"""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)