Author: Lyric, ChainCatcher
Editor: Nianqing, ChainCatcher
MEV (Maximum Extractable Value) refers to the additional value that miners or validators obtain by manipulating the order and selection of transactions. In simple terms, MEV reflects the additional profit that miners obtain by adjusting the order of transactions. With the increasing popularity of smart contract platforms such as Ethereum, MEV has gradually become an important research area, promoting the development of multiple new solutions and protocols aimed at reducing its negative impact on users.
Recently, Sorella Labs, a crypto startup that aims to solve the Ethereum MEV problem, announced a $7.5 million seed financing led by Paradigm, with participation from Uniswap Ventures, Bankless Ventures, Robot Ventures and Nascent. However, this round of financing was completed in September last year. At the same time as announcing the financing, Sorella Labs also announced the launch of its product Brontes. In addition, another tool under development, Angstrom, is expected to be released later this year after Uniswap V4 goes online on the mainnet.
Team Background
Karthik Srinivasan, co-founder and CTO of Sorella Labs, was an intern at Citadel. Ludwig Thouvenin, another co-founder and CEO, was an intern at Ubisoft and other companies. The two met at the University of Chicago, and their strong interest in blockchain technology prompted them to leave campus and co-found Sorella Labs to explore the unlimited potential of encryption.
It is reported that Sorella Labs is developing two tools, Brontes and Angstrom. Brontes has been launched, and Angstrom is expected to be released after Uniswap V4 goes online on the mainnet later this year.
Brontes Architecture
Brontes is a blockchain analysis pipeline built on Reth. It can be used to preprocess transaction data. The architecture is mainly divided into three parts: block tree, database (including table architecture, price table, block table, metadata table, classification table, Mev block table, miscellaneous table), and checker. The checker framework includes CEX-DEX arbitrage checker, sandwich attack checker, quantum arbitrage attack checker, JIT liquidity checker, and clearing inspector. (The following is a brief analysis of the working principle of the CEX-DEX arbitrage checker in the experiment)
How CEX-DEX Arbitrage Checker Works
This checker is used to identify arbitrage opportunities between centralized exchanges (CEX) and decentralized exchanges (DEX). It evaluates transaction costs and information content by analyzing the effective spread and realized spread. Here is how it works:
1. Identify potential arbitrage transactions:
The checker will collect all block transactions involving swap, transfer, eth_transfer, aggregator_swap operations and process the transaction information: discard transaction information from settlement or DeFi automated robots, extract DEX exchanges and transfers: if no exchange is found, try to reconstruct the exchange from the transfer, and discard transactions representing atomic arbitrage (transactions form a closed loop).
2. Merge order exchange:
That is, 50 A tokens are exchanged for 10 B tokens, and 10 B tokens are exchanged for 2 C tokens, which will be merged into 50 A tokens for 2 C tokens. This is very similar to the merger of Flashswap in Uniswap.
3. CEX price estimation (two methods)
1. Set dynamic time window VWAP: Calculate the volume weighted average price (VWAP) within the dynamic time window around each block. Simply put, it is to calculate the volume weighted average price within a certain period of time. The time window will be expanded in three stages:
Default window block time around -20/+80 ms
Initial expansion: If transaction volume is insufficient, increase the post-block time in 10 ms increments to 350 ms
Full expansion increases pre- and post-blocking time to -10/+20 seconds
2. Optimistic execution calculation: Make an optimistic estimate of potential arbitrage profitability. The process is as follows:
Dynamic time window: The initial window (±200ms around the block time) can be expanded: Extend the post-block time up to 450ms in 10ms increments. If necessary, extend the pre- and post-block time up to -5/+8 seconds.
Capacity allocation: Calculate the total amount required for arbitrage (x) and the total transaction volume of all time periods; for any period of time i, the formula for calculating capacity allocation is
Vi=（z/y）*x （z is the capacity of time period i）
Trade sorting and selection: Within each time basket: Trades are sorted by price (best to worst) and then the best trades are selected based on quality parameters (e.g. top 20%).
Progressive filling: Start with the time basket closest to the blocking time. If one basket cannot complete its allocation, the remainder is allocated to subsequent baskets
Calculate the final price using volume weight
4. Calculate potential arbitrage profit: Calculate the price difference between DEX and CEX. Estimate potential profit by comparing the amount a trader would get for buying tokens on CEX and the amount of token outputs exchanged. Calculate profit using the mid-price and the asking price.
5. Aggregate and analyze the results: Calculate the profit for each CEX individually and for the global VWAP of all exchanges. Identify the most profitable route across all exchanges. Calculate the optimistic profit based on the VWAP.
6. Calculate gas cost: Subtract the gas cost of the transaction from the calculated profit for each scenario.
7. Verify and screen potential arbitrage: If a transaction meets any of the following conditions, it is a valid arbitrage:
Profits can be achieved based on global VWAP or optimistic estimates.
Profit on multiple exchanges.
Performed by addresses with extensive Cex-Dex arbitrage history (>40 previous trades).
Flagged as a known Cex-Dex arbitrageur.
8. Handling edge cases and outliers: Highly profitable outliers (profit > $10,000) that are only profitable on exchanges with lower liquidity will be filtered out.
Current MEV Status
According to eigenphi data, the proportion of profits obtained by arbitrage in MEV is extremely high, but in fact, the number of sandwich attacks is much higher. This situation has triggered widespread discussion on market fairness and transparency. A sandwich attack is an algorithmic trading strategy in which the attacker first buys before the user places an order, and then quickly sells it after the user's transaction is completed to make a profit. This behavior not only harms the interests of users and makes them pay higher slippage, but also exacerbates the imbalance of the market.
As sandwich attacks occur more frequently, traders begin to realize that it is increasingly important to protect their own interests. After suffering from such attacks, many users choose to seek safer trading methods, and even turn to platforms that provide protection mechanisms. This also prompted developers to start designing new protocols and tools to reduce the risk of sandwich attacks and improve the security and transparency of transactions.
MEV is currently becoming more and more important in the blockchain ecosystem, especially in the context of the rapid development of DeFi. With the popularity of DeFi applications and the increase in complex trading strategies, MEV's influence has expanded significantly. It has also caused widespread concern and controversy, especially when ordinary users face possible unfair treatment.
In this context, emerging technologies and protocols such as Flashbots continue to emerge, including Brontes mentioned in this article, which are trying to solve the MEV problem. These tools help traders understand the existence of MEV and its impact, thereby reducing unfair competition among traders to a certain extent. This transparency measure not only helps to enhance user trust, but also helps to reduce market distortions caused by MEV.
However, the existence of MEV is not without cost. It changes the fundamental dynamics of the market, forcing traders to constantly adjust their strategies to adapt to the new market environment. Participants may use high-frequency trading and algorithmic trading more frequently in competition, making the market as a whole more complex. In this case, psychological factors and market behavior become particularly important, and traders need to conduct a deeper analysis of market dynamics and sometimes unpredictable behavior.
At the same time, regulators have begun to pay attention to the compliance and ethical issues brought about by MEV. As blockchain technology continues to develop, how to ensure the fairness and order of the market while enjoying the convenience brought by technology will become an important challenge. People look forward to designing more efficient and fair trading mechanisms in future technological explorations to reduce the negative impact caused by MEV. Through innovation, the industry has the opportunity to develop in a more sustainable and healthy direction, allowing blockchain technology to truly serve every user.
