This repository is the work related to the masters thesis Deep Reinforcement Learning for algorithmic stock trading. written in 2020/2021.
The goal was to train some DRL Agents for trading stocks and investigate limitations and chances for this algorithmic trading strategy. Additionally I compared performance to benchmarks and other basic algorithms.
At first setup your environment. I developed with Python v3.6.12 due to some restrictions at university server. There is a requirements file. Use it to install all necesarry packages: pip install -r requirements.txt.
I hardly recommend to use VisualStudio Code with Development container. There is a complete configuration. So you only have to startup Docker and open VSCode. Just click "Reopen in Container" and docker will do the rest.
If you are not able to use Docker and VSCode create a virtual environment and install packages there.
At first you should check the README of etl folder to see what kind of data you need. Because of licensing limitations I am not able to provide the data but you can re-download it for yourself.
- Run the ETL pipeline. (See the readme there)
- Run
python main.py --algo A2Cto train, evaluate and test an A2C Agent.
There are lot more parameters you can pass to the script. Just run python main.py --help for help or check the config for further information.
You can run some experiments programatically by modifying and running run_experiment.py.
It generates CLI-Commands for configurations you want to test on. Although it uses multiprocessing to leverage the training speed. It is best for letting it run on a server.
I'm open for questions and discussions. Just open a github issue.
Usage of the software on your own risk. There are no warranties for anything!
See LICENSE file in this repo. In short: It's MIT.