wip
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Dobromir Popov
@ -139,4 +139,190 @@ python main_clean.py --mode rl
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- **Feature processing**: 26 indicators in < 1 second
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- **Memory usage**: Monitored and limited per model
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- **Chart updates**: 2-second refresh for real-time display
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- **Decision latency**: Optimized for scalping (< 100ms target)
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- **Decision latency**: Optimized for scalping (< 100ms target)
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## 🚀 **VSCode Launch Configurations**
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### **1. Core Trading Modes**
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#### **Live Trading (Demo)**
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```json
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"name": "Live Trading (Demo)"
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"program": "main.py"
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"args": ["--mode", "live", "--demo", "true", "--symbol", "ETH/USDT", "--timeframe", "1m"]
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```
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- **Purpose**: Safe demo trading with virtual funds
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- **Environment**: Paper trading mode
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- **Risk**: Zero (no real money)
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#### **Live Trading (Real)**
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```json
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"name": "Live Trading (Real)"
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"program": "main.py"
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"args": ["--mode", "live", "--demo", "false", "--symbol", "ETH/USDT", "--leverage", "50"]
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```
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- **Purpose**: Real trading with actual funds
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- **Environment**: Live exchange API
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- **Risk**: High (real money)
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### **2. Training & Development Modes**
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#### **Train Bot**
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```json
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"name": "Train Bot"
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"program": "main.py"
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"args": ["--mode", "train", "--episodes", "100"]
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```
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- **Purpose**: Standard RL agent training
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- **Duration**: 100 episodes
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- **Output**: Trained model files
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#### **Evaluate Bot**
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```json
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"name": "Evaluate Bot"
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"program": "main.py"
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"args": ["--mode", "eval", "--episodes", "10"]
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```
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- **Purpose**: Model performance evaluation
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- **Duration**: 10 test episodes
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- **Output**: Performance metrics
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### **3. Neural Network Training**
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#### **NN Training Pipeline**
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```json
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"name": "NN Training Pipeline"
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"module": "NN.realtime_main"
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"args": ["--mode", "train", "--model-type", "cnn", "--epochs", "10"]
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```
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- **Purpose**: Deep learning model training
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- **Framework**: PyTorch
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- **Monitoring**: Automatic TensorBoard integration
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#### **Quick CNN Test (Real Data + TensorBoard)**
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```json
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"name": "Quick CNN Test (Real Data + TensorBoard)"
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"program": "test_cnn_only.py"
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```
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- **Purpose**: Fast CNN validation with real market data
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- **Duration**: 2 epochs, 500 samples
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- **Output**: `test_models/quick_cnn.pt`
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- **Monitoring**: TensorBoard metrics
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### **4. 🔥 Realtime RL Training + Monitoring**
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#### **Realtime RL Training + TensorBoard + Web UI**
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```json
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"name": "Realtime RL Training + TensorBoard + Web UI"
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"program": "train_realtime_with_tensorboard.py"
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"args": ["--episodes", "50", "--symbol", "ETH/USDT", "--web-port", "8051"]
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```
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- **Purpose**: Advanced RL training with comprehensive monitoring
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- **Features**:
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- Real-time TensorBoard metrics logging
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- Live web dashboard at http://localhost:8051
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- Episode rewards, balance tracking, win rates
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- Trading performance metrics
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- Agent learning progression
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- **Data**: 100% real ETH/USDT market data from Binance
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- **Monitoring**: Dual monitoring (TensorBoard + Web UI)
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- **Duration**: 50 episodes with real-time feedback
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### **5. Monitoring & Visualization**
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#### **TensorBoard Monitor (All Runs)**
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```json
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"name": "TensorBoard Monitor (All Runs)"
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"program": "run_tensorboard.py"
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```
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- **Purpose**: Monitor all training sessions
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- **Features**: Auto-discovery of training logs
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- **Access**: http://localhost:6006
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#### **Realtime Charts with NN Inference**
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```json
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"name": "Realtime Charts with NN Inference"
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"program": "realtime.py"
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```
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- **Purpose**: Live trading charts with ML predictions
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- **Features**: Real-time price updates + model inference
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- **Models**: CNN + RL integration
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### **6. Advanced Training Modes**
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#### **TRAIN Realtime Charts with NN Inference**
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```json
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"name": "TRAIN Realtime Charts with NN Inference"
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"program": "train_rl_with_realtime.py"
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"args": ["--episodes", "100", "--max-position", "0.1"]
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```
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- **Purpose**: RL training with live chart integration
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- **Features**: Visual training feedback
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- **Position limit**: 10% portfolio allocation
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## 📊 **Monitoring URLs**
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### **Development**
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- **TensorBoard**: http://localhost:6006
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- **Web Dashboard**: http://localhost:8051
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- **Training Status**: `python monitor_training.py`
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### **Production**
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- **Live Trading Dashboard**: Integrated in trading interface
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- **Performance Metrics**: Real-time P&L tracking
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- **Risk Management**: Position size and drawdown monitoring
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## 🎯 **Quick Start Recommendations**
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### **For CNN Development**
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1. **Start**: "Quick CNN Test (Real Data + TensorBoard)"
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2. **Monitor**: Open TensorBoard at http://localhost:6006
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3. **Validate**: Check `test_models/` for output files
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### **For RL Development**
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1. **Start**: "Realtime RL Training + TensorBoard + Web UI"
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2. **Monitor**: TensorBoard (http://localhost:6006) + Web UI (http://localhost:8051)
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3. **Track**: Episode rewards, balance progression, win rates
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### **For Production Trading**
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1. **Test**: "Live Trading (Demo)" first
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2. **Validate**: Confirm strategy performance
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3. **Deploy**: "Live Trading (Real)" with appropriate risk management
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## ⚡ **Performance Features**
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### **GPU Acceleration**
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- Automatic CUDA detection and utilization
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- Mixed precision training support
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- Memory optimization for large datasets
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### **Real-time Data**
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- Direct Binance API integration
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- Multi-timeframe data synchronization
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- Live price feed with minimal latency
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### **Professional Monitoring**
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- Industry-standard TensorBoard integration
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- Custom web dashboards for trading metrics
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- Real-time performance tracking
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## 🛡️ **Safety Features**
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### **Pre-launch Tasks**
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- **Kill Stale Processes**: Automatic cleanup before launch
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- **Port Management**: Intelligent port allocation
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- **Resource Monitoring**: Memory and GPU usage tracking
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### **Real Market Data Policy**
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- ✅ **No Synthetic Data**: All training uses authentic exchange data
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- ✅ **Live API Integration**: Direct connection to cryptocurrency exchanges
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- ✅ **Data Validation**: Quality checks for completeness and consistency
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- ✅ **Multi-timeframe Sync**: Aligned data across all time horizons
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---
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✅ **Launch configuration** - Clean, modular mode selection
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✅ **Professional monitoring** - TensorBoard + custom dashboards
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✅ **Real market data** - Authentic cryptocurrency price data
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✅ **Safety features** - Risk management and validation
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✅ **GPU acceleration** - Optimized for high-performance training
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