Market Making via Reinforcement Learning

Thomas Spooner, John Fearnley, Rahul Savani and Andreas Koukorinis

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Abstract: 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.

Date: 2018-04
New Economics Papers: this item is included in nep-des and nep-mst
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Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1804.04216

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