misc

.vscode/launch.json

@@ -204,6 +204,49 @@
             ],
             "console": "integratedTerminal",
             "justMyCode": true
+        },
+        {
+            "name": "Realtime RL Training + TensorBoard + Web UI",
+            "type": "python",
+            "request": "launch",
+            "program": "train_realtime_with_tensorboard.py",
+            "args": [
+                "--episodes",
+                "50",
+                "--symbol",
+                "ETH/USDT",
+                "--balance",
+                "1000.0",
+                "--web-port",
+                "8051"
+            ],
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "env": {
+                "PYTHONUNBUFFERED": "1",
+                "ENABLE_REAL_DATA_ONLY": "1"
+            },
+            "preLaunchTask": "Kill Stale Processes"
+        },
+        {
+            "name": "Quick CNN Test (Real Data + TensorBoard)",
+            "type": "python",
+            "request": "launch",
+            "program": "test_cnn_only.py",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "env": {
+                "PYTHONUNBUFFERED": "1"
+            },
+            "preLaunchTask": "Kill Stale Processes"
+        },
+        {
+            "name": "TensorBoard Monitor (All Runs)",
+            "type": "python",
+            "request": "launch",
+            "program": "run_tensorboard.py",
+            "console": "integratedTerminal",
+            "justMyCode": true
         }
     ]
 }

TENSORBOARD_MONITORING.md

@@ -0,0 +1,332 @@
+# TensorBoard Monitoring Guide
+
+## Overview
+
+The trading system now uses **TensorBoard** for real-time training monitoring instead of static charts. This provides dynamic, interactive visualizations that update during training.
+
+## 🚨 CRITICAL: Real Market Data Only
+
+All TensorBoard metrics are derived from **REAL market data training**. No synthetic or generated data is used.
+
+## Quick Start
+
+### 1. Start Training with TensorBoard
+```bash
+# CNN Training with TensorBoard
+python main_clean.py --mode cnn --symbol ETH/USDT
+
+# RL Training with TensorBoard  
+python train_rl_with_realtime.py --episodes 10
+
+# Quick CNN Test
+python test_cnn_only.py
+```
+
+### 2. Launch TensorBoard
+```bash
+# Option 1: Direct command
+tensorboard --logdir=runs
+
+# Option 2: Convenience script
+python run_tensorboard.py
+```
+
+### 3. Access TensorBoard
+Open your browser to: **http://localhost:6006**
+
+## Available Metrics
+
+### CNN Training Metrics
+
+#### **Training Progress**
+- `Training/EpochLoss` - Training loss per epoch
+- `Training/EpochAccuracy` - Training accuracy per epoch  
+- `Training/BatchLoss` - Batch-level loss
+- `Training/BatchAccuracy` - Batch-level accuracy
+- `Training/BatchConfidence` - Model confidence scores
+- `Training/LearningRate` - Learning rate schedule
+- `Training/EpochTime` - Time per epoch
+
+#### **Validation Metrics**
+- `Validation/Loss` - Validation loss
+- `Validation/Accuracy` - Validation accuracy
+- `Validation/AvgConfidence` - Average confidence on validation set
+- `Validation/Class_0_Accuracy` - BUY class accuracy
+- `Validation/Class_1_Accuracy` - SELL class accuracy  
+- `Validation/Class_2_Accuracy` - HOLD class accuracy
+
+#### **Best Model Tracking**
+- `Best/ValidationLoss` - Best validation loss achieved
+- `Best/ValidationAccuracy` - Best validation accuracy achieved
+
+#### **Data Statistics**
+- `Data/TotalSamples` - Number of training samples from real data
+- `Data/Features` - Number of features (detected from real data)
+- `Data/Timeframes` - Number of timeframes used
+- `Data/WindowSize` - Window size for temporal patterns
+- `Data/Class_X_Count` - Sample count per class
+- `Data/Feature_X_Mean/Std` - Feature statistics
+
+#### **Model Architecture**
+- `Model/TotalParameters` - Total model parameters
+- `Model/TrainableParameters` - Trainable parameters
+
+#### **Training Configuration**
+- `Config/LearningRate` - Learning rate used
+- `Config/BatchSize` - Batch size
+- `Config/MaxEpochs` - Maximum epochs
+
+### RL Training Metrics
+
+#### **Episode Performance**
+- `Episode/TotalReward` - Total reward per episode
+- `Episode/FinalBalance` - Final balance after episode
+- `Episode/TotalReturn` - Return percentage
+- `Episode/Steps` - Steps taken in episode
+
+#### **Trading Performance**
+- `Trading/TotalTrades` - Number of trades executed
+- `Trading/WinRate` - Percentage of profitable trades
+- `Trading/ProfitFactor` - Gross profit / gross loss ratio
+- `Trading/MaxDrawdown` - Maximum drawdown percentage
+
+#### **Agent Learning**
+- `Agent/Epsilon` - Exploration rate (epsilon)
+- `Agent/LearningRate` - Agent learning rate
+- `Agent/MemorySize` - Experience replay buffer size
+- `Agent/Loss` - Training loss from experience replay
+
+#### **Moving Averages**
+- `Moving_Average/Reward_50ep` - 50-episode average reward
+- `Moving_Average/Return_50ep` - 50-episode average return
+
+#### **Best Performance**
+- `Best/Return` - Best return percentage achieved
+
+## Directory Structure
+
+```
+runs/
+├── cnn_training_1748043814/     # CNN training session
+│   ├── events.out.tfevents.*    # TensorBoard event files
+│   └── ...
+├── rl_training_1748043920/      # RL training session  
+│   ├── events.out.tfevents.*
+│   └── ...
+└── ...                          # Other training sessions
+```
+
+## TensorBoard Features
+
+### **Scalars Tab**
+- Real-time line charts of all metrics
+- Smoothing controls for noisy metrics
+- Multiple run comparisons
+- Download data as CSV
+
+### **Images Tab**
+- Model architecture visualizations
+- Training progression images
+
+### **Graphs Tab**
+- Computational graph of models
+- Network architecture visualization
+
+### **Histograms Tab**
+- Weight and gradient distributions
+- Activation patterns over time
+
+### **Projector Tab**
+- High-dimensional data visualization
+- Feature embeddings
+
+## Usage Examples
+
+### 1. Monitor CNN Training
+```bash
+# Start CNN training (generates TensorBoard logs)
+python main_clean.py --mode cnn --symbol ETH/USDT
+
+# In another terminal, start TensorBoard
+tensorboard --logdir=runs
+
+# Open browser to http://localhost:6006
+# Navigate to Scalars tab to see:
+# - Training/EpochLoss declining over time
+# - Validation/Accuracy improving
+# - Training/LearningRate schedule
+```
+
+### 2. Compare Multiple Training Runs
+```bash
+# Run multiple training sessions
+python test_cnn_only.py  # Creates cnn_training_X
+python test_cnn_only.py  # Creates cnn_training_Y
+
+# TensorBoard automatically shows both runs
+# Compare performance across runs in the same charts
+```
+
+### 3. Monitor RL Agent Training
+```bash
+# Start RL training with TensorBoard logging
+python main_clean.py --mode rl --symbol ETH/USDT
+
+# View in TensorBoard:
+# - Episode/TotalReward trending up
+# - Trading/WinRate improving
+# - Agent/Epsilon decreasing (less exploration)
+```
+
+## Real-Time Monitoring
+
+### Key Indicators to Watch
+
+#### **CNN Training Health**
+- ✅ `Training/EpochLoss` should decrease over time
+- ✅ `Validation/Accuracy` should increase
+- ⚠️ Watch for overfitting (val loss increases while train loss decreases)
+- ✅ `Training/LearningRate` should follow schedule
+
+#### **RL Training Health** 
+- ✅ `Episode/TotalReward` trending upward
+- ✅ `Trading/WinRate` above 50%
+- ✅ `Moving_Average/Return_50ep` positive and stable
+- ⚠️ `Agent/Epsilon` should decay over time
+
+### Warning Signs
+- **Loss not decreasing**: Check learning rate, data quality
+- **Accuracy plateauing**: May need more data or different architecture
+- **RL rewards oscillating**: Unstable learning, adjust hyperparameters
+- **Win rate dropping**: Strategy not working, need different approach
+
+## Configuration
+
+### Custom TensorBoard Setup
+```python
+from torch.utils.tensorboard import SummaryWriter
+
+# Custom log directory
+writer = SummaryWriter(log_dir='runs/my_experiment')
+
+# Log custom metrics
+writer.add_scalar('Custom/Metric', value, step)
+writer.add_histogram('Custom/Weights', weights, step)
+```
+
+### Advanced Features
+```bash
+# Start TensorBoard with custom port
+tensorboard --logdir=runs --port=6007
+
+# Enable debugging
+tensorboard --logdir=runs --debugger_port=6064
+
+# Profile performance
+tensorboard --logdir=runs --load_fast=false
+```
+
+## Integration with Training
+
+### CNN Trainer Integration
+- Automatically logs all training metrics
+- Model architecture visualization
+- Real data statistics tracking
+- Best model checkpointing based on TensorBoard metrics
+
+### RL Trainer Integration  
+- Episode-by-episode performance tracking
+- Trading strategy effectiveness monitoring
+- Agent learning progress visualization
+- Hyperparameter optimization guidance
+
+## Benefits Over Static Charts
+
+### ✅ **Real-Time Updates**
+- See training progress as it happens
+- No need to wait for training completion
+- Immediate feedback on hyperparameter changes
+
+### ✅ **Interactive Exploration**
+- Zoom, pan, and explore metrics
+- Smooth noisy data with built-in controls
+- Compare multiple training runs side-by-side
+
+### ✅ **Rich Visualizations**
+- Scalars, histograms, images, and graphs
+- Model architecture visualization
+- High-dimensional data projections
+
+### ✅ **Data Export**
+- Download metrics as CSV
+- Programmatic access to training data
+- Integration with external analysis tools
+
+## Troubleshooting
+
+### TensorBoard Not Starting
+```bash
+# Check if TensorBoard is installed
+pip install tensorboard
+
+# Verify runs directory exists
+dir runs  # Windows
+ls runs   # Linux/Mac
+
+# Kill existing TensorBoard processes
+taskkill /F /IM tensorboard.exe  # Windows
+pkill -f tensorboard             # Linux/Mac
+```
+
+### No Data Showing
+- Ensure training is generating logs in `runs/` directory
+- Check browser console for errors
+- Try refreshing the page
+- Verify correct port (default 6006)
+
+### Performance Issues
+- Use `--load_fast=true` for faster loading
+- Clear old log directories
+- Reduce logging frequency in training code
+
+## Best Practices
+
+### 🎯 **Regular Monitoring**
+- Check TensorBoard every 10-20 epochs during CNN training
+- Monitor RL agents every 50-100 episodes
+- Look for concerning trends early
+
+### 📊 **Metric Organization**
+- Use clear naming conventions (Training/, Validation/, etc.)
+- Group related metrics together
+- Log at appropriate frequencies (not every step)
+
+### 💾 **Data Management**
+- Archive old training runs periodically
+- Keep successful run logs for reference
+- Document experiment parameters in run names
+
+### 🔍 **Hyperparameter Tuning**
+- Compare multiple runs with different hyperparameters
+- Use TensorBoard data to guide optimization
+- Track which settings produce best results
+
+---
+
+## Summary
+
+TensorBoard integration provides **real-time, interactive monitoring** of training progress using **only real market data**. This replaces static plots with dynamic visualizations that help optimize model performance and catch issues early.
+
+**Key Commands:**
+```bash
+# Train with TensorBoard logging
+python main_clean.py --mode cnn --symbol ETH/USDT
+
+# Start TensorBoard
+python run_tensorboard.py
+
+# Access dashboard
+http://localhost:6006
+```
+
+All metrics are derived from **real cryptocurrency market data** to ensure authentic trading model development. 

