Reinforcement Learning in Non-Markov Market-Making

TL;DR

This paper introduces a deep reinforcement learning framework using the Soft Actor-Critic algorithm for optimal market-making in complex price environments with semi-Markov and Hawkes jump-diffusion dynamics, demonstrating its effectiveness through detailed simulations.

Contribution

It develops a novel RL approach tailored for non-Markovian market dynamics, integrating advanced jump-diffusion models and addressing adverse selection in market-making strategies.

Findings

RL framework effectively models complex price dynamics

Deep RL outperforms traditional methods in simulated environments

Insights into the evolution of trading variables and reward functions

Abstract

We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used, where we deployed the state-of-the-art Soft Actor-Critic (SAC) algorithm for the deep learning part. The SAC algorithm is an off-policy entropy maximization algorithm more suitable for tackling complex, high-dimensional problems with continuous state and action spaces like in optimal market-making (MM). We introduce the optimal MM problem considered, where we detail all the deterministic and stochastic processes that go into setting up an environment for simulating this strategy. Here we also give an in-depth overview of the jump-diffusion pricing dynamics used, our method for dealing with adverse selection within the limit order book, and we highlight the working parts of our optimization problem. Next, we discuss training and testing results, where we give visuals of how important deterministic and stochastic processes such as the bid/ask, trade executions, inventory, and the reward function evolved. We include a discussion on the limitations of these results, which are important points to note for most diffusion models in this setting.