feat: CSI500 universe + reversal_vol signal + argparse CLI

signals/__init__.py

@@ -1,5 +1,6 @@
 """Alpha signal abstractions."""
 from signals.base import AlphaSignal
 from signals.reversal import ReversalSignal
+from signals.reversal_vol import ReversalVolSignal
 
-__all__ = ["AlphaSignal", "ReversalSignal"]
+__all__ = ["AlphaSignal", "ReversalSignal", "ReversalVolSignal"]

signals/reversal_vol.py

@@ -0,0 +1,27 @@
+"""Volatility-scaled short-horizon reversal signal."""
+import pandas as pd
+
+from signals.base import AlphaSignal
+
+
+class ReversalVolSignal(AlphaSignal):
+    """Reversal score normalized by trailing volatility.
+
+    The raw reversal ``-close.pct_change(lookback)`` is divided by the rolling
+    standard deviation of daily returns over ``vol_window``. Scaling by
+    volatility damps the score of noisy, high-vol names so the signal favors
+    oversold stocks whose move is large *relative* to their own volatility.
+    """
+
+    def __init__(self, lookback: int = 5, vol_window: int = 20):
+        self.lookback = lookback
+        self.vol_window = vol_window
+
+    def compute(self, df: pd.DataFrame) -> pd.Series:
+        reversal = -df["close"].pct_change(self.lookback)
+        vol = df["close"].pct_change().rolling(self.vol_window).std()
+        return reversal / vol
+
+    @property
+    def name(self) -> str:
+        return f"reversal_vol_{self.lookback}d_{self.vol_window}d"