Model-based gym environments for limit order book trading (2209.07823v1)

Abstract: 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.

• The paper introduces 'mbt', a Python module that provides model-based limit order book trading environments for training RL agents.
• It highlights a modular and vectorized simulation design that accelerates experimentation with diverse LOB models and market-making strategies.
• The research validates benchmark agents using PPO for market-making and outlines a roadmap for expanding the module’s capabilities in finance.

Overview of "Model-based Gym Environments for Limit Order Book Trading"

This paper introduces "mbt," a Python module that provides a collection of OpenAI Gym environments specifically designed for training Reinforcement Learning (RL) agents to tackle model-based limit order book (LOB) trading problems. The emphasis on model-based approaches addresses several longstanding challenges in the mathematical finance and RL communities.

Key Contributions

This research offers several significant contributions to the paper and application of RL in financial markets:

1. Introduction of the mbt Library: The paper presents mbt, which includes a robust suite of environments catering to a variety of LOB models. This enhances flexibility and depth for researchers and practitioners operating in algorithmic trading.
2. Extensibility and Efficiency: The authors highlight the particular modular design of mbt, which permits integration of distinct model components. This flexibility facilitates rapid adaptation and experimentation with different environmental setups and reward dynamics. Moreover, the vectorized environment implementation significantly accelerates the training of RL agents by allowing parallel processing of multiple trading trajectories efficiently.
3. Technical Implementation: The mbt module is structured to support various critical financial and computational components such as arrival processes, mid-price processes, fill probability models, action spaces, and reward functions. Each of these is extensible to fit new models or variants from existing literature.
4. Benchmark Agents: Pre-defined baseline agents are provided to facilitate comparative assessments, including optimal agents for certain market-making scenarios used for benchmarking RL-generated policies.
5. Empirical Demonstration and Roadmap: The paper demonstrates using proximal policy optimization (PPO) to learn policies for market-making scenarios and measures the effectiveness of vectorizing simulations. Additionally, it outlines development plans for expanding the module's capabilities and usability in broader settings.

Implications and Future Directions

This work has several implications for both the fields of mathematical finance and RL. By providing an alternative to traditional PDE-based approaches, mbt allows researchers to explore richer and higher-dimensional models that are difficult to handle analytically. This is particularly relevant in models where the curse of dimensionality or the specificity of solutions impedes traditional techniques.

For the RL community, the environments offered by mbt present new opportunities to test and benchmark RL algorithms on financial datasets, potentially sparking innovative algorithmic developments and improvements in sample efficiency.

Speculated Developments in AI and Finance

The integration of advanced RL methods with financial models through platforms like mbt is poised to accelerate developments in AI-driven trading strategies. This could lead to more adaptable and resilient market models that better capture the complexity and dynamism of real-world financial systems. In future work, expanding the module to include multi-agent scenarios or integrating with leading RL libraries like RLLib and RLax could open new avenues of exploration and application.

By drawing upon and contributing to both the computational and financial aspects of trading, mbt represents a bridging technology with the potential to significantly influence the future landscape of automated trading and financial market analysis.