Trading and Liquidity Provision in Concentrated-Liquidity AMMs
Summary
The thesis develops models and strategies for constant-product automated market makers with concentrated liquidity. It covers both liquidity takers, including large orders and statistical arbitrage, and liquidity providers managing positions in one or multiple pools. Market data motivates the modeling, including data from Uniswap v3 and analyses of price, liquidity, trading costs, co-movement, lead-lag effects, spillovers, and causality across venues.
For liquidity takers, the work derives strategies that use prices on competing venues as signals and that trade baskets of co-moving cryptocurrencies. For providers, it presents a stochastic-control strategy that selects a liquidity-range width based on pool profitability, position dynamics, and concentration risk, as well as a model-free approach using an LSTM network for multiple pools. The document describes data-based motivation and strategy derivations, but gives no numerical performance results in the supplied text; outcomes depend on model assumptions, data, and implementation.
Key ideas
- The thesis studies trading and liquidity provision in constant-product AMMs with concentrated liquidity.
- Liquidity-taker strategies use competing-venue prices and cross-asset relationships to guide large trades and statistical arbitrage.
- The analysis examines venue price, liquidity, trading costs, lead-lag relationships, spillovers, and causality.
- A stochastic-control method sets a provider’s liquidity-range width using pool profitability, position dynamics, and concentration risk.
- A model-free LSTM approach estimates liquidity provision strategies across multiple pools.
Tags
Full text
# Automated Market Making and Decentralized Finance # Automated Market Making and Decentralized Finance Automated market makers (AMMs) are a new type of trading venues which are revolutionising the way market participants interact. At present, the majority of AMMs are constant function market makers (CFMMs) where a deterministic trading function determines how markets are cleared. Within CFMMs, we focus on constant product market makers (CPMMs) which implements the concentrated liquidity (CL) feature. In this thesis we formalise and study the trading mechanism of CPMMs with CL, and we develop liquidity provision and liquidity taking strategies. Our models are motivated and tested with market data. We derive optimal strategies for liquidity takers (LTs) who trade orders of large size and execute statistical arbitrages. First, we consider an LT who trades in a CPMM with CL and uses the dynamics of prices in competing venues as market signals. We use Uniswap v3 data to study price, liquidity, and trading cost dynamics, and to motivate the model. Next, we consider an LT who trades a basket of crypto-currencies whose constituents co-move. We use market data to study lead-lag effects, spillover effects, and causality between trading venues. We derive optimal strategies for strategic liquidity providers (LPs) who provide liquidity in CPMM with CL. First, we use stochastic control tools to derive a self-financing and closed-form optimal liquidity provision strategy where the width of the LP's liquidity range is determined by the profitability of the pool, the dynamics of the LP's position, and concentration risk. Next, we use a model-free approach to solve the problem of an LP who provides liquidity in multiple CPMMs with CL. We do not specify a model for the stochastic processes observed by LPs, and use a long short-term memory (LSTM) neural network to approximate the optimal liquidity provision strategy.
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