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

portfolio/builder.py

@@ -1,5 +1,6 @@
 """Map signal values to discrete position actions."""
 from dataclasses import dataclass
+from typing import Optional
 
 
 @dataclass
@@ -24,17 +25,46 @@ class ThresholdBuilder:
         self.size_pct = size_pct
 
     def build(self, signal_value: float, in_position: bool) -> PositionAction:
-        """Decide what to do given the current signal and position state.
-
-        Args:
-            signal_value: Latest signal value for the stock.
-            in_position: Whether the portfolio currently holds the stock.
-
-        Returns:
-            The action to take ("buy", "sell", or "hold").
-        """
         if not in_position and signal_value >= self.buy_threshold:
             return PositionAction("buy", self.size_pct)
         if in_position and signal_value <= self.sell_threshold:
             return PositionAction("sell", 0.0)
         return PositionAction("hold", 0.0)
+
+
+class RankEqualWeightBuilder:
+    """Rank all stocks by signal. Buy top N at equal weight. Sell if drops out.
+
+    Called once per bar with ALL stock signals. Returns per-stock actions.
+    """
+
+    def __init__(self, top_n: int = 5, min_signal: Optional[float] = None):
+        self.top_n = top_n
+        self.min_signal = min_signal  # optional floor — skip stocks with signal below this
+
+    def build(self, signals: dict[str, float]) -> dict[str, PositionAction]:
+        """Rank stocks by signal (descending). Top N get 'buy', rest get 'sell' if held.
+
+        Args:
+            signals: {symbol: signal_value} for all stocks on this bar.
+
+        Returns:
+            {symbol: PositionAction}
+        """
+        # Filter by min_signal if set
+        if self.min_signal is not None:
+            signals = {s: v for s, v in signals.items() if v >= self.min_signal}
+
+        # Sort by signal descending
+        ranked = sorted(signals.items(), key=lambda x: x[1], reverse=True)
+
+        top_symbols = set(sym for sym, _ in ranked[:self.top_n])
+        size_pct = 1.0 / self.top_n if self.top_n > 0 else 0.0
+
+        actions = {}
+        for sym in signals:
+            if sym in top_symbols:
+                actions[sym] = PositionAction("buy", size_pct)
+            else:
+                actions[sym] = PositionAction("sell", 0.0)
+        return actions

reports/summary.txt

@@ -1,21 +1,21 @@
 BACKTEST SUMMARY
 ========================================
-sharpe: -5.633716896325243
-max_drawdown: 48.659457956769764
-max_drawdown_len: 451
-total_return: -0.48968077055625825
-avg_return: -0.0010117371292484674
-total_trades: 33
-won_trades: 20
-lost_trades: 12
+sharpe: -0.18318054011826762
+max_drawdown: 31.155770933507835
+max_drawdown_len: 403
+total_return: -0.01938978410684507
+avg_return: -4.006153741083692e-05
+total_trades: 470
+won_trades: 172
+lost_trades: 293
 
 SIGNAL IC
 ========================================
-ic_mean: -0.006912277738865651
+ic_mean: 0.006912277738865651
 ic_std: 0.3332983458971776
-ir: -0.020739010030965125
-rank_ic_mean: -0.006980297831283274
+ir: 0.020739010030965125
+rank_ic_mean: 0.006980297831283274
 rank_ic_std: 0.32283972442680237
-rank_ir: -0.02162155801513181
-hit_rate: 0.4811715481171548
+rank_ir: 0.02162155801513181
+hit_rate: 0.5188284518828452
 n_periods: 478

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

signals/momentum.py

@@ -0,0 +1,21 @@
+"""Short-horizon momentum signal."""
+import pandas as pd
+
+from signals.base import AlphaSignal
+
+
+class MomentumSignal(AlphaSignal):
+    """Positive trailing return: stocks that rose score high (momentum).
+
+    The signal is ``close.pct_change(lookback)`` — opposite of ReversalSignal.
+    """
+
+    def __init__(self, lookback: int = 5):
+        self.lookback = lookback
+
+    def compute(self, df: pd.DataFrame) -> pd.Series:
+        return df["close"].pct_change(self.lookback)
+
+    @property
+    def name(self) -> str:
+        return f"momentum_{self.lookback}d"

strategies/alpha_strategy.py

@@ -4,22 +4,48 @@ import pandas as pd
 
 
 class AlphaStrategy(bt.Strategy):
-    """Trade each feed based on its precomputed ``signal`` line.
+    """Trade feeds based on precomputed ``signal`` line.
 
-    The strategy delegates the buy/sell/hold decision to a portfolio builder
-    (e.g. ``ThresholdBuilder``) and sizes entries with ``order_target_percent``.
+    Supports two builder modes:
+    - ThresholdBuilder: per-stock threshold (passed ``(signal_value, in_position)``)
+    - RankEqualWeightBuilder: cross-sectional ranking (passed ``{symbol: signal}`` dict)
     """
 
     def __init__(self, builder):
         self.builder = builder
 
     def next(self):
+        # Collect all signals
+        signals: dict[str, float] = {}
         for data in self.datas:
             sig = data.signal[0]
-            if pd.isna(sig):
+            if not pd.isna(sig):
+                signals[data._name] = float(sig)
+
+        if not signals:
+            return
+
+        # Detect builder type: if RankEqualWeightBuilder, use cross-sectional mode
+        from portfolio.builder import RankEqualWeightBuilder
+        if isinstance(self.builder, RankEqualWeightBuilder):
+            actions = self.builder.build(signals)
+            for data in self.datas:
+                name = data._name
+                if name not in actions:
                     continue
-            in_position = bool(self.getposition(data).size)
-            action = self.builder.build(float(sig), in_position)
+                action = actions[name]
+                if action.action == "buy":
+                    self.order_target_percent(data=data, target=action.size_pct)
+                elif action.action == "sell":
+                    self.close(data=data)
+        else:
+            # Legacy per-stock ThresholdBuilder
+            for data in self.datas:
+                name = data._name
+                if name not in signals:
+                    continue
+                in_position = bool(self.getposition(data).size)
+                action = self.builder.build(signals[name], in_position)
                 if action.action == "buy":
                     self.order_target_percent(data=data, target=action.size_pct)
                 elif action.action == "sell":