Adriano
0e47956f7a
- src/strategies/base.py: Strategy ABC con Signal, BacktestResult, YearlyStats - src/strategies/indicators.py: keltner_ratio, detect_squeezes, ema, atr, rv, corr - scripts/strategies/: SQ01-SQ04 (squeeze puro/filtri), ML01 (squeeze+GBM) - scripts/waste/: W01-W22 script scartati + REF originali - scripts/analysis/: compare, best_yearly, final_report, paper_status - CLAUDE.md aggiornato con nuova struttura e tabella strategie Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
"""SQ02 — Squeeze Breakout + Anti-Fakeout + Volume Confirmation.
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Migliora SQ01 con due filtri:
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1. Anti-fakeout: scarta breakout dove la candela ritraccia >60% del range
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2. Volume confirm: volume al breakout deve essere >1.3× la media durante squeeze
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IN:
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- OHLCV DataFrame
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- Parametri: bb_window (14), sq_threshold (0.8), retrace_limit (0.6),
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vol_multiplier (1.3)
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OUT:
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- Lista di Signal filtrati
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- BacktestResult
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Risultati tipici:
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BTC 15m: 79.7% acc, 1250 trades, DD 6.5%, €5.23/day — SOLIDO 9/9 anni
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ETH 15m: 78.6% acc, 942 trades, DD 3.4%, €4.33/day
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BTC 1h: 78.0% acc, 473 trades, DD 3.5%, Sharpe 6.57
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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
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from src.strategies.indicators import keltner_ratio, detect_squeezes
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class SqueezeAntifakeVol(Strategy):
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name = "SQ02_antifake_vol"
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description = "Squeeze + antifakeout + volume confirmation"
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default_assets = ["BTC", "ETH"]
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default_timeframes = ["15m", "1h"]
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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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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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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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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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br = h[i] - l[i]
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if br > 0:
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if c[i] > c[i - 1]:
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if (h[i] - c[i]) / br > retrace_limit:
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continue
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else:
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if (c[i] - l[i]) / br > retrace_limit:
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continue
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avg_v = np.mean(v[ev["sq_start"]:i])
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if avg_v > 0 and v[i] <= avg_v * vol_mult:
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continue
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signals.append(Signal(
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idx=i,
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direction=1 if first_ret > 0 else -1,
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entry_price=c[i - 1],
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metadata={"dur": ev["dur"], "vol_ratio": v[i] / avg_v if avg_v > 0 else 0},
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))
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return signals
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if __name__ == "__main__":
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strategy = SqueezeAntifakeVol()
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strategy.report()
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