refactor: class-based alpha factory + month-partitioned data pipeline

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>

pipeline/alpha/library/reversal_vol.py

@@ -0,0 +1,26 @@
+"""Volatility-scaled short-horizon reversal alpha."""
+import pandas as pd
+
+from pipeline.alpha.base import BaseAlpha
+from pipeline.alpha.registry import register_alpha
+
+
+@register_alpha
+class ReversalVolAlpha(BaseAlpha):
+    """Reversal scaled by trailing volatility.
+
+    The raw reversal ``-close.pct_change(lookback)`` is divided by the rolling
+    standard deviation of daily returns over ``vol_window``, so the score favors
+    oversold names whose move is large *relative* to their own volatility.
+    """
+
+    name = "reversal_vol"
+
+    def __init__(self, lookback: int = 5, vol_window: int = 20):
+        self.lookback = lookback
+        self.vol_window = vol_window
+
+    def signal(self, close: pd.DataFrame) -> pd.DataFrame:
+        reversal = -close.pct_change(self.lookback)
+        vol = close.pct_change().rolling(self.vol_window).std()
+        return reversal / vol