TRAINING_STATUS.md

@@ -0,0 +1 @@
+ 

main_clean.py

@@ -82,72 +82,50 @@ def run_data_test():
         logger.error(traceback.format_exc())
         raise
 
-def run_cnn_training():
-    """Train CNN models only with comprehensive pipeline"""
+def run_cnn_training(config: Config, symbol: str):
+    """Run CNN training mode with TensorBoard monitoring"""
+    logger.info("Starting CNN Training Mode...")
+    
+    # Initialize data provider and trainer
+    data_provider = DataProvider(config)
+    trainer = CNNTrainer(config)
+    
+    # Use configured symbols or provided symbol
+    symbols = config.symbols if symbol == "ETH/USDT" else [symbol] + config.symbols
+    save_path = f"models/cnn/scalping_cnn_trained.pt"
+    
+    logger.info(f"Training CNN for symbols: {symbols}")
+    logger.info(f"Will save to: {save_path}")
+    logger.info(f"🔗 Monitor training: tensorboard --logdir=runs")
+    
     try:
-        logger.info("Starting CNN Training Mode...")
+        # Train model with TensorBoard logging
+        results = trainer.train(symbols, save_path=save_path)
         
-        # Initialize components
-        data_provider = DataProvider(
-            symbols=['ETH/USDT', 'BTC/USDT'], 
-            timeframes=['1s', '1m', '5m', '1h', '4h']
-        )
-        
-        # Import and create CNN trainer
-        from training.cnn_trainer import CNNTrainer
-        trainer = CNNTrainer(data_provider)
-        
-        # Configure training
-        trainer.num_samples = 20000  # Training samples
-        trainer.batch_size = 64
-        trainer.num_epochs = 100
-        trainer.patience = 15
-        
-        # Train the model
-        symbols = ['ETH/USDT', 'BTC/USDT']
-        save_path = 'models/cnn/scalping_cnn_trained.pt'
-        
-        logger.info(f"Training CNN for symbols: {symbols}")
-        logger.info(f"Will save to: {save_path}")
-        
-        results = trainer.train(symbols, save_path)
-        
-        # Log results
         logger.info("CNN Training Results:")
         logger.info(f"  Best validation accuracy: {results['best_val_accuracy']:.4f}")
         logger.info(f"  Best validation loss: {results['best_val_loss']:.4f}")
         logger.info(f"  Total epochs: {results['total_epochs']}")
-        logger.info(f"  Training time: {results['total_time']:.2f} seconds")
+        logger.info(f"  Training time: {results['training_time']:.2f} seconds")
+        logger.info(f"  TensorBoard logs: {results['tensorboard_dir']}")
         
-        # Plot training history
-        try:
-            plot_path = 'models/cnn/training_history.png'
-            trainer.plot_training_history(plot_path)
-            logger.info(f"Training plots saved to: {plot_path}")
-        except Exception as e:
-            logger.warning(f"Could not save training plots: {e}")
+        logger.info(f"📊 View training progress: tensorboard --logdir=runs")
+        logger.info("Evaluating CNN on test data...")
         
-        # Evaluate on test data
-        try:
-            logger.info("Evaluating CNN on test data...")
-            test_symbols = ['ETH/USDT']  # Use subset for testing
-            eval_results = trainer.evaluate_model(test_symbols)
-            
-            logger.info("CNN Evaluation Results:")
-            logger.info(f"  Test accuracy: {eval_results['test_accuracy']:.4f}")
-            logger.info(f"  Test loss: {eval_results['test_loss']:.4f}")
-            logger.info(f"  Average confidence: {eval_results['avg_confidence']:.4f}")
-            
-        except Exception as e:
-            logger.warning(f"Could not run evaluation: {e}")
+        # Quick evaluation on same symbols
+        test_results = trainer.evaluate(symbols[:1])  # Use first symbol for quick test
+        logger.info("CNN Evaluation Results:")
+        logger.info(f"  Test accuracy: {test_results['test_accuracy']:.4f}")
+        logger.info(f"  Test loss: {test_results['test_loss']:.4f}")
+        logger.info(f"  Average confidence: {test_results['avg_confidence']:.4f}")
         
         logger.info("CNN training completed successfully!")
         
     except Exception as e:
-        logger.error(f"Error in CNN training: {e}")
-        import traceback
-        logger.error(traceback.format_exc())
+        logger.error(f"CNN training failed: {e}")
         raise
+    finally:
+        trainer.close_tensorboard()
 
 def run_rl_training():
     """Train RL agents only with comprehensive pipeline"""
@@ -404,7 +382,7 @@ async def main():
         if args.mode == 'test':
             run_data_test()
         elif args.mode == 'cnn':
-            run_cnn_training()
+            run_cnn_training(get_config(), args.symbol)
         elif args.mode == 'rl':
             run_rl_training()
         elif args.mode == 'train':

monitor_training.py

@@ -0,0 +1,83 @@
+#!/usr/bin/env python3
+"""
+Training Monitor Script
+
+Quick script to check the status of realtime training and show key metrics.
+"""
+
+import os
+import time
+from pathlib import Path
+from datetime import datetime
+import glob
+
+def check_training_status():
+    """Check status of training processes and logs"""
+    print("=" * 60)
+    print("REALTIME RL TRAINING STATUS CHECK")
+    print("=" * 60)
+    
+    # Check TensorBoard logs
+    runs_dir = Path("runs")
+    if runs_dir.exists():
+        log_dirs = list(runs_dir.glob("rl_training_*"))
+        recent_logs = sorted(log_dirs, key=lambda x: x.name)[-3:]  # Last 3 sessions
+        
+        print("\n📊 RECENT TENSORBOARD LOGS:")
+        for log_dir in recent_logs:
+            # Get creation time
+            stat = log_dir.stat()
+            created = datetime.fromtimestamp(stat.st_ctime)
+            
+            # Check for event files
+            event_files = list(log_dir.glob("*.tfevents.*"))
+            
+            print(f"  📁 {log_dir.name}")
+            print(f"     Created: {created.strftime('%Y-%m-%d %H:%M:%S')}")
+            print(f"     Event files: {len(event_files)}")
+            
+            if event_files:
+                latest_event = max(event_files, key=lambda x: x.stat().st_mtime)
+                modified = datetime.fromtimestamp(latest_event.stat().st_mtime)
+                print(f"     Last update: {modified.strftime('%Y-%m-%d %H:%M:%S')}")
+            print()
+    
+    # Check running processes
+    print("🔍 PROCESS STATUS:")
+    try:
+        import subprocess
+        result = subprocess.run(['tasklist'], capture_output=True, text=True, shell=True)
+        python_processes = [line for line in result.stdout.split('\n') if 'python.exe' in line]
+        print(f"  Python processes running: {len(python_processes)}")
+        for i, proc in enumerate(python_processes[:5]):  # Show first 5
+            print(f"  {i+1}. {proc.strip()}")
+    except Exception as e:
+        print(f"  Error checking processes: {e}")
+    
+    # Check web services
+    print("\n🌐 WEB SERVICES:")
+    print("  TensorBoard: http://localhost:6006")
+    print("  Web Dashboard: http://localhost:8051")
+    
+    # Check model saves
+    models_dir = Path("models/rl")
+    if models_dir.exists():
+        model_files = list(models_dir.glob("realtime_agent_*.pt"))
+        print(f"\n💾 SAVED MODELS: {len(model_files)}")
+        for model_file in sorted(model_files, key=lambda x: x.stat().st_mtime)[-3:]:
+            modified = datetime.fromtimestamp(model_file.stat().st_mtime)
+            print(f"  📄 {model_file.name} - {modified.strftime('%Y-%m-%d %H:%M:%S')}")
+    
+    print("\n" + "=" * 60)
+    print("✅ MONITORING URLs:")
+    print("📊 TensorBoard: http://localhost:6006")
+    print("🌐 Dashboard:   http://localhost:8051")
+    print("=" * 60)
+
+if __name__ == "__main__":
+    try:
+        check_training_status()
+    except KeyboardInterrupt:
+        print("\nMonitoring stopped.")
+    except Exception as e:
+        print(f"Error: {e}") 

readme.md

@@ -80,24 +80,53 @@ gogo2/
 
 ## Training Modes
 
-### CNN Training
+### CNN Training with TensorBoard
 ```bash
-# Train on real ETH/USDT data
+# Train on real ETH/USDT data with TensorBoard monitoring
 python main_clean.py --mode cnn --symbol ETH/USDT
 
+# Monitor training in real-time
+tensorboard --logdir=runs
+
+# Or use the convenience script
+python run_tensorboard.py
+
 # Quick test with real data
 python test_cnn_only.py
 ```
 
