refactor(strategie): tieni solo MR01 mean-reversion, squeeze -> waste

L'analisi out-of-sample fee-aware ha dimostrato che l'intera famiglia squeeze-breakout (SQ01-04, MT01, ML01, AD01, CM01, PD01) non ha edge: le accuratezze storiche 76-82% erano un artefatto di look-ahead (ingresso a close[i-1] con direzione decisa da close[i]). Sotto ingresso onesto a close[i] e fee reali tutte perdono, anche a fee zero. - nuova MR01_bollinger_fade (mean-reversion): edge netto validato OOS, robusto su griglia parametri e fino a 0.20% fee RT. BTC 1h n50 k2.5: +201% OOS, DD 15% - 9 strategie squeeze spostate in scripts/waste/ - strategy_loader + strategies.yml: solo MR01 (BTC/ETH 1h) - signal_engine.train: validazione OOS (accuratezza test + signal precision) - scripts/analysis/strategy_research.py: harness di ricerca fee-aware NOTA: lo StrategyWorker va aggiornato per usare gli exit TP/SL passati in metadata prima di tradare MR01 dal vivo (ora esce solo a hold_bars/stop fisso). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-28 20:22:11 +00:00
parent ca88e62a11
commit 9879b46688
14 changed files with 506 additions and 47 deletions
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+"""SQ02 — Squeeze Breakout + Anti-Fakeout + Volume Confirmation.
+
+Migliora SQ01 con due filtri:
+  1. Anti-fakeout: scarta breakout dove la candela ritraccia >60% del range
+  2. Volume confirm: volume al breakout deve essere >1.3× la media durante squeeze
+
+IN:
+    - OHLCV DataFrame
+    - Parametri: bb_window (14), sq_threshold (0.8), retrace_limit (0.6),
+                 vol_multiplier (1.3)
+
+OUT:
+    - Lista di Signal filtrati
+    - BacktestResult
+
+Risultati tipici:
+    BTC 15m: 79.7% acc, 1250 trades, DD 6.5%, €5.23/day — SOLIDO 9/9 anni
+    ETH 15m: 78.6% acc, 942 trades, DD 3.4%, €4.33/day
+    BTC 1h:  78.0% acc, 473 trades, DD 3.5%, Sharpe 6.57
+"""
+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
+from src.strategies.indicators import keltner_ratio, detect_squeezes
+
+
+class SqueezeAntifakeVol(Strategy):
+    name = "SQ02_antifake_vol"
+    description = "Squeeze + antifakeout + volume confirmation"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        c = df["close"].values
+        h = df["high"].values
+        l = df["low"].values
+        v = df["volume"].values
+        n = len(c)
+
+        bb_w = params.get("bb_window", 14)
+        sq_thr = params.get("sq_threshold", 0.8)
+        retrace_limit = params.get("retrace_limit", 0.6)
+        vol_mult = params.get("vol_multiplier", 1.3)
+
+        kcr = keltner_ratio(c, h, l, bb_w)
+        events = detect_squeezes(c, h, l, kcr, sq_thr)
+
+        signals = []
+        for ev in events:
+            i = ev["idx"]
+            if i < 1 or i >= n:
+                continue
+            first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+
+            br = h[i] - l[i]
+            if br > 0:
+                if c[i] > c[i - 1]:
+                    if (h[i] - c[i]) / br > retrace_limit:
+                        continue
+                else:
+                    if (c[i] - l[i]) / br > retrace_limit:
+                        continue
+
+            avg_v = np.mean(v[ev["sq_start"]:i])
+            if avg_v > 0 and v[i] <= avg_v * vol_mult:
+                continue
+
+            signals.append(Signal(
+                idx=i,
+                direction=1 if first_ret > 0 else -1,
+                entry_price=c[i - 1],
+                metadata={"dur": ev["dur"], "vol_ratio": v[i] / avg_v if avg_v > 0 else 0},
+            ))
+        return signals
+
+
+if __name__ == "__main__":
+    strategy = SqueezeAntifakeVol()
+    strategy.report()