fix merge

core/training_integration.py

@@ -21,15 +21,6 @@ Key Features:
 import asyncio
 import logging
 import numpy as np
-<<<<<<< HEAD
-from core.reward_calculator import RewardCalculator
-=======
-import pandas as pd
-import torch
-from datetime import datetime, timedelta
-from typing import Dict, List, Optional, Tuple, Any, Callable
-from dataclasses import dataclass
->>>>>>> d49a473ed6f4aef55bfdd47d6370e53582be6b7b
 import threading
 import time
 from collections import deque
@@ -186,48 +177,6 @@ class TrainingIntegration:
                 collection_time = time.time() - start_time
                 self._update_collection_stats(collection_time)
                 
-<<<<<<< HEAD
-                # Get the model's device to ensure tensors are on the same device
-                model_device = next(cnn_model.parameters()).device
-                
-                # Create tensors
-                features_tensor = torch.FloatTensor(features).unsqueeze(0).to(model_device)
-                target_tensor = torch.LongTensor([target]).to(model_device)
-                
-                # Training step
-                cnn_model.train()
-                cnn_model.optimizer.zero_grad()
-                
-                outputs = cnn_model(features_tensor)
-                
-                # Handle different output formats
-                if isinstance(outputs, dict):
-                    if 'main_output' in outputs:
-                        logits = outputs['main_output']
-                    elif 'action_logits' in outputs:
-                        logits = outputs['action_logits']
-                    else:
-                        logits = list(outputs.values())[0]
-                else:
-                    logits = outputs
-                
-                # Calculate loss with reward weighting
-                loss_fn = torch.nn.CrossEntropyLoss()
-                loss = loss_fn(logits, target_tensor)
-                
-                # Weight loss by reward magnitude
-                weighted_loss = loss * abs(reward)
-                
-                # Backward pass
-                weighted_loss.backward()
-                cnn_model.optimizer.step()
-                
-                logger.info(f"CNN trained on trade outcome: P&L=${pnl:.2f}, loss={loss.item():.4f}")
-                return True
-=======
-                # Wait for next collection cycle
-                time.sleep(self.config.collection_interval)
->>>>>>> d49a473ed6f4aef55bfdd47d6370e53582be6b7b
                 
             except Exception as e:
                 logger.error(f"Error in data collection worker: {e}")