # Worst year
+        worst_year_acc = 100
+        worst_year_name = ""
+        for y, ypnls in sorted(yearly.items()):
+            yw = sum(1 for p in ypnls if p > 0) / len(ypnls) * 100 if ypnls else 0
+            if yw < worst_year_acc:
+                worst_year_acc = yw
+                worst_year_name = str(y)
+
+        # Compounded return
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+        for pnl in all_pnls:
+            capital += pnl * (capital / INITIAL)
+            capital = max(capital, 10)
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+        n_years = len(yearly)
+        ann = ((capital / INITIAL) ** (1 / n_years) - 1) * 100 if capital > 0 and n_years > 0 else -100
+
+        results_table.append({
+            "name": name,
+            "trades": len(all_pnls),
+            "acc": acc,
+            "avg_pnl": avg_pnl,
+            "avg_win": avg_win,
+            "avg_loss": avg_loss,
+            "ann": ann,
+            "max_dd": max_dd * 100,
+            "worst_year": f"{worst_year_name}({worst_year_acc:.0f}%)",
+            "capital": capital,
+        })
+
+    # Sort by accuracy
+    results_table.sort(key=lambda x: x["acc"], reverse=True)
+
+    print(f"\n  {'Name':.<30s} {'Trades':>7s} {'Acc':>6s} {'AvgPnL':>8s} {'AvgWin':>8s} {'AvgLoss':>8s} {'Ann%':>7s} {'DD%':>5s} {'Worst_Yr':>12s} {'Capital':>10s}")
+    print(f"  {'-'*105}")
+    for r in results_table:
+        tag = "✅✅" if r["acc"] >= 75 else "✅" if r["acc"] >= 70 else ""
+        print(f"  {r['name']:.<30s} {r['trades']:>7d} {r['acc']:>5.1f}% {r['avg_pnl']:>+7.2f}€ {r['avg_win']:>+7.2f}€ {r['avg_loss']:>+7.2f}€ {r['ann']:>+6.1f}% {r['max_dd']:>4.1f}% {r['worst_year']:>12s} €{r['capital']:>9,.0f} {tag}")
+
+    # Dettaglio per anno del migliore
+    best = results_table[0]
+    print(f"\n  MIGLIORE: {best['name']} → {best['acc']:.1f}% acc, {best['ann']:+.1f}% ann, DD {best['max_dd']:.1f}%")
+
+    # Rerun best per year
+    best_name = best["name"]
+    best_cfg = None
+    for cfg in filter_configs:
+        if cfg[0] == best_name:
+            best_cfg = cfg
+            break
+
+    if best_cfg:
+        _, f_sq, f_reg, f_mom, f_vol, sq_thr, reg_thr, mom_thr, vol_thr = best_cfg
+        yearly_detail = {}
+
+        for i in range(170, n - dte):
+            if ts.iloc[i].hour != 8:
+                continue
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+                continue
+
+            skip = False
+            if f_sq:
+                if any(not np.isnan(kcr[j]) and kcr[j] < sq_thr for j in range(max(0, i - 5), i + 1)):
+                    skip = True
+            if f_reg and rv_l > 0 and rv_s / rv_l > reg_thr:
+                skip = True
+            if f_mom and ret_4h[i] > mom_thr:
+                skip = True
+            if f_vol and not np.isnan(vol_avg_48[i]) and vol_avg_48[i] > 0 and volume[i] > vol_avg_48[i] * vol_thr:
+                skip = True
+            if skip:
+                continue
+
+            regime = rv_s / rv_l if rv_l > 0 else 1.0
+            iv_pf = {True: 0.9, False: 1.2}[regime > 2.0] if regime > 2.0 else (1.0 if regime > 1.5 else (1.1 if regime > 1.0 else 1.2))
+            iv = rv_l * iv_pf
+            prem = straddle_prem(iv, dte)
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            move = abs(close[exit_idx] - spot) / spot
+            pos_size = INITIAL * 0.10
+            if move <= prem:
+                raw = (prem - move) * pos_size
+            else:
+                raw = max(-(move - prem) * pos_size, -pos_size * 0.05)
+            net = raw - FEE_ROUNDTRIP * pos_size
+
+            year = ts.iloc[i].year
+            if year not in yearly_detail:
+                yearly_detail[year] = []
+            yearly_detail[year].append(net)
+
+        print(f"\n  Dettaglio per anno ({best_name}):")
+        for y in sorted(yearly_detail):
+            pnls = yearly_detail[y]
+            w = sum(1 for p in pnls if p > 0)
+            a = w / len(pnls) * 100
+            tag = " ← CRASH" if y in [2020, 2021, 2022] else ""
+            print(f"    {y}: trades={len(pnls):4d} acc={a:.1f}% avg=€{np.mean(pnls):+.2f} tot=€{np.sum(pnls):+.0f}{tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_filtered(asset, dte=48)
+    run_filtered(asset, dte=24)

scripts/s2_11_vrp_real_dvol.py

@@ -0,0 +1,152 @@
+"""S2-11: VRP con DVOL REALE — unico test valido.
