Model-based gym environments for limit order book trading
This tool addresses the need for efficient and extensible environments for RL research in algorithmic trading, though it is incremental as it builds on existing mathematical models.
The paper introduces MBTGym, a Python module providing gym environments for training reinforcement learning agents on model-based limit order book trading problems, such as market-making and optimal execution, and demonstrates its use on standard and non-standard problems from the literature.
Within the mathematical finance literature there is a rich catalogue of mathematical models for studying algorithmic trading problems -- such as market-making and optimal execution -- in limit order books. This paper introduces \mbtgym, a Python module that provides a suite of gym environments for training reinforcement learning (RL) agents to solve such model-based trading problems. The module is set up in an extensible way to allow the combination of different aspects of different models. It supports highly efficient implementations of vectorized environments to allow faster training of RL agents. In this paper, we motivate the challenge of using RL to solve such model-based limit order book problems in mathematical finance, we explain the design of our gym environment, and then demonstrate its use in solving standard and non-standard problems from the literature. Finally, we lay out a roadmap for further development of our module, which we provide as an open source repository on GitHub so that it can serve as a focal point for RL research in model-based algorithmic trading.