IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-031-07535-3_13.html
   My bibliography  Save this book chapter

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Guillermo Angeris & Alex Evans & Tarun Chitra, 2023. "Replicating market makers," Digital Finance, Springer, vol. 5(2), pages 367-387, June.

    More about this item

    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:spr:spochp:978-3-031-07535-3_13. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.