Yuxuan Yan
07ed6ad917
reversal_rank weights the 5-day reversal signal by bounded cross-sectional rank instead of z-score, so a few extreme A-share pct_change outliers (newly listed / post-suspension / limit-up names) can no longer dominate the book. compute_alpha gains an optional per-date investable-universe mask (tradable, non-ST, seasoned, top-liquidity) applied to the signal before weighting, exposed via --liquid-universe/--universe-top-n. combo combine now accepts a single alpha as an identity passthrough so a one-alpha pipeline run needs no synthetic second input. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
"""Outlier-robust short-horizon reversal alpha."""
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import pandas as pd
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from pipeline.alpha.base import BaseAlpha
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from pipeline.alpha.registry import register_alpha
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@register_alpha
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class ReversalRankAlpha(BaseAlpha):
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"""Reversal weighted by cross-sectional rank instead of z-score.
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The signal is the same trailing-return reversal as :class:`ReversalAlpha`,
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but :meth:`to_weights` converts it with a cross-sectional rank that is then
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demeaned. Rank weighting is bounded and monotone, so it does not dump the
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book into a handful of extreme movers the way raw z-scoring does — the
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failure mode that makes plain ``reversal`` collapse on the A-share universe,
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where newly listed / post-suspension / limit-up names produce huge
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``pct_change`` outliers.
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"""
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name = "reversal_rank"
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def __init__(self, lookback: int = 5):
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self.lookback = lookback
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def signal(self, close: pd.DataFrame) -> pd.DataFrame:
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return -close.pct_change(self.lookback, fill_method=None)
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def to_weights(self, signal: pd.DataFrame) -> pd.DataFrame:
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signal = signal.dropna(how="all")
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ranks = signal.rank(axis=1)
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weights = ranks.subtract(ranks.mean(axis=1), axis=0)
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return weights.fillna(0.0)
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