feat: include signal facts in research artifact

src/quant_engine/artifact.py

@@ -51,6 +51,7 @@ class ResearchRunArtifact:
 
     schema_version: str
     _run: pd.DataFrame
+    _signals: pd.DataFrame
     _nav: pd.DataFrame
     _trades: pd.DataFrame
     _positions: pd.DataFrame
@@ -67,6 +68,10 @@ class ResearchRunArtifact:
     def nav(self) -> pd.DataFrame:
         return _frame_copy(self._nav)
 
+    @property
+    def signals(self) -> pd.DataFrame:
+        return _frame_copy(self._signals)
+
     @property
     def trades(self) -> pd.DataFrame:
         return _frame_copy(self._trades)
@@ -95,6 +100,7 @@ class ResearchRunArtifact:
         """Return isolated table snapshots keyed by stable logical table name."""
         return {
             "run": self.run,
+            "signals": self.signals,
             "nav": self.nav,
             "trades": self.trades,
             "positions": self.positions,
@@ -140,6 +146,7 @@ class ResearchRunArtifact:
     def _internal_table_frames(self) -> Mapping[str, pd.DataFrame]:
         return {
             "run": self._run,
+            "signals": self._signals,
             "nav": self._nav,
             "trades": self._trades,
             "positions": self._positions,
@@ -234,6 +241,34 @@ def _build_nav(
     return nav
 
 
+def _build_signals(result: FactorBacktestResult, run_id: str) -> pd.DataFrame:
+    columns = [
+        "run_id",
+        "signal_date",
+        "execution_date",
+        "asset_id",
+        "factor_score",
+        "target_weight",
+    ]
+    rows: list[dict[str, object]] = []
+    for signal_date, scores in result.factor_scores.iterrows():
+        execution_date = pd.Timestamp(result.schedule.signal_to_execution.at[signal_date]).date()
+        for asset, score in scores.items():
+            rows.append(
+                {
+                    "run_id": run_id,
+                    "signal_date": pd.Timestamp(signal_date).date(),
+                    "execution_date": execution_date,
+                    "asset_id": asset,
+                    "factor_score": float(score),
+                    "target_weight": float(
+                        result.schedule.decision_weights.at[signal_date, asset]
+                    ),
+                }
+            )
+    return pd.DataFrame(rows, columns=columns)
+
+
 def _build_trades(result: FactorBacktestResult, run_id: str) -> pd.DataFrame:
     trades = result.execution.trades_frame.copy(deep=True)
     trades.insert(0, "run_id", run_id)
@@ -445,6 +480,7 @@ def build_research_run_artifact(
     return ResearchRunArtifact(
         schema_version=RESEARCH_ARTIFACT_SCHEMA_VERSION,
         _run=run,
+        _signals=_build_signals(result, normalized_run_id),
         _nav=_build_nav(result, normalized_run_id, benchmark_returns),
         _trades=_build_trades(result, normalized_run_id),
         _positions=_build_positions(result, normalized_run_id),