more features, but dead-end

crypto/gogo2/.vscode/launch.json

@@ -5,8 +5,8 @@
             "name": "Train Bot",
             "type": "python",
             "request": "launch",
-            "program": "main.py",
+            "program": "main_multiu_broken.py",
-            "args": ["--mode", "train", "--episodes", "100"],
+            "args": ["--mode", "train", "--episodes", "10000"],
             "console": "integratedTerminal",
             "justMyCode": true
         },
@@ -14,7 +14,7 @@
             "name": "Evaluate Bot",
             "type": "python",
             "request": "launch",
-            "program": "main.py",
+            "program": "main_multiu_broken.py",
             "args": ["--mode", "eval", "--episodes", "10"],
             "console": "integratedTerminal",
             "justMyCode": true
@@ -23,7 +23,7 @@
             "name": "Live Trading (Demo)",
             "type": "python",
             "request": "launch",
-            "program": "main.py",
+            "program": "main_multiu_broken.py",
             "args": ["--mode", "live", "--demo"],
             "console": "integratedTerminal",
             "justMyCode": true
@@ -32,7 +32,7 @@
             "name": "Live Trading (Real)",
             "type": "python",
             "request": "launch",
-            "program": "main.py",
+            "program": "main_multiu_broken.py",
             "args": ["--mode", "live"],
             "console": "integratedTerminal",
             "justMyCode": true
@@ -41,7 +41,7 @@
             "name": "Continuous Training",
             "type": "python",
             "request": "launch",
-            "program": "main.py",
+            "program": "main_multiu_broken.py",
             "args": ["--mode", "continuous", "--refresh-data"],
             "console": "integratedTerminal",
             "justMyCode": true

crypto/gogo2/enhanced_models.py

@@ -201,6 +201,28 @@ class EnhancedReplayBuffer:
         self.gamma = gamma  # Discount factor
         self.n_step_buffer = []
         self.max_priority = 1.0
+    
+    def add(self, state, action, reward, next_state, done):
+        """
+        Add a new experience to the buffer (simplified version of push for compatibility)
+        
+        Args:
+            state: Current state
+            action: Action taken
+            reward: Reward received
+            next_state: Next state
+            done: Whether the episode is done
+        """
+        # Store in replay buffer with max priority
+        if len(self.buffer) < self.capacity:
+            self.buffer.append(None)
+        self.buffer[self.position] = (state, action, reward, next_state, done)
+        
+        # Set priority to max priority to ensure it gets sampled
+        self.priorities[self.position] = self.max_priority
+        
+        # Move position pointer
+        self.position = (self.position + 1) % self.capacity
         
     def push(self, state, action, reward, next_state, done):
         # Store experience in n-step buffer
@@ -278,15 +300,8 @@ class EnhancedReplayBuffer:
             next_states.append(next_state)
             dones.append(done)
             
-        return (
+        # Return only the states, actions, rewards, next_states, dones for compatibility with learn function
-            torch.stack(states),
+        return states, actions, rewards, next_states, dones
-            torch.tensor(actions),
-            torch.tensor(rewards, dtype=torch.float32),
-            torch.stack(next_states),
-            torch.tensor(dones, dtype=torch.float32),
-            indices,
-            weights
-        )
     
     def update_priorities(self, indices, td_errors):
         for idx, td_error in zip(indices, td_errors):

crypto/gogo2/enhanced_training.py

@@ -2,6 +2,7 @@ import os
 import torch
 import torch.nn as nn
 import torch.optim as optim
+import torch.nn.functional as F
 from torch.amp import GradScaler, autocast
 import numpy as np
 import matplotlib.pyplot as plt
@@ -9,7 +10,7 @@ from datetime import datetime
 from tensorboardX import SummaryWriter
 
 # Import our enhanced models
-from enhanced_models import EnhancedPricePredictionModel, EnhancedDQN, EnhancedReplayBuffer, train_price_predictor, prepare_multi_timeframe_data
+from enhanced_models import EnhancedPricePredictionModel, EnhancedDQN, EnhancedReplayBuffer
 
 # Constants
 TIMEFRAMES = ['1m', '15m', '1h']
@@ -218,285 +219,453 @@ def load_checkpoint(price_model, dqn_model, optimizer, episode=None):
         print("No checkpoint found, starting training from scratch")
         return 0, [], [], []
 
-def enhanced_train_agent(exchange, num_episodes=NUM_EPISODES, continuous=CONTINUOUS_MODE, start_episode=CONTINUOUS_START_EPISODE):
+def enhanced_train_agent(exchange, num_episodes=NUM_EPISODES, continuous=CONTINUOUS_MODE, start_episode=CONTINUOUS_START_EPISODE, verbose=False):
     """
-    Train the enhanced trading agent using multi-timeframe data
+    Train an enhanced trading agent with multi-timeframe models
     
     Args:
-        exchange: Exchange object to fetch data from
+        exchange: Exchange simulator or real exchange
         num_episodes: Number of episodes to train for
         continuous: Whether to continue training from a checkpoint
-        start_episode: Episode to start from if continuous training
+        start_episode: Episode to start from for continuous training
+        verbose: Whether to enable verbose logging
     """
-    print(f"Training on device: {DEVICE}")
-    
     # Set up TensorBoard
     writer = setup_tensorboard()
     
     # Initialize models
-    state_dim = 100  # Increased state dimension for multi-timeframe features
+    state_dim = 100  # Increased state dimension for enhanced features
-    action_dim = 3   # Buy, Sell, Hold
+    action_dim = 3   # 0: HOLD, 1: BUY, 2: SELL
     
+    # Initialize price prediction model with multi-timeframe support
     price_model = EnhancedPricePredictionModel(
         input_dim=2,  # Price and volume
         hidden_dim=256,
         num_layers=3,
-        output_dim=5,  # Predict next 5 candles
+        output_dim=5,  # OHLCV prediction
         num_timeframes=len(TIMEFRAMES)
     ).to(DEVICE)
     
+    # Initialize DQN model with enhanced architecture
     dqn_model = EnhancedDQN(
         state_dim=state_dim,
         action_dim=action_dim,
         hidden_dim=512
     ).to(DEVICE)
     
-    target_dqn = EnhancedDQN(
+    # Initialize target network
+    target_model = EnhancedDQN(
         state_dim=state_dim,
         action_dim=action_dim,
         hidden_dim=512
     ).to(DEVICE)
-    
+    target_model.load_state_dict(dqn_model.state_dict())
-    # Copy initial weights to target network
-    target_dqn.load_state_dict(dqn_model.state_dict())
     
     # Initialize optimizer
-    optimizer = optim.Adam(list(price_model.parameters()) + list(dqn_model.parameters()), lr=LEARNING_RATE)
+    optimizer = optim.Adam(dqn_model.parameters(), lr=LEARNING_RATE)
     
-    # Initialize replay buffer
+    # Initialize replay buffer with prioritized experience replay
-    replay_buffer = EnhancedReplayBuffer(
+    replay_buffer = EnhancedReplayBuffer(REPLAY_BUFFER_SIZE)
-        capacity=REPLAY_BUFFER_SIZE,
-        alpha=0.6,
-        beta=0.4,
-        beta_increment=0.001,
-        n_step=3,
-        gamma=GAMMA
-    )
     
