fix predict

cbpred/latest_predict.py

@@ -10,6 +10,7 @@ from datetime import datetime, timezone
 from pathlib import Path
 from urllib.parse import urlparse
 
+import joblib
 import pandas as pd
 
 from .pipeline import load_database_url, parse_time_ms, predict_from_csv, prepare_data
@@ -84,9 +85,28 @@ def ensure_database(
 
 def build_predictions(data_dir: Path, artifact: Path, output_dir: Path) -> dict[str, object]:
     manifest = json.loads((data_dir / "manifest.json").read_text())
+    artifact_data = joblib.load(artifact)
+    trained_symbols = set(artifact_data.get("asset_profiles", {}))
+    if trained_symbols:
+        skipped_symbols = [
+            item["symbol"]
+            for item in manifest["symbols"]
+            if item["symbol"] not in trained_symbols
+        ]
+        prediction_items = [
+            item for item in manifest["symbols"] if item["symbol"] in trained_symbols
+        ]
+    else:
+        skipped_symbols = []
+        prediction_items = manifest["symbols"]
+    if skipped_symbols:
+        print(
+            f"[latest-predict] skipped {len(skipped_symbols)} symbols without stored training profiles",
+            flush=True,
+        )
     rows: list[dict[str, object]] = []
-    total = len(manifest["symbols"])
-    for index, item in enumerate(manifest["symbols"], 1):
+    total = len(prediction_items)
+    for index, item in enumerate(prediction_items, 1):
         csv_path = data_dir / item["path"]
         frame = pd.read_csv(csv_path).sort_values("timestamp").drop_duplicates("timestamp")
         latest = frame.iloc[-1]
@@ -128,6 +148,7 @@ def build_predictions(data_dir: Path, artifact: Path, output_dir: Path) -> dict[
     summary = {
         "model": str(artifact.resolve()),
         "symbols": len(rows),
+        "skipped_symbols": skipped_symbols,
         "eligible_predictions": sum(row["drawdown_point_pct"] is not None for row in rows),
         "latest_bar_start_utc": rows[0]["latest_bar_start_utc"] if rows else None,
         "csv": str(output_csv.resolve()),