Author: Lyric, ChainCatcher

Editor: Nianqing, ChainCatcher

MEV (Maximum Extractable Value) refers to the additional value obtained by miners or validators through manipulating transaction order and selection. In simple terms, MEV reflects the additional profit obtained by miners through adjusting transaction order. With the increasing popularity of smart contract platforms such as Ethereum, MEV has gradually become an important research area, driving the development of various new solutions and protocols aimed at reducing its negative impact on users.

Recently, Sorella Labs, a crypto startup aimed at addressing the Ethereum MEV problem, announced a $7.5 million seed funding round led by Paradigm, with participation from Uniswap Ventures, Bankless Ventures, Robot Ventures, and Nascent, among others. However, this round of funding was completed in September last year. Alongside the funding announcement, Sorella Labs also announced the launch of its product Brontes. Additionally, another tool under development, Angstrom, is expected to be released later this year after the launch of the mainnet for Uniswap V4.

Team Background

Karthik Srinivasan, co-founder and CTO of Sorella Labs, previously interned at Citadel. The other co-founder and CEO, Ludwig Thouvenin, interned at Ubisoft and other companies. The two met at the University of Chicago, and their strong interest in blockchain technology led them to leave campus and co-found Sorella Labs to explore the infinite potential of crypto.

It is reported that Sorella Labs is developing two tools, Brontes and Angstrom, with Brontes already launched and Angstrom expected to be released later this year after the launch of the mainnet for Uniswap V4.

Brontes Architecture

Brontes is a blockchain analysis pipeline built on Reth, which can be used for preprocessing transaction data. The architecture is mainly divided into three parts: block tree, database (including table structure, price table, block table, metadata table, category 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 liquidation inspection. (The following is a simple analysis of the working principle of the CEX-DEX arbitrage checker currently being experimented with)

Working Principle of CEX-DEX Arbitrage Checker
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 effective spreads and realized spreads. Its working principle is as follows:

1. Identify potential arbitrage trades:

The checker will collect all block transactions involving swap, transfer, ethtransfer, and aggregatorswap operations and process transaction information: discard transactions from settlements or DeFi automated bots, extract exchanges and transfers from DEX: if no exchange is found, attempt to reconstruct the exchange from the transfer, and discard transactions representing atomic arbitrage (closed-loop transactions).

2. Merge sequential exchanges:

That is, exchanging 50 A Tokens for 10 B Tokens and then exchanging 10 B Tokens for 2 C Tokens will be merged into exchanging 50 A Tokens for 2 C Tokens. This is similar to the merging in Uniswap's Flashswap.

3. CEX price estimation (two methods)

4. Set dynamic time window VWAP: Calculate the volume-weighted average price (VWAP) within a dynamic time window around each block. In simple terms, it calculates the volume-weighted average price over a certain period of time, which will be divided into three stages of expansion:

   • Default window around block time -20/+80 milliseconds
   • Initial expansion: If the trading volume is insufficient, the rear blocking time will be incrementally extended by 10 milliseconds up to 350 milliseconds
   • Full expansion will extend the blocking time before and after to -10/+20 seconds

5. Optimistic execution calculation: Make an optimistic estimate of the arbitrage profitability. The process is as follows:

   • Dynamic time window: The initial window (block time left and right ±200ms) can be expanded: the rear blocking time will be incrementally extended by 10 milliseconds up to 450 milliseconds. If necessary, the blocking time before and after can be extended to -5/+8 seconds.

   • Capacity allocation: Calculate the total amount required for arbitrage (x) and the total trading volume for all time periods; for any time period i, calculate the capacity allocation using the formula

     Vi = (z/y) * x (where z is the capacity of time period i)

   • Trade classification and selection: Within each time bucket, sort trades by price (from best to worst), and then select the best trades based on quality parameters (e.g., top 20%).

   • Progressive filling: Start from the time bucket closest to the blocking time. If a bucket cannot fulfill its allocation, the remaining portion will be allocated to subsequent buckets.

   • Calculate the final price using trade volume weighting

6. Calculate potential arbitrage profits: Calculate the price difference between DEX and CEX. Estimate potential profits by comparing the amount obtained by traders purchasing tokens on CEX and the token output amount from the exchange. Use the mid-price and ask price to calculate profits.

7. Summarize and analyze results: Calculate the profit for each CEX separately and the global VWAP profit for all exchanges. Determine the most profitable route among all exchanges based on VWAP calculations.

8. Calculate gas costs: Subtract the transaction gas costs from the calculated profits for each scenario.

9. Verify and filter potential arbitrage opportunities: If a trade meets any of the following conditions, it is considered a valid arbitrage:

• Profit can be realized based on global VWAP or optimistic estimates.
• Profit is made on multiple exchanges.
• The address has a history of a large number of CEX-DEX arbitrage trades (>40 previous trades).
• Marked as a known CEX-DEX arbitrage trader.

3. Handle edge cases and outliers: High-profit outliers (profit > $10,000) that only occur on exchanges with low liquidity will be filtered out.

Current MEV Situation

According to eigenphi's data, the proportion of profits obtained through arbitrage in MEV is extremely high, and in fact, sandwich attacks occur much more frequently. This situation has sparked widespread discussions about market fairness and transparency. A sandwich attack is an algorithmic trading strategy where the attacker first buys ahead of the user's order and then quickly sells after the user's transaction is completed to profit from it. This behavior not only harms the interests of users, causing them to pay higher slippage, but also exacerbates market imbalance.

With the frequent occurrence of sandwich attacks, traders are increasingly realizing the importance of protecting their own interests. Many users, after being subjected to such attacks, choose to seek more secure trading methods or even turn to platforms that provide protective mechanisms. This has also prompted developers to start designing new protocols and tools aimed at reducing the risk of sandwich attacks and improving the security and transparency of transactions.

MEV has become increasingly important in the blockchain ecosystem, especially against the backdrop of the rapid development of DeFi. With the popularity of DeFi applications and the increase in complex trading strategies, the influence of MEV has significantly expanded. This has sparked widespread attention and controversy, especially when ordinary users face potential unfair treatment.

In this context, emerging technologies and protocols such as Flashbots continue to emerge, including Brontes mentioned in this article, all of which are attempting to address the MEV problem. These tools help traders understand the existence of MEV and its impact, thereby reducing unfair competition among traders to some extent. This transparency measure not only helps to build 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 engage in more frequent use of high-frequency trading and algorithmic trading, making the market overall more complex. In this scenario, psychological factors and market behavior become particularly important, and traders need to conduct deeper analysis of market dynamics and sometimes unpredictable behavior.

At the same time, regulatory authorities are beginning to focus on the compliance and ethical issues brought about by MEV. With the continuous development of blockchain technology, ensuring market fairness and order while enjoying the convenience brought by the 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 move towards a more sustainable and healthy direction, allowing blockchain technology to truly serve every user.

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