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Arbitrageurs' profits, LVR, and sandwich attacks: batch trading as an AMM design response (2307.02074v5)

Published 5 Jul 2023 in cs.DC and econ.TH

Abstract: We study a novel automated market maker design: the function maximizing AMM (FM-AMM). Our central assumption is that trades are batched before execution. Because of competition between arbitrageurs, the FM-AMM eliminates arbitrage profits (or LVR) and sandwich attacks, currently the two main problems in decentralized finance and blockchain design more broadly. We then consider 11 token pairs and use Binance price data to simulate the lower bound to the return of providing liquidity to an FM-AMM. Such a lower bound is, for the most part, slightly higher than the empirical returns of providing liquidity on Uniswap v3 (currently the dominant AMM).

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References (28)
  1. Aoyagi, J. (2020). Liquidity provision by automated market makers. working paper.
  2. Enter the hydra: Towards principled bug bounties and {{\{{Exploit-Resistant}}\}} smart contracts. In 27th USENIX Security Symposium (USENIX Security 18), pp. 1335–1352.
  3. The high-frequency trading arms race: Frequent batch auctions as a market design response. The Quarterly Journal of Economics 130(4), 1547–1621.
  4. Commitment against front-running attacks. Management Science.
  5. Batching Trades on Automated Market Makers. In J. Bonneau and S. M. Weinberg (Eds.), 5th Conference on Advances in Financial Technologies (AFT 2023), Volume 282 of Leibniz International Proceedings in Informatics (LIPIcs), Dagstuhl, Germany, pp. 24:1–24:17. Schloss Dagstuhl – Leibniz-Zentrum für Informatik.
  6. The adoption of blockchain-based decentralized exchanges. arXiv preprint arXiv:2103.08842.
  7. Chainlink (2020). What is the blockchain oracle problem? Retrieved from https://chain.link/education-hub/oracle-problem on May 24, 2023. Online forum post.
  8. Mev minimizing amm (minmev amm). Retrieved from https://ethresear.ch/t/mev-minimizing-amm-minmev-amm/13775 on May 24, 2023. Online forum post.
  9. Ferreira, M. V. X. and D. C. Parkes (2023). Credible decentralized exchange design via verifiable sequencing rules.
  10. Can markets be fully automated? evidence from an automated market maker. Technical report, Working Paper, Macquarie University.
  11. Gans, J. S. and R. T. Holden (2022). A solomonic solution to ownership disputes: An application to blockchain front-running. Technical report, National Bureau of Economic Research.
  12. Ethereum’s proposer-builder separation: Promises and realities. In Proceedings of the 2023 ACM on Internet Measurement Conference, pp.  406–420.
  13. Non-atomic arbitrage in decentralized finance. arXiv preprint arXiv:2401.01622.
  14. Risks and returns of uniswap v3 liquidity providers. arXiv preprint arXiv:2205.08904.
  15. Behavior of liquidity providers in decentralized exchanges.
  16. Concave pro-rata games. arXiv preprint arXiv:2302.02126.
  17. Josojo (2022, August 4). Mev capturing amm (mcamm). Retrieved from https://ethresear.ch/t/mev-capturing-amm-mcamm/13336 on May 24, 2023. Online forum post.
  18. Lehar, A. and C. A. Parlour (2021). Decentralized exchanges. working paper.
  19. Cow native amms (aka surplus capturing amms with single price clearing). Retrieved from https://forum.cow.fi/t/cow-native-amms-aka-surplus-capturing-amms-with-single-price-clearing/1219/1 on May 24, 2023. Online forum post.
  20. Impermanent loss in uniswap v3.
  21. Learning from defi: Would automated market makers improve equity trading?
  22. Automated market making and arbitrage profits in the presence of fees. arXiv preprint arXiv:2305.14604.
  23. Automated market making and loss-versus-rebalancing. arXiv preprint arXiv:2208.06046.
  24. Park, A. (2023). The conceptual flaws of decentralized automated market making. Management Science 69(11), 6731–6751.
  25. Quantifying blockchain extractable value: How dark is the forest? In 2022 IEEE Symposium on Security and Privacy (SP), pp. 198–214. IEEE.
  26. Augmenting batch exchanges with constant function market makers. arXiv preprint arXiv:2210.04929.
  27. Axioms for constant function amms. arXiv preprint arXiv:2210.00048.
  28. Frontrunner jones and the raiders of the dark forest: An empirical study of frontrunning on the ethereum blockchain. In 30th USENIX Security Symposium (USENIX Security 21), pp. 1343–1359.
Citations (11)
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Summary

  • The paper introduces FM-AMM, a batch-trading AMM that mitigates LVR and sandwich attacks with a novel design approach.
  • It employs traditional finance techniques to counteract latency vulnerabilities and minimize MEV extraction in DeFi systems.
  • The study outlines theoretical advancements paving the way for enhanced security and fairness in decentralized financial markets.

Overview of "Arbitrageurs' Profits, LVR, and Sandwich Attacks: Batch Trading as an AMM Design Response"

This paper makes significant contributions to the field of decentralized finance (DeFi) by proposing a novel design for automated market makers (AMMs). The paper, authored by Andrea and Robin, introduces FM-AMM—a function-maximizing automated market maker—intended to mitigate critical problems associated with latency vulnerability races (LVR) and sandwich attacks prevalent in DeFi systems.

Key Contributions

  1. Introduction of FM-AMM: The FM-AMM design implements batch trading as a core mechanism to counteract LVR and sandwich attacks. These issues, well-documented in the DeFi landscape, primarily arise from the poor structuring of transaction orderings on the blockchain. The proposed model adapts concepts from traditional finance, particularly those related to mitigating latency races, to improve on-chain financial exchanges.
  2. Counteraction of Maximal Extractable Value (MEV): By resolving LVR and sandwich attacks, the FM-AMM effectively eliminates a majority of MEV, which has been a significant impediment in maintaining the fundamental nature of blockchain systems as permissionless and decentralized. The concentration of MEV extraction has presented challenges by centralizing control away from the decentralized ethos of blockchain technologies.

Implications

The elimination of LVR and sandwich attacks through the FM-AMM design is pivotal for the future of decentralized financial systems. Firstly, it aligns with the longstanding objective within the blockchain community to enhance transparency, integrity, and decentralization. The reduction of MEV—facilitated by this design—removes some control from a small cadre of builders, fostering a more equitable transaction validation environment.

Practical Impact and Future Directions

Although at this moment, FM-AMM is a theoretical construct without any existing deployment, its potential has been recognized by at least two entities that have initiated development efforts inspired by this research. This lays the groundwork for future adoption and adaptation of batch trading techniques in practical environments.

The implications of FM-AMM are further extended by its relevance to current projects, such as the BIS Mariana project's exploration into utilizing AMMs for cross-border transactions. Continued research and real-world trials in such applications can potentially transform financial ecosystems through enhanced efficiency and security measures this model promises.

Conclusion

The paper's substantial theoretical advancements, aligned with its forward-looking practical implications, indicate an evolutive step in automated market-making within decentralized finance. Future research may focus on exploring the scalability of the FM-AMM model, addressing potential obstacles in heterogeneous trading environments, and further refining the conditions under which batch trading can be universally beneficial. These developments are anticipated to advance the discourse and operational infrastructures in the interface between decentralized systems and financial technology.

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