stats

test_model_stats.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python3
+"""
+Test script to verify model stats functionality
+"""
+
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+import logging
+from core.orchestrator import TradingOrchestrator
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_model_stats():
+    """Test the model stats functionality"""
+    try:
+        logger.info("Testing model stats functionality...")
+        
+        # Create orchestrator instance (this will initialize model states)
+        orchestrator = TradingOrchestrator()
+        
+        # Sync with dashboard values
+        orchestrator.sync_model_states_with_dashboard()
+        
+        # Get current model stats
+        stats = orchestrator.get_model_training_stats()
+        
+        logger.info("Current model training stats:")
+        for model_name, model_stats in stats.items():
+            if model_stats['current_loss'] is not None:
+                logger.info(f"  {model_name.upper()}: {model_stats['current_loss']:.4f} loss, {model_stats['improvement_pct']:.1f}% improvement")
+            else:
+                logger.info(f"  {model_name.upper()}: No training data yet")
+        
+        # Test updating a model loss
+        orchestrator.update_model_loss('cnn', 0.0001)
+        logger.info("Updated CNN loss to 0.0001")
+        
+        # Get updated stats
+        updated_stats = orchestrator.get_model_training_stats()
+        cnn_stats = updated_stats['cnn']
+        logger.info(f"CNN updated: {cnn_stats['current_loss']:.4f} loss, {cnn_stats['improvement_pct']:.1f}% improvement")
+        
+        return True
+        
+    except Exception as e:
+        logger.error(f"❌ Model stats test failed: {e}")
+        return False
+
+if __name__ == "__main__":
+    success = test_model_stats()
+    sys.exit(0 if success else 1)