-### RL Training
+### RL Training with TensorBoard
 ```bash
 # Train RL agent with real data
 python main_clean.py --mode rl --symbol ETH/USDT
 
 # Real-time RL training
 python train_rl_with_realtime.py --episodes 10
+
+# Monitor RL training metrics
+tensorboard --logdir=runs
 ```
 
+## TensorBoard Monitoring
+
+All training sessions are logged to TensorBoard for real-time monitoring:
+
+```bash
+# Start TensorBoard server
+tensorboard --logdir=runs
+
+# Or use the convenience script
+python run_tensorboard.py
+```
+
+**Metrics Available:**
+- **CNN Training**: Loss, accuracy, confidence scores, feature statistics
+- **RL Training**: Rewards, returns, win rates, epsilon values, trading metrics
+- **Model Architecture**: Parameter counts, memory usage
+- **Real-time Updates**: Batch-level and epoch-level metrics
+
+Access TensorBoard at: http://localhost:6006
+
 ## Performance
 
 - **Memory Usage**: <2GB per model

run_tensorboard.py

@@ -1,69 +1,74 @@
-import os
-import sys
-import subprocess
-import webbrowser
-import time
-import argparse
+#!/usr/bin/env python3
+"""
+TensorBoard Launch Script
 
-def run_tensorboard():
-    """Run TensorBoard server and open browser"""
-    parser = argparse.ArgumentParser(description='TensorBoard Launcher')
-    parser.add_argument('--port', type=int, default=6006, help='Port for TensorBoard server')
-    parser.add_argument('--logdir', type=str, default='runs', help='Log directory for TensorBoard')
-    parser.add_argument('--no-browser', action='store_true', help='Do not open browser automatically')
-    args = parser.parse_args()
+Starts TensorBoard server for monitoring training progress.
+"""
+
+import subprocess
+import sys
+import os
+import time
+import webbrowser
+from pathlib import Path
+
+def main():
+    """Launch TensorBoard"""
     
-    # Create log directory if it doesn't exist
-    os.makedirs(args.logdir, exist_ok=True)
+    # Check if runs directory exists
+    runs_dir = Path("runs")
+    if not runs_dir.exists():
+        print("❌ No 'runs' directory found.")
+        print("   Start training first to generate TensorBoard logs.")
+        return
     
-    # Print banner
-    print("\n" + "="*60)
-    print("📊 TRADING BOT - TENSORBOARD MONITORING 📊")
-    print("="*60)
-    print(f"Starting TensorBoard server on port {args.port}")
-    print(f"Log directory: {args.logdir}")
-    print("Press Ctrl+C to stop the server")
-    print("="*60 + "\n")
+    # Check if there are any log directories
+    log_dirs = list(runs_dir.glob("*"))
+    if not log_dirs:
+        print("❌ No training logs found in 'runs' directory.")
+        print("   Start training first to generate TensorBoard logs.")
+        return
     
-    # Start TensorBoard server
-    cmd = ["tensorboard", "--logdir", args.logdir, "--port", str(args.port)]
+    print("🚀 Starting TensorBoard...")
+    print(f"📁 Log directory: {runs_dir.absolute()}")
+    print(f"📊 Found {len(log_dirs)} training sessions")
     
+    # List available sessions
+    print("\nAvailable training sessions:")
+    for i, log_dir in enumerate(sorted(log_dirs), 1):
+        print(f"  {i}. {log_dir.name}")
+    
+    # Start TensorBoard
     try:
+        port = 6006
+        print(f"\n🌐 Starting TensorBoard on port {port}...")
+        print(f"🔗 Access at: http://localhost:{port}")
+        
+        # Try to open browser automatically
+        try:
+            webbrowser.open(f"http://localhost:{port}")
+            print("🌍 Browser opened automatically")
+        except:
+            pass
+        
         # Start TensorBoard process
-        process = subprocess.Popen(
-            cmd, 
-            stdout=subprocess.PIPE,
-            stderr=subprocess.STDOUT,
-            universal_newlines=True
-        )
+        cmd = [sys.executable, "-m", "tensorboard.main", "--logdir", str(runs_dir), "--port", str(port)]
         
-        # Wait for TensorBoard to start
-        time.sleep(3)
+        print("\n" + "="*50)
+        print("🔥 TensorBoard is running!")
+        print(f"📈 View training metrics at: http://localhost:{port}")
+        print("⏹️  Press Ctrl+C to stop TensorBoard")
+        print("="*50 + "\n")
         
-        # Open browser
-        if not args.no_browser:
-            url = f"http://localhost:{args.port}"
-            print(f"Opening browser to {url}")
-            webbrowser.open(url)
+        # Run TensorBoard
+        subprocess.run(cmd)
         
-        # Print TensorBoard output
-        while True:
-            output = process.stdout.readline()
-            if output == '' and process.poll() is not None:
-                break
-            if output:
-                print(output.strip())
-        
-        return process.poll()
-    
     except KeyboardInterrupt:
-        print("\nStopping TensorBoard server...")
-        process.terminate()
-        return 0
+        print("\n🛑 TensorBoard stopped")
+    except FileNotFoundError:
+        print("❌ TensorBoard not found. Install with: pip install tensorboard")
     except Exception as e:
-        print(f"Error running TensorBoard: {str(e)}")
-        return 1
+        print(f"❌ Error starting TensorBoard: {e}")
 
 if __name__ == "__main__":
-    exit_code = run_tensorboard()
-    sys.exit(exit_code) 
+    main() 

test_cnn_only.py

@@ -1,54 +1,65 @@
 #!/usr/bin/env python3
 """
-Quick CNN training test
+Quick CNN Training Test - Real Market Data Only
+
+This script tests CNN training with a small dataset for quick validation.
+All training metrics are logged to TensorBoard for real-time monitoring.
 """
 
-import sys
-from pathlib import Path
-sys.path.insert(0, str(Path(__file__).parent))
-
-from core.config import setup_logging
+import logging
+from core.config import setup_logging, get_config
 from core.data_provider import DataProvider
 from training.cnn_trainer import CNNTrainer
 
 def main():
+    """Test CNN training with real market data"""
     setup_logging()
     
     print("Setting up CNN training test...")
+    print("📊 Monitor training: tensorboard --logdir=runs")
     
-    # Setup
-    data_provider = DataProvider(['ETH/USDT'], ['1m', '5m', '1h'])
-    trainer = CNNTrainer(data_provider)
+    # Configure test parameters
+    config = get_config()
     
-    # Configure for quick test
-    trainer.num_samples = 500   # Very small dataset
-    trainer.num_epochs = 2      # Just 2 epochs
-    trainer.batch_size = 16
-    trainer.timeframes = ['1m', '5m', '1h']  # Skip 1s for now
-    trainer.n_timeframes = 3
+    # Test configuration
+    symbols = ['ETH/USDT']
+    timeframes = ['1m', '5m', '1h']
+    num_samples = 500
+    epochs = 2
+    batch_size = 16
     
-    print(f"Configuration:")
-    print(f"  Samples: {trainer.num_samples}")
-    print(f"  Epochs: {trainer.num_epochs}")
-    print(f"  Batch size: {trainer.batch_size}")
-    print(f"  Timeframes: {trainer.timeframes}")
+    # Override config for quick test
+    config._config['timeframes'] = timeframes  # Direct config access
+    
+    trainer = CNNTrainer(config)
+    trainer.batch_size = batch_size
+    trainer.epochs = epochs
+    
+    print("Configuration:")
+    print(f"  Symbols: {symbols}")
+    print(f"  Timeframes: {timeframes}")
+    print(f"  Samples: {num_samples}")
+    print(f"  Epochs: {epochs}")
+    print(f"  Batch size: {batch_size}")
+    print("  Data source: REAL market data from exchange APIs")
     
