Gahow Wang
04e2c5c3be
Studies run by earlier sessions and documented in CLAUDE.md, but never committed: FINRA short volume (SVR) and the research-only svr_momentum veto, SEC fails-to-deliver, 13D activists and the 13D event sleeve, 13F institutional flow, GDELT news volume, S&P 400/600 Form 4 insiders, union_shield, shield_lab; plus the mid/small insider and Tiingo fetcher changes and midsmall_rebuild. overnight_midsmall: the overnight-only momentum book on S&P 400/600 earns 0.93 net Sharpe at 3 bp a side (2019-26) with corr 0.83 to the S&P 500 book (STOP). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
92 lines
3.3 KiB
Python
92 lines
3.3 KiB
Python
"""Short-volume veto overlay test on the momentum sleeve (official engine).
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Hypothesis from the first scan (research/lab/short_volume.py): names in the
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top quintile of 21-day mean REG SHO short-volume ratio (SVR = ShortVolume /
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TotalVolume) underperform over the next month (S&P 500: IC21 -0.020, t -3.1;
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Q5-Q1 spread -0.40%/mo). Test: veto those names at selection time (score ->
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NaN before top-N selection), on the flagship overnight_recovery_top10 sleeve.
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PIT: the SVR panel is shifted by 2 sessions inside the veto (REG SHO files
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are published after the close), matching the conservative convention of the
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scan.
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PYTHONPATH=. uv run python -m research.svr_overlay
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"""
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from __future__ import annotations
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import pickle
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import numpy as np
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import pandas as pd
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import trader
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from strategies.robust_momentum import RobustMomentumStrategy, select_with_buffer
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import research.tr9_lab as lab
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SVR_PATH = "data/research/short_volume.pkl"
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_SVR_CACHE: dict = {}
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def svr21_panel() -> pd.DataFrame:
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if "svr" not in _SVR_CACHE:
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with open(SVR_PATH, "rb") as fh:
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d = pickle.load(fh)
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svr = (d["short"] / d["total"].replace(0.0, np.nan))
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_SVR_CACHE["svr"] = svr.rolling(21, min_periods=10).mean()
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return _SVR_CACHE["svr"]
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class RobustMomentumSvr(RobustMomentumStrategy):
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"""RobustMomentum + veto of top-quantile short-volume names at selection."""
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def __init__(self, signal: str, svr_quant: float = 0.8, svr_lag: int = 2, **kw) -> None:
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super().__init__(signal, **kw)
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self.svr_quant = svr_quant
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self.svr_lag = svr_lag
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def generate_weights(self, bars: dict[str, pd.DataFrame]) -> pd.DataFrame:
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close = bars["close"]
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panel = {f: bars[f].reindex(index=close.index, columns=close.columns) for f in self.fields}
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if "eligible" in bars:
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panel["eligible"] = bars["eligible"]
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score = self._score(panel)
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eligible = close.notna()
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if "eligible" in bars:
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eligible &= bars["eligible"].reindex_like(close).fillna(False).astype(bool)
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score = score.where(eligible)
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svr = svr21_panel().reindex(index=close.index, columns=close.columns).shift(self.svr_lag)
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q = svr.rank(axis=1, pct=True)
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veto = (q >= self.svr_quant) & svr.notna()
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score = score.where(~veto)
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weights = select_with_buffer(score, self.top_n, self.rebal_freq, self.buffer, self.warmup,
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prices=close)
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return weights.shift(1).fillna(0.0)
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def register() -> list[str]:
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reg = trader.STRATEGY_REGISTRY
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names = []
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reg["overnight_recovery_svr80"] = lambda **kw: RobustMomentumSvr(
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"overnight_recovery", top_n=10, rebal_freq=10, buffer=2.0, svr_quant=0.8)
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names.append("overnight_recovery_svr80")
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reg["overnight_recovery_svr90"] = lambda **kw: RobustMomentumSvr(
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"overnight_recovery", top_n=10, rebal_freq=10, buffer=2.0, svr_quant=0.9)
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names.append("overnight_recovery_svr90")
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reg["overnight_recovery_svr70"] = lambda **kw: RobustMomentumSvr(
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"overnight_recovery", top_n=10, rebal_freq=10, buffer=2.0, svr_quant=0.7)
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names.append("overnight_recovery_svr70")
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return names
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def main() -> None:
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names = register()
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curves = lab.run(["overnight_recovery_top10", "lowrisk_top20"] + names)
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lab.report(curves, "SVR veto overlay")
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if __name__ == "__main__":
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main()
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