Evaluate weights against next-period returns to avoid look-ahead

Weights formed from close[t] now earn the t→t+1 return: forward returns
are computed on the full market calendar before selecting signal dates,
so a sparse signal grid earns the next available return rather than the
next signal date, and the final signal date (no forward return) is
dropped. Signal pct_change uses fill_method=None so suspended names do
not inherit stale prices.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tests/test_portfolio.py

@@ -65,7 +65,7 @@ def _make_data(n_days: int = 40, symbols=_SYMBOLS, start="2024-01-01",
 def _make_weights(data: pd.DataFrame, name="combo") -> pd.DataFrame:
     """Demeaned per-date signed weights so the cross-section is dollar-neutral."""
     close = data.pivot_table(index="date", columns="symbol_id", values="close").sort_index()
-    raw = -close.pct_change(5)
+    raw = -close.pct_change(5, fill_method=None)
     demeaned = raw.sub(raw.mean(axis=1), axis=0)
     long = demeaned.reset_index().melt(id_vars="date", var_name="symbol_id",
                                        value_name="weight").dropna()
@@ -535,3 +535,27 @@ def test_evaluate_portfolio_keys_no_ic():
         assert key in metrics
     assert "ic" not in metrics
     assert "rank_ic" not in metrics
+
+
+def test_evaluate_portfolio_excludes_signal_without_forward_return():
+    dates = pd.date_range("2024-01-01", periods=3)
+    data = pd.DataFrame([
+        {"symbol_id": sym, "date": d, "close": price}
+        for d, prices in zip(dates, [(100.0, 100.0), (100.0, 100.0), (200.0, 100.0)])
+        for sym, price in zip(("sh600000", "sz000001"), prices)
+    ])
+    positions = pd.DataFrame({
+        "symbol_id": ["sh600000", "sz000001", "sh600000", "sz000001"],
+        "date": [dates[1], dates[1], dates[2], dates[2]],
+        "portfolio_name": ["run1"] * 4,
+        "target_weight": [0.5, -0.5, -0.5, 0.5],
+        "target_value": [500.0, -500.0, -500.0, 500.0],
+        "target_shares": [5.0, -5.0, -2.5, 5.0],
+        "position_shares": [5, -5, -2, 5],
+        "position_value": [500.0, -500.0, -400.0, 500.0],
+        "price": [100.0, 100.0, 200.0, 100.0],
+    })
+
+    metrics = evaluate_portfolio(positions, data)
+
+    assert metrics["n_dates"] == 1