stored

NN/training/enhanced_realtime_training.py

@@ -22,11 +22,24 @@ from typing import Dict, List, Optional, Any, Tuple
 from collections import deque
 import random
 import math
+import sys
+import os
 
 import torch
 import torch.nn as nn
 import torch.optim as optim
 
+# Add parent directory to path for imports
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+
+# Import prediction database
+try:
+    from core.prediction_database import get_prediction_db
+except ImportError:
+    # Fallback if prediction database is not available
+    def get_prediction_db():
+        logger.warning("Prediction database not available, using mock")
+        return None
 
 logger = logging.getLogger(__name__)
 
@@ -2364,7 +2377,13 @@ class EnhancedRealtimeTrainingSystem:
             # Use DQN model to predict action (if available)
             if (self.orchestrator and hasattr(self.orchestrator, 'rl_agent') 
                 and self.orchestrator.rl_agent):
-                
+                # Get prediction from DQN agent
+                state = self.orchestrator._create_rl_state(symbol)
+                if state is not None:
+                    action, confidence, q_values = self.orchestrator.rl_agent.act_with_confidence(state)
+                else:
+                    # Fallback if state creation fails
+                    action, q_values, confidence = self._technical_analysis_prediction(symbol)
             else:
                 # Fallback to technical analysis-based prediction
                 action, q_values, confidence = self._technical_analysis_prediction(symbol)
@@ -2725,8 +2744,9 @@ class EnhancedRealtimeTrainingSystem:
                 
         except Exception as e:
             logger.debug(f"Error estimating price change: {e}")
-            return 0.0     d
-ef _save_model_checkpoint(self, model_name: str, model_obj, loss: float):
+            return 0.0
+    
+    def _save_model_checkpoint(self, model_name: str, model_obj, loss: float):
         """
         Save model checkpoint after training if performance improved