Optimal Market Making by Reinforcement Learning

Proceedings of the 2021 MACI

4 Pages Posted: 28 Apr 2021

See all articles by Matias Selser

Matias Selser

Universidad Torcuato Di Tella

Javier Kreiner

affiliation not provided to SSRN

Manuel Maurette

Universidad Torcuato Di Tella - Finance; Universidad del CEMA

Date Written: April 9, 2021

Abstract

We apply Reinforcement Learning algorithms to solve the classic quantitative finance Market Making problem, in which an agent provides liquidity to the market by placing buy and sell orders while maximizing a utility function. The optimal agent has to find a delicate balance between the price risk of her inventory and the profits obtained by capturing the bid-ask spread. We design an environment with a reward function that determines an order relation between policies equivalent to the original utility function. When comparing our agents with the optimal solution and a benchmark symmetric agent, we find that the Deep Q-Learning algorithm manages to recover the optimal agent.

Keywords: reinforcement learning, market making, Q-Learning, quantitative finance

JEL Classification: G12, G13

Suggested Citation

Selser, Matias and Kreiner, Javier and Maurette, Manuel, Optimal Market Making by Reinforcement Learning (April 9, 2021). Proceedings of the 2021 MACI, Available at SSRN: https://ssrn.com/abstract=3829984 or http://dx.doi.org/10.2139/ssrn.3829984

Matias Selser (Contact Author)

Universidad Torcuato Di Tella ( email )

Minones 2159
C1428ATG Buenos Aires, 1428
Argentina

Javier Kreiner

affiliation not provided to SSRN

Manuel Maurette

Universidad Torcuato Di Tella - Finance ( email )

United States

Universidad del CEMA ( email )

Córdoba 374
Buenos Aires, 1044
Argentina

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