fix(training): use selected chart and include TA-Lib span sources

services/ml/app/annotation_ingestion.py

@@ -99,7 +99,7 @@ class AnnotationIngestion:
     def load_annotations_from_db(
         self, 
         chart_name: str,
-        source: str = "human"
+        source: Optional[str] = "human"
     ) -> List[Dict[str, Any]]:
         """
         Load annotations directly from PostgreSQL database.
@@ -108,7 +108,8 @@ class AnnotationIngestion:
         
         Args:
             chart_name: Name of the chart to load annotations for
-            source: Filter by annotation source ('human', 'model', 'hybrid')
+            source: Optional source filter (e.g. 'human', 'talib', 'model').
+                When None, includes all sources.
             
         Returns:
             List of annotation dictionaries compatible with existing processing
@@ -545,7 +546,7 @@ class AnnotationIngestion:
         self,
         enriched_df: pd.DataFrame,
         chart_name: str,
-        source: str = "human"
+        source: Optional[str] = "human"
     ) -> pd.DataFrame:
         """
         Main processing pipeline using direct database access.
@@ -555,7 +556,8 @@ class AnnotationIngestion:
         Args:
             enriched_df: DataFrame with engineered features
             chart_name: Name of the chart to load annotations for
-            source: Filter by annotation source ('human', 'model', 'hybrid')
+            source: Optional source filter (e.g. 'human', 'talib', 'model').
+                When None, includes all sources.
             
         Returns:
             Labeled DataFrame ready for training

services/ml/app/data_access.py

@@ -59,6 +59,25 @@ class DataAccess:
             if result:
                 return dict(result._mapping)
             return None
+
+    def get_chart_by_id(self, chart_id: int) -> Optional[Dict[str, Any]]:
+        """
+        Get chart by ID.
+
+        Args:
+            chart_id: Chart ID
+
+        Returns:
+            Chart dictionary or None if not found
+        """
+        with get_db() as db:
+            chart_id = int(chart_id)
+            stmt = select(self.charts).where(self.charts.c.id == chart_id)
+            result = db.execute(stmt).fetchone()
+
+            if result:
+                return dict(result._mapping)
+            return None
     
     def get_candles(
         self, 

services/ml/app/main.py

@@ -1039,6 +1039,11 @@ class TrainingStartRequest(BaseModel):
         "random_forest",
         description="Model type: random_forest or xgboost",
     )
+    chart_id: Optional[int] = Field(
+        default=None,
+        ge=1,
+        description="Chart ID to train on. If omitted, falls back to first chart.",
+    )
 
 
 class TrainingStartResponse(BaseModel):
@@ -1072,7 +1077,7 @@ class DatasetInfoResponse(BaseModel):
     row_count: Optional[int] = None
 
 
-def build_dataset_from_db(config: PipelineConfig) -> dict:
+def build_dataset_from_db(config: PipelineConfig, chart_id: Optional[int] = None) -> dict:
     """
     Build the labeled training dataset directly from the database.
 
@@ -1090,18 +1095,25 @@ def build_dataset_from_db(config: PipelineConfig) -> dict:
 
     data_access = DataAccess()
 
-    # Find all charts, use the first one (single-chart app)
-    charts_df = data_access.get_all_charts()
-    if charts_df.empty:
-        raise ValueError("No charts found in database. Upload candle data first.")
+    # Resolve target chart
+    if chart_id is not None:
+        chart = data_access.get_chart_by_id(chart_id)
+        if not chart:
+            raise ValueError(f"Chart not found: id={chart_id}")
+        chart_name = chart["name"]
+        chart_id_int = int(chart["id"])
+    else:
+        charts_df = data_access.get_all_charts()
+        if charts_df.empty:
+            raise ValueError("No charts found in database. Upload candle data first.")
+        chart = charts_df.iloc[0]
+        chart_name = chart["name"]
+        chart_id_int = int(chart["id"])
 
-    chart = charts_df.iloc[0]
-    chart_name = chart["name"]
-    chart_id = int(chart["id"])
-    logger.info(f"Building dataset for chart: {chart_name} (id={chart_id})")
+    logger.info(f"Building dataset for chart: {chart_name} (id={chart_id_int})")
 
     # Step 1: Export candles to raw CSV
-    candles_df = data_access.get_candles(chart_id)
+    candles_df = data_access.get_candles(chart_id_int)
     if candles_df.empty:
         raise ValueError(f"No candles found for chart: {chart_name}")
 
@@ -1121,7 +1133,9 @@ def build_dataset_from_db(config: PipelineConfig) -> dict:
     # Step 3: Run annotation ingestion from database
     enriched_df = pd.read_csv(enriched_path, parse_dates=["time"])
     ingestion = AnnotationIngestion(config.stages.annotation_ingestion)
-    labeled_df = ingestion.process_from_db(enriched_df, chart_name, source="human")
+    # Include all annotation sources so TA-Lib generated spans (source='talib')
+    # can be used for training alongside manual labels.
+    labeled_df = ingestion.process_from_db(enriched_df, chart_name, source=None)
 
     if labeled_df.empty:
         raise ValueError(
@@ -1145,7 +1159,12 @@ def build_dataset_from_db(config: PipelineConfig) -> dict:
     return result
 
 
-def _run_training_background(run_id: str, model_type: str, config: PipelineConfig) -> None:
+def _run_training_background(
+    run_id: str,
+    model_type: str,
+    config: PipelineConfig,
+    chart_id: Optional[int] = None,
+) -> None:
     """
     Background thread target: build dataset then train a model.
 
@@ -1160,7 +1179,7 @@ def _run_training_background(run_id: str, model_type: str, config: PipelineConfi
 
         # Build dataset from database (feature engineering + annotation ingestion)
         logger.info("Building dataset from database...")
-        build_dataset_from_db(config)
+        build_dataset_from_db(config, chart_id=chart_id)
 
         labeled_path = Path(config.data.labeled_path)
         if not labeled_path.exists():
@@ -1336,6 +1355,15 @@ async def training_start(request: TrainingStartRequest):
             detail=f"Unsupported model type. Available: {', '.join(SUPPORTED_MODEL_TYPES)}",
         )
 
+    if request.chart_id is not None:
+        from app.data_access import DataAccess
+        chart = DataAccess().get_chart_by_id(request.chart_id)
+        if not chart:
+            raise HTTPException(
+                status_code=status.HTTP_400_BAD_REQUEST,
+                detail=f"Chart not found: id={request.chart_id}",
+            )
+
     # Reject concurrent runs (atomic check-and-set)
     with state.training_lock:
         if state.active_training_run_id is not None:
@@ -1385,7 +1413,7 @@ async def training_start(request: TrainingStartRequest):
     # Launch background thread (daemon so it doesn't block process exit)
     thread = threading.Thread(
         target=_run_training_background,
-        args=(run_id, request.model_type, config),
+        args=(run_id, request.model_type, config, request.chart_id),
         daemon=True,
         name=f"training-{run_id[:8]}",
     )