-    # Train
     try:
-        results = trainer.train(['ETH/USDT'], save_path='test_models/quick_cnn.pt')
+        # Train model with TensorBoard logging
+        results = trainer.train(symbols, save_path='test_models/quick_cnn.pt', num_samples=num_samples)
         
         print(f"\n✅ CNN Training completed!")
         print(f"  Best accuracy: {results['best_val_accuracy']:.4f}")
         print(f"  Total epochs: {results['total_epochs']}")
-        print(f"  Training time: {results['total_time']:.2f}s")
+        print(f"  Training time: {results['training_time']:.2f}s")
+        print(f"  TensorBoard logs: {results['tensorboard_dir']}")
+        print(f"\n📊 View training progress: tensorboard --logdir=runs")
         
     except Exception as e:
-        print(f"\n❌ Training failed: {e}")
+        print(f"❌ Training failed: {e}")
         import traceback
         traceback.print_exc()
-        return 1
-    
-    return 0
+    finally:
+        trainer.close_tensorboard()
 
 if __name__ == "__main__":
-    sys.exit(main()) 
+    main() 

train_realtime_with_tensorboard.py

@@ -0,0 +1,420 @@
+#!/usr/bin/env python3
+"""
+Realtime RL Training with TensorBoard and Web UI Monitoring
+
+This script runs RL training with:
+- TensorBoard monitoring for training metrics  
+- Web UI for real-time trading visualization
+- Real market data integration
+- PnL tracking and performance analysis
+"""
+
+import asyncio
+import threading
+import time
+import logging
+import argparse
+from datetime import datetime
+import os
+import sys
+from pathlib import Path
+
+# Add project path
+project_root = Path(__file__).parent
+sys.path.insert(0, str(project_root))
+
+from core.config import setup_logging, get_config
+from core.data_provider import DataProvider
+from training.rl_trainer import RLTrainer
+from torch.utils.tensorboard import SummaryWriter
+
+logger = logging.getLogger(__name__)
+
+class RealtimeRLTrainer:
+    """Realtime RL Trainer with TensorBoard and Web UI"""
+    
+    def __init__(self, symbol="ETH/USDT", initial_balance=1000.0):
+        self.symbol = symbol
+        self.initial_balance = initial_balance
+        
+        # Initialize data provider
+        self.data_provider = DataProvider(
+            symbols=[symbol],
+            timeframes=['1s', '1m', '5m', '15m', '1h']
+        )
+        
+        # Initialize RL trainer with TensorBoard
+        self.rl_trainer = RLTrainer(self.data_provider)
+        
+        # Training state
+        self.current_episode = 0
+        self.session_trades = []
+        self.session_balance = initial_balance
+        self.session_pnl = 0.0
+        self.training_active = False
+        
+        # Web dashboard
+        self.dashboard = None
+        self.dashboard_thread = None
+        
+        logger.info(f"RealtimeRLTrainer initialized for {symbol}")
+        logger.info(f"TensorBoard logs: {self.rl_trainer.tensorboard_dir}")
+    
+    def setup_web_dashboard(self, port=8051):
+        """Setup web dashboard for monitoring"""
+        try:
+            import dash
+            from dash import dcc, html, Input, Output
+            import plotly.graph_objects as go
+            import plotly.express as px
+            
+            # Create Dash app
+            app = dash.Dash(__name__)
+            
+            # Layout
+            app.layout = html.Div([
+                html.H1(f"RL Training Monitor - {self.symbol}", 
+                       style={'textAlign': 'center', 'color': '#2c3e50'}),
+                
+                # Refresh interval
+                dcc.Interval(
+                    id='interval-component',
+                    interval=2000,  # Update every 2 seconds
+                    n_intervals=0
+                ),
+                
+                # Status row
+                html.Div([
+                    html.Div([
+                        html.H3("Training Status", style={'color': '#34495e'}),
+                        html.P(id='training-status', style={'fontSize': 18})
+                    ], className='three columns'),
+                    
+                    html.Div([
+                        html.H3("Current Episode", style={'color': '#34495e'}),
+                        html.P(id='current-episode', style={'fontSize': 18})
+                    ], className='three columns'),
+                    
+                    html.Div([
+                        html.H3("Session Balance", style={'color': '#27ae60'}),
+                        html.P(id='session-balance', style={'fontSize': 18})
+                    ], className='three columns'),
+                    
+                    html.Div([
+                        html.H3("Session PnL", style={'color': '#e74c3c'}),
+                        html.P(id='session-pnl', style={'fontSize': 18})
+                    ], className='three columns'),
+                ], className='row', style={'margin': '20px'}),
+                
+                # Charts row
+                html.Div([
+                    html.Div([
+                        dcc.Graph(id='rewards-chart')
+                    ], className='six columns'),
+                    
+                    html.Div([
+                        dcc.Graph(id='balance-chart')
+                    ], className='six columns'),
+                ], className='row'),
+                
+                html.Div([
+                    html.Div([
+                        dcc.Graph(id='trades-chart')
+                    ], className='six columns'),
+                    
+                    html.Div([
+                        dcc.Graph(id='win-rate-chart')
+                    ], className='six columns'),
+                ], className='row'),
+                
+                # TensorBoard link
+                html.Div([
+                    html.H3("TensorBoard Monitoring"),
+                    html.A("Open TensorBoard", 
+                          href="http://localhost:6006", 
+                          target="_blank",
+                          style={'fontSize': 16, 'color': '#3498db'})
+                ], style={'textAlign': 'center', 'margin': '20px'})
+            ])
+            
+            # Callbacks
+            @app.callback(
+                [Output('training-status', 'children'),
+                 Output('current-episode', 'children'),
+                 Output('session-balance', 'children'),
+                 Output('session-pnl', 'children'),
+                 Output('rewards-chart', 'figure'),
+                 Output('balance-chart', 'figure'),
+                 Output('trades-chart', 'figure'),
+                 Output('win-rate-chart', 'figure')],
+                [Input('interval-component', 'n_intervals')]
+            )
+            def update_dashboard(n):
+                # Status updates
+                status = "TRAINING" if self.training_active else "IDLE"
+                episode = f"{self.current_episode}"
+                balance = f"${self.session_balance:.2f}"
+                pnl = f"${self.session_pnl:.2f}"
+                
+                # Create charts
+                rewards_fig = self._create_rewards_chart()
+                balance_fig = self._create_balance_chart()
+                trades_fig = self._create_trades_chart()
+                win_rate_fig = self._create_win_rate_chart()
+                
+                return status, episode, balance, pnl, rewards_fig, balance_fig, trades_fig, win_rate_fig
+            
+            self.dashboard = app
+            logger.info(f"Web dashboard created for port {port}")
+            
+        except Exception as e:
+            logger.error(f"Error setting up web dashboard: {e}")
+            self.dashboard = None
+    
+    def _create_rewards_chart(self):
+        """Create rewards chart"""
+        import plotly.graph_objects as go
+        
+        if not self.rl_trainer.episode_rewards:
+            fig = go.Figure()
+            fig.add_annotation(text="No data yet", x=0.5, y=0.5, xref="paper", yref="paper")
+        else:
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(
+                y=self.rl_trainer.episode_rewards,
+                mode='lines',
+                name='Episode Rewards',
+                line=dict(color='#3498db')
+            ))
+            
+            # Add moving average if enough data
+            if len(self.rl_trainer.avg_rewards) > 0:
+                fig.add_trace(go.Scatter(
+                    y=self.rl_trainer.avg_rewards,
+                    mode='lines',
+                    name='Moving Average',
+                    line=dict(color='#e74c3c', width=2)
+                ))
+        
+        fig.update_layout(title="Episode Rewards", xaxis_title="Episode", yaxis_title="Reward")
+        return fig
+    
+    def _create_balance_chart(self):
+        """Create balance chart"""
+        import plotly.graph_objects as go
+        
+        if not self.rl_trainer.episode_balances:
+            fig = go.Figure()
+            fig.add_annotation(text="No data yet", x=0.5, y=0.5, xref="paper", yref="paper")
+        else:
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(
+                y=self.rl_trainer.episode_balances,
+                mode='lines',
+                name='Balance',
+                line=dict(color='#27ae60')
+            ))
+            
+            # Add initial balance line
+            fig.add_hline(y=self.initial_balance, line_dash="dash", 
+                         annotation_text="Initial Balance")
+        
+        fig.update_layout(title="Portfolio Balance", xaxis_title="Episode", yaxis_title="Balance ($)")
+        return fig
+    
+    def _create_trades_chart(self):
+        """Create trades per episode chart"""
+        import plotly.graph_objects as go
+        
+        if not self.rl_trainer.episode_trades:
+            fig = go.Figure()
+            fig.add_annotation(text="No data yet", x=0.5, y=0.5, xref="paper", yref="paper")
+        else:
+            fig = go.Figure()
+            fig.add_trace(go.Bar(
+                y=self.rl_trainer.episode_trades,
+                name='Trades per Episode',
+                marker_color='#f39c12'
+            ))
+        
+        fig.update_layout(title="Trades per Episode", xaxis_title="Episode", yaxis_title="Number of Trades")
+        return fig
+    
+    def _create_win_rate_chart(self):
+        """Create win rate chart"""
+        import plotly.graph_objects as go
+        
+        if not self.rl_trainer.win_rates:
+            fig = go.Figure()
+            fig.add_annotation(text="No data yet", x=0.5, y=0.5, xref="paper", yref="paper")
+        else:
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(
+                y=self.rl_trainer.win_rates,
+                mode='lines+markers',
+                name='Win Rate',
+                line=dict(color='#9b59b6')
+            ))
+            
+            # Add 50% line
+            fig.add_hline(y=0.5, line_dash="dash", 
+                         annotation_text="Break Even")
+        
+        fig.update_layout(title="Win Rate", xaxis_title="Evaluation", yaxis_title="Win Rate")
+        return fig
+    
+    def start_web_dashboard(self, port=8051):
+        """Start web dashboard in background thread"""
+        if self.dashboard is None:
+            self.setup_web_dashboard(port)
+        
+        if self.dashboard is not None:
+            def run_dashboard():
+                try:
+                    # Use run instead of run_server for newer Dash versions
+                    self.dashboard.run(port=port, debug=False, use_reloader=False)
+                except Exception as e:
+                    logger.error(f"Error running dashboard: {e}")
+            
+            self.dashboard_thread = threading.Thread(target=run_dashboard, daemon=True)
+            self.dashboard_thread.start()
+            logger.info(f"Web dashboard started on http://localhost:{port}")
+        else:
+            logger.warning("Dashboard not available")
+    
+    async def train_realtime(self, episodes=100, evaluation_interval=10):
+        """Run realtime training with monitoring"""
+        logger.info(f"Starting realtime RL training for {episodes} episodes")
+        logger.info(f"TensorBoard: http://localhost:6006")
+        logger.info(f"Web UI: http://localhost:8051")
+        
+        self.training_active = True
+        
+        # Setup environment and agent
+        environment, agent = self.rl_trainer.setup_environment_and_agent()
+        
+        # Training loop
+        for episode in range(episodes):
+            self.current_episode = episode
+            
+            # Run episode
+            episode_start = time.time()
+            results = self.rl_trainer.run_episode(episode, training=True)
+            episode_time = time.time() - episode_start
+            
+            # Update session tracking
+            self.session_balance = results.get('balance', self.initial_balance)
+            self.session_pnl = self.session_balance - self.initial_balance
+            
+            # Log episode metrics to TensorBoard
+            self.rl_trainer.log_episode_metrics(episode, {
+                'total_reward': results['reward'],
+                'final_balance': results['balance'],
+                'total_return': results['pnl_percentage'],
+                'steps': results['steps'],
+                'total_trades': results['trades'],
+                'win_rate': 1.0 if results['pnl'] > 0 else 0.0,
+                'epsilon': agent.epsilon,
+                'memory_size': len(agent.memory) if hasattr(agent, 'memory') else 0
+            })
+            
+            # Log progress
+            if episode % 10 == 0:
+                logger.info(
+                    f"Episode {episode}/{episodes} - "
+                    f"Reward: {results['reward']:.4f}, "
+                    f"Balance: ${results['balance']:.2f}, "
+                    f"PnL: {results['pnl_percentage']:.2f}%, "
+                    f"Trades: {results['trades']}, "
+                    f"Time: {episode_time:.2f}s"
+                )
+            
+            # Evaluation
+            if episode % evaluation_interval == 0 and episode > 0:
+                eval_results = self.rl_trainer.evaluate_agent(num_episodes=3)
+                logger.info(
+                    f"Evaluation - Avg Reward: {eval_results['avg_reward']:.4f}, "
+                    f"Win Rate: {eval_results['win_rate']:.2%}"
+                )
+            
+            # Small delay to allow UI updates
+            await asyncio.sleep(0.1)
+        
+        self.training_active = False
+        logger.info("Training completed!")
+        
+        # Save final model
+        save_path = f"models/rl/realtime_agent_{int(time.time())}.pt"
+        agent.save(save_path)
+        logger.info(f"Model saved: {save_path}")
+        
+        return {
+            'episodes': episodes,
+            'final_balance': self.session_balance,
+            'final_pnl': self.session_pnl,
+            'model_path': save_path
+        }
+
+async def main():
+    """Main function"""
+    parser = argparse.ArgumentParser(description='Realtime RL Training with Monitoring')
+    parser.add_argument('--symbol', type=str, default='ETH/USDT', help='Trading symbol')
+    parser.add_argument('--episodes', type=int, default=50, help='Number of episodes')
+    parser.add_argument('--balance', type=float, default=1000.0, help='Initial balance')
+    parser.add_argument('--web-port', type=int, default=8051, help='Web dashboard port')
+    
+    args = parser.parse_args()
+    
+    # Setup logging
+    setup_logging()
+    
+    logger.info("=" * 60)
+    logger.info("REALTIME RL TRAINING WITH MONITORING")
+    logger.info(f"Symbol: {args.symbol}")
+    logger.info(f"Episodes: {args.episodes}")
+    logger.info(f"Initial Balance: ${args.balance:.2f}")
+    logger.info("=" * 60)
+    
+    try:
+        # Create trainer
+        trainer = RealtimeRLTrainer(
+            symbol=args.symbol,
+            initial_balance=args.balance
+        )
+        
+        # Start web dashboard
+        trainer.start_web_dashboard(port=args.web_port)
+        
+        # Wait for dashboard to start
+        await asyncio.sleep(2)
+        
+        logger.info("MONITORING READY!")
+        logger.info(f"TensorBoard: http://localhost:6006")
+        logger.info(f"Web Dashboard: http://localhost:{args.web_port}")
+        logger.info("=" * 60)
+        
+        # Run training
+        results = await trainer.train_realtime(
+            episodes=args.episodes,
+            evaluation_interval=10
+        )
+        
+        logger.info("Training Results:")
+        logger.info(f"  Final Balance: ${results['final_balance']:.2f}")
+        logger.info(f"  Final PnL: ${results['final_pnl']:.2f}")
+        logger.info(f"  Model Saved: {results['model_path']}")
+        
+        # Keep running for monitoring
+        logger.info("Training complete. Press Ctrl+C to exit monitoring.")
+        while True:
+            await asyncio.sleep(1)
+            
+    except KeyboardInterrupt:
+        logger.info("Training stopped by user")
+    except Exception as e:
+        logger.error(f"Error in training: {e}")
+        import traceback
+        logger.error(traceback.format_exc())
+
+if __name__ == "__main__":
+    asyncio.run(main()) 

