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>
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
"""
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Re-run the existing factor_combo champions under the research engine, with
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the same rules every new candidate gets: next-open execution, PIT universe,
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costs, and averaging over rebalance phases.
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PYTHONPATH=. uv run python -m research.lab.baselines --market us
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"""
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import argparse
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import warnings
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import pandas as pd
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from research.lab.factor_scan import load, phase_avg
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from research.lab.report import fmt
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from strategies.factor_combo import SIGNAL_REGISTRY
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warnings.filterwarnings("ignore")
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CHAMPIONS = {
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"us": ["rec_mfilt+deep_upvol", "ma200+mom7m+rec126", "rec_mfilt+ma200", "mom7m+rec126"],
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"cn": ["up_cap+quality_mom", "down_resil+qual_mom", "rec63+mom_gap", "up_cap+mom_gap"],
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}
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def baseline_scores(p, market: str) -> dict[str, pd.DataFrame]:
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mk = "us" if market == "us" else "cn"
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# factor_combo signals were built on prices masked to index membership
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px = p.close.where(p.member).ffill(limit=5)
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return {name: SIGNAL_REGISTRY[name](px) for name in CHAMPIONS[mk]}
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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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args = ap.parse_args()
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p, periods = load(args.market)
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scores = baseline_scores(p, args.market)
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for name, s in scores.items():
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for every, label in [(5, "weekly"), (21, "monthly")]:
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for per, (a, b) in periods.items():
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st = phase_avg(p, s, a, b, 10, every)
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print(f"{name:22s} top10 {label:8s} {per:8s} {fmt(st)}", flush=True)
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if __name__ == "__main__":
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main()
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