timezones

test_training_fixes.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+"""
+Test Training Fixes
+
+This script tests the fixes for CNN adapter and DQN training issues.
+"""
+
+import asyncio
+import time
+import numpy as np
+from datetime import datetime
+from core.orchestrator import TradingOrchestrator
+from core.data_provider import DataProvider
+
+async def test_training_fixes():
+    """Test the training fixes"""
+    print("=== Testing Training Fixes ===")
+    
+    # Initialize orchestrator
+    print("1. Initializing orchestrator...")
+    data_provider = DataProvider()
+    orchestrator = TradingOrchestrator(data_provider=data_provider)
+    
+    # Wait for initialization
+    await asyncio.sleep(3)
+    
+    # Check CNN adapter initialization
+    print("\n2. Checking CNN adapter initialization:")
+    if hasattr(orchestrator, 'cnn_adapter') and orchestrator.cnn_adapter:
+        print("   ✅ CNN adapter is properly initialized")
+        print(f"   CNN adapter type: {type(orchestrator.cnn_adapter)}")
+    else:
+        print("   ❌ CNN adapter is None or missing")
+    
+    # Check DQN agent initialization
+    print("\n3. Checking DQN agent initialization:")
+    if hasattr(orchestrator, 'rl_agent') and orchestrator.rl_agent:
+        print("   ✅ DQN agent is properly initialized")
+        print(f"   DQN agent type: {type(orchestrator.rl_agent)}")
+        if hasattr(orchestrator.rl_agent, 'policy_net'):
+            print("   ✅ DQN policy network is available")
+        else:
+            print("   ❌ DQN policy network is missing")
+    else:
+        print("   ❌ DQN agent is None or missing")
+    
+    # Test CNN predictions
+    print("\n4. Testing CNN predictions:")
+    try:
+        predictions = await orchestrator._get_all_predictions('ETH/USDT')
+        cnn_predictions = [p for p in predictions if 'cnn' in p.model_name.lower()]
+        if cnn_predictions:
+            print(f"   ✅ Got {len(cnn_predictions)} CNN predictions")
+            for pred in cnn_predictions:
+                print(f"   CNN prediction: {pred.action} (confidence: {pred.confidence:.3f})")
+        else:
+            print("   ❌ No CNN predictions received")
+    except Exception as e:
+        print(f"   ❌ CNN prediction failed: {e}")
+    
+    # Test training with validation
+    print("\n5. Testing training with validation:")
+    for i in range(3):
+        print(f"   Training iteration {i+1}/3...")
+        
+        # Create training records for different models
+        training_records = [
+            {
+                'model_name': 'enhanced_cnn',
+                'model_input': np.random.randn(7850),
+                'prediction': {'action': 'BUY', 'confidence': 0.7},
+                'symbol': 'ETH/USDT',
+                'timestamp': datetime.now()
+            },
+            {
+                'model_name': 'dqn_agent',
+                'model_input': np.random.randn(7850),
+                'prediction': {'action': 'SELL', 'confidence': 0.8},
+                'symbol': 'ETH/USDT',
+                'timestamp': datetime.now()
+            }
+        ]
+        
+        for record in training_records:
+            try:
+                success = await orchestrator._train_model_on_outcome(
+                    record, True, 0.5, 1.0
+                )
+                if success:
+                    print(f"   ✅ Training succeeded for {record['model_name']}")
+                else:
+                    print(f"   ⚠️ Training failed for {record['model_name']}")
+            except Exception as e:
+                print(f"   ❌ Training error for {record['model_name']}: {e}")
+        
+        await asyncio.sleep(1)
+    
+    # Show final statistics
+    print("\n6. Final model statistics:")
+    orchestrator.log_model_statistics(detailed=True)
+    
+    # Check for overfitting warnings
+    print("\n7. Checking for training quality:")
+    summary = orchestrator.get_model_statistics_summary()
+    for model_name, stats in summary.items():
+        if stats['total_trainings'] > 0:
+            print(f"   {model_name}: {stats['total_trainings']} trainings, "
+                  f"avg time: {stats['average_training_time_ms']:.1f}ms")
+            if stats['current_loss'] is not None:
+                if stats['current_loss'] < 0.001:
+                    print(f"   ⚠️ {model_name} has very low loss ({stats['current_loss']:.6f}) - check for overfitting")
+                else:
+                    print(f"   ✅ {model_name} has reasonable loss ({stats['current_loss']:.6f})")
+    
+    print("\n✅ Training fixes test completed!")
+
+if __name__ == "__main__":
+    asyncio.run(test_training_fixes())