Cryptocurrency Trading Agent with Reinforcement Learning

Description

This project implements a reinforcement learning-based trading agent for cryptocurrencies. It uses the Alpaca API for data retrieval and trading, and implements a custom gym environment for training the agent. The project includes modules for creating a trading environment, processing data, training models, and performing live trading.

Table of Contents

• Installation
• Usage
• Features
• Project Structure
• Contributing
• License

Installation

To set up the project, follow these steps:

git clone https://github.com/wayy-research/rl-agent.git
cd rl-agent
pip install -r requirements.txt
pip install .

Make sure to set up your Alpaca API credentials in a .env file:

ALPACA_API_KEY=your_api_key
ALPACA_SECRET_KEY=your_secret_key
ALPACA_BASE_URL=https://paper-api.alpaca.markets

Usage

To train the agent:

python rl_agent/train.py --mode train --start_date 2020-01-01 --end_date 2023-01-01 --symbols BTC/USD ETH/USD

To backtest the trained agent:

python rl_agent/train.py --mode backtest --start_date 2023-01-01 --end_date 2023-12-31 --symbols BTC/USD ETH/USD

To run live trading:

python rl_agent/live_trader.py

Features

• Custom OpenAI Gym environment for cryptocurrency trading
• Data processing and technical indicator calculation
• Integration with Alpaca API for real-time data and trading
• Reinforcement learning model training using Stable Baselines3
• Backtesting capabilities
• Live trading functionality

Project Structure

• rl_agent/
  • trading_env.py: Custom OpenAI Gym environment for cryptocurrency trading
  • data_processing.py: Functions for data download and preprocessing
  • train.py: Script for training and backtesting the RL agent
  • live_trader.py: Implementation of live trading using the trained agent
• requirements.txt: List of project dependencies
• setup.py: Setup script for the project

Contributing

Contributions to this project are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature/YourFeature)
3. Make your changes and commit them (git commit -m 'Add some feature')
4. Push to the branch (git push origin feature/YourFeature)
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.