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Optimizing Market Making using Multi-Agent Reinforcement Learning

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  • Yagna Patel

Abstract

In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold an asset. The micro-agent optimizes on placing limit orders within the limit order book. For the context of this paper, the proposed framework is applied and studied on the Bitcoin cryptocurrency market. The goal of this paper is to show that reinforcement learning is a viable strategy that can be applied to complex problems (with complex environments) such as market making.

Suggested Citation

  • Yagna Patel, 2018. "Optimizing Market Making using Multi-Agent Reinforcement Learning," Papers 1812.10252, arXiv.org.
    • Handle: RePEc:arx:papers:1812.10252
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    File URL: http://arxiv.org/pdf/1812.10252
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    References listed on IDEAS

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    1. Olivier Guéant, 2017. "Optimal market making," Applied Mathematical Finance, Taylor & Francis Journals, vol. 24(2), pages 112-154, March.
    2. Matthew F Dixon, 2017. "A High Frequency Trade Execution Model for Supervised Learning," Papers 1710.03870, arXiv.org, revised Dec 2017.
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    Cited by:

    1. Jiafa He & Cong Zheng & Can Yang, 2023. "Integrating Tick-level Data and Periodical Signal for High-frequency Market Making," Papers 2306.17179, arXiv.org.
    2. Schnaubelt, Matthias, 2020. "Deep reinforcement learning for the optimal placement of cryptocurrency limit orders," FAU Discussion Papers in Economics 05/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    4. Bruno Gašperov & Stjepan Begušić & Petra Posedel Šimović & Zvonko Kostanjčar, 2021. "Reinforcement Learning Approaches to Optimal Market Making," Mathematics, MDPI, vol. 9(21), pages 1-22, October.
    5. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
    6. Michael Karpe & Jin Fang & Zhongyao Ma & Chen Wang, 2020. "Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation," Papers 2006.05574, arXiv.org, revised Sep 2020.
    7. Hui Niu & Siyuan Li & Jiahao Zheng & Zhouchi Lin & Jian Li & Jian Guo & Bo An, 2023. "IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making," Papers 2308.08918, arXiv.org.
    8. Joseph Jerome & Gregory Palmer & Rahul Savani, 2022. "Market Making with Scaled Beta Policies," Papers 2207.03352, arXiv.org, revised Sep 2022.
    9. Jonathan Sadighian, 2020. "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers 2004.06985, arXiv.org.
    10. Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.
    11. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.

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