-    # Initialize gradient scaler for mixed precision training
+    # Initialize training metrics
-    scaler = GradScaler(enabled=(DEVICE.type == 'cuda'))
-    
-    # Initialize tracking variables
     rewards = []
     profits = []
     win_rates = []
     best_reward = float('-inf')
     best_pnl = float('-inf')
-    best_winrate = float('-inf')
+    best_winrate = 0
     
-    # Load checkpoint if continuous training
+    # Initialize epsilon for exploration
+    epsilon = EPSILON_START
+    
+    # Load checkpoint if continuing training
     if continuous:
-        start_episode, rewards, profits, win_rates = load_checkpoint(
+        try:
-            price_model, dqn_model, optimizer, start_episode
+            checkpoint_path = 'models/enhanced_trading_agent_latest.pt'
-        )
+            if os.path.exists(checkpoint_path):
+                checkpoint = torch.load(checkpoint_path, map_location=DEVICE)
+                price_model.load_state_dict(checkpoint['price_model_state_dict'])
+                dqn_model.load_state_dict(checkpoint['dqn_model_state_dict'])
+                target_model.load_state_dict(checkpoint['dqn_model_state_dict'])
+                optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+                
+                if 'rewards' in checkpoint:
+                    rewards = checkpoint['rewards']
+                if 'profits' in checkpoint:
+                    profits = checkpoint['profits']
+                if 'win_rates' in checkpoint:
+                    win_rates = checkpoint['win_rates']
+                if 'best_reward' in checkpoint:
+                    best_reward = checkpoint['best_reward']
+                if 'best_pnl' in checkpoint:
+                    best_pnl = checkpoint['best_pnl']
+                if 'best_winrate' in checkpoint:
+                    best_winrate = checkpoint['best_winrate']
+                
+                print(f"Loaded checkpoint from {checkpoint_path}")
+                print(f"Continuing from episode {start_episode}")
+                
+                # Decay epsilon based on start episode
+                for _ in range(start_episode):
+                    epsilon = max(EPSILON_END, epsilon * EPSILON_DECAY)
+            else:
+                print(f"No checkpoint found at {checkpoint_path}, starting from scratch")
+        except Exception as e:
+            print(f"Error loading checkpoint: {e}")
+            print("Starting from scratch")
     
-    # Prepare multi-timeframe data for price prediction model training
+    # Set models to training mode
-    data_loaders = prepare_multi_timeframe_data(exchange, TIMEFRAMES)
+    price_model.train()
+    dqn_model.train()
+    
+    # Initialize gradient scaler for mixed precision training
+    scaler = GradScaler()
+    
+    print(f"Training on device: {DEVICE}")
     
     # Pre-train price prediction model
     print("Pre-training price prediction model...")
-    train_price_predictor(price_model, data_loaders, optimizer, DEVICE, epochs=5)
+    train_price_predictor(price_model, exchange, TIMEFRAMES, DEVICE, num_epochs=5, batch_size=32)
     
     # Main training loop
-    epsilon = EPSILON_START
+    for episode in range(start_episode, start_episode + num_episodes):
-    
+        # Initialize state
-    for episode in range(start_episode, num_episodes):
-        print(f"Episode {episode+1}/{num_episodes}")
-        
-        # Reset environment
         state = initialize_state(exchange, TIMEFRAMES)
-        total_reward = 0
+        
+        # Reset environment for new episode
+        exchange.reset()
+        
+        # Track episode metrics
+        episode_reward = 0
+        episode_steps = 0
+        done = False
         trades = []
         wins = 0
         losses = 0
         
+        # Enable verbose logging for prediction validation if requested
+        if verbose:
+            # Set logging level to DEBUG for more detailed logs
+            import logging
+            logging.getLogger("trading_bot").setLevel(logging.DEBUG)
+            
+            # Add a hook to log prediction validations
+            def log_prediction_validation(pred, actual, was_correct):
+                if was_correct:
+                    print(f"CORRECT prediction: predicted={pred:.2f}, actual={actual:.2f}")
+                else:
+                    print(f"INCORRECT prediction: predicted={pred:.2f}, actual={actual:.2f}")
+            
+            # Monkey patch the validate_predictions method if possible
+            if hasattr(exchange, 'validate_predictions'):
+                original_validate = exchange.validate_predictions
+                
+                def verbose_validate(self, new_candle):
+                    result = original_validate(new_candle)
+                    print(f"Validated predictions for candle at {new_candle['timestamp']}")
+                    if hasattr(self, 'prediction_history'):
+                        validated = [p for p in self.prediction_history if p['validated']]
+                        correct = [p for p in validated if p['was_correct']]
+                        if validated:
+                            accuracy = len(correct) / len(validated)
+                            print(f"Prediction accuracy: {accuracy:.2f} ({len(correct)}/{len(validated)})")
+                    return result
+                
+                exchange.validate_predictions = verbose_validate.__get__(exchange, exchange.__class__)
+        
         # Episode loop
-        for step in range(MAX_STEPS_PER_EPISODE):
+        while not done and episode_steps < MAX_STEPS_PER_EPISODE:
             # Epsilon-greedy action selection
             if np.random.random() < epsilon:
                 action = np.random.randint(0, action_dim)
             else:
                 with torch.no_grad():
-                    state_tensor = torch.FloatTensor(state).unsqueeze(0).to(DEVICE)
+                    q_values = dqn_model(torch.FloatTensor(state).unsqueeze(0).to(DEVICE))
-                    q_values, _, _ = dqn_model(state_tensor)
                     action = q_values.argmax().item()
             
-            # Execute action and get next state and reward
+            # Take action in environment
-            next_state, reward, done, trade_info = step_environment(
+            next_state, reward, done, info = exchange.step(action)
-                exchange, state, action, price_model, TIMEFRAMES, DEVICE
-            )
             
             # Store transition in replay buffer
-            replay_buffer.push(
+            replay_buffer.add(state, action, reward, next_state, done)
-                torch.FloatTensor(state),
-                action,
-                reward,
-                torch.FloatTensor(next_state),
-                done
-            )
             
             # Update state and accumulate reward
             state = next_state
-            total_reward += reward
+            episode_reward += reward
             
             # Track trade outcomes
-            if trade_info is not None:
+            if 'trade' in info and info['trade']:
-                trades.append(trade_info)
+                trades.append(info['trade'])
-                if trade_info['pnl'] > 0:
+                if 'pnl_dollar' in info['trade']:
-                    wins += 1
+                    if info['trade']['pnl_dollar'] > 0:
-                elif trade_info['pnl'] < 0:
+                        wins += 1
-                    losses += 1
+                    elif info['trade']['pnl_dollar'] < 0:
+                        losses += 1
+                    
+                    # Log trade to TensorBoard
+                    if writer:
+                        writer.add_scalar('Trade/PnL', info['trade']['pnl_dollar'], episode)
+                        if 'duration' in info['trade']:
+                            writer.add_scalar('Trade/Duration', info['trade']['duration'], episode)
+                    
+                    # Log prediction validation metrics if available
+                    if verbose and 'prediction_accuracy' in info:
+                        writer.add_scalar('Prediction/Accuracy', info['prediction_accuracy'], episode)
+                        if 'low_prediction_accuracy' in info:
+                            writer.add_scalar('Prediction/LowAccuracy', info['low_prediction_accuracy'], episode)
+                        if 'high_prediction_accuracy' in info:
+                            writer.add_scalar('Prediction/HighAccuracy', info['high_prediction_accuracy'], episode)
             
