BTGym

Scalable event-driven RL-friendly backtesting library. Build on top of Backtrader with OpenAI Gym environment API.

Backtrader is open-source algorithmic trading library:
GitHub: http://github.com/mementum/backtrader
Documentation and community:
http://www.backtrader.com/

OpenAI Gym is..., well, everyone knows Gym:
GitHub: http://github.com/openai/gym
Documentation and community:
https://gym.openai.com/


Outline

General purpose of this project is to provide gym-integrated framework for running reinforcement learning experiments in [close to] real world algorithmic trading environments.

DISCLAIMER:
Code presented here is research/development grade.
Can be unstable, buggy, poor performing and is subject to change.

Note that this package is neither out-of-the-box-moneymaker, nor it provides ready-to-converge RL solutions.
Think of it as framework for setting experiments with complex non-stationary stochastic environments.

As a research project BTGym in its current stage can hardly deliver easy end-user experience in as sense that
setting meaninfull  experiments will require some practical programming experience as well as general knowledge
of reinforcement learning theory.

GitHub

https://github.com/Kismuz/btgym