change: rank allocator top_n=5 -> top_pct=0.2 (20% of universe)

portfolio/builder.py

@@ -33,24 +33,17 @@ class ThresholdBuilder:
 
 
 class RankEqualWeightBuilder:
-    """Rank all stocks by signal. Buy top N at equal weight. Sell if drops out.
+    """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):
+    def __init__(self, top_n: Optional[int] = None, top_pct: float = 0.2, min_signal: Optional[float] = None):
         self.top_n = top_n
-        self.min_signal = min_signal  # optional floor — skip stocks with signal below this
+        self.top_pct = top_pct
+        self.min_signal = min_signal
 
     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}
@@ -58,8 +51,14 @@ class RankEqualWeightBuilder:
         # 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
+        # Determine top N: explicit count or percentage of available stocks
+        if self.top_n is not None:
+            n = self.top_n
+        else:
+            n = max(1, int(len(signals) * self.top_pct))
+
+        top_symbols = set(sym for sym, _ in ranked[:n])
+        size_pct = 1.0 / n if n > 0 else 0.0
 
         actions = {}
         for sym in signals: