This repository contains source code for the course "Deep Reinforcement Learning for High-Frequency Trading" at the Ukrainian Catholic University (UCU) / Czech Technical University (CTU) in July 2022.
Sooner or later, every Data Scientist meets with general financial tasks and automation of trading on a stock exchange – in particular. But not every Data Scientist knows how to apply Deep Reinforcement Learning to these assignments effectively. This course is designed to teach you just that – with real examples.
Go to: the University course page for more detail.
- Intro
- Understand your homework & resources
- Understand the data, features, targets, etc.
- Implement the baselines (hard rule, Supervised trader - RF)
- Brief Deep Reinforcement Learning literacy campaign
- Implement & train Double-Duelling Ape-X DQN
- Implement & train PPO
- Conclusions
- Jupyter Notebook / Google Colab
- Python
- PyTorch, pandas
Course playlist on YouTube: Deep Reinforcement Learning for High Frequency Trading.
Your goal: maximize investment return.
- Double DQN & PPO for HFT - paper
- Random Forest for HFT - paper
- Random Forest for HFT - source code