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>

scripts/waste/W28_squeeze_mr.py

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+"""HY01 — Squeeze + Mean Reversion Ibrida.
+
+Insight: durante lo squeeze (bassa volatilità), il prezzo mean-reverte
+DENTRO il range compresso. Autocorrelazione negativa a 15m conferma.
+Invece di aspettare il BREAKOUT, tradi la MEAN REVERSION dentro lo squeeze.
+
+Completamente diverso da SQ01-SQ04 che aspettano il RILASCIO.
+
+IN:
+    - OHLCV DataFrame
+    - Parametri: bb_window, sq_threshold, rsi_period, rsi_levels,
+                 vol_filter, bb_touch (prezzo tocca banda Bollinger)
+
+OUT:
+    - Signal: long quando RSI oversold DURANTE squeeze, short quando overbought
+    - BacktestResult
+
+Logica:
+    1. Verifica che siamo IN squeeze (BB dentro KC)
+    2. Prezzo tocca banda inferiore BB → LONG (tornerà alla media)
+    3. Prezzo tocca banda superiore BB → SHORT (tornerà alla media)
+    4. Conferma RSI: deve essere estremo nella direzione
+    5. Hold corto (2-3 barre) — target: ritorno alla media
+    6. Stop: se prezzo rompe lo squeeze → chiudi subito
+"""
+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
+
+
+def rsi(close, period=14):
+    delta = np.diff(close)
+    gain = np.where(delta > 0, delta, 0)
+    loss = np.where(delta < 0, -delta, 0)
+    result = np.full(len(close), 50.0)
+    if len(gain) < period:
+        return result
+    ag = np.mean(gain[:period])
+    al = np.mean(loss[:period])
+    for i in range(period, len(delta)):
+        ag = (ag * (period - 1) + gain[i]) / period
+        al = (al * (period - 1) + loss[i]) / period
+        result[i + 1] = 100 if al == 0 else 100 - 100 / (1 + ag / al)
+    return result
+
+
+def bollinger(close, window=14):
+    n = len(close)
+    upper = np.full(n, np.nan)
+    lower = np.full(n, np.nan)
+    mid = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i - window:i]
+        m = np.mean(wc)
+        s = np.std(wc)
+        mid[i] = m
+        upper[i] = m + 2 * s
+        lower[i] = m - 2 * s
+    return upper, mid, lower
+
+
+class SqueezeMeanReversion(Strategy):
+    name = "HY01_squeeze_mr"
+    description = "Mean reversion DENTRO lo squeeze — fade estremi in range compresso"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+    fee_rt = 0.002
+
+    def generate_signals(self, df, ts, **params):
+        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)
+        rsi_period = params.get("rsi_period", 14)
+        rsi_low = params.get("rsi_oversold", 30)
+        rsi_high = params.get("rsi_overbought", 70)
+        use_bb_touch = params.get("bb_touch", True)
+        cooldown = params.get("cooldown", 3)
+
+        kcr = keltner_ratio(c, h, l, bb_w)
+        rsi_vals = rsi(c, rsi_period)
+        bb_upper, bb_mid, bb_lower = bollinger(c, bb_w)
+
+        signals = []
+        last_idx = -cooldown
+
+        for i in range(bb_w + 1, n):
+            if i - last_idx < cooldown:
+                continue
+            if np.isnan(kcr[i]) or np.isnan(bb_lower[i]):
+                continue
+
+            # Must be IN squeeze
+            if kcr[i] >= sq_thr:
+                continue
+
+            direction = 0
+
+            if use_bb_touch:
+                # Prezzo tocca/rompe BB lower → long (mean reversion up)
+                if c[i] <= bb_lower[i] and rsi_vals[i] < rsi_low:
+                    direction = 1
+                # Prezzo tocca/rompe BB upper → short (mean reversion down)
+                elif c[i] >= bb_upper[i] and rsi_vals[i] > rsi_high:
+                    direction = -1
+            else:
+                # Solo RSI
+                if rsi_vals[i] < rsi_low:
+                    direction = 1
+                elif rsi_vals[i] > rsi_high:
+                    direction = -1
+
+            if direction == 0:
+                continue
+
+            signals.append(Signal(
+                idx=i, direction=direction, entry_price=c[i],
+                metadata={
+                    "rsi": float(rsi_vals[i]),
+                    "kcr": float(kcr[i]),
+                    "bb_pos": "lower" if direction == 1 else "upper",
+                },
+            ))
+            last_idx = i
+
+        return signals
+
+
+if __name__ == "__main__":
+    strategy = SqueezeMeanReversion()
+
+    configs = [
+        ("bb+rsi30/70", {"bb_touch": True, "rsi_oversold": 30, "rsi_overbought": 70}),
+        ("bb+rsi25/75", {"bb_touch": True, "rsi_oversold": 25, "rsi_overbought": 75}),
+        ("bb+rsi35/65", {"bb_touch": True, "rsi_oversold": 35, "rsi_overbought": 65}),
+        ("rsi30/70 only", {"bb_touch": False, "rsi_oversold": 30, "rsi_overbought": 70}),
+        ("rsi25/75 only", {"bb_touch": False, "rsi_oversold": 25, "rsi_overbought": 75}),
+        ("sq<0.7 bb+rsi30", {"bb_touch": True, "sq_threshold": 0.7, "rsi_oversold": 30, "rsi_overbought": 70}),
+        ("sq<0.9 bb+rsi30", {"bb_touch": True, "sq_threshold": 0.9, "rsi_oversold": 30, "rsi_overbought": 70}),
+        ("sq<0.9 rsi35/65", {"bb_touch": False, "sq_threshold": 0.9, "rsi_oversold": 35, "rsi_overbought": 65}),
+    ]
+
+    all_results = []
+    for label, params in configs:
+        for asset in ["BTC", "ETH"]:
+            for tf in ["15m", "1h"]:
+                for hold in [2, 3, 4]:
+                    r = strategy.backtest(asset, tf, hold=hold, **params)
+                    if r and r.trades >= 30:
+                        r.strategy_name = f"HY01 {label} h={hold}"
+                        all_results.append(r)
+
+    all_results.sort(key=lambda r: r.accuracy, reverse=True)
+    print(f"\n{'=' * 130}")
+    print(f"  HY01 SQUEEZE MEAN REVERSION — TOP 25")
+    print(f"{'=' * 130}")
+    for r in all_results[:25]:
+        r.print_summary()
+    if all_results:
+        all_results[0].print_yearly()