IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2211.01346.html
   My bibliography  Save this paper

Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning

Author

Listed:
  • Tristan Lim

Abstract

The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2211.01346
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2211.01346
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mohsen Pourpouneh & Kurt Nielsen & Omri Ross, 2020. "Automated Market Makers," IFRO Working Paper 2020/08, University of Copenhagen, Department of Food and Resource Economics.
    2. Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.
    3. Jonathan Sadighian, 2020. "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers 2004.06985, arXiv.org.
    4. Guillermo Angeris & Tarun Chitra, 2020. "Improved Price Oracles: Constant Function Market Makers," Papers 2003.10001, arXiv.org, revised Jun 2020.
    5. Nicholas T. Chan and Christian Shelton, 2001. "An Adaptive Electronic Market-Maker," Computing in Economics and Finance 2001 146, Society for Computational Economics.
    6. Lioba Heimbach & Eric Schertenleib & Roger Wattenhofer, 2022. "Risks and Returns of Uniswap V3 Liquidity Providers," Papers 2205.08904, arXiv.org, revised Sep 2022.
    7. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    8. Thomas Spooner & John Fearnley & Rahul Savani & Andreas Koukorinis, 2018. "Market Making via Reinforcement Learning," Papers 1804.04216, arXiv.org.
    9. Jonathan Sadighian, 2019. "Deep Reinforcement Learning in Cryptocurrency Market Making," Papers 1911.08647, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tristan Lim, 2024. "Predictive crypto-asset automated market maker architecture for decentralized finance using deep reinforcement learning," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-29, December.
    2. 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.
    3. Hong Guo & Jianwu Lin & Fanlin Huang, 2023. "Market Making with Deep Reinforcement Learning from Limit Order Books," Papers 2305.15821, arXiv.org.
    4. Luca Lalor & Anatoliy Swishchuk, 2024. "Reinforcement Learning in Non-Markov Market-Making," Papers 2410.14504, arXiv.org, revised Nov 2024.
    5. Jiafa He & Cong Zheng & Can Yang, 2023. "Integrating Tick-level Data and Periodical Signal for High-frequency Market Making," Papers 2306.17179, arXiv.org.
    6. Thomas Spooner & Rahul Savani, 2020. "Robust Market Making via Adversarial Reinforcement Learning," Papers 2003.01820, arXiv.org, revised Jul 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. Gao, Xuefeng & Xu, Tianrun, 2022. "Order scoring, bandit learning and order cancellations," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    9. Abbas Haider & Hui Wang & Bryan Scotney & Glenn Hawe, 2022. "Predictive Market Making via Machine Learning," SN Operations Research Forum, Springer, vol. 3(1), pages 1-21, March.
    10. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
    11. 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.
    12. 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.
    13. Matheus V. X. Ferreira & David C. Parkes, 2022. "Credible Decentralized Exchange Design via Verifiable Sequencing Rules," Papers 2209.15569, arXiv.org, revised Apr 2023.
    14. Nelson Vadori & Leo Ardon & Sumitra Ganesh & Thomas Spooner & Selim Amrouni & Jared Vann & Mengda Xu & Zeyu Zheng & Tucker Balch & Manuela Veloso, 2022. "Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations," Papers 2210.07184, arXiv.org, revised Aug 2023.
    15. Vincent Gramlich & Tobias Guggenberger & Marc Principato & Benjamin Schellinger & Nils Urbach, 2023. "A multivocal literature review of decentralized finance: Current knowledge and future research avenues," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-37, December.
    16. Joseph Jerome & Gregory Palmer & Rahul Savani, 2022. "Market Making with Scaled Beta Policies," Papers 2207.03352, arXiv.org, revised Sep 2022.
    17. Joseph Jerome & Leandro Sanchez-Betancourt & Rahul Savani & Martin Herdegen, 2022. "Model-based gym environments for limit order book trading," Papers 2209.07823, arXiv.org.
    18. Vijay Mohan, 2022. "Automated market makers and decentralized exchanges: a DeFi primer," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-48, December.
    19. Jonathan Sadighian, 2020. "Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making," Papers 2004.06985, arXiv.org.
    20. Sumitra Ganesh & Nelson Vadori & Mengda Xu & Hua Zheng & Prashant Reddy & Manuela Veloso, 2019. "Reinforcement Learning for Market Making in a Multi-agent Dealer Market," Papers 1911.05892, arXiv.org.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2211.01346. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.