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Constant Function Market Makers: Multi-asset Trades via Convex Optimization

In: Handbook on Blockchain

Author

Listed:
  • Guillermo Angeris

    (Stanford University)

  • Akshay Agrawal

    (Stanford University)

  • Alex Evans

    (Bain Capital Crypto)

  • Tarun Chitra

    (Gauntlet Networks)

  • Stephen Boyd

    (Stanford University)

Abstract

The rise of Ethereum and other blockchains that support smart contracts has led to the creation of decentralized exchanges (DEXs), such as Uniswap, Balancer, Curve, mStable, and SushiSwap, which enable agents to trade cryptocurrencies without trusting a centralized authority. While traditional exchanges use order books to match and execute trades, DEXs are typically organized as constant function market makers (CFMMs). CFMMs accept and reject proposed trades based on the evaluation of a function that depends on the proposed trade and the current reserves of the DEX. For trades that involve only two assets, CFMMs are easy to understand, via two functions that give the quantity of one asset that must be tendered to receive a given quantity of the other, and vice versa. When more than two assets are being exchanged, it is harder to understand the landscape of possible trades. We observe that various problems of choosing a multi-asset trade can be formulated as convex optimization problems and can therefore be reliably and efficiently solved.

Suggested Citation

  • Guillermo Angeris & Akshay Agrawal & Alex Evans & Tarun Chitra & Stephen Boyd, 2022. "Constant Function Market Makers: Multi-asset Trades via Convex Optimization," Springer Optimization and Its Applications, in: Duc A. Tran & My T. Thai & Bhaskar Krishnamachari (ed.), Handbook on Blockchain, pages 415-444, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-07535-3_13
    DOI: 10.1007/978-3-031-07535-3_13
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    Cited by:

    1. C. Escudero & F. Lara & M. Sama, 2024. "Optimal Trade Characterizations in Multi-Asset Crypto-Financial Markets," Papers 2405.06854, arXiv.org.
    2. Guillermo Angeris & Alex Evans & Tarun Chitra, 2023. "Replicating market makers," Digital Finance, Springer, vol. 5(2), pages 367-387, June.
    3. Sebastian Jaimungal & Yuri F. Saporito & Max O. Souza & Yuri Thamsten, 2023. "Optimal Trading in Automatic Market Makers with Deep Learning," Papers 2304.02180, arXiv.org.

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