Market Making via Reinforcement Learning

Spooner, Thomas, Fearnley, John, Savani, Rahul ORCID: 0000-0003-1262-7831 and Koukorinis, Andreas (2018) Market Making via Reinforcement Learning .

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Description

Market making is a fundamental trading problem in which an agent provides liquidity by continually offering to buy and sell a security. The problem is challenging due to inventory risk, the risk of accumulating an unfavourable position and ultimately losing money. In this paper, we develop a high-fidelity simulation of limit order book markets, and use it to design a market making agent using temporal-difference reinforcement learning. We use a linear combination of tile codings as a value function approximator, and design a custom reward function that controls inventory risk. We demonstrate the effectiveness of our approach by showing that our agent outperforms both simple benchmark strategies and a recent online learning approach from the literature.

Item Type:	Other
Additional Information:	10 pages, 5 figures, AAMAS2018 Conference Proceedings
Uncontrolled Keywords:	Market Making, Limit Order Books, TD Learning, Tile Coding
Depositing User:	Symplectic Admin
Date Deposited:	06 Nov 2020 16:06
Last Modified:	23 May 2026 01:30
Related Websites:	Author
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3020818
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