CLVR Ordering of Transactions on AMMs

Abstract: Trading on decentralized exchanges via an Automated Market Maker (AMM) mechanism has been massively adopted, with a daily trading volume reaching $1B. This trading method has also received close attention from researchers, central banks, and financial firms, who have the potential to adopt it to traditional financial markets such as foreign exchanges and stock markets. A critical challenge of AMM-powered trading is that transaction order has high financial value, so a policy or method to order transactions in a "good" (optimal) manner is vital. We offer economic measures of both price stability (low volatility) and inequality that inform how a "social planner" should pick an optimal ordering. We show that there is a trade-off between achieving price stability and reducing inequality, and that policymakers must choose which to prioritize. In addition, picking the optimal order can often be costly, especially when performing an exhaustive search over trade orderings (permutations). As an alternative we provide a simple algorithm, Clever Look-ahead Volatility Reduction (CLVR). This algorithm constructs an ordering which approximately minimizes price volatility with a small computation cost. We also provide insight into the strategy changes that may occur if traders are subject to this sequencing algorithm.

• The paper introduces CLVR, an algorithm that minimizes price volatility on AMMs by choosing transactions with the least impact on price shifts.
• It demonstrates trade-offs between improved price stability and increased economic inequality, measured via volatility metrics and the Gini coefficient.
• Empirical simulations with Uniswap V2 data reveal that larger block sizes and transaction splitting significantly enhance market stability.

Overview of CLVR Ordering of Transactions on AMMs

This paper by McLaughlin et al. investigates an important problem in the domain of decentralized finance (DeFi), specifically within the context of Automated Market Makers (AMMs). The focus is on finding optimal transaction ordering to achieve goals such as price stability and economic equality among traders.

Introduction and Motivation

The paper begins by highlighting the rising importance and adoption of AMMs in DeFi. AMMs facilitate a considerable volume of trading on decentralized exchanges (DEXs), with daily volumes reaching \$1B. Given the transparency and continuous price updates enabled by blockchain technology, the order of transactions can significantly impact financial outcomes.

The authors address a critical challenge: identifying optimal transaction ordering on AMMs. They propose economic measures to evaluate an ordering scheme's effectiveness in terms of price stability and inequality reduction. They introduce a novel algorithm, Clever Look-ahead Volatility Reduction (CLVR), designed to minimize price volatility with minimal computational effort.

Economic Measures

The paper defines two primary economic measures:

1. Price Stability: The authors focus on an objective function that minimizes price volatility relative to the initial price. This measure aims to ensure stability in the AMM’s price movements.
2. Inequality: Measured by the Gini Coefficient, this assesses the distribution of wealth (in terms of token amounts received) post-trade to gauge inequality among traders.

Key Findings and Contributions

1. Trade-offs Between Stability and Inequality: The optimal ordering to achieve price stability often leads to higher inequality, and vice versa, presenting a dilemma for policymakers.
2. CLVR Algorithm: The CLVR algorithm is proposed as a practical method to achieve relatively minimal price volatility. It accomplishes this by selecting the next transaction that causes the least deviation from the initial price, computing this in polynomial time.
3. Block Size Implications: Experimentation shows that larger block sizes improve the flexibility of achieving price stability by allowing better organization of trades.
4. Impact of Transaction Splitting: It is shown that price volatility can be significantly reduced if traders break their transactions into smaller orders, particularly under the CLVR ordering mechanism.

Methodology

The research employs both theoretical analysis and empirical simulations. A key part of the methodology involves generating synthetic workloads based on real-world trading data from the Uniswap V2 USDC-USDT AMM. This allows the authors to create robust simulations that mirror actual trading environments.

Simulations and Results

The paper presents keen insights from various simulations:

• Optimal Ordering: Through brute-force algorithms, orderings minimizing volatility are contrasted with those minimizing the Gini coefficient, illustrating the inherent trade-offs.
• Algorithm Performance: CLVR outperforms Volume Heuristic Greedy Sequencing Rule (VHGSR) concerning minimizing price volatility across multiple trials and varying block sizes.
• Block Size: Larger blocks enable better price stability, indicating that batched processing could be beneficial.
• Empirical Data: Applying the CLVR algorithm to empirical Uniswap V2 data demonstrates substantial price volatility reduction, supporting the practical applicability of the proposed approach.

Practical and Theoretical Implications

Practical Implications:

• AMM protocols can integrate CLVR to achieve price stability, potentially attracting more traders by offering better price assurances.
• Encouraging transaction splitting can further enhance market stability.

Theoretical Implications:

• This study underscores the importance of transaction ordering beyond MEV mitigation, reinforcing that transaction sequencing can achieve broader economic goals.
• It prompts further research on integrating such ordering mechanisms in private AMMs or adapting these insights to traditional financial systems.

Future Directions

The paper identifies several future research avenues:

• Incorporating Slippage Tolerances: Future work can consider traders' slippage settings within the ordering algorithms.
• Router-based Trades: The effect of routing trades through multiple pools warrants additional exploration.
• Incentives and Gas Costs: Examining the impact of network fees on the practical adoption of transaction splitting strategies provides another research direction.
• Market Behavior Over Time: Longitudinal studies on how transaction sequencing impacts price discovery and market efficiency can offer deeper insights.

Conclusion

McLaughlin et al. offer significant contributions to the field of blockchain finance by addressing optimal transaction ordering on AMMs. The findings not only suggest robust theoretical implications but also provide practical algorithms like CLVR which can be progressively adopted in the evolving DeFi landscape. This work stands as a valuable resource for both policymakers and researchers seeking to optimize AMM transaction sequencing to balance price stability with economic equality.