Gahow Wang
0e378efa67
Event-driven long-only simulator with next-open fills, CN tradability (halts, limit-up/down, stamp duty), PIT S&P 500 / CSI 300 membership, rebalance-phase averaging, factor IC scans, maximin stepwise composite search across 5-year blocks, walk-forward LightGBM/ridge combiners, overlays, and deflated Sharpe. Fetchers pull 20y US OHLCV from Yahoo and CSI 300/500 ever-members (membership snapshot from baostock, bars from Yahoo). Research data lives in data/research/ (ignored). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
53 lines
1.9 KiB
Python
53 lines
1.9 KiB
Python
"""
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Walk-forward ML combination experiment.
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PYTHONPATH=. uv run --with lightgbm python -m research.lab.run_ml --market us
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"""
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import argparse
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import os
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import pickle
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import time
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import warnings
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from research.lab import ml
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from research.lab.factor_scan import load, phase_avg
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from research.lab.factors import FACTORS
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from research.lab.report import fmt
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warnings.filterwarnings("ignore")
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OUT = os.path.join("data", "research", "results")
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--market", default="us")
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ap.add_argument("--models", nargs="*", default=["lgbm", "ridge", "ic_weight"])
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ap.add_argument("--horizons", nargs="*", type=int, default=[5, 20])
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ap.add_argument("--first-test-year", type=int, default=2010)
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args = ap.parse_args()
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mk = "us" if args.market == "us" else "cn"
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p, periods = load(args.market)
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names = [n for n, f in FACTORS.items() if mk in f.markets]
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t = time.time()
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feats = ml.feature_ranks(p, names)
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print(f"{len(names)} features in {time.time() - t:.0f}s", flush=True)
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evalp = {"design": (f"{args.first_test_year}-01-01", periods["design"][1]), "holdout": periods["holdout"]}
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os.makedirs(OUT, exist_ok=True)
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for model in args.models:
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for h in args.horizons:
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t = time.time()
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score = ml.walk_forward(p, feats, model=model, horizon=h, first_test_year=args.first_test_year)
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with open(os.path.join(OUT, f"ml_{args.market}_{model}_h{h}.pkl"), "wb") as fh:
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pickle.dump(score, fh)
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for top_n in (10, 20):
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parts = []
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for per, (a, b) in evalp.items():
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parts.append(f"{per}: {fmt(phase_avg(p, score, a, b, top_n, 5))}")
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print(f"{model:9s} h{h:<3d} top{top_n:<3d}", " | ".join(parts), f"[{time.time() - t:.0f}s]", flush=True)
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if __name__ == "__main__":
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main()
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