             # Learn from experiences if enough samples
             if len(replay_buffer) > BATCH_SIZE:
-                learn(dqn_model, target_dqn, replay_buffer, optimizer, scaler, DEVICE)
+                learn(dqn_model, target_model, replay_buffer, optimizer, scaler, DEVICE)
             
-            if done:
+            episode_steps += 1
-                break
+            
+            # Log verbose information about the current state if requested
+            if verbose and episode_steps % 10 == 0:
+                print(f"Episode {episode+1}, Step {episode_steps}, Action: {['HOLD', 'BUY', 'SELL'][action]}, Reward: {reward:.4f}")
+                if hasattr(exchange, 'position') and exchange.position != 'FLAT':
+                    print(f"  Position: {exchange.position}, Entry Price: {exchange.entry_price:.2f}, Current PnL: {exchange.calculate_pnl():.2f}")
+                
+                # Log prediction validation status
+                if hasattr(exchange, 'prediction_history'):
+                    recent_predictions = [p for p in exchange.prediction_history if p['validated']][-5:]
+                    if recent_predictions:
+                        print("  Recent prediction validations:")
+                        for pred in recent_predictions:
+                            status = "✓" if pred['was_correct'] else "✗"
+                            print(f"    {status} {pred['type']} prediction: {pred['predicted_value']:.2f} vs {pred['actual_value']:.2f}")
         
         # Update target network
         if episode % TARGET_UPDATE == 0:
-            target_dqn.load_state_dict(dqn_model.state_dict())
+            target_model.load_state_dict(dqn_model.state_dict())
         
         # Calculate episode metrics
-        avg_reward = total_reward / (step + 1)
+        avg_reward = episode_reward / max(1, episode_steps)
-        total_pnl = sum(trade['pnl'] for trade in trades) if trades else 0
+        total_pnl = sum(trade.get('pnl_dollar', 0) for trade in trades) if trades else 0
         win_rate = (wins / (wins + losses) * 100) if (wins + losses) > 0 else 0
         
-        # Decay epsilon
+        # Store metrics
-        epsilon = max(EPSILON_END, epsilon * EPSILON_DECAY)
-        
-        # Track metrics
         rewards.append(avg_reward)
         profits.append(total_pnl)
         win_rates.append(win_rate)
         
+        # Decay epsilon
+        epsilon = max(EPSILON_END, epsilon * EPSILON_DECAY)
+        
+        # Log episode metrics
+        print(f"Episode {episode+1}: Reward={avg_reward:.4f}, PnL=${total_pnl:.2f}, Win Rate={win_rate:.1f}%, Epsilon={epsilon:.4f}")
+        
         # Log to TensorBoard
-        writer.add_scalar('Training/Reward', avg_reward, episode)
+        if writer:
-        writer.add_scalar('Training/Profit', total_pnl, episode)
+            writer.add_scalar('Metrics/Reward', avg_reward, episode)
-        writer.add_scalar('Training/WinRate', win_rate, episode)
+            writer.add_scalar('Metrics/PnL', total_pnl, episode)
-        writer.add_scalar('Training/Epsilon', epsilon, episode)
+            writer.add_scalar('Metrics/WinRate', win_rate, episode)
+            writer.add_scalar('Metrics/Epsilon', epsilon, episode)
+            
+            # Log prediction validation metrics if available
+            if hasattr(exchange, 'prediction_history'):
+                validated_predictions = [p for p in exchange.prediction_history if p['validated']]
+                if validated_predictions:
+                    correct_predictions = [p for p in validated_predictions if p['was_correct']]
+                    accuracy = len(correct_predictions) / len(validated_predictions)
+                    writer.add_scalar('Prediction/OverallAccuracy', accuracy, episode)
+                    
+                    # Log separate accuracies for low and high predictions
+                    low_predictions = [p for p in validated_predictions if p['type'] == 'low']
+                    high_predictions = [p for p in validated_predictions if p['type'] == 'high']
+                    
+                    if low_predictions:
+                        correct_lows = [p for p in low_predictions if p['was_correct']]
+                        low_accuracy = len(correct_lows) / len(low_predictions)
+                        writer.add_scalar('Prediction/LowAccuracy', low_accuracy, episode)
+                    
+                    if high_predictions:
+                        correct_highs = [p for p in high_predictions if p['was_correct']]
+                        high_accuracy = len(correct_highs) / len(high_predictions)
+                        writer.add_scalar('Prediction/HighAccuracy', high_accuracy, episode)
+                    
+                    if verbose:
+                        print(f"Prediction accuracy: {accuracy:.2f} ({len(correct_predictions)}/{len(validated_predictions)})")
+                        if low_predictions:
+                            print(f"  Low prediction accuracy: {low_accuracy:.2f} ({len(correct_lows)}/{len(low_predictions)})")
+                        if high_predictions:
+                            print(f"  High prediction accuracy: {high_accuracy:.2f} ({len(correct_highs)}/{len(high_predictions)})")
         
-        # Print episode summary
+        # Save best models
-        print(f"Episode {episode+1} - Avg Reward: {avg_reward:.2f}, PnL: ${total_pnl:.2f}, Win Rate: {win_rate:.1f}%")
+        if avg_reward > best_reward:
+            best_reward = avg_reward
+            torch.save({
+                'price_model_state_dict': price_model.state_dict(),
+                'dqn_model_state_dict': dqn_model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'episode': episode,
+                'reward': best_reward,
+                'pnl': total_pnl,
+                'win_rate': win_rate
+            }, 'models/enhanced_trading_agent_best_reward.pt')
+            print(f"Saved best reward model: {best_reward:.4f}")
         
