Guillermo Angeris

Chief Scientist

Mandy Campbell

Partner and Head of Marketing

Michael Chagnon

Vice President, Fund Finance

Parth Chopra

Principal

Stefan Cohen

Partner

Andrew Cleverdon

Chief Financial Officer

Khurram Dara

Regulatory and Policy Principal

Alex Evans

Partner

grjte

Research Collaborator

Kobi Gurkan

Partner and Head of Research

Charlotte Kyle

Executive Assistant

Haley MacCormac

Senior Finance Associate

Myles Maloof

Finance Associate

Nicolas Mohnblatt

Research Collaborator

Natalie Mullins

Investor

Andrija Novakovic

Researcher

Kaley Petulla

Executive Assistant

Justin Rattigan

General Counsel

Kara Reusch

Vice President, Product Specialist Investor Relations

Kimleang Svay

Executive Assistant

Kevin Zhang

Venture Partner

Alex Evans

Partner

BIO

Prior to joining Bain Capital Crypto, Alex was a researcher at Placeholder, where he focused on early-stage investing in DeFi, blockchain scalability, and privacy technologies. Drawn to problems that bridge theoretical ideas to practical implementation, he has published research on financial protocols, privacy, and consensus.

Insights

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

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.

Guillermo Angeris,
Akshay Agrawal,
Alex Evans,
Tarun Chitra,
Stephen Boyd

Basics,
DeFi

Liquidity Provider Returns in Geometric Mean Markets

Geometric mean market makers (G3Ms), such as Uniswap and Balancer, comprise a popular class of automated market makers (AMMs) defined by the following rule: the reserves of the AMM before and after each trade must have the same (weighted) geometric mean. This paper extends several results known for constant-weight G3Ms to the general case of G3Ms with time-varying and potentially stochastic weights. These results include the returns and no-arbitrage prices of liquidity pool (LP) shares that investors receive for supplying liquidity to G3Ms. Using these expressions, we show how to create G3Ms whose LP shares replicate the payoffs of financial derivatives. The resulting hedges are model-independent and exact for derivative contracts whose payoff functions satisfy an elasticity constraint. These strategies allow LP shares to replicate various trading strategies and financial contracts, including standard options. G3Ms are thus shown to be capable of recreating a variety of active trading strategies through passive positions in LP shares.

Alex Evans

DeFi