Add Jared read-only token OHLCV ingestion and manual contract lookup

core/train_eval.py

@@ -90,7 +90,16 @@ def inverse_target_scale(values, target_scaler):
     """Inverse-transform a single target feature for any batch/horizon shape."""
     arr = np.asarray(values, dtype=float)
     original_shape = arr.shape
-    restored = target_scaler.inverse_transform(arr.reshape(-1, 1))
+    flat = arr.reshape(-1, 1)
+    # Be defensive against an older/cached preprocessing path that fitted the
+    # target scaler with more than one column. Only the first target column is
+    # relevant; sklearn's multi-column inverse_transform would broadcast-fail.
+    if getattr(target_scaler, "n_features_in_", 1) != 1:
+        scale = float(np.asarray(target_scaler.scale_).reshape(-1)[0])
+        offset = float(np.asarray(target_scaler.min_).reshape(-1)[0])
+        restored = flat / scale + offset
+    else:
+        restored = target_scaler.inverse_transform(flat)
     return restored.reshape(original_shape)
 
 # ---------------- Feature selection ----------------