training/cnn_trainer.py

@@ -1,31 +1,23 @@
 """
-CNN Training Pipeline - Scalping Pattern Recognition
+CNN Training Pipeline
 
-Comprehensive training pipeline for multi-timeframe CNN models:
-- Automated data generation and preprocessing
-- Training with validation and early stopping
-- Memory-efficient batch processing
-- Model evaluation and metrics
+This module handles training of the CNN model using ONLY real market data.
+All training metrics are logged to TensorBoard for real-time monitoring.
 """
 
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import Dataset, DataLoader, random_split
+from torch.utils.tensorboard import SummaryWriter
 import numpy as np
 import pandas as pd
 import logging
 from typing import Dict, List, Tuple, Optional
-import time
 from pathlib import Path
+import time
 from sklearn.metrics import classification_report, confusion_matrix
-from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
-
-# Add project imports
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+import json
 
 from core.config import get_config
 from core.data_provider import DataProvider
@@ -33,13 +25,12 @@ from models.cnn.scalping_cnn import MultiTimeframeCNN, ScalpingDataGenerator
 
 logger = logging.getLogger(__name__)
 
-class TradingDataset(Dataset):
-    """PyTorch dataset for trading data"""
+class CNNDataset(Dataset):
+    """Dataset for CNN training with real market data"""
     
-    def __init__(self, features: np.ndarray, labels: np.ndarray, metadata: Optional[Dict] = None):
+    def __init__(self, features: np.ndarray, labels: np.ndarray):
         self.features = torch.FloatTensor(features)
-        self.labels = torch.FloatTensor(labels)
-        self.metadata = metadata or {}
+        self.labels = torch.LongTensor(np.argmax(labels, axis=1))  # Convert one-hot to class indices
         
     def __len__(self):
         return len(self.features)
@@ -48,431 +39,437 @@ class TradingDataset(Dataset):
         return self.features[idx], self.labels[idx]
 
 class CNNTrainer:
-    """
-    CNN Training Pipeline for Scalping
-    """
+    """CNN Trainer using ONLY real market data with TensorBoard monitoring"""
     
-    def __init__(self, data_provider: DataProvider, config: Optional[Dict] = None):
-        self.data_provider = data_provider
+    def __init__(self, config: Optional[Dict] = None):
+        """Initialize CNN trainer"""
         self.config = config or get_config()
-        
-        # Training parameters
-        self.learning_rate = 1e-4
-        self.batch_size = 64
-        self.num_epochs = 100
-        self.patience = 15
-        self.validation_split = 0.2
-        
-        # Data parameters
-        self.timeframes = ['1s', '1m', '5m', '1h']
-        self.window_size = 20
-        self.num_samples = 20000
-        
-        # Model parameters
-        self.n_timeframes = len(self.timeframes)
-        self.n_features = 26  # Number of technical indicators
-        self.n_classes = 3    # BUY, SELL, HOLD
-        
-        # Device
         self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
         
-        # Initialize data generator
-        self.data_generator = ScalpingDataGenerator(data_provider, self.window_size)
+        # Training parameters
+        self.learning_rate = self.config.training.get('learning_rate', 0.001)
+        self.batch_size = self.config.training.get('batch_size', 32)
+        self.epochs = self.config.training.get('epochs', 100)
+        self.validation_split = self.config.training.get('validation_split', 0.2)
+        self.early_stopping_patience = self.config.training.get('early_stopping_patience', 10)
         
-        # Training state
+        # Model parameters - will be updated based on real data
+        self.n_timeframes = len(self.config.timeframes)
+        self.window_size = self.config.cnn.get('window_size', 20)
+        self.n_features = self.config.cnn.get('features', 26)  # Will be dynamically updated
+        self.n_classes = 3  # BUY, SELL, HOLD
+        
+        # Initialize components
+        self.data_provider = DataProvider(self.config)
+        self.data_generator = ScalpingDataGenerator(self.data_provider, self.window_size)
         self.model = None
-        self.train_losses = []
-        self.val_losses = []
-        self.train_accuracies = []
-        self.val_accuracies = []
+        
+        # TensorBoard setup
+        self.setup_tensorboard()
         
         logger.info(f"CNNTrainer initialized with {self.n_timeframes} timeframes, {self.n_features} features")
+        logger.info("Will use ONLY real market data for training")
     
-    def prepare_data(self, symbols: List[str]) -> Tuple[DataLoader, DataLoader, Dict]:
-        """Prepare training and validation data"""
-        logger.info("Preparing training data...")
+    def setup_tensorboard(self):
+        """Setup TensorBoard logging"""
+        # Create tensorboard logs directory
+        log_dir = Path("runs") / f"cnn_training_{int(time.time())}"
+        log_dir.mkdir(parents=True, exist_ok=True)
         
-        all_features = []
-        all_labels = []
-        all_metadata = {'symbols': []}
+        self.writer = SummaryWriter(log_dir=str(log_dir))
+        self.tensorboard_dir = log_dir
         