+Solo 90 giorni di dati, ma REALI.
+Confronta DVOL (IV reale Deribit) vs RV realizzata.
+"""
+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_ROUNDTRIP = 0.0052
+INITIAL = 1000
+
+
+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 straddle_prem(iv_pct, dte_h):
+    """iv_pct è la IV in decimale (es 0.50 = 50%)."""
+    if iv_pct <= 0 or dte_h <= 0:
+        return 0
+    return iv_pct * np.sqrt(dte_h / (24 * 365)) * 0.8
+
+
+for asset in ["ETH", "BTC"]:
+    print(f"\n{'='*70}")
+    print(f"  {asset} — VRP CON DVOL REALE (90 giorni)")
+    print(f"{'='*70}")
+
+    df_price = load_data(asset, "1h")
+    df_dvol = pd.read_parquet(f"data/raw/{asset.lower()}_dvol.parquet")
+
+    close = df_price["close"].values
+    ts_price = df_price["timestamp"].values
+    n = len(close)
+
+    dvol_ts = df_dvol["timestamp"].values
+    dvol_vals = df_dvol["dvol"].values / 100  # converti a decimale
+
+    rv_24 = rv_ann(close, 24)
+    rv_48 = rv_ann(close, 48)
+
+    # Allinea DVOL ai candles 1h (DVOL è giornaliero)
+    dvol_aligned = np.full(n, np.nan)
+    for j in range(len(dvol_ts)):
+        mask = (ts_price >= dvol_ts[j]) & (ts_price < dvol_ts[j] + 86400000)
+        dvol_aligned[mask] = dvol_vals[j]
+
+    valid_count = np.sum(~np.isnan(dvol_aligned))
+    print(f"  Candele con DVOL reale: {valid_count}")
+    print(f"  DVOL range: {np.nanmin(dvol_aligned)*100:.1f}% — {np.nanmax(dvol_aligned)*100:.1f}%")
+
+    # Analisi IV vs RV reale
+    iv_rv_ratios = []
+    for i in range(n):
+        if np.isnan(dvol_aligned[i]) or np.isnan(rv_24[i]) or rv_24[i] <= 0:
+            continue
+        iv_rv_ratios.append(dvol_aligned[i] / rv_24[i])
+
+    if iv_rv_ratios:
+        print(f"\n  IV/RV ratio REALE:")
+        print(f"    Mean:   {np.mean(iv_rv_ratios):.3f}")
+        print(f"    Median: {np.median(iv_rv_ratios):.3f}")
+        print(f"    Min:    {np.min(iv_rv_ratios):.3f}")
+        print(f"    Max:    {np.max(iv_rv_ratios):.3f}")
+        print(f"    >1 (VRP+): {sum(1 for r in iv_rv_ratios if r > 1)/len(iv_rv_ratios)*100:.0f}% del tempo")
+
+    # Backtest VRP reale
+    for dte in [24, 48]:
+        print(f"\n  --- DTE={dte}h ---")
+        capital = float(INITIAL)
+        trades = []
+        daily_done = set()
+
+        for i in range(100, n - dte):
+            if np.isnan(dvol_aligned[i]) or np.isnan(rv_24[i]):
+                continue
+
+            ts_dt = pd.Timestamp(ts_price[i], unit="ms", tz="UTC")
+            if ts_dt.hour != 8:
+                continue
+
+            day = ts_dt.strftime("%Y-%m-%d")
+            if day in daily_done:
+                continue
+
+            iv = dvol_aligned[i]
+            rv = rv_24[i]
+
+            # Filtro regime: skip se RV > IV (no premium)
+            if rv > iv:
+                continue
+
+            prem = straddle_prem(iv, dte)
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot) / spot
+
+            pos_pct = 0.10
+            if actual_move <= prem:
+                raw = (prem - actual_move) * pos_pct
+            else:
+                raw = -(actual_move - prem) * pos_pct
+                raw = max(raw, -pos_pct * 0.05)
+
+            net = raw - FEE_ROUNDTRIP * pos_pct
+            capital += capital * net
+            capital = max(capital, 10)
+
+            trades.append({
+                "day": day,
+                "iv": iv * 100,
+                "rv": rv * 100,
+                "premium": prem * 100,
+                "move": actual_move * 100,
+                "pnl": net * capital,
+                "win": raw > 0,
+            })
+            daily_done.add(day)
+
+        if not trades:
+            print("    Nessun trade!")
