feat(strategy4): MT01 squeeze+MTF 82.7% acc — batte SQ02, 6 strategie scartate

Nuova strategia MT01: squeeze 15m + momentum EMA 1h BTC 15m: 82.7% acc, 503 trades, DD 5.9%, 9/9 anni, worst 72% ETH 15m: 81.2% acc, 404 trades, DD 2.9%, 9/9 anni, worst 73% Strategie testate e scartate (waste W23-W28): IB01 inside bar (58.7%, no edge) DC01 donchian (48%, sotto random) SB01 retest (52%, no edge) MR01 mean reversion RSI (62.9%, DD 29%) VO01 volume spike (64.2%, DD 34%) HY01 squeeze+MR (64.6%, DD 14.5%) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-28 00:38:11 +02:00
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+"""VO01 — Volume Spike Reversal.
+
+Quando il volume esplode (>3× media) con un forte move direzionale,
+il mercato è in eccesso → fade il move (mean reversion).
+
+Diverso dallo squeeze: non cerca compressione, cerca ECCESSO.
+Il volume spike indica panico/euforia → reversal probabile.
+
+IN:
+    - OHLCV DataFrame
+    - Parametri: vol_mult (3), move_threshold (0.005), hold
+
+OUT:
+    - Signal: fade la direzione del volume spike
+    - BacktestResult
+
+Logica:
+    1. Volume > vol_mult × media 20 periodi
+    2. Move nella candela > move_threshold (0.5%)
+    3. Direzione: opposta al move (mean reversion)
+    4. Filtro: non entrare se già in trend forte (EMA slope)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.strategies.base import Strategy, Signal
+
+
+class VolumeSpikeReversal(Strategy):
+    name = "VO01_vol_spike_reversal"
+    description = "Volume spike reversal — fade eccessi di volume/prezzo"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+    fee_rt = 0.002
+
+    def generate_signals(self, df, ts, **params):
+        c = df["close"].values
+        o = df["open"].values
+        h = df["high"].values
+        l = df["low"].values
+        v = df["volume"].values
+        n = len(c)
+
+        vol_mult = params.get("vol_mult", 3.0)
+        move_thr = params.get("move_threshold", 0.005)
+        use_trend_filter = params.get("trend_filter", False)
+        cooldown = params.get("cooldown", 4)
+
+        # Volume media rolling
+        vol_ma = np.full(n, np.nan)
+        for i in range(20, n):
+            vol_ma[i] = np.mean(v[i - 20:i])
+
+        # EMA per trend filter
+        ema_20 = np.full(n, np.nan)
+        k = 2 / 21
+        ema_20[19] = np.mean(c[:20])
+        for i in range(20, n):
+            ema_20[i] = c[i] * k + ema_20[i - 1] * (1 - k)
+
+        signals = []
+        last_idx = -cooldown
+
+        for i in range(21, n):
+            if i - last_idx < cooldown:
+                continue
+            if np.isnan(vol_ma[i]):
+                continue
+
+            # Volume spike
+            if v[i] < vol_ma[i] * vol_mult:
+                continue
+
+            # Price move
+            move = (c[i] - o[i]) / o[i] if o[i] > 0 else 0
+            if abs(move) < move_thr:
+                continue
+
+            # Fade: opposto al move
+            direction = -1 if move > 0 else 1
+
+            # Trend filter: non fare mean reversion contro trend forte
+            if use_trend_filter and not np.isnan(ema_20[i]):
+                ema_slope = (ema_20[i] - ema_20[max(0, i - 5)]) / ema_20[max(0, i - 5)]
+                if direction == -1 and ema_slope > 0.005:
+                    continue
+                if direction == 1 and ema_slope < -0.005:
+                    continue
+
+            signals.append(Signal(
+                idx=i, direction=direction, entry_price=c[i],
+                metadata={"vol_ratio": float(v[i] / vol_ma[i]), "move_pct": round(move * 100, 3)},
+            ))
+            last_idx = i
+
+        return signals
+
+
+if __name__ == "__main__":
+    strategy = VolumeSpikeReversal()
+
+    configs = [
+        ("v3x m0.5%", {"vol_mult": 3.0, "move_threshold": 0.005}),
+        ("v3x m1%", {"vol_mult": 3.0, "move_threshold": 0.01}),
+        ("v4x m0.5%", {"vol_mult": 4.0, "move_threshold": 0.005}),
+        ("v4x m1%", {"vol_mult": 4.0, "move_threshold": 0.01}),
+        ("v3x m0.5%+tf", {"vol_mult": 3.0, "move_threshold": 0.005, "trend_filter": True}),
+        ("v3x m1%+tf", {"vol_mult": 3.0, "move_threshold": 0.01, "trend_filter": True}),
+        ("v5x m1%", {"vol_mult": 5.0, "move_threshold": 0.01}),
+        ("v5x m1%+tf", {"vol_mult": 5.0, "move_threshold": 0.01, "trend_filter": True}),
+    ]
+
+    all_results = []
+    for label, params in configs:
+        for asset in ["BTC", "ETH"]:
+            for tf in ["15m", "1h"]:
+                for hold in [3, 6]:
+                    r = strategy.backtest(asset, tf, hold=hold, **params)
+                    if r and r.trades >= 30:
+                        r.strategy_name = f"VO01 {label} h={hold}"
+                        all_results.append(r)
+
+    all_results.sort(key=lambda r: r.accuracy, reverse=True)
+    print(f"\n{'=' * 120}")
+    print(f"  VO01 VOLUME SPIKE REVERSAL — TOP 20")
+    print(f"{'=' * 120}")
+    for r in all_results[:20]:
+        r.print_summary()
+    if all_results:
+        all_results[0].print_yearly()