-        # Save models and plot results
+        if total_pnl > best_pnl:
-        if episode % SAVE_INTERVAL == 0 or episode == num_episodes - 1:
+            best_pnl = total_pnl
-            best_reward, best_pnl, best_winrate = save_models(
+            torch.save({
-                price_model, dqn_model, optimizer, episode, 
+                'price_model_state_dict': price_model.state_dict(),
-                rewards, profits, win_rates, 
+                'dqn_model_state_dict': dqn_model.state_dict(),
-                best_reward, best_pnl, best_winrate
+                'optimizer_state_dict': optimizer.state_dict(),
-            )
+                'episode': episode,
-            plot_training_results(rewards, profits, win_rates, episode)
+                'reward': avg_reward,
+                'pnl': best_pnl,
+                'win_rate': win_rate
+            }, 'models/enhanced_trading_agent_best_pnl.pt')
+            print(f"Saved best PnL model: ${best_pnl:.2f}")
+        
+        if win_rate > best_winrate:
+            best_winrate = win_rate
+            torch.save({
+                'price_model_state_dict': price_model.state_dict(),
+                'dqn_model_state_dict': dqn_model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'episode': episode,
+                'reward': avg_reward,
+                'pnl': total_pnl,
+                'win_rate': best_winrate
+            }, 'models/enhanced_trading_agent_best_winrate.pt')
+            print(f"Saved best win rate model: {best_winrate:.1f}%")
+        
+        # Save latest model for continuous training
+        torch.save({
+            'price_model_state_dict': price_model.state_dict(),
+            'dqn_model_state_dict': dqn_model.state_dict(),
+            'optimizer_state_dict': optimizer.state_dict(),
+            'episode': episode,
+            'rewards': rewards,
+            'profits': profits,
+            'win_rates': win_rates,
+            'best_reward': best_reward,
+            'best_pnl': best_pnl,
+            'best_winrate': best_winrate
+        }, 'models/enhanced_trading_agent_latest.pt')
+        
+        # Add additional verbose logging at the end of each episode
+        if verbose:
+            print("\nEpisode Summary:")
+            print(f"  Total Steps: {episode_steps}")
+            print(f"  Total Trades: {len(trades)}")
+            print(f"  Wins/Losses: {wins}/{losses}")
+            print(f"  Average Trade PnL: ${total_pnl/max(1, len(trades)):.2f}")
+            
+            # Log prediction validation summary
+            if hasattr(exchange, 'prediction_history'):
+                validated = [p for p in exchange.prediction_history if p['validated']]
+                if validated:
+                    correct = [p for p in validated if p['was_correct']]
+                    print("\nPrediction Validation Summary:")
+                    print(f"  Total Predictions: {len(exchange.prediction_history)}")
+                    print(f"  Validated Predictions: {len(validated)}")
+                    print(f"  Correct Predictions: {len(correct)}")
+                    print(f"  Accuracy: {len(correct)/len(validated):.2f}")
+                    
+                    # Reset prediction history for next episode if it's getting too large
+                    if len(exchange.prediction_history) > 1000:
+                        print("  Resetting prediction history (too large)")
+                        exchange.prediction_history = exchange.prediction_history[-100:]
     
-    # Close TensorBoard writer
+    # Return final metrics
-    writer.close()
+    return rewards, profits, win_rates
-    
-    # Final save and plot
-    best_reward, best_pnl, best_winrate = save_models(
-        price_model, dqn_model, optimizer, num_episodes - 1, 
-        rewards, profits, win_rates, 
-        best_reward, best_pnl, best_winrate
-    )
-    plot_training_results(rewards, profits, win_rates, num_episodes - 1)
-    
-    print("Training complete!")
-    return price_model, dqn_model
 
 def learn(dqn, target_dqn, replay_buffer, optimizer, scaler, device):
-    """Update the DQN model using experiences from the replay buffer"""
+    """
-    # Sample from replay buffer
+    Update the DQN model using experiences from the replay buffer
-    states, actions, rewards, next_states, dones, indices, weights = replay_buffer.sample(BATCH_SIZE)
     
-    # Move to device
+    Args:
-    states = states.to(device)
+        dqn: The DQN model to update
-    actions = actions.to(device)
+        target_dqn: The target DQN model for stable Q-value estimates
-    rewards = rewards.to(device)
+        replay_buffer: Replay buffer containing experiences
-    next_states = next_states.to(device)
+        optimizer: Optimizer for updating the DQN model
-    dones = dones.to(device)
+        scaler: Gradient scaler for mixed precision training
-    weights = weights.to(device)
+        device: Device to train on (CPU or GPU)
+    """
+    if len(replay_buffer) < BATCH_SIZE:
+        return
+    
+    # Sample batch from replay buffer
+    states, actions, rewards, next_states, dones = replay_buffer.sample(BATCH_SIZE)
+    
+    # Convert to tensors
+    states = torch.FloatTensor(states).to(device)
+    actions = torch.LongTensor(actions).to(device)
+    rewards = torch.FloatTensor(rewards).to(device)
+    next_states = torch.FloatTensor(next_states).to(device)
+    dones = torch.FloatTensor(dones).to(device)
     
     # Get current Q values
-    if device.type == 'cuda':
+    with autocast(device_type='cuda' if device.type == 'cuda' else 'cpu'):
-        with autocast(device_type='cuda', enabled=True):
+        q_values = dqn(states)
-            current_q_values, _, _ = dqn(states)
+        if isinstance(q_values, tuple):
-            current_q_values = current_q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
+            q_values = q_values[0]  # Extract the tensor from the tuple
-            
+        q_values = q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
-            # Compute target Q values
-            with torch.no_grad():
-                next_q_values, _, _ = target_dqn(next_states)
-                max_next_q_values = next_q_values.max(1)[0]
-                target_q_values = rewards + (1 - dones) * GAMMA * max_next_q_values
-            
-            # Compute loss with importance sampling weights
-            td_errors = target_q_values - current_q_values
-            loss = (weights * td_errors.pow(2)).mean()
-    else:
-        # CPU version without autocast
-        current_q_values, _, _ = dqn(states)
-        current_q_values = current_q_values.gather(1, actions.unsqueeze(1)).squeeze(1)
         
-        # Compute target Q values
+        # Get next Q values from target network
         with torch.no_grad():
-            next_q_values, _, _ = target_dqn(next_states)
+            next_q_values = target_dqn(next_states)
-            max_next_q_values = next_q_values.max(1)[0]
+            if isinstance(next_q_values, tuple):
-            target_q_values = rewards + (1 - dones) * GAMMA * max_next_q_values
+                next_q_values = next_q_values[0]  # Extract the tensor from the tuple
+            next_q_values = next_q_values.max(1)[0]
+            target_q_values = rewards + GAMMA * next_q_values * (1 - dones)
         
-        # Compute loss with importance sampling weights
+        # Calculate loss
-        td_errors = target_q_values - current_q_values
+        loss = F.smooth_l1_loss(q_values, target_q_values)
-        loss = (weights * td_errors.pow(2)).mean()
     
-    # Update priorities in replay buffer
+    # Optimize the model
-    replay_buffer.update_priorities(indices, td_errors.abs().detach().cpu().numpy())
-    
-    # Optimize the model with mixed precision
     optimizer.zero_grad()
-    
+    scaler.scale(loss).backward()
-    if device.type == 'cuda':
+    scaler.step(optimizer)
-        scaler.scale(loss).backward()
+    scaler.update()
-        scaler.unscale_(optimizer)
-        torch.nn.utils.clip_grad_norm_(dqn.parameters(), max_norm=1.0)
-        scaler.step(optimizer)
-        scaler.update()
-    else:
-        # CPU version without scaler
-        loss.backward()
-        torch.nn.utils.clip_grad_norm_(dqn.parameters(), max_norm=1.0)
-        optimizer.step()
 
 def initialize_state(exchange, timeframes):
-    """Initialize the state with data from multiple timeframes"""
+    """
+    Initialize the state for the trading agent using multi-timeframe data
+    
+    Args:
+        exchange: Exchange object to fetch data from
+        timeframes: List of timeframes to use
+    
+    Returns:
+        state: Initial state vector
+    """
+    # Initialize empty state
+    state = []
+    
     # Fetch data for each timeframe
     timeframe_data = {}
     for tf in timeframes:
         candles = exchange.fetch_ohlcv(timeframe=tf, limit=30)
-        timeframe_data[tf] = candles
+        if not candles or len(candles) < 10:
-    
+            print(f"Not enough data for timeframe {tf}, using zeros")
-    # Extract features from each timeframe
+            # Use zeros if not enough data
-    state = []
+            state.extend([0] * 20)  # 20 features per timeframe
-    
+            continue
-    for tf in timeframes:
-        candles = timeframe_data[tf]
         
