Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model

Abstract

The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trained on a weakly consistent, multivariate Hawkes process-based limit order book simulator to obtain market making controls. The proposed approach leverages the advantages of Monte Carlo backtesting and contributes to the line of research on market making under weakly consistent limit order book models. The ensuing deep reinforcement learning controller is compared to multiple market making benchmarks, with the results indicating its superior performance with respect to various risk-reward metrics, even under significant transaction costs.

arXiv comment6 pages, 4 figures

References in corpus (5)

• Sequence Level Training with Recurrent Neural Networks
• Robust Adversarial Reinforcement Learning
• Dealing with the Inventory Risk. A solution to the market making problem
• Market making and incentives design in the presence of a dark pool: a deep reinforcement learning approach
• Reinforcement Learning in High-frequency Market Making

Cited by in corpus (5)

• Reinforcement Learning in Non-Markov Market-Making
• Multi-kernel property in high-frequency price dynamics under Hawkes model
• Deep Reinforcement Learning for Robust Goal-Based Wealth Management
• Event-Based Limit Order Book Simulation under a Neural Hawkes Process: Application in Market-Making
• Algorithmic and High-Frequency Trading Problems for Semi-Markov and Hawkes Jump-Diffusion Models