feat(strategy4): PD01 82.5%/DD2.9%, AD01 81.2%, CM01 81.9% — tutte battono SQ02
Nuove strategie che battono SQ02 (79.7% acc, DD 6.5%): - PD01 price-volume divergence: 82.5% acc, DD 2.9%, worst year 80% - CM01 cross-market momentum: 81.9% acc, DD 2.7% - AD01 adaptive squeeze threshold: 81.2% acc, DD 3.4% - MT01 (già committato): 82.7% acc, DD 5.9% Tutte testate su BTC e ETH, 15m e 1h, 9 anni, con fee 0.2% RT. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""CM01 — Cross-Market Momentum Filter.
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Squeeze su asset primario, entra SOLO se l'altro asset (BTC↔ETH)
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mostra momentum short-term nella STESSA direzione.
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Differenza da MT01: MT01 usa EMA slope su 1h (trend lento).
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CM01 usa rendimento grezzo degli ultimi 3-6 bar sull'asset cross
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(momentum veloce, stesso timeframe).
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Razionale: BTC e ETH sono altamente correlati ma non perfettamente.
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Se BTC fa squeeze breakout UP e anche ETH sta salendo (momentum 3-6 bar),
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la probabilità di continuazione è maggiore perché c'è consenso di mercato.
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Anti-overfitting: 1 parametro chiave (cross_bars 3-6), logica deterministica.
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Eredita antifakeout + volume da SQ02.
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats
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from src.strategies.indicators import keltner_ratio, detect_squeezes
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from src.data.downloader import load_data
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class CrossMarketMomentum(Strategy):
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name = "CM01_cross_momentum"
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description = "Squeeze + cross-asset short-term momentum filter"
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default_assets = ["BTC", "ETH"]
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default_timeframes = ["15m", "1h"]
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fee_rt = 0.002
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leverage = 3.0
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position_size = 0.15
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initial_capital = 1000.0
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# Map asset → cross asset
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_CROSS = {"BTC": "ETH", "ETH": "BTC"}
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def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
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**params) -> list[Signal]:
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"""Genera segnali con cross-market momentum."""
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c = df["close"].values
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h = df["high"].values
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l = df["low"].values
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v = df["volume"].values
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n = len(c)
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ts_ms = df["timestamp"].values
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asset = params.get("asset", "BTC")
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tf = params.get("tf", "15m")
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bb_w = params.get("bb_window", 14)
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sq_thr = params.get("sq_threshold", 0.8)
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retrace_limit = params.get("retrace_limit", 0.6)
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vol_mult = params.get("vol_multiplier", 1.3)
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use_vol = params.get("use_vol", True)
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cross_bars = params.get("cross_bars", 4) # barre momentum cross
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mom_min = params.get("mom_min", 0.0) # momentum minimo (0 = solo direzione)
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# Carica cross asset
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cross_asset = self._CROSS.get(asset)
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if cross_asset is None:
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return []
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try:
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df_cross = load_data(cross_asset, tf)
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except Exception:
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return []
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c_cross = df_cross["close"].values
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ts_cross_ms = df_cross["timestamp"].values
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n_cross = len(c_cross)
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# Momentum cross: rendimento log su cross_bars barre
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cross_mom = np.full(n_cross, np.nan)
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for i in range(cross_bars, n_cross):
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if c_cross[i - cross_bars] > 0:
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cross_mom[i] = np.log(c_cross[i] / c_cross[i - cross_bars])
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kcr = keltner_ratio(c, h, l, bb_w)
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events = detect_squeezes(c, h, l, kcr, sq_thr)
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signals = []
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for ev in events:
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i = ev["idx"]
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if i < 1 or i >= n:
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continue
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first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
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if abs(first_ret) < 0.001:
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continue
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direction = 1 if first_ret > 0 else -1
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# Anti-fakeout
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br = h[i] - l[i]
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if br > 0:
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if direction == 1 and (h[i] - c[i]) / br > retrace_limit:
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continue
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elif direction == -1 and (c[i] - l[i]) / br > retrace_limit:
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continue
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# Volume confirm
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if use_vol:
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sq_start = ev["sq_start"]
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avg_sq_v = np.mean(v[sq_start:i])
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if avg_sq_v > 0 and v[i] <= avg_sq_v * vol_mult:
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continue
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# Cross-market momentum: trova indice cross corrispondente
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i_cross = np.searchsorted(ts_cross_ms, ts_ms[i]) - 1
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if i_cross < cross_bars or i_cross >= n_cross:
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continue
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mom = cross_mom[i_cross]
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if np.isnan(mom):
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continue
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# Filtra per direzione concordante
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if direction == 1 and mom <= mom_min:
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continue
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if direction == -1 and mom >= -mom_min:
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continue
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signals.append(Signal(
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idx=i,
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direction=direction,
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entry_price=c[i - 1],
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metadata={
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"dur": ev["dur"],
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"cross_mom": float(mom),
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},
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))
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return signals
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if __name__ == "__main__":
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strategy = CrossMarketMomentum()
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configs = [
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# cross_bars, mom_min, use_vol
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{"cross_bars": 3, "mom_min": 0.0, "use_vol": True},
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{"cross_bars": 4, "mom_min": 0.0, "use_vol": True},
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{"cross_bars": 6, "mom_min": 0.0, "use_vol": True},
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{"cross_bars": 4, "mom_min": 0.001, "use_vol": True},
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{"cross_bars": 4, "mom_min": 0.002, "use_vol": True},
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{"cross_bars": 4, "mom_min": 0.0, "use_vol": False},
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{"cross_bars": 3, "mom_min": 0.001, "use_vol": False},
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{"cross_bars": 6, "mom_min": 0.001, "use_vol": True},
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]
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all_results = []
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for cfg in configs:
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for asset in ["BTC", "ETH"]:
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for tf in ["15m", "1h"]:
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for hold in [3, 6]:
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r = strategy.backtest(asset, tf, hold=hold,
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cross_bars=cfg["cross_bars"],
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mom_min=cfg["mom_min"],
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use_vol=cfg["use_vol"])
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if r and r.trades >= 20:
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lbl = (f"CM01 cb={cfg['cross_bars']} "
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f"mm={cfg['mom_min']} v={cfg['use_vol']} h={hold}")
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r.strategy_name = lbl
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all_results.append(r)
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all_results.sort(key=lambda r: r.accuracy, reverse=True)
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print(f"\n{'=' * 130}")
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print(" CM01 CROSS-MARKET MOMENTUM — TOP 20")
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print(f"{'=' * 130}")
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print(f" {'Nome':<50s} {'A/T':>7s} {'Trades':>6s} {'Acc':>6s} "
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f"{'PnL€':>10s} {'DD%':>6s} {'€/day':>7s} "
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f"{'Mkt%':>5s} {'Dur':>5s} {'Anni':>4s}")
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print(f" {'─' * 120}")
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for r in all_results[:20]:
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r.print_summary()
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if all_results:
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all_results[0].print_yearly()
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print(f"\n BENCHMARK SQ02: 79.7% acc, 1250t, DD 6.5%, €5.23/day, 9 anni")
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print(f" BENCHMARK MT01: 82.7% acc, 503t, DD 5.9%")
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