Selected Publications

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.
In AAMAS, 2018

Projects

RSRL

A fast, safe and easy to use reinforcement learning framework in Rust.

spaces

Set/space primitives for defining machine learning problems.

Contact

  • Ashton Building, University of Liverpool, UK, L69 3BX