+            continue
+
+        wins = sum(1 for t in trades if t["win"])
+        acc = wins / len(trades) * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        avg_iv = np.mean([t["iv"] for t in trades])
+        avg_rv = np.mean([t["rv"] for t in trades])
+        avg_prem = np.mean([t["premium"] for t in trades])
+        avg_move = np.mean([t["move"] for t in trades])
+
+        print(f"    Trades:   {len(trades)}")
+        print(f"    Accuracy: {acc:.1f}%")
+        print(f"    Return:   {ret:+.1f}%")
+        print(f"    Capital:  €{capital:.0f}")
+        print(f"    Avg IV:   {avg_iv:.1f}%")
+        print(f"    Avg RV:   {avg_rv:.1f}%")
+        print(f"    Avg Prem: {avg_prem:.2f}%")
+        print(f"    Avg Move: {avg_move:.2f}%")
+        print(f"    IV > Move (win condition): {sum(1 for t in trades if t['premium'] > t['move'])/len(trades)*100:.0f}%")
+
+        # Worst trade
+        worst = min(trades, key=lambda t: t["pnl"])
+        print(f"    Worst:    day={worst['day']} iv={worst['iv']:.1f}% move={worst['move']:.2f}%")

scripts/s2_12_perpetual_only.py

@@ -0,0 +1,320 @@
+"""S2-12: Strategie SOLO su perpetual — dati 100% reali.
+Niente opzioni, niente IV stimata. Solo prezzo OHLCV.
+Mix di approcci diversi da quelli già testati su main.
+
+1. Intraday range breakout con filtro volatilità
+2. Daily open range breakout (prima ora di trading)
+3. RSI divergence (prezzo fa nuovo min/max, RSI no)
+4. Close-to-close momentum filtrato da volatilità regime
+5. Multi-timeframe confirmation (15m signal + 1h trend)
+
+Test per-anno, onesto, con fee reali perpetual (0.05% taker × 2 = 0.1% roundtrip).
+"""
+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  # 0.1% taker roundtrip
+INITIAL = 1000
+LEVERAGE = 3
+
+
+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
+        if al == 0:
+            result[i + 1] = 100
+        else:
+            result[i + 1] = 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
+
+
+def run_all_perpetual(asset):
+    print(f"\n{'#'*70}")
+    print(f"  {asset} — STRATEGIE PERPETUAL (dati reali)")
+    print(f"  Fee: {FEE_RT*100}% roundtrip, Leva: {LEVERAGE}x")
+    print(f"{'#'*70}")
+
+    df_1h = load_data(asset, "1h")
+    df_15m = load_data(asset, "15m")
+    c1h = df_1h["close"].values
+    h1h = df_1h["high"].values
+    l1h = df_1h["low"].values
+    v1h = df_1h["volume"].values
+    n1h = len(c1h)
+    ts1h = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+
+    rsi_14 = rsi(c1h, 14)
+    ema_20 = ema(c1h, 20)
+    ema_50 = ema(c1h, 50)
+
+    results = {}
+
+    # ======================================================
+    # STRAT 1: Daily Open Range Breakout
+    # Prima ora (08-09 UTC) definisce il range. Breakout = entrata.
