feat(live): worker con exit TP/SL/max_bars per MR01 + doc aggiornata

StrategyWorker ora supporta exit guidati dalla strategia via Signal.metadata (take-profit alla media / stop-loss ad ATR / time-limit), con fallback al vecchio hold_bars/stop -2% per strategie senza metadata. Usa fee_rt della strategia (MR01 = 0.10% RT reale Deribit, non piu' 0.20% hardcoded). Persistenza di tp/sl/max_bars in status.json per resume. Re-validato col worker reale (replay finestre mobili 1h, fee 0.10%): BTC 1h MR01: +196% OOS, ETH 1h: +251% OOS (nov 2023->mag 2026) — coerente col backtest. README + CLAUDE.md riscritti: squeeze = artefatto di look-ahead -> waste, MR01 mean-reversion unica attiva, metodologia anti-look-ahead e fee reali 0.10% RT. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-28 20:46:35 +00:00
parent 9879b46688
commit 48435f6858
4 changed files with 218 additions and 94 deletions
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+"""Re-validazione: il StrategyWorker REALE tradi MR01 con edge netto?
+
+Guida il worker vero (generate_signals + nuova logica exit TP/SL/max_bars) su
+finestre mobili di dati 1h storici, simulando il polling live. Conferma che
+sulla finestra OOS l'edge netto (dopo fee 0.10% RT) sopravvive alla meccanica
+del worker (exit su prezzo corrente, piu' conservativa del backtest high/low).
+"""
+from __future__ import annotations
+
+import contextlib
+import os
+import sys
+from pathlib import Path
+
+PROJECT_ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(PROJECT_ROOT))
+
+from src.data.downloader import load_data
+from src.live.strategy_loader import load_strategy
+from src.live.strategy_worker import StrategyWorker
+
+OOS_FRAC = 0.30
+WIN = 250  # barre per finestra di poll (warmup bb_window=50 + ATR)
+
+
+def replay(asset: str, params: dict):
+    df = load_data(asset, "1h").reset_index(drop=True)
+    n = len(df)
+    split = int(n * (1 - OOS_FRAC))
+    strat = load_strategy("MR01_bollinger_fade")
+    w = StrategyWorker(strat, asset, "1h", capital=1000.0, position_size=0.15,
+                       leverage=3.0, hold_bars=3, params=params,
+                       data_dir=Path(f"/tmp/replay_{asset}"))
+    w._notify = lambda *a, **k: None
+    # stato pulito
+    for attr, val in dict(capital=1000.0, in_position=False, direction=0, entry_price=0,
+                          bars_held=0, total_trades=0, total_wins=0, last_bar_ts=0,
+                          tp=0.0, sl=0.0, max_bars=0).items():
+        setattr(w, attr, val)
+
+    start = max(split, WIN)
+    with contextlib.redirect_stdout(open(os.devnull, "w")):
+        for j in range(start, n):
+            w.tick(df.iloc[j - WIN + 1 : j + 1])
+
+    ret = (w.capital / 1000 - 1) * 100
+    acc = w.total_wins / w.total_trades * 100 if w.total_trades else 0.0
+    import pandas as pd
+    period = (f"{pd.to_datetime(df['timestamp'].iloc[start], unit='ms', utc=True).date()}"
+              f"->{pd.to_datetime(df['timestamp'].iloc[-1], unit='ms', utc=True).date()}")
+    return w.total_trades, acc, ret, w.capital, period
+
+
+def main():
+    print("=" * 90)
+    print("  RE-VALIDAZIONE WORKER REALE su MR01 (OOS, fee 0.10% RT, leva 3x) — finestra poll 250b")
+    print("=" * 90)
+    params = dict(bb_window=50, k=2.5, sl_atr=2.0, max_bars=24)
+    print(f"  {'Asset':>6s}{'Periodo OOS':>26s}{'Trade':>7s}{'Win%':>7s}{'Ret%':>9s}{'Cap€':>9s}")
+    print("  " + "-" * 80)
+    for asset in ["BTC", "ETH"]:
+        t, acc, ret, cap, period = replay(asset, params)
+        print(f"  {asset:>6s}{period:>26s}{t:>7d}{acc:>7.1f}{ret:>+9.1f}{cap:>9.0f}")
+    print("  " + "-" * 80)
+    print("  Atteso: Ret% positivo (l'edge mean-reversion sopravvive alla meccanica del worker).")
+
+
+if __name__ == "__main__":
+    main()