-        # Price features
+        timeframe_data[tf] = candles
+        
+        # Extract features for this timeframe
         prices = [candle[4] for candle in candles[-10:]]  # Last 10 close prices
         price_changes = [prices[i]/prices[i-1] - 1 for i in range(1, len(prices))]
         
-        # Volume features
         volumes = [candle[5] for candle in candles[-10:]]  # Last 10 volumes
         volume_changes = [volumes[i]/volumes[i-1] - 1 for i in range(1, len(volumes))]
         
         # Technical indicators
-        # Simple Moving Averages
         sma_5 = sum(prices[-5:]) / 5
         sma_10 = sum(prices) / 10
         
-        # Relative Strength Index (simplified)
+        # RSI (simplified)
-        gains = [max(0, price_changes[i]) for i in range(len(price_changes))]
+        gains = [max(0, pc) for pc in price_changes]
-        losses = [max(0, -price_changes[i]) for i in range(len(price_changes))]
+        losses = [max(0, -pc) for pc in price_changes]
-        avg_gain = sum(gains) / len(gains)
+        avg_gain = sum(gains) / len(gains) if gains else 0
-        avg_loss = sum(losses) / len(losses)
+        avg_loss = sum(losses) / len(losses) if losses else 1e-10
-        rs = avg_gain / (avg_loss + 1e-10)  # Avoid division by zero
+        rs = avg_gain / avg_loss
         rsi = 100 - (100 / (1 + rs))
         
         # Add features to state
@@ -506,85 +675,15 @@ def initialize_state(exchange, timeframes):
         state.append(sma_10 / prices[-1] - 1)  # 1 feature
         state.append(rsi / 100)  # 1 feature
     
-    # Add market regime features
+    # Add market regime features (placeholder)
-    # This is a placeholder - in a real implementation, you would use the market_regime_classifier
+    state.extend([0, 0, 0])  # 3 features for market regime
-    # from the DQN model to predict the current market regime
-    state.extend([0, 0, 0])  # 3 features for market regime (one-hot encoded)
     
-    # Add additional features to reach the expected dimension of 100
+    # Pad state to reach expected dimension of 100
-    # Calculate more technical indicators
+    if len(state) < 100:
-    for tf in timeframes:
+        state.extend([0] * (100 - len(state)))
-        candles = timeframe_data[tf]
+    elif len(state) > 100:
-        prices = [candle[4] for candle in candles[-20:]]  # Last 20 close prices
-        
-        # Bollinger Bands
-        window = 20
-        if len(prices) >= window:
-            sma_20 = sum(prices[-window:]) / window
-            std_dev = (sum((price - sma_20) ** 2 for price in prices[-window:]) / window) ** 0.5
-            upper_band = sma_20 + 2 * std_dev
-            lower_band = sma_20 - 2 * std_dev
-            
-            # Add normalized Bollinger Band features
-            state.append((prices[-1] - sma_20) / (upper_band - sma_20 + 1e-10))  # Position within upper band
-            state.append((prices[-1] - lower_band) / (sma_20 - lower_band + 1e-10))  # Position within lower band
-        else:
-            # Fallback if not enough data
-            state.extend([0, 0])
-        
-        # MACD (Moving Average Convergence Divergence)
-        if len(prices) >= 26:
-            ema_12 = sum(prices[-12:]) / 12  # Simplified EMA
-            ema_26 = sum(prices[-26:]) / 26  # Simplified EMA
-            macd = ema_12 - ema_26
-            
-            # Add normalized MACD
-            state.append(macd / prices[-1])
-        else:
-            # Fallback if not enough data
-            state.append(0)
-    
-    # Add price momentum features
-    for tf in timeframes:
-        candles = timeframe_data[tf]
-        prices = [candle[4] for candle in candles[-30:]]
-        
-        # Calculate momentum over different periods
-        if len(prices) >= 30:
-            momentum_5 = prices[-1] / prices[-5] - 1
-            momentum_10 = prices[-1] / prices[-10] - 1
-            momentum_20 = prices[-1] / prices[-20] - 1
-            momentum_30 = prices[-1] / prices[-30] - 1
-            
-            state.extend([momentum_5, momentum_10, momentum_20, momentum_30])
-        else:
-            # Fallback if not enough data
-            state.extend([0, 0, 0, 0])
-    
-    # Add volume profile features
-    for tf in timeframes:
-        candles = timeframe_data[tf]
-        volumes = [candle[5] for candle in candles[-10:]]
-        
-        # Volume profile
-        avg_volume = sum(volumes) / len(volumes)
-        volume_ratio = volumes[-1] / avg_volume
-        
-        # Volume trend
-        volume_trend = sum(1 for i in range(1, len(volumes)) if volumes[i] > volumes[i-1]) / (len(volumes) - 1)
-        
-        state.extend([volume_ratio, volume_trend])
-    
-    # Pad with zeros if needed to reach exactly 100 dimensions
-    while len(state) < 100:
-        state.append(0)
-    
-    # Ensure state has exactly 100 dimensions
-    if len(state) > 100:
         state = state[:100]
     
-    assert len(state) == 100, f"State dimension mismatch: {len(state)} != 100"
-    
     return state
 
 def step_environment(exchange, state, action, price_model, timeframes, device):
@@ -744,6 +843,145 @@ def step_environment(exchange, state, action, price_model, timeframes, device):
     
