This repository provides a Gym environment for analyzing the stock market using reinforcement learning techniques. The environment is compatible with MuZero and enables researchers and practitioners to train and evaluate RL agents in simulated stock market scenarios.
To install the TradingGym package from this GitHub repository, run the following command:
pip install git+https://github.com/drlove2002/TradingGym.gitTo use the TradingGym, import it into your Python code as follows:
import gymnasium as gym
import trading_gym as tg
env = gym.make('stocks-v0')For more information on how to use Gym environments, see the Gym documentation.
To see an example of how to use the TradingGym package, check out the test_render.ipynb notebook in the tests folder. This notebook demonstrates how to create and render a simple stock market environment using the TradingGym package.
You can run the notebook locally by cloning the TradingGym repository and installing the necessary dependencies. For example:
git clone https://github.com/drlove2002/TradingGym.git
cd TradingGym
pip install -r requirements.txt
jupyter notebook tests/test_render.ipynbThis will open the notebook in your browser, where you can run each cell to see the environment in action.
This Gym environment is compatible with MuZero, an RL algorithm that can learn to play complex games and solve planning problems without knowing the rules in advance. For more information on MuZero, see the original paper.
Contributions to this repository are welcome! If you find a bug or would like to suggest an enhancement, please open an issue or submit a pull request.
Before contributing, please read the contributing guidelines for more information on how to contribute to this project.
This project is licensed under the MIT License - see the LICENSE file for details.