fix(ml): complete ML pipeline fixes and setup

- Fix CCI indicator to use HLC prices instead of close only
- Parse datetime column when loading enriched CSV
- Strip timezone from annotation timestamps
- Fix TA-Lib pattern names (CDL3WHITESOLDIERS, CDL3BLACKCROWS)
- Exclude programmatic label columns from training features
- Fix classification report to handle missing classes
- Update MLflow tracking to use localhost:5000
- Grant PostgreSQL permissions to ml_user

Pipeline now runs successfully end-to-end:
- Feature engineering: 2543 rows, 31 columns
- Annotation ingestion: 286 samples
- Training: 89.47% test accuracy with Random Forest

services/ml/training/train.py

@@ -208,7 +208,10 @@ def train(
         raise ValueError("Labeled dataset must have 'label' column")
     
     label_col = 'label'
-    feature_cols = [col for col in df.columns if col not in ['label', 'time', 'timestamp']]
+    # Exclude label columns, time columns, and programmatic label columns (which contain string values)
+    feature_cols = [col for col in df.columns 
+                    if col not in ['label', 'time', 'timestamp'] 
+                    and not col.startswith('label_programmatic_')]
     
     X = df[feature_cols].values
     y = df[label_col].values