Add minute bar feature pipeline

tests/test_alpha.py

@@ -347,3 +347,69 @@ def test_universe_filter_does_not_corrupt_signal_history():
     held = set(filtered.loc[filtered["weight"] != 0.0, "symbol_id"].unique())
     # The two most liquid names (highest amount) are sh600519, sz300750.
     assert held == {"sh600519", "sz300750"}
+
+
+# --- feature-aware alpha integration ----------------------------------------
+
+def test_compute_alpha_without_feature_path_matches_empty_feature_paths():
+    data = _make_data()
+
+    base = compute_alpha(data, "rev5", "reversal", lookback=5)
+    with_empty_features = compute_alpha(
+        data,
+        "rev5",
+        "reversal",
+        lookback=5,
+        feature_paths=[],
+    )
+
+    pd.testing.assert_frame_equal(base, with_empty_features)
+
+
+def test_feature_aware_alpha_reads_joined_feature_column(tmp_path):
+    module_path = tmp_path / "feature_aware_alpha.py"
+    module_path.write_text(textwrap.dedent('''
+        import pandas as pd
+        from pipeline.alpha.base import BaseAlpha
+        from pipeline.alpha.registry import register_alpha
+
+        @register_alpha
+        class FeatureAwareAlpha(BaseAlpha):
+            name = "feature_aware_test_alpha"
+
+            def signal_from_data(
+                self,
+                data: pd.DataFrame,
+                close: pd.DataFrame,
+            ) -> pd.DataFrame:
+                signal = data.pivot_table(
+                    index="date",
+                    columns="symbol_id",
+                    values="toy_feature",
+                    aggfunc="first",
+                )
+                return signal.reindex(index=close.index, columns=close.columns)
+    '''))
+
+    data = _make_data()
+    feature = data[["symbol_id", "date"]].copy()
+    feature["toy_feature"] = feature["symbol_id"].map({
+        "sh600000": 1.0,
+        "sz000001": 2.0,
+        "sh600519": 3.0,
+    })
+    feature_path = tmp_path / "toy_feature.pq"
+    feature.to_parquet(feature_path, index=False)
+
+    load_alpha_module(str(module_path))
+    result = compute_alpha(
+        data,
+        "feature_run",
+        "feature_aware_test_alpha",
+        feature_paths=[str(feature_path)],
+    )
+
+    assert list(result.columns) == ALPHA_COLUMNS
+    assert (result["alpha_name"] == "feature_run").all()
+    last = result[result["date"] == result["date"].max()]
+    assert last.set_index("symbol_id")["weight"].idxmax() == "sh600519"