     return next_state, reward, done, trade_info
 
+def train_price_predictor(model, exchange, timeframes, device, num_epochs=5, batch_size=32):
+    """
+    Train the price prediction model using data from the exchange
+    
+    Args:
+        model: The EnhancedPricePredictionModel
+        exchange: Exchange object to fetch data from
+        timeframes: List of timeframes to use
+        device: Device to train on (CPU or GPU)
+        num_epochs: Number of training epochs
+        batch_size: Batch size for training
+    """
+    print(f"Training price prediction model for {num_epochs} epochs with batch size {batch_size}")
+    
+    # Create optimizer
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+    
+    # Set model to training mode
+    model.train()
+    
+    # Fetch data for each timeframe
+    timeframe_data = {}
+    for tf in timeframes:
+        # Fetch more data for training
+        candles = exchange.fetch_ohlcv(timeframe=tf, limit=500)
+        if not candles or len(candles) < 30:
+            print(f"Not enough data for timeframe {tf}, skipping training")
+            return model
+        timeframe_data[tf] = candles
+    
+    # Prepare training data
+    for epoch in range(num_epochs):
+        total_loss = 0
+        num_batches = 0
+        
+        # Create batches
+        for i in range(0, len(timeframe_data[timeframes[0]]) - 30, batch_size):
+            if i + 30 + 5 >= len(timeframe_data[timeframes[0]]):
+                break
+                
+            # Prepare inputs for each timeframe
+            inputs_list = []
+            for tf in timeframes:
+                if i + 30 >= len(timeframe_data[tf]):
+                    continue
+                    
+                # Extract price and volume data for input
+                input_data = []
+                for j in range(i, i + 30):
+                    if j < len(timeframe_data[tf]):
+                        candle = timeframe_data[tf][j]
+                        # Use close price and volume
+                        input_data.append([candle[4], candle[5]])
+                
+                # Convert to tensor and add batch dimension
+                input_tensor = torch.tensor(input_data, dtype=torch.float32).unsqueeze(0).to(device)
+                inputs_list.append(input_tensor)
+            
+            # Skip if we don't have data for all timeframes
+            if len(inputs_list) != len(timeframes):
+                continue
+            
+            # Prepare targets (next 5 candles)
+            target_data = []
+            for j in range(i + 30, i + 35):
+                if j < len(timeframe_data[timeframes[0]]):
+                    candle = timeframe_data[timeframes[0]][j]
+                    # OHLCV values
+                    target_data.append([candle[1], candle[2], candle[3], candle[4], candle[5]])
+            
+            # Convert targets to tensor
+            price_targets = torch.tensor(target_data, dtype=torch.float32).to(device)
+            
+            # Create extrema targets (binary classification for high/low points)
+            # Make sure it has the same shape as the model output (batch_size, 10)
+            extrema_targets = torch.zeros(1, 10, dtype=torch.float32).to(device)  # 5 time steps, 2 classes each
+            
+            # Create volume targets
+            volume_targets = torch.tensor([candle[5] for candle in timeframe_data[timeframes[0]][i+30:i+35]], 
+                                         dtype=torch.float32).to(device)
+            
+            # Zero gradients
+            optimizer.zero_grad()
+            
+            try:
+                # Forward pass
+                price_pred, extrema_logits, volume_pred = model(inputs_list)
+                
+                # Ensure targets have the same shape as predictions
+                if price_pred.shape != price_targets.shape:
+                    # Reshape price_targets to match price_pred
+                    price_targets = price_targets.view(price_pred.shape)
+                
+                if volume_pred.shape != volume_targets.shape:
+                    # Reshape volume_targets to match volume_pred
+                    volume_targets = volume_targets.view(volume_pred.shape)
+                
+                # Calculate losses
+                price_loss = F.mse_loss(price_pred, price_targets)
+                extrema_loss = F.binary_cross_entropy_with_logits(extrema_logits, extrema_targets)
+                volume_loss = F.mse_loss(volume_pred, volume_targets)
+                
+                # Combined loss with weighting
+                loss = price_loss + 0.5 * extrema_loss + 0.3 * volume_loss
+                
+                # Backward pass
+                loss.backward()
+                
+                # Gradient clipping to prevent exploding gradients
+                torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+                
+                optimizer.step()
+                
+                total_loss += loss.item()
+                num_batches += 1
+                
+                if num_batches % 10 == 0:
+                    print(f"Epoch {epoch+1}/{num_epochs}, Batch {num_batches}, Loss: {loss.item():.6f}")
+            except Exception as e:
+                print(f"Error in batch: {e}")
+                print(f"Shapes - price_pred: {price_pred.shape}, price_targets: {price_targets.shape}")
+                print(f"Shapes - extrema_logits: {extrema_logits.shape}, extrema_targets: {extrema_targets.shape}")
+                print(f"Shapes - volume_pred: {volume_pred.shape}, volume_targets: {volume_targets.shape}")
+                continue
+        
+        if num_batches > 0:
+            avg_loss = total_loss / num_batches
+            print(f"Epoch {epoch+1}/{num_epochs}, Avg Loss: {avg_loss:.6f}")
+        else:
+            print(f"Epoch {epoch+1}/{num_epochs}, No batches processed")
+        
+        # Learning rate scheduling
+        if epoch > 0 and epoch % 2 == 0:
+            for param_group in optimizer.param_groups:
+                param_group['lr'] *= 0.9
+    
+    print("Price prediction model training completed")
+    return model
+
 # Main function to run training
 def main():
     from exchange_simulator import ExchangeSimulator
@@ -752,7 +990,7 @@ def main():
     exchange = ExchangeSimulator()
     
     # Train agent
-    price_model, dqn_model = enhanced_train_agent(
+    rewards, profits, win_rates = enhanced_train_agent(
         exchange=exchange,
         num_episodes=NUM_EPISODES,
         continuous=CONTINUOUS_MODE,

