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2bbc43c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | #!/usr/bin/env python3
"""Run the offline backtest on a historical data file.
Usage:
python scripts/run_backtest.py --data data/BTCUSDT_2021-05-19.parquet
"""
import argparse
import logging
import sys
from pathlib import Path
# Insert the ml directory at the FRONT of sys.path so we import the local
# package (with all submodules: data/, features/, models/, eval/, alert/)
# rather than any pip-installed version that might be incomplete.
ML_DIR = Path(__file__).resolve().parent.parent / "ml"
sys.path.insert(0, str(ML_DIR))
# Also add the project root so 'flash_crash_watchdog' resolves
PROJECT_ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(PROJECT_ROOT))
from flash_crash_watchdog.cascade import DetectionCascade
from flash_crash_watchdog.data.historical_loader import load_parquet
from flash_crash_watchdog.eval.backtest import run_backtest
def main() -> int:
parser = argparse.ArgumentParser(description="Run offline backtest")
parser.add_argument("--data", required=True, help="Parquet or CSV file")
parser.add_argument("--config", default="configs/pipeline.yml")
parser.add_argument("--output", default="results/backtest_results.json")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
df = load_parquet(args.data)
cascade = DetectionCascade.from_config(args.config)
results = run_backtest(cascade, df)
results.print_summary()
output_path = Path(args.output)
output_path.parent.mkdir(parents=True, exist_ok=True)
results.save(output_path)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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