bassemfg / Deep-Q-Learning-Applied-to-Algorithmic-Trading

Deep Q-Learning Applied to Algorithmic Trading

Deep Q-Learning Applied to Algorithmic Trading

The notebook with the experiments is available here The study is a replication and enhancement of the DQN by Theate, Thibaut and Ernst, Damien(2020).

Dataset

The Data is provided by me in the data directory

GPU and Linux

Windows WSL

It's recommend to run this on a linux system.

If you are on windows, install WSL on an admin powershell: wsl --install

update your distribution: sudo apt-get update and sudo apt-get install wget ca-certificates. From your WSL cli, run code . and it will open a VSCode for you. Install the VSCode WSL extension for a seamless developer experience, see their tutorial here.

Cuda and GPUs

Install the NVidia drivers on your Linux box. Check if installation was good: nvidia-smi

Install TensorFlow's cuda framework: pip install tensorflow[and-cuda] and verify the installation: python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Credits and Citations

The DQN model was inspired by the paper:

@article{theate2021application,
  title={An application of deep reinforcement learning to algorithmic trading},
  author={Th{\'e}ate, Thibaut and Ernst, Damien},
  journal={Expert Systems with Applications},
  volume={173},
  pages={114632},
  year={2021},
  publisher={Elsevier}
}