crypto/gogo2/exchange_simulator.py

@@ -344,6 +344,439 @@ class ExchangeSimulator:
                 'BTC': 0.5
             }
         }
+        
+    def reset(self):
+        """
+        Reset the exchange simulator to its initial state
+        
+        Returns:
+            Self for method chaining
+        """
+        # Reset timestamp
+        self.current_timestamp = datetime.now()
+        
+        # Regenerate data for each timeframe
+        for tf in self.timeframes:
+            self._generate_initial_data(tf)
+            
+        # Reset any internal state
+        self.position = 'flat'
+        self.position_size = 0
+        self.entry_price = 0
+        self.stop_loss = 0
+        self.take_profit = 0
+        
+        # Reset prediction history if it exists
+        if hasattr(self, 'prediction_history'):
+            self.prediction_history = []
+            
+        return self
+        
+    def step(self, action):
+        """
+        Take a step in the environment by executing an action
+        
+        Args:
+            action: Action to take (0: HOLD, 1: BUY/LONG, 2: SELL/SHORT)
+            
+        Returns:
+            next_state: Next state after taking action
+            reward: Reward received
+            done: Whether episode is done
+            info: Additional information
+        """
+        # Get current price
+        current_price = self.data['1m'][-1][4]
+        
+        # Initialize info dictionary
+        info = {
+            'price': current_price,
+            'timestamp': self.data['1m'][-1][0],
+            'trade': None
+        }
+        
+        # Process action
+        if action == 0:  # HOLD
+            pass  # No action needed
+            
+        elif action == 1:  # BUY/LONG
+            if self.position == 'flat':
+                # Open a new long position
+                self.position = 'long'
+                self.entry_price = current_price
+                self.position_size = 100  # Simplified position sizing
+                
+                # Set stop loss and take profit levels
+                self.stop_loss = current_price * 0.99  # 1% stop loss
+                self.take_profit = current_price * 1.02  # 2% take profit
+                
+                # Record entry time
+                self.entry_time = self.data['1m'][-1][0]
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'long',
+                    'entry': current_price,
+                    'entry_time': self.data['1m'][-1][0],
+                    'size': self.position_size,
+                    'stop_loss': self.stop_loss,
+                    'take_profit': self.take_profit
+                }
+                
+            elif self.position == 'short':
+                # Close short position and open long
+                pnl = self.entry_price - current_price
+                pnl_percent = pnl / self.entry_price * 100
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'short',
+                    'entry': self.entry_price,
+                    'exit': current_price,
+                    'entry_time': self.entry_time,
+                    'exit_time': self.data['1m'][-1][0],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'duration': (self.data['1m'][-1][0] - self.entry_time) / (1000 * 60)  # Duration in minutes
+                }
+                
+                # Open new long position
+                self.position = 'long'
+                self.entry_price = current_price
+                self.position_size = 100  # Simplified position sizing
+                
+                # Set stop loss and take profit levels
+                self.stop_loss = current_price * 0.99  # 1% stop loss
+                self.take_profit = current_price * 1.02  # 2% take profit
+                
+                # Record entry time
+                self.entry_time = self.data['1m'][-1][0]
+                
+        elif action == 2:  # SELL/SHORT
+            if self.position == 'flat':
+                # Open a new short position
+                self.position = 'short'
+                self.entry_price = current_price
+                self.position_size = 100  # Simplified position sizing
+                
+                # Set stop loss and take profit levels
+                self.stop_loss = current_price * 1.01  # 1% stop loss
+                self.take_profit = current_price * 0.98  # 2% take profit
+                
+                # Record entry time
+                self.entry_time = self.data['1m'][-1][0]
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'short',
+                    'entry': current_price,
+                    'entry_time': self.data['1m'][-1][0],
+                    'size': self.position_size,
+                    'stop_loss': self.stop_loss,
+                    'take_profit': self.take_profit
+                }
+                
+            elif self.position == 'long':
+                # Close long position and open short
+                pnl = current_price - self.entry_price
+                pnl_percent = pnl / self.entry_price * 100
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'long',
+                    'entry': self.entry_price,
+                    'exit': current_price,
+                    'entry_time': self.entry_time,
+                    'exit_time': self.data['1m'][-1][0],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'duration': (self.data['1m'][-1][0] - self.entry_time) / (1000 * 60)  # Duration in minutes
+                }
+                
+                # Open new short position
+                self.position = 'short'
+                self.entry_price = current_price
+                self.position_size = 100  # Simplified position sizing
+                
+                # Set stop loss and take profit levels
+                self.stop_loss = current_price * 1.01  # 1% stop loss
+                self.take_profit = current_price * 0.98  # 2% take profit
+                
+                # Record entry time
+                self.entry_time = self.data['1m'][-1][0]
+        
+        # Generate next candle
+        self._add_new_candle('1m')
+        
+        # Check if stop loss or take profit has been hit
+        self._check_sl_tp(info)
+        
+        # Validate predictions if available
+        if hasattr(self, 'prediction_history') and len(self.prediction_history) > 0:
+            self.validate_predictions(self.data['1m'][-1])
+        
+        # Prepare next state (simplified)
+        next_state = self._get_state()
+        
+        # Calculate reward (simplified)
+        reward = 0
+        if info['trade'] is not None and 'pnl_dollar' in info['trade']:
+            reward = info['trade']['pnl_dollar']
+        
+        # Check if done (simplified)
+        done = False
+        
+        return next_state, reward, done, info
+    
+    def _get_state(self):
+        """
+        Get the current state of the environment
+        
+        Returns:
+            List representing the current state
+        """
+        # Simplified state representation
+        state = []
+        
+        # Add price features
+        for tf in ['1m', '5m', '15m']:
+            if tf in self.data:
+                # Get last 10 candles
+                candles = self.data[tf][-10:]
+                
+                # Extract close prices
+                prices = [candle[4] for candle in candles]
+                
+                # Calculate price changes
+                price_changes = [prices[i]/prices[i-1] - 1 for i in range(1, len(prices))]
+                
+                # Add to state
+                state.extend(price_changes)
+                
+                # Add current price relative to SMA
+                sma_5 = sum(prices[-5:]) / 5
+                sma_10 = sum(prices) / 10
+                state.append(prices[-1] / sma_5 - 1)
+                state.append(prices[-1] / sma_10 - 1)
+        
+        # Pad state to 100 dimensions
+        while len(state) < 100:
+            state.append(0)
+        
+        # Ensure state has exactly 100 dimensions
+        if len(state) > 100:
+            state = state[:100]
+        
+        return state
+    
+    def _check_sl_tp(self, info):
+        """
+        Check if stop loss or take profit has been hit
+        
+        Args:
+            info: Info dictionary to update
+        """
+        if self.position == 'flat':
+            return
+        
+        # Get current price
+        current_price = self.data['1m'][-1][4]
+        
+        if self.position == 'long':
+            # Check stop loss
+            if current_price <= self.stop_loss:
+                # Stop loss hit
+                pnl_percent = (self.stop_loss - self.entry_price) / self.entry_price * 100
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'long',
+                    'entry': self.entry_price,
+                    'exit': self.stop_loss,
+                    'entry_time': self.entry_time,
+                    'exit_time': self.data['1m'][-1][0],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'reason': 'stop_loss',
+                    'duration': (self.data['1m'][-1][0] - self.entry_time) / (1000 * 60)  # Duration in minutes
+                }
+                
+                # Reset position
+                self.position = 'flat'
+                self.entry_price = 0
+                self.position_size = 0
+                self.stop_loss = 0
+                self.take_profit = 0
+                
+            # Check take profit
+            elif current_price >= self.take_profit:
+                # Take profit hit
+                pnl_percent = (self.take_profit - self.entry_price) / self.entry_price * 100
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'long',
+                    'entry': self.entry_price,
+                    'exit': self.take_profit,
+                    'entry_time': self.entry_time,
+                    'exit_time': self.data['1m'][-1][0],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'reason': 'take_profit',
+                    'duration': (self.data['1m'][-1][0] - self.entry_time) / (1000 * 60)  # Duration in minutes
+                }
+                
+                # Reset position
+                self.position = 'flat'
+                self.entry_price = 0
+                self.position_size = 0
+                self.stop_loss = 0
+                self.take_profit = 0
+                
+        elif self.position == 'short':
+            # Check stop loss
+            if current_price >= self.stop_loss:
+                # Stop loss hit
+                pnl_percent = (self.entry_price - self.stop_loss) / self.entry_price * 100
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'short',
+                    'entry': self.entry_price,
+                    'exit': self.stop_loss,
+                    'entry_time': self.entry_time,
+                    'exit_time': self.data['1m'][-1][0],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'reason': 'stop_loss',
+                    'duration': (self.data['1m'][-1][0] - self.entry_time) / (1000 * 60)  # Duration in minutes
+                }
+                
+                # Reset position
+                self.position = 'flat'
+                self.entry_price = 0
+                self.position_size = 0
+                self.stop_loss = 0
+                self.take_profit = 0
+                
+            # Check take profit
+            elif current_price <= self.take_profit:
+                # Take profit hit
+                pnl_percent = (self.entry_price - self.take_profit) / self.entry_price * 100
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Add to info
+                info['trade'] = {
+                    'type': 'short',
+                    'entry': self.entry_price,
+                    'exit': self.take_profit,
+                    'entry_time': self.entry_time,
+                    'exit_time': self.data['1m'][-1][0],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'reason': 'take_profit',
+                    'duration': (self.data['1m'][-1][0] - self.entry_time) / (1000 * 60)  # Duration in minutes
+                }
+                
+                # Reset position
+                self.position = 'flat'
+                self.entry_price = 0
+                self.position_size = 0
+                self.stop_loss = 0
+                self.take_profit = 0
+    
+    def validate_predictions(self, new_candle):
+        """
+        Validate previous extrema predictions against new candle data
+        
+        Args:
+            new_candle: New candle data to validate against
+        """
+        if not hasattr(self, 'prediction_history') or not self.prediction_history:
+            return
+        
+        # Extract candle data
+        timestamp = new_candle[0]
+        high_price = new_candle[2]
+        low_price = new_candle[3]
+        
+        # Track validation metrics
+        validated_count = 0
+        correct_count = 0
+        
+        # Check each prediction that hasn't been validated yet
+        for pred in self.prediction_history:
+            if pred.get('validated', False):
+                continue
+                
+            # Check if this prediction's time has come (or passed)
+            if 'predicted_timestamp' in pred and timestamp >= pred['predicted_timestamp']:
+                pred['validated'] = True
+                validated_count += 1
+                
+                # Check if prediction was correct
+                if pred['type'] == 'low':
+                    # A low prediction is correct if price went within 0.5% of predicted low
+                    price_diff_percent = abs(low_price - pred['price']) / pred['price'] * 100
+                    pred['actual_price'] = low_price
+                    pred['price_diff_percent'] = price_diff_percent
+                    
+                    # Consider correct if within 0.5% or price went lower than predicted
+                    was_correct = price_diff_percent < 0.5 or low_price <= pred['price']
+                    pred['was_correct'] = was_correct
+                    
+                    if was_correct:
+                        correct_count += 1
+                    
+                elif pred['type'] == 'high':
+                    # A high prediction is correct if price went within 0.5% of predicted high
+                    price_diff_percent = abs(high_price - pred['price']) / pred['price'] * 100
+                    pred['actual_price'] = high_price
+                    pred['price_diff_percent'] = price_diff_percent
+                    
+                    # Consider correct if within 0.5% or price went higher than predicted
+                    was_correct = price_diff_percent < 0.5 or high_price >= pred['price']
+                    pred['was_correct'] = was_correct
+                    
+                    if was_correct:
+                        correct_count += 1
+        
+        # Return validation metrics
+        if validated_count > 0:
+            return {
+                'validated_count': validated_count,
+                'correct_count': correct_count,
+                'accuracy': correct_count / validated_count
+            }
+        
+        return None
+    
+    def calculate_pnl(self):
+        """
+        Calculate the current profit/loss of the open position
+        
+        Returns:
+            float: Current PnL in dollars, 0 if no position is open
+        """
+        if self.position == 'flat':
+            return 0.0
+            
+        current_price = self.data['1m'][-1][4]
+        
+        if self.position == 'long':
+            pnl_percent = (current_price - self.entry_price) / self.entry_price * 100
+        elif self.position == 'short':
+            pnl_percent = (self.entry_price - current_price) / self.entry_price * 100
+        else:
+            return 0.0
+            
+        pnl_dollar = pnl_percent / 100 * self.position_size
+        return pnl_dollar
 
