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Optimal Market Making by Reinforcement Learning

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  • Matias Selser
  • Javier Kreiner
  • Manuel Maurette

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.

Suggested Citation

  • Matias Selser & Javier Kreiner & Manuel Maurette, 2021. "Optimal Market Making by Reinforcement Learning," Papers 2104.04036, arXiv.org.
    • Handle: RePEc:arx:papers:2104.04036
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    File URL: http://arxiv.org/pdf/2104.04036
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    References listed on IDEAS

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    1. Angelos Filos, 2019. "Reinforcement Learning for Portfolio Management," Papers 1909.09571, arXiv.org.
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    Cited by:

    1. Bruno Gašperov & Stjepan Begušić & Petra Posedel Šimović & Zvonko Kostanjčar, 2021. "Reinforcement Learning Approaches to Optimal Market Making," Mathematics, MDPI, vol. 9(21), pages 1-22, October.

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