Yuxuan Yan
0a6f367fbf
Weights formed from close[t] now earn the t→t+1 return: forward returns are computed on the full market calendar before selecting signal dates, so a sparse signal grid earns the next available return rather than the next signal date, and the final signal date (no forward return) is dropped. Signal pct_change uses fill_method=None so suspended names do not inherit stale prices. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
27 lines
915 B
Python
27 lines
915 B
Python
"""Volatility-scaled 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 ReversalVolAlpha(BaseAlpha):
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"""Reversal scaled by trailing volatility.
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The raw reversal ``-close.pct_change(lookback)`` is divided by the rolling
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standard deviation of daily returns over ``vol_window``, so the score favors
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oversold names whose move is large *relative* to their own volatility.
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"""
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name = "reversal_vol"
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def __init__(self, lookback: int = 5, vol_window: int = 20):
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self.lookback = lookback
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self.vol_window = vol_window
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def signal(self, close: pd.DataFrame) -> pd.DataFrame:
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reversal = -close.pct_change(self.lookback, fill_method=None)
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vol = close.pct_change(fill_method=None).rolling(self.vol_window).std()
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return reversal / vol
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