fix: rank-based allocator + momentum signal (reversal->momentum flip)

run_example.py

@@ -1,10 +1,6 @@
 #!/usr/bin/env python3
-"""End-to-end pipeline: HS300 universe -> reversal signal -> cross-sectional IC
--> multi-stock backtest (AlphaStrategy + PercentSizer) -> reports.
-
-Note: this runs the first 30 HS300 constituents to keep runtime manageable.
-Downloading daily bars for the full ~300 names takes roughly 10 minutes.
-"""
+"""End-to-end pipeline: HS300 universe -> momentum signal -> cross-sectional IC
+-> multi-stock backtest (AlphaStrategy + RankEqualWeightBuilder) -> reports."""
 import logging
 
 import pandas as pd
@@ -15,8 +11,8 @@ from backtest.runner import BacktestRunner
 from data.downloader import download_batch
 from data.universe import SYMBOLS
 from eval.metrics import evaluate_cross_sectional
-from portfolio.builder import ThresholdBuilder
-from signals.reversal import ReversalSignal
+from portfolio.builder import RankEqualWeightBuilder
+from signals.momentum import MomentumSignal
 from strategies.alpha_strategy import AlphaStrategy
 
 logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
@@ -33,8 +29,8 @@ def main():
     data = {s: df for s, df in data.items() if df is not None and not df.empty}
     logger.info(f"Downloaded {len(data)}/{len(symbols)} symbols")
 
-    # 3. Compute the reversal signal per stock.
-    signal = ReversalSignal(lookback=5)
+    # 3. Compute the momentum signal per stock.
+    signal = MomentumSignal(lookback=5)
     signal_series: dict[str, pd.Series] = {}
     forward_returns: dict[str, pd.Series] = {}
     for sym, df in data.items():
@@ -60,7 +56,7 @@ def main():
         sizer_percent=0.95,
     )
     runner = BacktestRunner(config)
-    builder = ThresholdBuilder(buy_threshold=0.02, sell_threshold=-0.02, size_pct=0.95)
+    builder = RankEqualWeightBuilder(top_n=5)
     for sym, df in data.items():
         df = df.copy()
         df["signal"] = signal.compute(df).values