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>
77 lines
3.0 KiB
Python
77 lines
3.0 KiB
Python
"""RobustMomentum + short-volume veto (FINRA REG SHO). RESEARCH ONLY — not registered.
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Status: a 70%-quantile veto on overnight_recovery_top10 looked good on the
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2026-10-07 window (Sharpe 0.87 -> 0.89, MaxDD -47.4% -> -39.4%), but the
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quantile neighborhood killed it: veto at 0.60/0.75/0.80 all score BELOW the
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baseline (Sharpe 0.81/0.82/0.84 vs 0.87). A lone spike, not a factor — the
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strategy was registered as `overnight_recovery_svr70` and then reverted the
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same day. Kept as an artifact; the veto idea is not robust on this sleeve.
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Context: the raw cross-sectional reading stands (21-day SVR is a negative
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predictor on the S&P 500 panel, IC -0.020 with t -3.1; within small-cap
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momentum the high-SVR half underperforms -0.61%/mo), but turning that into a
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top-10-portfolio exclusion rule is too concentrated to be stable.
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Data: data/research/short_volume.pkl (research/lab/fetch_shortvolume.py;
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missing file = no veto). REG SHO files publish after the close, so the veto
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inside is lagged 2 sessions.
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"""
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from __future__ import annotations
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import os
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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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from strategies.robust_momentum import RobustMomentumStrategy, select_with_buffer
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SVR_PATH = os.path.join("data", "research", "short_volume.pkl")
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_SVR_CACHE: dict = {}
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def svr21_panel() -> pd.DataFrame | None:
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if "svr" not in _SVR_CACHE:
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if not os.path.exists(SVR_PATH):
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_SVR_CACHE["svr"] = None
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else:
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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 with a top-quantile short-volume veto at selection."""
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def __init__(self, signal: str, svr_quant: float = 0.7, 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()
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if svr is not None:
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svr = svr.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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__all__ = ["RobustMomentumSvr"]
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