Optimal Market Making by Reinforcement Learning

Matias Selser, Javier Kreiner and Manuel Maurette

Papers from arXiv.org

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.

Date: 2021-04
New Economics Papers: this item is included in nep-cmp, nep-cwa, nep-mst and nep-upt
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Citations: View citations in EconPapers (1)

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