-        # Generate data for each symbol
-        for symbol in symbols:
-            logger.info(f"Generating data for {symbol}...")
-            
-            features, labels, metadata = self.data_generator.generate_training_cases(
-                symbol, self.timeframes, self.num_samples // len(symbols)
-            )
-            
-            if features is not None and labels is not None:
-                all_features.append(features)
-                all_labels.append(labels)
-                all_metadata['symbols'].extend([symbol] * len(features))
-                
-                logger.info(f"Generated {len(features)} samples for {symbol}")
-                
-                # Update feature count based on actual data
-                if len(all_features) == 1:
-                    actual_features = features.shape[-1]
-                    if actual_features != self.n_features:
-                        logger.info(f"Updating feature count from {self.n_features} to {actual_features}")
-                        self.n_features = actual_features
-            else:
-                logger.warning(f"No data generated for {symbol}")
-        
-        if not all_features:
-            raise ValueError("No training data generated")
-        
-        # Combine all data
-        combined_features = np.concatenate(all_features, axis=0)
-        combined_labels = np.concatenate(all_labels, axis=0)
-        
-        logger.info(f"Total dataset: {len(combined_features)} samples")
-        logger.info(f"Features shape: {combined_features.shape}")
-        logger.info(f"Labels shape: {combined_labels.shape}")
-        
-        # Split into train/validation
-        X_train, X_val, y_train, y_val = train_test_split(
-            combined_features, combined_labels, 
-            test_size=self.validation_split, 
-            stratify=np.argmax(combined_labels, axis=1),
-            random_state=42
-        )
-        
-        # Create datasets
-        train_dataset = TradingDataset(X_train, y_train)
-        val_dataset = TradingDataset(X_val, y_val)
-        
-        # Create data loaders
-        train_loader = DataLoader(
-            train_dataset, 
-            batch_size=self.batch_size, 
-            shuffle=True,
-            num_workers=0,  # Set to 0 to avoid multiprocessing issues
-            pin_memory=True if torch.cuda.is_available() else False
-        )
-        
-        val_loader = DataLoader(
-            val_dataset, 
-            batch_size=self.batch_size, 
-            shuffle=False,
-            num_workers=0,
-            pin_memory=True if torch.cuda.is_available() else False
-        )
-        
-        # Prepare metadata for return
-        dataset_info = {
-            'train_size': len(train_dataset),
-            'val_size': len(val_dataset),
-            'feature_shape': combined_features.shape[1:],
-            'label_distribution': {
-                'train': np.bincount(np.argmax(y_train, axis=1)),
-                'val': np.bincount(np.argmax(y_val, axis=1))
-            }
-        }
-        
-        logger.info(f"Train samples: {dataset_info['train_size']}")
-        logger.info(f"Validation samples: {dataset_info['val_size']}")
-        logger.info(f"Train label distribution: {dataset_info['label_distribution']['train']}")
-        logger.info(f"Val label distribution: {dataset_info['label_distribution']['val']}")
-        
-        return train_loader, val_loader, dataset_info
+        logger.info(f"TensorBoard logging to: {log_dir}")
+        logger.info(f"Run: tensorboard --logdir=runs")
     
+    def log_model_architecture(self):
+        """Log model architecture to TensorBoard"""
+        if self.model is not None:
+            # Log model graph (requires a dummy input)
+            dummy_input = torch.randn(1, self.n_timeframes, self.window_size, self.n_features).to(self.device)
+            try:
+                self.writer.add_graph(self.model, dummy_input)
+                logger.info("Model architecture logged to TensorBoard")
+            except Exception as e:
+                logger.warning(f"Could not log model graph: {e}")
+            
+            # Log model parameters count
+            total_params = sum(p.numel() for p in self.model.parameters())
+            trainable_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad)
+            
+            self.writer.add_scalar('Model/TotalParameters', total_params, 0)
+            self.writer.add_scalar('Model/TrainableParameters', trainable_params, 0)
+            
     def create_model(self) -> MultiTimeframeCNN:
-        """Create and initialize the CNN model"""
+        """Create CNN model"""
         model = MultiTimeframeCNN(
             n_timeframes=self.n_timeframes,
             window_size=self.window_size,
             n_features=self.n_features,
-            n_classes=self.n_classes
+            n_classes=self.n_classes,
+            dropout_rate=self.config.cnn.get('dropout', 0.2)
         )
         
-        model.to(self.device)
+        model = model.to(self.device)
         
         # Log model info
         total_params = sum(p.numel() for p in model.parameters())
         trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        memory_usage = model.get_memory_usage()
         
         logger.info(f"Model created with {total_params:,} total parameters")
         logger.info(f"Trainable parameters: {trainable_params:,}")
-        logger.info(f"Estimated memory usage: {model.get_memory_usage()}MB")
+        logger.info(f"Estimated memory usage: {memory_usage}MB")
         
         return model
     
+    def prepare_data(self, symbols: List[str], num_samples: int = 10000) -> Tuple[np.ndarray, np.ndarray, Dict]:
+        """Prepare training data from REAL market data"""
+        logger.info("Preparing training data...")
+        logger.info("Data source: REAL market data from exchange APIs")
+        
+        all_features = []
+        all_labels = []
+        all_metadata = []
+        
+        for symbol in symbols:
+            logger.info(f"Generating data for {symbol}...")
+            
+            features, labels, metadata = self.data_generator.generate_training_cases(
+                symbol=symbol,
+                timeframes=self.config.timeframes,
+                num_samples=num_samples
+            )
+            
+            if features is not None:
+                all_features.append(features)
+                all_labels.append(labels)
+                all_metadata.append(metadata)
+                
+                logger.info(f"Generated {len(features)} samples for {symbol}")
+                
+                # Update feature count if needed
+                actual_features = features.shape[-1]
+                if actual_features != self.n_features:
+                    logger.info(f"Updating feature count from {self.n_features} to {actual_features}")
+                    self.n_features = actual_features
+        
+        if not all_features:
+            raise ValueError("No training data generated from real market data")
+        
+        # Combine all data
+        features = np.concatenate(all_features, axis=0)
+        labels = np.concatenate(all_labels, axis=0)
+        
+        # Log data statistics to TensorBoard
+        self.log_data_statistics(features, labels)
+        
+        return features, labels, all_metadata
+    
+    def log_data_statistics(self, features: np.ndarray, labels: np.ndarray):
+        """Log data statistics to TensorBoard"""
+        # Dataset size
+        self.writer.add_scalar('Data/TotalSamples', len(features), 0)
+        self.writer.add_scalar('Data/Features', features.shape[-1], 0)
+        self.writer.add_scalar('Data/Timeframes', features.shape[1], 0)
+        self.writer.add_scalar('Data/WindowSize', features.shape[2], 0)
+        
+        # Class distribution
+        class_counts = np.bincount(np.argmax(labels, axis=1))
+        for i, count in enumerate(class_counts):
+            self.writer.add_scalar(f'Data/Class_{i}_Count', count, 0)
+        
+        # Feature statistics
+        feature_means = features.mean(axis=(0, 1, 2))
+        feature_stds = features.std(axis=(0, 1, 2))
+        
+        for i in range(min(10, len(feature_means))):  # Log first 10 features
+            self.writer.add_scalar(f'Data/Feature_{i}_Mean', feature_means[i], 0)
+            self.writer.add_scalar(f'Data/Feature_{i}_Std', feature_stds[i], 0)
+    
     def train_epoch(self, model: nn.Module, train_loader: DataLoader, 
-                   optimizer: optim.Optimizer, criterion: nn.Module) -> Tuple[float, float]:
-        """Train for one epoch"""
+                   optimizer: torch.optim.Optimizer, criterion: nn.Module, epoch: int) -> Tuple[float, float]:
+        """Train for one epoch with TensorBoard logging"""
         model.train()
         total_loss = 0.0
-        correct_predictions = 0
-        total_predictions = 0
+        correct = 0
+        total = 0
         
         for batch_idx, (features, labels) in enumerate(train_loader):
-            features = features.to(self.device)
-            labels = labels.to(self.device)
+            features, labels = features.to(self.device), labels.to(self.device)
             
-            # Zero gradients
             optimizer.zero_grad()
-            
-            # Forward pass
             predictions = model(features)
-            
-            # Calculate loss (multi-task loss)
-            action_loss = criterion(predictions['action'], labels)
-            
-            # Additional losses for auxiliary tasks
-            confidence_loss = torch.mean(torch.abs(predictions['confidence'] - 0.5))  # Encourage diversity
-            
-            # Total loss
-            total_loss_batch = action_loss + 0.1 * confidence_loss
-            
-            # Backward pass
-            total_loss_batch.backward()
-            
-            # Gradient clipping
-            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
-            
-            # Update weights
+            loss = criterion(predictions['action'], labels)
+            loss.backward()
             optimizer.step()
             
-            # Track metrics
-            total_loss += total_loss_batch.item()
+            total_loss += loss.item()
+            _, predicted = torch.max(predictions['action'].data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
             
-            # Calculate accuracy
-            pred_classes = torch.argmax(predictions['action'], dim=1)
-            true_classes = torch.argmax(labels, dim=1)
-            correct_predictions += (pred_classes == true_classes).sum().item()
-            total_predictions += labels.size(0)
+            # Log batch metrics
+            step = epoch * len(train_loader) + batch_idx
+            self.writer.add_scalar('Training/BatchLoss', loss.item(), step)
             
-            # Log progress
-            if batch_idx % 100 == 0:
-                logger.debug(f"Batch {batch_idx}/{len(train_loader)}, Loss: {total_loss_batch.item():.4f}")
+            if batch_idx % 50 == 0:  # Log every 50 batches
+                batch_acc = 100. * (predicted == labels).sum().item() / labels.size(0)
+                self.writer.add_scalar('Training/BatchAccuracy', batch_acc, step)
+                
+                # Log confidence scores
+                avg_confidence = predictions['confidence'].mean().item()
+                self.writer.add_scalar('Training/BatchConfidence', avg_confidence, step)
         
