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

Guillermo Angeris (), Akshay Agrawal (), Alex Evans (), Tarun Chitra () and Stephen Boyd ()
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Guillermo Angeris: Stanford University
Akshay Agrawal: Stanford University
Alex Evans: Bain Capital Crypto
Tarun Chitra: Gauntlet Networks
Stephen Boyd: Stanford University

A chapter in Handbook on Blockchain, 2022, pp 415-444 from Springer

Abstract: 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.

Date: 2022
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DOI: 10.1007/978-3-031-07535-3_13

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