Add outlier-robust reversal_rank alpha and investable-universe filter

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>

pipeline/alpha/library/reversal_rank.py

@@ -0,0 +1,33 @@
+"""Outlier-robust short-horizon reversal alpha."""
+import pandas as pd
+
+from pipeline.alpha.base import BaseAlpha
+from pipeline.alpha.registry import register_alpha
+
+
+@register_alpha
+class ReversalRankAlpha(BaseAlpha):
+    """Reversal weighted by cross-sectional rank instead of z-score.
+
+    The signal is the same trailing-return reversal as :class:`ReversalAlpha`,
+    but :meth:`to_weights` converts it with a cross-sectional rank that is then
+    demeaned. Rank weighting is bounded and monotone, so it does not dump the
+    book into a handful of extreme movers the way raw z-scoring does — the
+    failure mode that makes plain ``reversal`` collapse on the A-share universe,
+    where newly listed / post-suspension / limit-up names produce huge
+    ``pct_change`` outliers.
+    """
+
+    name = "reversal_rank"
+
+    def __init__(self, lookback: int = 5):
+        self.lookback = lookback
+
+    def signal(self, close: pd.DataFrame) -> pd.DataFrame:
+        return -close.pct_change(self.lookback, fill_method=None)
+
+    def to_weights(self, signal: pd.DataFrame) -> pd.DataFrame:
+        signal = signal.dropna(how="all")
+        ranks = signal.rank(axis=1)
+        weights = ranks.subtract(ranks.mean(axis=1), axis=0)
+        return weights.fillna(0.0)