Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model

Bruno Gašperov, Zvonko Kostanjčar
7/20/2022
q-fin.GNcs.LGq-fin.TR

No official code found. Best implementation: BGasperov/drlformm (71 stars, last commit 2023-03-19).

Abstract

The stochastic control problem of optimal market making is among the central problems in quantitative finance. In this paper, a deep reinforcement learning-based controller is trained on a weakly consistent, multivariate Hawkes process-based limit order book simulator to obtain market making controls. The proposed approach leverages the advantages of Monte Carlo backtesting and contributes to the line of research on market making under weakly consistent limit order book models. The ensuing deep reinforcement learning controller is compared to multiple market making benchmarks, with the results indicating its superior performance with respect to various risk-reward metrics, even under significant transaction costs.

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BGasperov/drlformmCommunity100%

This repository contains the main code used in the paper "Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model"

7125Jupyter NotebookMar 5, 20223 years ago

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Cite this paper

@article{gaperov2022deep,
  title  = {Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model},
  author = {Bruno Gašperov and Zvonko Kostanjčar},
  year   = {2022},
  eprint = {2207.09951},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.09951v1}
}

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