-        avg_loss = total_loss / len(train_loader)
-        accuracy = correct_predictions / total_predictions
+        epoch_loss = total_loss / len(train_loader)
+        epoch_accuracy = correct / total
         
-        return avg_loss, accuracy
+        return epoch_loss, epoch_accuracy
     
     def validate_epoch(self, model: nn.Module, val_loader: DataLoader, 
-                      criterion: nn.Module) -> Tuple[float, float, Dict]:
-        """Validate for one epoch"""
+                      criterion: nn.Module, epoch: int) -> Tuple[float, float, Dict]:
+        """Validate for one epoch with TensorBoard logging"""
         model.eval()
         total_loss = 0.0
-        correct_predictions = 0
-        total_predictions = 0
-        
+        correct = 0
+        total = 0
         all_predictions = []
         all_labels = []
         all_confidences = []
         
         with torch.no_grad():
             for features, labels in val_loader:
-                features = features.to(self.device)
-                labels = labels.to(self.device)
+                features, labels = features.to(self.device), labels.to(self.device)
                 
-                # Forward pass
                 predictions = model(features)
-                
-                # Calculate loss
                 loss = criterion(predictions['action'], labels)
+                
                 total_loss += loss.item()
+                _, predicted = torch.max(predictions['action'].data, 1)
+                total += labels.size(0)
+                correct += (predicted == labels).sum().item()
                 
-                # Track predictions
-                pred_classes = torch.argmax(predictions['action'], dim=1)
-                true_classes = torch.argmax(labels, dim=1)
-                
-                correct_predictions += (pred_classes == true_classes).sum().item()
-                total_predictions += labels.size(0)
-                
-                # Store for detailed analysis
-                all_predictions.extend(pred_classes.cpu().numpy())
-                all_labels.extend(true_classes.cpu().numpy())
+                all_predictions.extend(predicted.cpu().numpy())
+                all_labels.extend(labels.cpu().numpy())
                 all_confidences.extend(predictions['confidence'].cpu().numpy())
         
-        avg_loss = total_loss / len(val_loader)
-        accuracy = correct_predictions / total_predictions
+        epoch_loss = total_loss / len(val_loader)
+        epoch_accuracy = correct / total
         
-        # Additional metrics
-        metrics = {
-            'predictions': np.array(all_predictions),
-            'labels': np.array(all_labels),
-            'confidences': np.array(all_confidences),
-            'accuracy_by_class': {},
-            'avg_confidence': np.mean(all_confidences)
-        }
+        # Calculate detailed metrics
+        metrics = self.calculate_detailed_metrics(all_predictions, all_labels, all_confidences)
         
-        # Calculate per-class accuracy
-        for class_idx in range(self.n_classes):
-            class_mask = metrics['labels'] == class_idx
-            if np.sum(class_mask) > 0:
-                class_accuracy = np.mean(metrics['predictions'][class_mask] == metrics['labels'][class_mask])
-                metrics['accuracy_by_class'][class_idx] = class_accuracy
+        # Log validation metrics to TensorBoard
+        self.writer.add_scalar('Validation/Loss', epoch_loss, epoch)
+        self.writer.add_scalar('Validation/Accuracy', epoch_accuracy, epoch)
+        self.writer.add_scalar('Validation/AvgConfidence', metrics['avg_confidence'], epoch)
         
-        return avg_loss, accuracy, metrics
+        for class_idx, acc in metrics['class_accuracies'].items():
+            self.writer.add_scalar(f'Validation/Class_{class_idx}_Accuracy', acc, epoch)
+        
+        return epoch_loss, epoch_accuracy, metrics
     
-    def train(self, symbols: List[str], save_path: Optional[str] = None) -> Dict:
-        """Train the CNN model"""
+    def calculate_detailed_metrics(self, predictions: List, labels: List, confidences: List) -> Dict:
+        """Calculate detailed training metrics"""
+        predictions = np.array(predictions)
+        labels = np.array(labels)
+        confidences = np.array(confidences)
+        
+        # Class-wise accuracies
+        class_accuracies = {}
+        for class_idx in range(self.n_classes):
+            class_mask = labels == class_idx
+            if class_mask.sum() > 0:
+                class_acc = (predictions[class_mask] == labels[class_mask]).mean()
+                class_accuracies[class_idx] = class_acc
+        
+        return {
+            'class_accuracies': class_accuracies,
+            'avg_confidence': confidences.mean(),
+            'confusion_matrix': confusion_matrix(labels, predictions)
+        }
+    
+    def train(self, symbols: List[str], save_path: str = 'models/cnn/scalping_cnn_trained.pt', 
+              num_samples: int = 10000) -> Dict:
+        """Train CNN model with TensorBoard monitoring"""
         logger.info("Starting CNN training...")
+        logger.info("Using ONLY real market data from exchange APIs")
         
-        # Prepare data first to get actual feature count
-        train_loader, val_loader, dataset_info = self.prepare_data(symbols)
+        # Prepare data
+        features, labels, metadata = self.prepare_data(symbols, num_samples)
         
-        # Create model with correct feature count
+        # Log training configuration
+        self.writer.add_text('Config/Symbols', str(symbols), 0)
+        self.writer.add_text('Config/Timeframes', str(self.config.timeframes), 0)
+        self.writer.add_scalar('Config/LearningRate', self.learning_rate, 0)
+        self.writer.add_scalar('Config/BatchSize', self.batch_size, 0)
+        self.writer.add_scalar('Config/MaxEpochs', self.epochs, 0)
+        
+        # Create datasets
+        dataset = CNNDataset(features, labels)
+        
+        # Split data
+        val_size = int(len(dataset) * self.validation_split)
+        train_size = len(dataset) - val_size
+        train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+        
+        # Create data loaders
+        train_loader = DataLoader(train_dataset, batch_size=self.batch_size, shuffle=True)
+        val_loader = DataLoader(val_dataset, batch_size=self.batch_size, shuffle=False)
+        
+        logger.info(f"Total dataset: {len(dataset)} samples")
+        logger.info(f"Features shape: {features.shape}")
+        logger.info(f"Labels shape: {labels.shape}")
+        logger.info(f"Train samples: {train_size}")
+        logger.info(f"Validation samples: {val_size}")
+        
+        # Log class distributions
+        train_labels = [dataset[i][1].item() for i in train_dataset.indices]
+        val_labels = [dataset[i][1].item() for i in val_dataset.indices]
+        
+        logger.info(f"Train label distribution: {np.bincount(train_labels)}")
+        logger.info(f"Val label distribution: {np.bincount(val_labels)}")
+        
+        # Create model
         self.model = self.create_model()
+        self.log_model_architecture()
         
         # Setup training
         criterion = nn.CrossEntropyLoss()
         optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)
-        scheduler = optim.lr_scheduler.ReduceLROnPlateau(
-            optimizer, mode='min', factor=0.5, patience=5, verbose=True
-        )
+        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=5, verbose=True)
         
-        # Training state
+        # Training loop
         best_val_loss = float('inf')
         best_val_accuracy = 0.0
         patience_counter = 0
         start_time = time.time()
         
-        # Training loop
-        for epoch in range(self.num_epochs):
-            epoch_start_time = time.time()
+        for epoch in range(self.epochs):
+            epoch_start = time.time()
             
             # Train
-            train_loss, train_accuracy = self.train_epoch(
-                self.model, train_loader, optimizer, criterion
-            )
+            train_loss, train_accuracy = self.train_epoch(self.model, train_loader, optimizer, criterion, epoch)
             
             # Validate
-            val_loss, val_accuracy, val_metrics = self.validate_epoch(
-                self.model, val_loader, criterion
-            )
+            val_loss, val_accuracy, val_metrics = self.validate_epoch(self.model, val_loader, criterion, epoch)
             
             # Update learning rate
             scheduler.step(val_loss)
+            current_lr = optimizer.param_groups[0]['lr']
             
-            # Track metrics
-            self.train_losses.append(train_loss)
-            self.val_losses.append(val_loss)
-            self.train_accuracies.append(train_accuracy)
-            self.val_accuracies.append(val_accuracy)
+            # Log epoch metrics
+            self.writer.add_scalar('Training/EpochLoss', train_loss, epoch)
+            self.writer.add_scalar('Training/EpochAccuracy', train_accuracy, epoch)
+            self.writer.add_scalar('Training/LearningRate', current_lr, epoch)
             
-            # Check for improvement
+            epoch_time = time.time() - epoch_start
+            self.writer.add_scalar('Training/EpochTime', epoch_time, epoch)
+            
+            # Save best model
             if val_loss < best_val_loss:
                 best_val_loss = val_loss
                 best_val_accuracy = val_accuracy
                 patience_counter = 0
                 
                 # Save best model
-                if save_path:
-                    best_path = save_path.replace('.pt', '_best.pt')
-                    self.model.save(best_path)
-                    logger.info(f"New best model saved: {best_path}")
+                best_path = save_path.replace('.pt', '_best.pt')
+                self.model.save(best_path)
+                logger.info(f"New best model saved: {best_path}")
+                
+                # Log best metrics
+                self.writer.add_scalar('Best/ValidationLoss', best_val_loss, epoch)
+                self.writer.add_scalar('Best/ValidationAccuracy', best_val_accuracy, epoch)
             else:
                 patience_counter += 1
             
-            # Log progress
-            epoch_time = time.time() - epoch_start_time
-            logger.info(
-                f"Epoch {epoch+1}/{self.num_epochs} - "
-                f"Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.4f} - "
-                f"Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f} - "
-                f"Time: {epoch_time:.2f}s"
-            )
+            logger.info(f"Epoch {epoch+1}/{self.epochs} - "
+                       f"Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.4f} - "
+                       f"Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.4f} - "
+                       f"Time: {epoch_time:.2f}s")
             
