Adriano
7b2e0049eb
MR01 BTC 1h, 4000 barre, no filtro trend: backtest build_trades +3.5%/73 trade vs worker replay intrabar +4.5%/78 trade -> gap +1.0pt (allineato). Conferma che il fix exit intrabar chiude il gap live-vs-backtest delle fade (residuo = bar-timing). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
"""Demo numerica: il worker fade col NUOVO exit intrabar riproduce il backtest intrabar?
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Replay bar-by-bar dello StrategyWorker (MR01 Bollinger fade) su una finestra storica e
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confronto del rendimento col backtest di riferimento build_trades (che esce intrabar su
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high/low al livello). Filtro trend disattivato in entrambi per isolare l'effetto-exit.
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Atteso: dopo il fix (worker esce su high/low al livello, SL prioritario, come build_trades)
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il rendimento del worker ≈ backtest. Prima del fix (exit solo sul close) divergeva.
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Run: uv run python scripts/analysis/validate_fade_intrabar.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import tempfile, shutil
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.data.downloader import load_data
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from src.live.strategy_worker import StrategyWorker
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from src.live.strategy_loader import load_strategy
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from scripts.analysis.risk_management import bollinger_fade, build_trades
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CORE = dict(n=50, k=2.5, sl_atr=2.0, max_bars=24) # MR01, niente filtro trend
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POS = 0.15
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def backtest_return(df) -> tuple[float, int]:
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ents = bollinger_fade(df, **CORE)
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trades = build_trades(ents, df, trend_max=None) # intrabar, no trend filter
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cap = 1000.0
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for _, _, ret in trades:
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cap = max(cap + cap * POS * ret, 10.0)
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return (cap / 1000 - 1) * 100, len(trades)
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def worker_replay_return(df) -> tuple[float, int]:
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tmp = Path(tempfile.mkdtemp())
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try:
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w = StrategyWorker(strategy=load_strategy("MR01_bollinger_fade"), asset="BTC", tf="1h",
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capital=1000.0, params=dict(CORE), data_dir=tmp)
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# niente I/O per tick (replay veloce)
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w._save_state = lambda *a, **k: None
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w._log = lambda *a, **k: None
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w._notify = lambda *a, **k: None
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n = len(df)
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for i in range(101, n):
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w.tick(df.iloc[: i + 1])
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return (w.capital / 1000 - 1) * 100, w.total_trades
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finally:
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shutil.rmtree(tmp, ignore_errors=True)
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def main():
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df = load_data("BTC", "1h").iloc[-4000:].reset_index(drop=True)
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print("=" * 84)
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print(" DEMO exit intrabar — worker fade MR01 (replay) vs backtest intrabar | BTC 1h, 4000 barre")
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print("=" * 84)
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bt_ret, bt_n = backtest_return(df)
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wk_ret, wk_n = worker_replay_return(df)
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gap = wk_ret - bt_ret
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print(f" backtest build_trades : {bt_ret:+.1f}% ({bt_n} trade)")
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print(f" worker replay (intrabar): {wk_ret:+.1f}% ({wk_n} trade)")
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print(f" gap = {gap:+.1f} punti % -> {'OK (allineato)' if abs(gap) < max(abs(bt_ret) * 0.10, 3) else 'DIVERGE'}")
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print("\n Col vecchio exit close-only il worker divergeva (usciva tardi/altrove);")
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print(" ora esce su high/low al livello come il backtest -> gap ridotto al bar-timing residuo.")
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if __name__ == "__main__":
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main()
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