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>
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
"""
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Construction sweep: holdings count, buffer (hysteresis), rebalance interval and
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weighting for a fixed score, reported per ~5-year block.
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PYTHONPATH=. uv run python -m research.lab.construction --market us --combo overnight_mom+rec_126
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"""
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import argparse
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import itertools
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import warnings
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import numpy as np
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import pandas as pd
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from research.lab.baselines import baseline_scores
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from research.lab.factor_scan import load, phase_avg
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from research.lab.factors import FACTORS, zrank
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from research.lab.stepwise import SUBPERIODS
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warnings.filterwarnings("ignore")
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def combo_score(p, combo: str, market: str) -> pd.DataFrame:
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if combo.startswith("baseline:"):
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return baseline_scores(p, market)[combo.split(":", 1)[1]]
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parts = combo.split("+")
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return sum(zrank(FACTORS[n](p), p.member) for n in parts) / len(parts)
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def block_stats(p, score, subs, **kw) -> dict:
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per = [phase_avg(p, score, a, b, **kw) for a, b in subs]
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return {"sharpe": np.mean([x["sharpe"] for x in per]), "min_sh": min(x["sharpe"] for x in per),
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"cagr": np.mean([x["cagr"] for x in per]), "mdd": min(x["mdd"] for x in per),
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"turn": np.mean([x["turnover"] for x in per]),
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"blocks": " ".join(f"{x['sharpe']:.2f}/{x['cagr']*100:.0f}%" for x in per)}
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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("--combo", nargs="+", required=True)
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ap.add_argument("--tops", nargs="*", type=int, default=[10, 15, 20, 30])
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ap.add_argument("--buffers", nargs="*", type=float, default=[0, 0.5, 1.0, 2.0])
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ap.add_argument("--everys", nargs="*", type=int, default=[5, 10])
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ap.add_argument("--weightings", nargs="*", default=["equal", "invvol"])
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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, _ = load(args.market)
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subs = SUBPERIODS[mk]
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for combo in args.combo:
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s = combo_score(p, combo, args.market)
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print(f"=== {combo}")
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for top, buf, every, wt in itertools.product(args.tops, args.buffers, args.everys, args.weightings):
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st = block_stats(p, s, subs, top_n=top, every=every, buffer=buf, weighting=wt)
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print(f" top{top:<3d} buf{buf:<4} every{every:<3d} {wt:7s} sh {st['sharpe']:.2f} min {st['min_sh']:.2f} "
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f"cagr {st['cagr']*100:5.1f}% worstMDD {st['mdd']*100:5.1f}% turn {st['turn']:4.1f}x [{st['blocks']}]",
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flush=True)
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if __name__ == "__main__":
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main()
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