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Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges

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  • Bhaskar Krishnamachari
  • Qi Feng
  • Eugenio Grippo

Abstract

One of the exciting recent developments in decentralized finance (DeFi) has been the development of decentralized cryptocurrency exchanges that can autonomously handle conversion between different cryptocurrencies. Decentralized exchange protocols such as Uniswap, Curve and other types of Automated Market Makers (AMMs) maintain a liquidity pool (LP) of two or more assets constrained to maintain at all times a mathematical relation to each other, defined by a given function or curve. Examples of such functions are the constant-sum and constant-product AMMs. Existing systems however suffer from several challenges. They require external arbitrageurs to restore the price of tokens in the pool to match the market price. Such activities can potentially drain resources from the liquidity pool. In particular, dramatic market price changes can result in low liquidity with respect to one or more of the assets and reduce the total value of the LP. We propose in this work a new approach to constructing the AMM by proposing the idea of dynamic curves. It utilizes input from a market price oracle to modify the mathematical relationship between the assets so that the pool price continuously and automatically adjusts to be identical to the market price. This approach eliminates arbitrage opportunities and, as we show through simulations, maintains liquidity in the LP for all assets and the total value of the LP over a wide range of market prices.

Suggested Citation

  • Bhaskar Krishnamachari & Qi Feng & Eugenio Grippo, 2021. "Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges," Papers 2101.02778, arXiv.org.
    • Handle: RePEc:arx:papers:2101.02778
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    References listed on IDEAS

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    1. Lawrence H. White, 2015. "The Market for Cryptocurrencies," Cato Journal, Cato Journal, Cato Institute, vol. 35(2), pages 383-402, Spring/Su.
    2. Robin Hanson, 2007. "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 3-15, February.
    3. Yongge Wang, 2020. "Automated Market Makers for Decentralized Finance (DeFi)," Papers 2009.01676, arXiv.org, revised Sep 2020.
    4. Guillermo Angeris & Tarun Chitra, 2020. "Improved Price Oracles: Constant Function Market Makers," Papers 2003.10001, arXiv.org, revised Jun 2020.
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    Cited by:

    1. Daniel Z. Zanger, 2022. "G3Ms:Generalized Mean Market Makers," Papers 2208.07305, arXiv.org.

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