Yuxuan Yan
1caa63faeb
Replace the old signal/strategy/backtest modules with a decoupled
data → alpha → combo pipeline (parquet between phases, .pq extension).
Alphas:
- BaseAlpha + @register_alpha factory/plugin registry; one file per
built-in (reversal, reversal_vol, momentum); external alphas via
--alpha-module. Alphas are z-scored position weights, not predictors.
Data:
- baostock primary / akshare fallback, treated consistently.
- New --universe all (~5000 A-shares via query_all_stock, filtered).
- login-once batch downloader; empty-string OHLCV coerced to NaN.
- Month-partitioned dataset {output_dir}/{universe}/month=YYYY-MM/*.pq
with chunked durability flushes; --data-path is the dataset dir.
CLI logs at INFO by default (--log-level) so progress is visible.
Docs (README, CLAUDE.md) updated incl. pipeline diagram and roadmap
TODOs for portfolio construction / backtest / paper trading.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
27 lines
881 B
Python
27 lines
881 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)
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vol = close.pct_change().rolling(self.vol_window).std()
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return reversal / vol
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