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Deep Reinforcement Learning in Cryptocurrency Market Making

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  • Jonathan Sadighian

Abstract

This paper sets forth a framework for deep reinforcement learning as applied to market making (DRLMM) for cryptocurrencies. Two advanced policy gradient-based algorithms were selected as agents to interact with an environment that represents the observation space through limit order book data, and order flow arrival statistics. Within the experiment, a forward-feed neural network is used as the function approximator and two reward functions are compared. The performance of each combination of agent and reward function is evaluated by daily and average trade returns. Using this DRLMM framework, this paper demonstrates the effectiveness of deep reinforcement learning in solving stochastic inventory control challenges market makers face.

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  • Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
    • Handle: RePEc:arx:papers:1911.08647
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    References listed on IDEAS

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    1. Maxime Morariu-Patrichi & Mikko S. Pakkanen, 2018. "State-dependent Hawkes processes and their application to limit order book modelling," Papers 1809.08060, arXiv.org, revised Sep 2021.
    2. Maxime Morariu-Patrichi & Mikko Pakkanen, 2018. "State-dependent Hawkes processes and their application to limit order book modelling," CREATES Research Papers 2018-26, Department of Economics and Business Economics, Aarhus University.
    3. Avraam Tsantekidis & Nikolaos Passalis & Anastasios Tefas & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Using Deep Learning for price prediction by exploiting stationary limit order book features," Papers 1810.09965, arXiv.org.
    4. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Papers 1803.06917, arXiv.org.
    5. Peng Wu & Marcello Rambaldi & Jean-Franc{c}ois Muzy & Emmanuel Bacry, 2019. "Queue-reactive Hawkes models for the order flow," Papers 1901.08938, arXiv.org.
    6. Rama Cont & Arseniy Kukanov & Sasha Stoikov, 2013. "The Price Impact of Order Book Events," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 47-88, December.
    7. David W. Lu, 2017. "Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks," Papers 1707.07338, arXiv.org.
    8. E. Bacry & J. F Muzy, 2013. "Hawkes model for price and trades high-frequency dynamics," Papers 1301.1135, arXiv.org.
    9. Justin Sirignano & Rama Cont, 2018. "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers hal-01754054, HAL.
    10. Yagna Patel, 2018. "Optimizing Market Making using Multi-Agent Reinforcement Learning," Papers 1812.10252, arXiv.org.
    11. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    12. Thomas Spooner & John Fearnley & Rahul Savani & Andreas Koukorinis, 2018. "Market Making via Reinforcement Learning," Papers 1804.04216, arXiv.org.
    13. Ke Xu & Martin D. Gould & Sam D. Howison, 2019. "Multi-Level Order-Flow Imbalance in a Limit Order Book," Papers 1907.06230, arXiv.org, revised Oct 2019.
    14. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    15. Baron Law & Frederi Viens, 2019. "Market Making under a Weakly Consistent Limit Order Book Model," Papers 1903.07222, arXiv.org, revised Jan 2020.
    16. Peng Wu & Marcello Rambaldi & Jean-François Muzy & Emmanuel Bacry, 2021. "Queue-reactive Hawkes models for the order flow," Working Papers hal-02409073, HAL.
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    Citations

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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. Bruno Gav{s}perov & Zvonko Kostanjv{c}ar, 2022. "Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book Model," Papers 2207.09951, arXiv.org.
    3. 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.
    4. Tristan Lim, 2022. "Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning," Papers 2211.01346, arXiv.org, revised Jan 2023.
    5. Ali Raheman & Anton Kolonin & Alexey Glushchenko & Arseniy Fokin & Ikram Ansari, 2022. "Adaptive Multi-Strategy Market-Making Agent For Volatile Markets," Papers 2204.13265, arXiv.org.
    6. 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.
    7. Shuyang Wang & Diego Klabjan, 2023. "An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading," Papers 2309.00626, 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. Xiao-Yang Liu & Hongyang Yang & Jiechao Gao & Christina Dan Wang, 2021. "FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance," Papers 2111.09395, arXiv.org.
    11. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    12. Zihao Zhang & Stefan Zohren, 2021. "Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units," Papers 2105.10430, arXiv.org, revised Aug 2021.
    13. Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.

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