Multi-Agent Reinforcement Learning for Assumption-Free Stock Market Modeling

An assumption-free microscopic stock market model built upon multi-agent reinforcement learning.

This is a final project for the course CS285 Deep Reinforcement Learning, Decision Making, and Control at UC Berkeley. Please see our final report for more details about this project.

Installation

Use the package manager pip to install the dependencies locally.

git clone -b master --depth 1 https://github.com/ChocolateDave/stock_market.git
cd stock_market & pip install -r requirements.txt & pip install -e .

Usage

We provide three scripts for running our codes on different settings.

• If you would like to explore agents trained on different learning rates, please run with

  bash stock_market/scripts/run_stock_market_diff_lr $GPU_ID

• If you would like to investigate agents trained on different budget discount over time, please run with

  bash stock_market/scripts/run_stock_market_budget_discount $GPU_ID

• If you would like to investigate agents trained on different worth of stock, please run with

  bash stock_market/scripts/run_stock_market_worth_of_stocks.sh $GPU_ID

• Or you can run the base training script for more flexibility

  python stock_market/train.py <$ARGUMENTS>

Citation

If you use this source code, please cite it using bibtex as below.

@misc{maverick2022,
  author = {Maverick, Zhang and Juanwu Lu},
  title = {Multi-Agent Reinforcement Learning for Assumption-Free Stock Market Modeling},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/ChocolateDave/stock_market}},
  commit = {2b675bac077bc695048ce0072f254de25c898050}
}

License

This project is licensed under the BSD 3-Clause License