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