fix: resolve numpy type conversion issues in ML service data access

- Convert numpy.int64 to Python int before passing to SQLAlchemy queries
- Prevents psycopg2.ProgrammingError: can't adapt type 'numpy.int64'
- Applied to get_candles(), get_span_annotations(), and get_point_annotations()
- All ML service database access tests now passing successfully

services/ml/app/data_access.py

@@ -78,6 +78,9 @@ class DataAccess:
             DataFrame with columns: id, chart_id, time, open, high, low, close
         """
         with get_db() as db:
+            # Convert numpy types to native Python types for SQLAlchemy
+            chart_id = int(chart_id)
+            
             # Build query
             stmt = select(self.candles).where(self.candles.c.chart_id == chart_id)
             
@@ -118,6 +121,11 @@ class DataAccess:
             DataFrame with span annotations
         """
         with get_db() as db:
+            # Convert numpy types to native Python types for SQLAlchemy
+            chart_id = int(chart_id)
+            if min_confidence is not None:
+                min_confidence = int(min_confidence)
+            
             # Build query
             stmt = select(self.span_annotations).where(
                 self.span_annotations.c.chart_id == chart_id
@@ -164,6 +172,9 @@ class DataAccess:
             DataFrame with point annotations
         """
         with get_db() as db:
+            # Convert numpy types to native Python types for SQLAlchemy
+            chart_id = int(chart_id)
+            
             # Build query
             stmt = select(self.annotations).where(
                 self.annotations.c.chart_id == chart_id