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QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols

Author

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  • Dev Churiwala
  • Bhaskar Krishnamachari

Abstract

Automated Market Makers (AMMs) have cemented themselves as an integral part of the decentralized finance (DeFi) space. AMMs are a type of exchange that allows users to trade assets without the need for a centralized exchange. They form the foundation for numerous decentralized exchanges (DEXs), which help facilitate the quick and efficient exchange of on-chain tokens. All present-day popular DEXs are static protocols, with fixed parameters controlling the fee and the curvature - they suffer from invariance and cannot adapt to quickly changing market conditions. This characteristic may cause traders to stay away during high slippage conditions brought about by intractable market movements. We propose an RL framework to optimize the fees collected on an AMM protocol. In particular, we develop a Q-Learning Agent for Market Making Protocols (QLAMMP) that learns the optimal fee rates and leverage coefficients for a given AMM protocol and maximizes the expected fee collected under a range of different market conditions. We show that QLAMMP is consistently able to outperform its static counterparts under all the simulated test conditions.

Suggested Citation

  • Dev Churiwala & Bhaskar Krishnamachari, 2022. "QLAMMP: A Q-Learning Agent for Optimizing Fees on Automated Market Making Protocols," Papers 2211.14977, arXiv.org.
    • Handle: RePEc:arx:papers:2211.14977
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    References listed on IDEAS

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    1. Yongge Wang, 2020. "Automated Market Makers for Decentralized Finance (DeFi)," Papers 2009.01676, arXiv.org, revised May 2024.
    2. Guillermo Angeris & Tarun Chitra, 2020. "Improved Price Oracles: Constant Function Market Makers," Papers 2003.10001, arXiv.org, revised Jun 2020.
    3. Alex Evans & Guillermo Angeris & Tarun Chitra, 2021. "Optimal Fees for Geometric Mean Market Makers," Papers 2104.00446, arXiv.org.
    4. 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.
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    Cited by:

    1. Viraj Nadkarni & Sanjeev Kulkarni & Pramod Viswanath, 2024. "Adaptive Curves for Optimally Efficient Market Making," Papers 2406.13794, arXiv.org.
    2. Daniel Kirste & Niclas Kannengie{ss}er & Ricky Lamberty & Ali Sunyaev, 2023. "How Automated Market Makers Approach the Thin Market Problem in Cryptoeconomic Systems," Papers 2309.12818, arXiv.org, revised Sep 2023.

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