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>
96 lines
3.8 KiB
Python
96 lines
3.8 KiB
Python
"""
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Single-factor scan: rank IC and long-only top-N backtests per period.
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PYTHONPATH=. uv run python -m research.lab.factor_scan --market us
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PYTHONPATH=. uv run python -m research.lab.factor_scan --market cn_hs300
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Writes data/research/results/factor_scan_{market}.csv.
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"""
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import argparse
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import os
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import time
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import warnings
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import pandas as pd
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from research.lab import panel as pnl
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from research.lab.engine import simulate
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from research.lab.factors import FACTORS
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from research.lab.report import (forward_returns, ic_series, ic_summary, stats,
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top_minus_universe)
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warnings.filterwarnings("ignore")
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PERIODS = {
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"us": {"design": ("2006-01-01", "2016-09-26"), "holdout": ("2016-09-27", "2026-09-24")},
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"cn": {"design": ("2010-01-01", "2016-09-26"), "holdout": ("2016-09-27", "2026-09-24")},
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}
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RESULTS = os.path.join("data", "research", "results")
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def load(market: str):
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if market == "us":
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return pnl.load_us(), PERIODS["us"]
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return pnl.load_cn(market.split("_", 1)[1]), PERIODS["cn"]
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def phase_avg(p, score, start, end, top_n, every, **kw) -> dict:
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"""Average stats over every rebalance phase (offset 0..every-1)."""
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w0 = p.dates.searchsorted(pd.Timestamp(start))
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sp = p.slice(p.dates[max(w0 - 5, 0)], end)
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rows = []
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for off in range(every):
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r = simulate(sp, score, top_n=top_n, every=every, offset=off, warmup=5, **kw)
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rows.append(stats(r.equity.loc[start:end], r.turnover))
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return pd.DataFrame(rows).mean().to_dict()
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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("--top-n", type=int, default=20)
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ap.add_argument("--every", type=int, default=5)
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ap.add_argument("--only", nargs="*")
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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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fwd5 = forward_returns(p, 5)
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fwd20 = forward_returns(p, 20)
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rows = []
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names = args.only or [n for n, f in FACTORS.items() if mk in f.markets]
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for name in names:
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t = time.time()
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try:
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s = FACTORS[name](p)
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except Exception as exc: # noqa: BLE001
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print(f"{name}: failed {exc}")
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continue
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row = {"factor": name}
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for per, (a, b) in periods.items():
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sl = slice(a, b)
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ic5 = ic_summary(ic_series(s.loc[sl], fwd5.loc[sl], p.member.loc[sl], 5))
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ic20 = ic_summary(ic_series(s.loc[sl], fwd20.loc[sl], p.member.loc[sl], 20), 12)
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tmu = top_minus_universe(s.loc[sl], fwd5.loc[sl], p.member.loc[sl], args.top_n, 5)
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st = phase_avg(p, s, a, b, args.top_n, args.every)
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row.update({f"{per}_ic5": ic5["ic"], f"{per}_ic5_t": ic5["t"],
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f"{per}_ic20": ic20["ic"], f"{per}_ic20_t": ic20["t"],
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f"{per}_top_excess_ann": tmu.mean() * 52,
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f"{per}_cagr": st["cagr"], f"{per}_sharpe": st["sharpe"],
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f"{per}_mdd": st["mdd"], f"{per}_turn": st["turnover"]})
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rows.append(row)
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print(f"{name:16s} "
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f"D: ic5 {row['design_ic5']:+.3f} (t{row['design_ic5_t']:+.1f}) ic20 {row['design_ic20']:+.3f} "
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f"xs {row['design_top_excess_ann']*100:+5.1f}% cagr {row['design_cagr']*100:5.1f}% sh {row['design_sharpe']:.2f} | "
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f"H: ic5 {row['holdout_ic5']:+.3f} (t{row['holdout_ic5_t']:+.1f}) ic20 {row['holdout_ic20']:+.3f} "
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f"xs {row['holdout_top_excess_ann']*100:+5.1f}% cagr {row['holdout_cagr']*100:5.1f}% sh {row['holdout_sharpe']:.2f} "
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f"turn {row['holdout_turn']:.0f}x [{time.time()-t:.0f}s]", flush=True)
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# equal-weight universe reference
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os.makedirs(RESULTS, exist_ok=True)
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out = pd.DataFrame(rows)
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out.to_csv(os.path.join(RESULTS, f"factor_scan_{args.market}.csv"), index=False)
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if __name__ == "__main__":
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main()
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