+    # ======================================================
+    for hold, stop_m in [(6, 1.0), (12, 1.5), (4, 0.8)]:
+        name = f"ORB_h{hold}_s{stop_m}"
+        capital = float(INITIAL)
+        yearly = {}
+
+        for i in range(50, n1h - hold):
+            if ts1h.iloc[i].hour != 9:  # fine della prima ora
+                continue
+            day = ts1h.iloc[i].strftime("%Y-%m-%d")
+            if day in yearly and len(yearly[day]) >= 1:
+                continue
+
+            range_high = h1h[i - 1]
+            range_low = l1h[i - 1]
+            range_size = range_high - range_low
+            if range_size <= 0:
+                continue
+
+            # ATR per stop
+            atr_14 = np.mean(h1h[max(0,i-14):i] - l1h[max(0,i-14):i])
+            if atr_14 <= 0:
+                continue
+
+            # Breakout detection: la candela attuale rompe il range
+            if c1h[i] > range_high:
+                direction = "long"
+            elif c1h[i] < range_low:
+                direction = "short"
+            else:
+                continue
+
+            entry = c1h[i]
+            stop_dist = atr_14 * stop_m
+            exit_price = c1h[min(i + hold, n1h - 1)]
+
+            for j in range(i + 1, min(i + hold + 1, n1h)):
+                if direction == "long":
+                    if l1h[j] <= entry - stop_dist:
+                        exit_price = entry - stop_dist
+                        break
+                    if h1h[j] >= entry + stop_dist * 2:
+                        exit_price = entry + stop_dist * 2
+                        break
+                else:
+                    if h1h[j] >= entry + stop_dist:
+                        exit_price = entry + stop_dist
+                        break
+                    if l1h[j] <= entry - stop_dist * 2:
+                        exit_price = entry - stop_dist * 2
+                        break
+                exit_price = c1h[j]
+
+            trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+            net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+
+            year = ts1h.iloc[i].year
+            if year not in yearly:
+                yearly[year] = []
+            yearly[year].append(net > 0)
+            if day not in yearly:
+                yearly[day] = []
+
+        if sum(len(v) for v in yearly.values() if isinstance(v, list) and all(isinstance(x, bool) for x in v)) > 30:
+            all_wins = [w for v in yearly.values() if isinstance(v, list) for w in v if isinstance(w, bool)]
+            acc = sum(all_wins) / len(all_wins) * 100
+            ret = (capital - INITIAL) / INITIAL * 100
+            results[name] = {"acc": acc, "ret": ret, "trades": len(all_wins), "capital": capital}
+
+    # ======================================================
+    # STRAT 2: RSI Divergence
+    # Prezzo fa nuovo low, RSI no = bullish divergence → long
+    # ======================================================
+    for lookback, rsi_thr_low, rsi_thr_high, hold in [(20, 30, 70, 6), (14, 25, 75, 8), (10, 35, 65, 4)]:
+        name = f"RSIdiv_lb{lookback}_h{hold}"
+        capital = float(INITIAL)
+        trades_list = []
+
+        for i in range(max(50, lookback + 1), n1h - hold):
+            day = ts1h.iloc[i].strftime("%Y-%m-%d")
+
+            # Bullish divergence: price new low, RSI higher low
+            price_new_low = c1h[i] < np.min(c1h[i - lookback : i])
+            rsi_higher = rsi_14[i] > np.min(rsi_14[i - lookback : i]) and rsi_14[i] < rsi_thr_low
+
+            # Bearish divergence: price new high, RSI lower high
+            price_new_high = c1h[i] > np.max(c1h[i - lookback : i])
+            rsi_lower = rsi_14[i] < np.max(rsi_14[i - lookback : i]) and rsi_14[i] > rsi_thr_high
+
+            direction = None
+            if price_new_low and rsi_higher:
+                direction = "long"
+            elif price_new_high and rsi_lower:
+                direction = "short"
+
+            if direction is None:
+                continue
+
+            entry = c1h[i]
+            exit_price = c1h[min(i + hold, n1h - 1)]
+            trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+            net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+            capital += capital * 0.12 * net
+            capital = max(capital, 10)
+            trades_list.append({"year": ts1h.iloc[i].year, "win": trade_ret > 0})
+
+        if len(trades_list) > 30:
+            acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
+            ret = (capital - INITIAL) / INITIAL * 100
+            results[name] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
+
