Optimal Market Making by Reinforcement Learning
Proceedings of the 2021 MACI
4 Pages Posted: 28 Apr 2021
Date Written: April 9, 2021
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
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