-            # Detailed validation metrics every 10 epochs
+            # Log detailed metrics every 10 epochs
             if (epoch + 1) % 10 == 0:
-                logger.info(f"Class accuracies: {val_metrics['accuracy_by_class']}")
+                logger.info(f"Class accuracies: {val_metrics['class_accuracies']}")
                 logger.info(f"Average confidence: {val_metrics['avg_confidence']:.4f}")
             
             # Early stopping
-            if patience_counter >= self.patience:
+            if patience_counter >= self.early_stopping_patience:
                 logger.info(f"Early stopping triggered after {epoch+1} epochs")
                 break
         
-        # Training complete
+        # Training completed
         total_time = time.time() - start_time
         logger.info(f"Training completed in {total_time:.2f} seconds")
         logger.info(f"Best validation loss: {best_val_loss:.4f}")
         logger.info(f"Best validation accuracy: {best_val_accuracy:.4f}")
         
-        # Save final model
-        if save_path:
-            self.model.save(save_path)
-            logger.info(f"Final model saved: {save_path}")
+        # Log final metrics
+        self.writer.add_scalar('Final/TotalTrainingTime', total_time, 0)
+        self.writer.add_scalar('Final/TotalEpochs', epoch + 1, 0)
         
-        # Prepare training results
-        results = {
+        # Save final model
+        self.model.save(save_path)
+        logger.info(f"Final model saved: {save_path}")
+        
+        # Log training summary
+        self.writer.add_text('Training/Summary', 
+                           f"Completed training with {len(features)} real market samples. "
+                           f"Best validation accuracy: {best_val_accuracy:.4f}", 0)
+        
+        return {
             'best_val_loss': best_val_loss,
             'best_val_accuracy': best_val_accuracy,
             'total_epochs': epoch + 1,
-            'total_time': total_time,
-            'train_losses': self.train_losses,
-            'val_losses': self.val_losses,
-            'train_accuracies': self.train_accuracies,
-            'val_accuracies': self.val_accuracies,
-            'dataset_info': dataset_info,
-            'final_metrics': val_metrics
+            'training_time': total_time,
+            'tensorboard_dir': str(self.tensorboard_dir)
         }
-        
-        return results
     
-    def evaluate_model(self, test_symbols: List[str]) -> Dict:
+    def evaluate(self, symbols: List[str], num_samples: int = 5000) -> Dict:
         """Evaluate trained model on test data"""
         if self.model is None:
             raise ValueError("Model not trained yet")
         
         logger.info("Evaluating model...")
         
-        # Generate test data
-        test_features = []
-        test_labels = []
+        # Generate test data from real market data
+        features, labels, metadata = self.prepare_data(symbols, num_samples)
         
-        for symbol in test_symbols:
-            features, labels, _ = self.data_generator.generate_training_cases(
-                symbol, self.timeframes, 5000
-            )
-            if features is not None:
-                test_features.append(features)
-                test_labels.append(labels)
-        
-        if not test_features:
-            raise ValueError("No test data generated")
-        
-        test_features = np.concatenate(test_features, axis=0)
-        test_labels = np.concatenate(test_labels, axis=0)
-        
-        # Create test loader
-        test_dataset = TradingDataset(test_features, test_labels)
+        # Create test dataset and loader
+        test_dataset = CNNDataset(features, labels)
         test_loader = DataLoader(test_dataset, batch_size=self.batch_size, shuffle=False)
         
         # Evaluate
         criterion = nn.CrossEntropyLoss()
         test_loss, test_accuracy, test_metrics = self.validate_epoch(
-            self.model, test_loader, criterion
+            self.model, test_loader, criterion, epoch=0
         )
         
-        # Generate classification report
+        # Generate detailed classification report
+        from sklearn.metrics import classification_report
         class_names = ['BUY', 'SELL', 'HOLD']
-        classification_rep = classification_report(
-            test_metrics['labels'],
-            test_metrics['predictions'],
-            target_names=class_names,
-            output_dict=True
-        )
+        all_predictions = []
+        all_labels = []
         
-        # Confusion matrix
-        conf_matrix = confusion_matrix(
-            test_metrics['labels'],
-            test_metrics['predictions']
+        with torch.no_grad():
+            for features_batch, labels_batch in test_loader:
+                features_batch = features_batch.to(self.device)
+                predictions = self.model(features_batch)
+                _, predicted = torch.max(predictions['action'].data, 1)
+                all_predictions.extend(predicted.cpu().numpy())
+                all_labels.extend(labels_batch.numpy())
+        
+        classification_rep = classification_report(
+            all_labels, all_predictions, target_names=class_names, output_dict=True
         )
         
         evaluation_results = {
             'test_loss': test_loss,
             'test_accuracy': test_accuracy,
             'classification_report': classification_rep,
-            'confusion_matrix': conf_matrix,
-            'class_accuracies': test_metrics['accuracy_by_class'],
-            'avg_confidence': test_metrics['avg_confidence']
+            'class_accuracies': test_metrics['class_accuracies'],
+            'avg_confidence': test_metrics['avg_confidence'],
+            'confusion_matrix': test_metrics['confusion_matrix']
         }
         
         logger.info(f"Test accuracy: {test_accuracy:.4f}")
@@ -480,40 +477,15 @@ class CNNTrainer:
         
         return evaluation_results
     
-    def plot_training_history(self, save_path: Optional[str] = None):
-        """Plot training history"""
-        if not self.train_losses:
-            logger.warning("No training history to plot")
-            return
-        
-        fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(12, 4))
-        
-        # Loss plot
-        epochs = range(1, len(self.train_losses) + 1)
-        ax1.plot(epochs, self.train_losses, 'b-', label='Training Loss')
-        ax1.plot(epochs, self.val_losses, 'r-', label='Validation Loss')
-        ax1.set_title('Training and Validation Loss')
-        ax1.set_xlabel('Epoch')
-        ax1.set_ylabel('Loss')
-        ax1.legend()
-        ax1.grid(True)
-        
-        # Accuracy plot
-        ax2.plot(epochs, self.train_accuracies, 'b-', label='Training Accuracy')
-        ax2.plot(epochs, self.val_accuracies, 'r-', label='Validation Accuracy')
-        ax2.set_title('Training and Validation Accuracy')
-        ax2.set_xlabel('Epoch')
-        ax2.set_ylabel('Accuracy')
-        ax2.legend()
-        ax2.grid(True)
-        
-        plt.tight_layout()
-        
-        if save_path:
-            plt.savefig(save_path, dpi=300, bbox_inches='tight')
-            logger.info(f"Training history plot saved: {save_path}")
-        
-        plt.show()
+    def close_tensorboard(self):
+        """Close TensorBoard writer"""
+        if hasattr(self, 'writer'):
+            self.writer.close()
+            logger.info("TensorBoard writer closed")
+    
+    def __del__(self):
+        """Cleanup"""
+        self.close_tensorboard()
 
 # Export
-__all__ = ['CNNTrainer', 'TradingDataset'] 
+__all__ = ['CNNTrainer', 'CNNDataset'] 

training/rl_trainer.py

@@ -18,6 +18,7 @@ from pathlib import Path
 import matplotlib.pyplot as plt
 from collections import deque
 import random
+from torch.utils.tensorboard import SummaryWriter
 
 # Add project imports
 import sys
@@ -75,8 +76,23 @@ class RLTrainer:
         self.win_rates = []
         self.avg_rewards = []
         
+        # TensorBoard setup
+        self.setup_tensorboard()
+        
         logger.info(f"RLTrainer initialized for symbols: {self.symbols}")
     
+    def setup_tensorboard(self):
+        """Setup TensorBoard logging"""
+        # Create tensorboard logs directory
+        log_dir = Path("runs") / f"rl_training_{int(time.time())}"
+        log_dir.mkdir(parents=True, exist_ok=True)
+        
+        self.writer = SummaryWriter(log_dir=str(log_dir))
+        self.tensorboard_dir = log_dir
+        
+        logger.info(f"TensorBoard logging to: {log_dir}")
+        logger.info(f"Run: tensorboard --logdir=runs")
+    
     def setup_environment_and_agent(self) -> Tuple[ScalpingEnvironment, ScalpingRLAgent]:
         """Setup trading environment and RL agent"""
         logger.info("Setting up environment and agent...")
@@ -443,6 +459,29 @@ class RLTrainer:
         
         plt.show()
 
+    def log_episode_metrics(self, episode: int, metrics: Dict):
+        """Log episode metrics to TensorBoard"""
+        # Main performance metrics
+        self.writer.add_scalar('Episode/TotalReward', metrics['total_reward'], episode)
+        self.writer.add_scalar('Episode/FinalBalance', metrics['final_balance'], episode)
+        self.writer.add_scalar('Episode/TotalReturn', metrics['total_return'], episode)
+        self.writer.add_scalar('Episode/Steps', metrics['steps'], episode)
+        
+        # Trading metrics
+        self.writer.add_scalar('Trading/TotalTrades', metrics['total_trades'], episode)
+        self.writer.add_scalar('Trading/WinRate', metrics['win_rate'], episode)
+        self.writer.add_scalar('Trading/ProfitFactor', metrics.get('profit_factor', 0), episode)
+        self.writer.add_scalar('Trading/MaxDrawdown', metrics.get('max_drawdown', 0), episode)
+        
+        # Agent metrics
+        self.writer.add_scalar('Agent/Epsilon', metrics['epsilon'], episode)
+        self.writer.add_scalar('Agent/LearningRate', metrics.get('learning_rate', self.learning_rate), episode)
+        self.writer.add_scalar('Agent/MemorySize', metrics.get('memory_size', 0), episode)
+        
+        # Loss metrics (if available)
+        if 'loss' in metrics:
+            self.writer.add_scalar('Agent/Loss', metrics['loss'], episode)
+
 class HybridTrainer:
     """
     Hybrid training pipeline combining CNN and RL