Adriano
4c184bb5f7
Script utility per validare OOS strategie discovered durante run Phase 2.5.
Carica un JSON strategia (formato Hypothesis output), fetcha OHLCV via
Cerbero, esegue BacktestEngine + FalsificationReport + AdversarialReport,
stampa metriche annualizzate (CAGR, Sharpe, max DD, Calmar).
Esempio:
uv run python scripts/backtest_strategy.py /tmp/strategy.json \
--start 2018-09-01 --end 2026-01-01 --label my-strategy
Validato sui top 2 genomi Phase 2.5 (flat-ablation e fitness-v2-combo):
flat-ablation top overfit su 7y (-37%), fitness-v2 top regge (+143% in 7y,
CAGR +12.8%). Conferma che strategie con time gate temporal feature
generalizzano meglio di strategie con SMA crossover hard-tied al regime
del training period.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
"""Backtest standalone di una strategia su range esteso.
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Carica un JSON strategia (formato del Hypothesis Agent output), fetcha OHLCV
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via Cerbero, esegue BacktestEngine + FalsificationReport + AdversarialReport,
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stampa metriche annualizzate.
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Esempio:
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uv run python scripts/backtest_strategy.py /tmp/strategy_e52604ba.json \
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--start 2019-01-01 --end 2026-01-01 --label flat-ablation-top
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"""
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from __future__ import annotations
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import argparse
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import json
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import math
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from datetime import datetime
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from pathlib import Path
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from multi_swarm.agents.adversarial import AdversarialAgent
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from multi_swarm.agents.falsification import FalsificationAgent
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from multi_swarm.cerbero.client import CerberoClient
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from multi_swarm.config import load_settings
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from multi_swarm.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
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from multi_swarm.protocol.parser import parse_strategy
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from multi_swarm.protocol.validator import validate_strategy
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def main() -> None:
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p = argparse.ArgumentParser()
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p.add_argument("strategy_file", type=Path)
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p.add_argument("--start", default="2019-01-01T00:00:00+00:00")
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p.add_argument("--end", default="2026-01-01T00:00:00+00:00")
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p.add_argument("--exchange", default="deribit")
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p.add_argument("--symbol", default="BTC-PERPETUAL")
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p.add_argument("--timeframe", default="1h")
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p.add_argument("--fees-bp", type=float, default=5.0)
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p.add_argument("--n-trials-dsr", type=int, default=50)
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p.add_argument("--label", default="strategy")
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args = p.parse_args()
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strategy_json = json.loads(args.strategy_file.read_text())
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raw = json.dumps(strategy_json)
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parsed = parse_strategy(raw)
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validate_strategy(parsed)
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print(f"Strategy '{args.label}' parsed OK: {len(parsed.rules)} rules")
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settings = load_settings()
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token = (
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settings.cerbero_mainnet_token.get_secret_value()
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if settings.cerbero_mainnet_token
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else settings.cerbero_testnet_token.get_secret_value()
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)
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cerbero = CerberoClient(
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base_url=settings.cerbero_base_url,
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token=token,
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bot_tag=settings.cerbero_bot_tag,
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)
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loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
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req = OHLCVRequest(
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symbol=args.symbol,
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timeframe=args.timeframe,
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start=datetime.fromisoformat(args.start),
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end=datetime.fromisoformat(args.end),
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exchange=args.exchange,
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)
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ohlcv = loader.load(req)
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n_bars = len(ohlcv)
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years = n_bars / (24 * 365.25)
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print(
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f"OHLCV loaded: {n_bars} bars "
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f"({ohlcv.index[0]} → {ohlcv.index[-1]}, ~{years:.2f} anni)"
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)
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fals_agent = FalsificationAgent(fees_bp=args.fees_bp, n_trials_dsr=args.n_trials_dsr)
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adv_agent = AdversarialAgent(fees_bp=args.fees_bp)
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fals = fals_agent.evaluate(parsed, ohlcv)
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adv = adv_agent.review(parsed, ohlcv)
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cagr = (1.0 + float(fals.total_return)) ** (1.0 / years) - 1.0 if years > 0 else float("nan")
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calmar = (cagr / float(fals.max_drawdown)) if fals.max_drawdown > 0 else float("inf")
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print(f"\n=== {args.label} on {args.symbol} {args.timeframe} ({years:.2f} anni) ===")
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print(f"n_trades: {fals.n_trades}")
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print(f"total_return: {fals.total_return:+.4f} ({fals.total_return * 100:+.2f}%)")
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print(f"CAGR: {cagr:+.4f} ({cagr * 100:+.2f}%)")
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print(f"Sharpe (ann): {fals.sharpe:+.3f}")
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print(f"DSR: {fals.dsr:.4f} (pvalue {fals.dsr_pvalue:.4f})")
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print(f"max_drawdown: {fals.max_drawdown:.4f} ({fals.max_drawdown * 100:.2f}%)")
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print(f"Calmar: {calmar:+.3f}")
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print(f"\nAdversarial findings:")
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if not adv.findings:
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print(" (none)")
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for f in adv.findings:
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print(f" [{f.severity.value:6s}] {f.name:30s} {f.detail}")
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if __name__ == "__main__":
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main()
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