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+# Crypto Trading Bot with Reinforcement Learning
+
+An automated cryptocurrency trading bot that uses Deep Q-Learning (DQN) to trade ETH/USDT on the MEXC exchange. The bot features a sophisticated neural network architecture with LSTM layers and attention mechanisms for better pattern recognition.
+
+## Features
+
+- Deep Q-Learning with experience replay
+- LSTM layers for sequential data processing
+- Multi-head attention mechanism
+- Dueling DQN architecture
+- Real-time trading capabilities
+- TensorBoard integration for monitoring
+- Comprehensive technical indicators
+- Demo and live trading modes
+- Automatic model checkpointing
+
+## Prerequisites
+
+- Python 3.8+
+- MEXC Exchange API credentials
+- GPU recommended but not required
+
+## Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/yourusername/crypto-trading-bot.git
+cd crypto-trading-bot
+```
+2. Create a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate # On Windows: venv\Scripts\activate
+```
+3. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+
+4. Create a `.env` file in the project root with your MEXC API credentials:
+
+```bash
+MEXC_API_KEY=your_api_key
+MEXC_API_SECRET=your_api_secret
+```
+## Usage
+
+The bot can be run in three modes:
+
+### Training Mode
+
+```bash
+python main.py --mode train --episodes 1000
+```
+
+### Evaluation Mode
+
+```bash
+python main.py --mode eval --episodes 10
+```
+
+### Live Trading Mode
+
+```bash
+# Demo mode (simulated trading with real market data)
+python main.py --mode live --demo
+
+# Real trading (actual trades on MEXC)
+python main.py --mode live
+```
+
+Demo mode simulates trading using real-time market data but does not execute actual trades. It still:
+- Logs all trading decisions and performance metrics
+- Updates the model based on market data (if in training mode)
+- Displays real-time analytics and position information
+- Calculates theoretical profits/losses
+- Saves performance data to TensorBoard
+
+This makes it perfect for testing strategies without financial risk.
+
+## Configuration
+
+Key parameters can be adjusted in `main.py`:
+
+- `INITIAL_BALANCE`: Starting balance for training/demo
+- `MAX_LEVERAGE`: Maximum leverage for trades
+- `STOP_LOSS_PERCENT`: Stop loss percentage
+- `TAKE_PROFIT_PERCENT`: Take profit percentage
+- `BATCH_SIZE`: Training batch size
+- `LEARNING_RATE`: Model learning rate
+- `STATE_SIZE`: Size of the state representation
+
+## Model Architecture
+
+The DQN model includes:
+- Input layer with technical indicators
+- LSTM layers for temporal pattern recognition
+- Multi-head attention mechanism
+- Dueling architecture for better Q-value estimation
+- Batch normalization for stable training
+
+## Monitoring
+
+Training progress can be monitored using TensorBoard:
+
+
+Training progress is logged to TensorBoard:
+
+```bash
+tensorboard --logdir=logs
+```
+
+This will show:
+- Training rewards
+- Account balance
+- Win rate
+- Loss metrics
+
+## Trading Strategy
+
+The bot makes decisions based on:
+- Price action
+- Technical indicators (RSI, MACD, Bollinger Bands, etc.)
+- Historical patterns through LSTM
+- Risk management with stop-loss and take-profit
+
+## Safety Features
+
+- Demo mode for safe testing
+- Automatic stop-loss
+- Position size limits
+- Error handling for API calls
+- Logging of all actions
+
+## Directory Structure
+├── main.py # Main bot implementation
+├── requirements.txt # Project dependencies
+├── .env # API credentials
+├── models/ # Saved model checkpoints
+├── runs/ # TensorBoard logs
+└── trading_bot.log # Activity logs
+
+
+## Warning
+
+Cryptocurrency trading carries significant risks. This bot is for educational purposes and should not be used with real money without thorough testing and understanding of the risks involved.
+
+## License
+
+[MIT License](LICENSE)
+
+The main changes I made:
+Fixed code block formatting by adding proper language identifiers
+Added missing closing code blocks
+Properly formatted directory structure
+Added complete sections that were cut off in the original
+Ensured consistent formatting throughout the document
+Added proper bash syntax highlighting for command examples
+The README.md now provides a complete guide for setting up and using the trading bot, with clear sections for installation, usage, configuration, and safety considerations.
+
+
+
+
+
+# Edits/improvements
+
+Fixes the shape mismatch by ensuring the state vector is exactly STATE_SIZE elements
+Adds robust error handling in the model's forward pass to handle mismatched inputs
+Adds a transformer encoder for more sophisticated pattern recognition
+Provides an expand_model method to increase model capacity while preserving learned weights
+Adds detailed logging about model size and shape mismatches
+The model now has:
+Configurable hidden layer sizes
+Transformer layers for complex pattern recognition
+LSTM layers for temporal patterns
+Attention mechanisms for focusing on important features
+Dueling architecture for better Q-value estimation
+With hidden_size=256, this model has about 1-2 million parameters. By increasing hidden_size to 512 or 1024, you can easily scale to 5-20 million parameters. For even larger models (billions of parameters), you would need to implement a more distributed architecture with multiple GPUs, which would require significant changes to the training loop.