This repository is the program code for the thesis of an undergraduate student at the University of Aizu.
The paper is An Evaluation on Deep Reinforcement Learning Algorithms for Stock Trading.
git clone https://github.com/napnel/UoA-thesis-s1260099.git && cd UoA-thesis-s1260099
docker build .
Please use Ubuntu as the OS; Rllib does not officially support windows.
git clone https://github.com/napnel/UoA-thesis-s1260099.git && cd UoA-thesis-s1260099
conda crate --name <env_name> python==3.8
codna activate <env_name>
pip install -r requirements.txt
python main.py -h
python main.py --local_dir ./ray_results --algo DQN --ticker ^N225
This example shows a Deep Q-Network learning on the Nikkei Stock Average.
The execution result is . /ray_results, and you can see the agent's behavior visualized in backtest.html.
If you want to reduce the computation time, increase the --num_workers or decrease the --num_samples.
python summarize_performance.py --local_dir ./ray_results
This run summarizes the computation time, learning curve, and investment performance of all the agents stored in ray_results.