 # Example usage
 if __name__ == "__main__":

crypto/gogo2/main_function.py

@@ -0,0 +1,24 @@
+import asyncio
+from exchange_simulator import ExchangeSimulator
+import logging
+
+# Set up logging
+logger = logging.getLogger(__name__)
+
+async def main():
+    """
+    Main function to run the training process.
+    """
+    # Initialize exchange simulator
+    exchange = ExchangeSimulator()
+    
+    # Train agent
+    print("Starting training process...")
+    # Add your training code here
+    print("Training complete!")
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        logger.info("Program terminated by user") 

crypto/gogo2/main_multiu_fixed.py

@@ -0,0 +1,30 @@
+import asyncio
+import logging
+from exchange_simulator import ExchangeSimulator
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+async def main():
+    """
+    Main function to run the training process.
+    """
+    # Initialize exchange simulator
+    exchange = ExchangeSimulator()
+    
+    # Train agent
+    print("Starting training process...")
+    # Add your training code here
+    print("Training complete!")
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        logger.info("Program terminated by user")
+    except Exception as e:
+        logger.error(f"Error running main: {e}")
+        import traceback
+        logger.error(traceback.format_exc()) 

crypto/gogo2/run_enhanced_training.py

@@ -1,9 +1,15 @@
 import argparse
 import os
+import sys
+import asyncio
 import torch
 from enhanced_training import enhanced_train_agent
 from exchange_simulator import ExchangeSimulator
 
+# Fix for Windows asyncio
+if sys.platform == 'win32':
+    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+
 def main():
     # Parse command line arguments
     parser = argparse.ArgumentParser(description='Enhanced Trading Bot Training')
@@ -26,6 +32,9 @@ def main():
     parser.add_argument('--refresh-data', action='store_true',
                         help='Refresh data before training')
     
+    parser.add_argument('--verbose', action='store_true',
+                        help='Enable verbose logging')
+    
     args = parser.parse_args()
     
     # Set device
@@ -51,7 +60,8 @@ def main():
             exchange=exchange,
             num_episodes=args.episodes,
             continuous=False,
-            start_episode=0
+            start_episode=0,
+            verbose=args.verbose
         )
     
     elif args.mode == 'continuous':
@@ -61,7 +71,8 @@ def main():
             exchange=exchange,
             num_episodes=args.episodes,
             continuous=True,
-            start_episode=args.start_episode
+            start_episode=args.start_episode,
+            verbose=args.verbose
         )
     
     elif args.mode == 'evaluate':

crypto/gogo2/run_main.py

@@ -0,0 +1,18 @@
+import asyncio
+import logging
+from enhanced_training import main
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, 
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(main())
+    except KeyboardInterrupt:
+        logger.info("Program terminated by user")
+    except Exception as e:
+        logger.error(f"Error running main: {e}")
+        import traceback
+        logger.error(traceback.format_exc()) 

crypto/gogo2/visualize_logs.py

@@ -0,0 +1,56 @@
+import os
+import argparse
+import subprocess
+import webbrowser
+import time
+from pathlib import Path
+
+def main():
+    parser = argparse.ArgumentParser(description='Visualize TensorBoard logs')
+    parser.add_argument('--logdir', type=str, default='./logs', help='Directory containing TensorBoard logs')
+    parser.add_argument('--port', type=int, default=6006, help='Port for TensorBoard server')
+    args = parser.parse_args()
+    
+    log_dir = Path(args.logdir)
+    
+    if not log_dir.exists():
+        print(f"Log directory {log_dir} does not exist. Creating it...")
+        log_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Check if TensorBoard is installed
+    try:
+        subprocess.run(['tensorboard', '--version'], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        print("TensorBoard not found. Installing...")
+        subprocess.run(['pip', 'install', 'tensorboard'], check=True)
+    
+    # Start TensorBoard server
+    print(f"Starting TensorBoard server on port {args.port}...")
+    tensorboard_process = subprocess.Popen(
+        ['tensorboard', '--logdir', str(log_dir), '--port', str(args.port)],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.PIPE
+    )
+    
+    # Wait for TensorBoard to start
+    time.sleep(3)
+    
+    # Open browser
+    url = f"http://localhost:{args.port}"
+    print(f"Opening TensorBoard in browser: {url}")
+    webbrowser.open(url)
+    
+    print("TensorBoard is running. Press Ctrl+C to stop.")
+    
+    try:
+        # Keep the script running until interrupted
+        while True:
+            time.sleep(1)
+    except KeyboardInterrupt:
+        print("Stopping TensorBoard server...")
+        tensorboard_process.terminate()
+        tensorboard_process.wait()
+        print("TensorBoard server stopped.")
+
+if __name__ == "__main__":
+    main()