+    # ======================================================
+    # STRAT 3: Momentum regime — trend following solo in low-vol regime
+    # ======================================================
+    for fast, slow, vol_w, vol_thr, hold in [
+        (8, 21, 48, 0.8, 12),
+        (5, 13, 24, 0.8, 6),
+        (13, 34, 72, 0.7, 24),
+        (8, 21, 48, 0.9, 8),
+    ]:
+        name = f"MomReg_f{fast}s{slow}_h{hold}"
+        ema_f = ema(c1h, fast)
+        ema_s = ema(c1h, slow)
+
+        rv_short = np.full(n1h, np.nan)
+        rv_long = np.full(n1h, np.nan)
+        lr = np.diff(np.log(np.where(c1h == 0, 1e-10, c1h)))
+        for idx in range(vol_w, len(lr)):
+            rv_short[idx + 1] = np.std(lr[idx - min(12, vol_w) : idx])
+            rv_long[idx + 1] = np.std(lr[idx - vol_w : idx])
+
+        capital = float(INITIAL)
+        trades_list = []
+        daily_done = set()
+
+        for i in range(max(60, slow + 1), n1h - hold):
+            if np.isnan(ema_f[i]) or np.isnan(ema_s[i]) or np.isnan(rv_short[i]) or np.isnan(rv_long[i]):
+                continue
+            if rv_long[i] <= 0:
+                continue
+
+            day = ts1h.iloc[i].strftime("%Y-%m-%d")
+            if day in daily_done:
+                continue
+
+            # Only trade in low-vol regime
+            vol_ratio = rv_short[i] / rv_long[i]
+            if vol_ratio > vol_thr:
+                continue
+
+            # EMA crossover signal
+            cross_up = ema_f[i] > ema_s[i] and ema_f[i - 1] <= ema_s[i - 1]
+            cross_down = ema_f[i] < ema_s[i] and ema_f[i - 1] >= ema_s[i - 1]
+
+            if not (cross_up or cross_down):
+                continue
+
+            direction = "long" if cross_up else "short"
+            entry = c1h[i]
+            exit_price = c1h[min(i + hold, n1h - 1)]
+            trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+            net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+            trades_list.append({"year": ts1h.iloc[i].year, "win": trade_ret > 0})
+            daily_done.add(day)
+
+        if len(trades_list) > 30:
+            acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
+            ret = (capital - INITIAL) / INITIAL * 100
+            results[name] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
+
+    # ======================================================
+    # STRAT 4: Multi-TF confirmation (15m entry, 1h trend)
+    # ======================================================
+    c15 = df_15m["close"].values
+    h15 = df_15m["high"].values
+    l15 = df_15m["low"].values
+    ts15 = df_15m["timestamp"].values
+    n15 = len(c15)
+
+    ema_1h_50 = ema(c1h, 50)
+    rsi_15m = rsi(c15, 14)
+
+    capital = float(INITIAL)
+    trades_list = []
+    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
+
+        # 15m signal: RSI extreme
+        if rsi_15m[i] > 35 and rsi_15m[i] < 65:
+            continue
+
+        # Find matching 1h candle
+        h_idx = np.searchsorted(ts1h.values.astype("int64"), ts15[i]) - 1
+        if h_idx < 50 or h_idx >= n1h or np.isnan(ema_1h_50[h_idx]):
+            continue
+
+        # 1h trend confirmation
+        trend_up = c1h[h_idx] > ema_1h_50[h_idx]
+        trend_down = c1h[h_idx] < ema_1h_50[h_idx]
+
+        direction = None
+        if rsi_15m[i] < 30 and trend_up:
+            direction = "long"  # oversold in uptrend
+        elif rsi_15m[i] > 70 and trend_down:
+            direction = "short"  # overbought in downtrend
+
+        if direction is None:
+            continue
+
+        entry = c15[i]
+        hold_bars = 12  # 12 × 15m = 3h
+        exit_price = c15[min(i + hold_bars, n15 - 1)]
+        trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+        net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+        capital += capital * 0.12 * net
+        capital = max(capital, 10)
+        trades_list.append({"year": ts_dt.year, "win": trade_ret > 0})
+        daily_done.add(day)
+
+    if len(trades_list) > 30:
+        acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        results["MultiTF_15m1h"] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
+
+    # === PRINT RESULTS ===
+    print(f"\n  {'Strategy':<25s} {'Trades':>7s} {'Acc':>6s} {'Return':>10s} {'Capital':>10s}")
+    print(f"  {'-'*60}")
+    for name, r in sorted(results.items(), key=lambda x: x[1]["acc"], reverse=True):
+        tag = "✅" if r["acc"] >= 60 and r["ret"] > 30 else ""
+        print(f"  {name:<25s} {r['trades']:>7d} {r['acc']:>5.1f}% {r['ret']:>+9.1f}% €{r['capital']:>9,.0f} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_all_perpetual(asset)