Playing the MEV Game on a First-Come-First-Served Blockchain (2401.07992v1)

Abstract: Maximal Extractable Value (MEV) searching has gained prominence on the Ethereum blockchain since the surge in Decentralized Finance activities. In Ethereum, MEV extraction primarily hinges on fee payments to block proposers. However, in First-Come-First-Served (FCFS) blockchain networks, the focus shifts to latency optimizations, akin to High-Frequency Trading in Traditional Finance. This paper illustrates the dynamics of the MEV extraction game in an FCFS network, specifically Algorand. We introduce an arbitrage detection algorithm tailored to the unique time constraints of FCFS networks and assess its effectiveness. Additionally, our experiments investigate potential optimizations in Algorand's network layer to secure optimal execution positions. Our analysis reveals that while the states of relevant trading pools are updated approximately every six blocks on median, pursuing MEV at the block state level is not viable on Algorand, as arbitrage opportunities are typically executed within the blocks they appear. Our algorithm's performance under varying time constraints underscores the importance of timing in arbitrage discovery. Furthermore, our network-level experiments identify critical transaction prioritization strategies for Algorand's FCFS network. Key among these is reducing latency in connections with relays that are well-connected to high-staked proposers.

• The paper introduces a two-phase methodology to detect real-time arbitrage opportunities and optimize transaction ordering in FCFS blockchains.
• It reveals that latency is critical in MEV extraction, directly influencing the success of transaction prioritization on networks like Algorand.
• The study highlights the impact of network topology, recommending optimal relay connections to high-staked nodes for enhanced transaction positioning.

Analysis of "Playing the MEV Game on a First-Come-First-Served Blockchain"

The paper "Playing the MEV Game on a First-Come-First-Served Blockchain" offers a comprehensive examination of Maximal Extractable Value (MEV) strategies, with a focus on First-Come-First-Served (FCFS) blockchain networks like Algorand. The authors present a two-phased methodology: detecting profitable arbitrage opportunities and optimizing transaction prioritization to extract these opportunities.

Arbitrage Detection

The initial phase focuses on developing a real-time algorithm designed for detecting arbitrage opportunities unique to the time constraints of FCFS networks. This algorithm employs a cyclic arbitrage detection mechanism that incorporates efficient input optimization strategies. To validate its efficacy, the authors gather state data from the Algorand blockchain and execute their algorithm post-block finality under various time constraints. Their findings show significant insights into timing's critical role in arbitrage discovery, with states of trading pools updating approximately every six blocks.

Network-Level MEV Strategy

The paper progresses to explore transaction prioritization strategies at the network level. A series of rigorous experiments conducted on a private Algorand network are outlined, designed to discern critical factors influencing the transaction ordering process under a competitive FCFS framework. These experiments conclude latency stands as the paramount determinant of transaction priority. Crucially, optimizing connections to relays with high-staked participation nodes is identified as a key strategy for ensuring top-tier transaction sequences in a block.

Key Findings and Implications

1. Real-Time Arbitrage: The work highlights that, due to the rapid operation of MEV participants within individual blocks, focusing solely on block state arbitrages is not viable. This is underscored by the stark dominance of realized arbitrage revenues on the Algorand blockchain.
2. Timing and Latency: The efficacy of the MEV search relies heavily on the timing, with latency acting as a critical factor in transaction sequencing. This dynamic is crucial for traders in FCFS networks, underpinning the need for optimized placement of transactions to exploit market opportunities effectively.
3. Network Topology Influence: The proximity of transaction nodes to high-staked proposers plays a pivotal role. Consequently, ensuring optimal connectivity to well-situated relays effectively enhances the likelihood of achieving favorable transaction positions.

The paper offers substantial contributions to understanding MEV strategies in FCFS blockchains, providing a pathway for future research. Expanding detection algorithms to broader asset pools and deploying experiments on the Algorand mainnet are indicated as logical continuations. These directions promise to refine MEV searching strategies further and potentially offer insights applicable across various blockchain frameworks.

In conclusion, this research enriches the discourse on MEV strategies, especially concerning their implementation in FCFS environments such as Algorand, emphasizing low-latency connections as a linchpin for competitive advantage. These insights not only bolster prevailing MEV methodologies but also chart a course for enhancing algorithmic trading efficiency within decentralized finance ecosystems.