RL Optimal Trade Execution

This is the code repository of "A Modular Framework for RL Optimal Execution".

Installation

We recommend running the experiments via the Dockerfile.

Alternatively, one can manually install all the necessary libraries in a virtual environment via

pip install -r requirements.txt

Entrypoints

The train_{algo}.py files are the entrypoints of the experiments. Training and evaluations can be carried out modifying their main() functions. Adding new agents and training on different periods (if the data is provided) can be done via modifying the config dict.

Framework Modules

src/core/data/historical:datafeed.py contains the implementation of the DataFeed class.

src/core/environment/limit_orders_setup contains the implementations of the Execution Algo, Broker classes as well as the gym environment.

Miscellaneous

src/tests contains the implementation of the multiple UnitTests applied to the environment.

The plot_schedule method of the Execution Algo class can reproduce Figures 5 & 6 of the paper.