fix predict

tests/test_latest_predict.py

@@ -1,6 +1,12 @@
+import json
+import tempfile
 import unittest
+from pathlib import Path
+from unittest.mock import patch
 
-from cbpred.latest_predict import OUTPUT_COLUMNS
+import pandas as pd
+
+from cbpred.latest_predict import OUTPUT_COLUMNS, build_predictions
 
 
 class LatestPredictTests(unittest.TestCase):
@@ -21,6 +27,51 @@ class LatestPredictTests(unittest.TestCase):
             ),
         )
 
+    def test_batch_skips_symbols_without_training_profiles(self):
+        with tempfile.TemporaryDirectory() as directory:
+            root = Path(directory)
+            data_dir = root / "data"
+            output_dir = root / "output"
+            data_dir.mkdir()
+            for symbol, filename in (
+                ("TRAIN/USDT:USDT", "train.csv"),
+                ("STALE/USDT:USDT", "stale.csv"),
+            ):
+                pd.DataFrame(
+                    [{"timestamp": 1, "quote": 10.0, "close": 100.0}]
+                ).to_csv(data_dir / filename, index=False)
+            (data_dir / "manifest.json").write_text(
+                json.dumps(
+                    {
+                        "symbols": [
+                            {"symbol": "TRAIN/USDT:USDT", "path": "train.csv"},
+                            {"symbol": "STALE/USDT:USDT", "path": "stale.csv"},
+                        ]
+                    }
+                )
+            )
+
+            result = {
+                "symbol": "TRAIN/USDT:USDT",
+                "timestamp": 1,
+                "close": 100.0,
+                "volatility_rank_200": 0.5,
+                "eligible_for_model": False,
+            }
+            with patch(
+                "cbpred.latest_predict.joblib.load",
+                return_value={"asset_profiles": {"TRAIN/USDT:USDT": {}}},
+            ), patch(
+                "cbpred.latest_predict.predict_from_csv", return_value=result
+            ) as predict:
+                summary = build_predictions(data_dir, root / "model.joblib", output_dir)
+
+            predict.assert_called_once_with(
+                root / "model.joblib", data_dir / "train.csv", symbol="TRAIN/USDT:USDT"
+            )
+            self.assertEqual(summary["symbols"], 1)
+            self.assertEqual(summary["skipped_symbols"], ["STALE/USDT:USDT"])
+
 
 if __name__ == "__main__":
     unittest.main()