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)
+    # Resolve target chart
-    charts_df = data_access.get_all_charts()
+    if chart_id is not None:
-    if charts_df.empty:
+        chart = data_access.get_chart_by_id(chart_id)
-        raise ValueError("No charts found in database. Upload candle data first.")
+        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]
+    logger.info(f"Building dataset for chart: {chart_name} (id={chart_id_int})")
-    chart_name = chart["name"]
-    chart_id = int(chart["id"])
-    logger.info(f"Building dataset for chart: {chart_name} (id={chart_id})")
 
     # 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]}",
     )

src/app/api/training/start/route.ts

@@ -6,6 +6,7 @@ const INFERENCE_API_TIMEOUT = parseInt(process.env.INFERENCE_API_TIMEOUT || '100
 
 const TrainingStartRequestSchema = z.object({
   model_type: z.string().min(1, 'model_type must be a non-empty string'),
+  chart_id: z.number().int().positive().optional(),
 });
 
 export async function POST(request: NextRequest) {

src/app/page.tsx

@@ -856,7 +856,7 @@ export default function Home() {
 
           {/* Training Panel */}
           <div className="px-3 py-2 border-b border-sidebar-border">
-            <TrainingPanel />
+            <TrainingPanel activeChartId={activeChartId} />
           </div>
 
           {/* Predictions */}

src/components/TrainingPanel.tsx

@@ -27,6 +27,10 @@ interface TrainingRun {
   error?: string;
 }
 
+interface TrainingPanelProps {
+  activeChartId: number | null;
+}
+
 const MODEL_TYPES = [
   { value: 'random_forest', label: 'Random Forest' },
   { value: 'xgboost', label: 'XGBoost' },
@@ -58,7 +62,7 @@ function StatusBadge({ status }: { status: string }) {
   );
 }
 
-export default function TrainingPanel() {
+export default function TrainingPanel({ activeChartId }: TrainingPanelProps) {
   const [expanded, setExpanded] = useState(false);
   const [modelType, setModelType] = useState('random_forest');
   const [datasetInfo, setDatasetInfo] = useState<DatasetInfo | null>(null);
@@ -172,13 +176,21 @@ export default function TrainingPanel() {
   }, [isTraining, activeRunId, fetchRuns, fetchActiveRun]);
 
   const handleStartTraining = async () => {
+    if (!activeChartId) {
+      setStatusMessage({
+        type: 'error',
+        text: 'Select a chart before starting training',
+      });
+      return;
+    }
+
     setStatusMessage(null);
     setIsTraining(true);
     try {
       const res = await fetch('/api/training/start', {
         method: 'POST',
         headers: { 'Content-Type': 'application/json' },
-        body: JSON.stringify({ model_type: modelType }),
+        body: JSON.stringify({ model_type: modelType, chart_id: activeChartId }),
       });
 
       if (res.status === 409) {
@@ -227,7 +239,7 @@ export default function TrainingPanel() {
     }
   };
 
-  const canTrain = !isTraining;
+  const canTrain = !isTraining && !!activeChartId;
 
   return (
     <div>
@@ -308,6 +320,11 @@ export default function TrainingPanel() {
               'Start Training'
             )}
           </button>
+          {!activeChartId && (
+            <p className="text-[10px] text-muted-foreground">
+              Select a chart to enable training.
+            </p>
+          )}
 
           {/* Status message */}
           {statusMessage && (