大额卖单的影响。

the price impact of large trades. Back runners place their orders after the victim’s trade, but still within the same block, to benefit from the price movement triggered by the victim’s trade.

大宗交易的价格影响。后继者在受害者交易之后下订单，但仍在同一区块内，以从受害者交易引发的价格变动中受益。

Back-running is a strategy that is often used in conjunction with front-running. It involves observing a large trade in the mempool and placing an order in the same direction, but later in the block. This strategy benefits from the price movement caused by the victim’s trade, but without the risk of being front-run.

反向交易是一种经常与抢先交易结合使用的策略。它涉及观察内存池中的大量交易，并在同一方向但稍后在区块中下订单。该策略受益于受害者交易引起的价格变动，但没有被抢先交易的风险。

Back-running is typically less profitable than front-running, as the price impact of a trade diminishes over time. However, it is still a viable strategy, especially when the victim’s trade is particularly large.

反向交易的利润通常低于抢先交易，因为交易的价格影响会随着时间的推移而减弱。不过，这仍然是一个可行的策略，尤其是当受害者的交易量特别大的时候。

Sandwich Attack

三明治攻击

A sandwich attack is a type of MEV strategy that involves placing two trades around a victim’s trade to profit from the price impact. The searcher first identifies a victim’s trade in the mempool and places a buy or sell order before it, and then places another order in the opposite direction after the victim’s trade.

三明治攻击是 MEV 策略的一种，涉及围绕受害者的交易进行两笔交易，以从价格影响中获利。搜索者首先在内存池中识别受害者的交易，并在其之前下达买入或卖出订单，然后在受害者交易后以相反方向下达另一个订单。

Sandwich attacks are a more advanced and profitable form of MEV strategy. They involve identifying a large trade in the mempool and placing two orders around it, one before and one after. The goal is to capture the price impact of the victim’s trade on both sides.

三明治攻击是一种更先进、更有利可图的 MEV 策略形式。它们涉及识别内存池中的一笔大额交易，并围绕该交易下两个订单，一个在之前，一个在之后。目标是捕捉受害者交易对双方的价格影响。

In this example, the searcher observed a pending sell trade for 100 SOL, which is likely to drive down the price of SOL. To capitalize on this, the searcher placed a buy trade for 50 SOL before the victim's trade, and then a sell trade for 50 SOL after the victim's trade. As a result, the searcher was able to buy at a lower price and sell at a higher price, making a profit of 0.0002 SOL (about $0.04) per sandwich transaction.

在此示例中，搜索者观察到 100 SOL 的待售交易，这可能会压低 SOL 的价格。为了利用这一点，搜索者在受害者交易之前进行了 50 SOL 的买入交易，然后在受害者交易之后进行了 50 SOL 的卖出交易。结果，搜索者能够以较低的价格买入并以较高的价格卖出，每笔三明治交易获利 0.0002 SOL（约 0.04 美元）。

Solana MEV Data

Solana MEV 数据

Below is a collection of relevant, quantifiable, and contextual data to illustrate the current scope and impact of MEV in Solana.

以下是相关、可量化和上下文数据的集合，用于说明 MEV 在 Solana 中的当前范围和影响。

Jito Bundles

吉托捆绑包

Jito bundles are the primary method for seekers to ensure profitable transaction ordering. However, Jito data does not cover the full spectrum of MEV activity; in particular, it does not capture seeker profits or activity through alternative mempools. In addition, many applications use Jito for non-MEV purposes, such as bypassing priority fees to ensure timely inclusion of transactions.

Jito捆绑是寻求者确保有利可图的交易订购的主要方法。然而，Jito 数据并未涵盖 MEV 活动的全部范围；特别是，它不会通过替代内存池获取寻求者的利润或活动。此外，许多应用程序将 Jito 用于非 MEV 目的，例如绕过优先费以确保及时纳入交易。

Jito has processed more than 3 billion transaction bundles over the past year, generating a total of 3.75 million SOL in tips. This activity has shown a clear upward trend, from a low of 781 SOL in tips on January 11, 2024, to 60,801 SOL on November 19.

Jito 在过去一年中处理了超过 30 亿笔交易捆绑，总共产生了 375 万个 SOL 提示。该活动呈现出明显的上升趋势，从 2024 年 1 月 11 日的低点 781 SOL 到 11 月 19 日的 60,801 SOL。

Jito Arbitrage Detection

Jito套利检测

Jito’s arbitrage detection algorithm analyzes all Solana transactions, including those outside of the Jito bundle, and has identified more than 90.44 million successful arbitrage trades over the past year. The average profit per arbitrage was $1.58, while the single most profitable arbitrage trade generated $3.7 million in gains, and these arbitrage trades generated a total of $142.8 million in profits.

Jito 的套利检测算法会分析所有 Solana 交易，包括 Jito 捆绑包之外的交易，并在过去一年中识别出超过 9044 万笔成功的套利交易。每次套利的平均利润为 1.58 美元，而单笔利润最高的套利交易产生了 370 万美元的收益，这些套利交易总共产生了 1.428 亿美元的利润。

DeezNode Sandwich Transactions

DeezNode 三明治交易

DeezNode runs a sandwich trading bot on an address starting with vpeNAL. Jito's internal analysis shows that almost half of the sandwich attacks against Solana can be attributed to this program. During a 30-day period (December 7 to January 5), the program performed 1.55 million sandwich transactions, making a profit of 65,880 SOL (about $13.43 million), with an average profit of 0.0425 SOL (about $8.67) per sandwich transaction. On an annual basis, the program will generate a profit of more than 801,500 SOL per year.

DeezNode 在以 vpeNAL 开头的地址上运行三明治交易机器人。 Jito 的内部分析显示，针对 Solana 的三明治攻击几乎有一半可归因于该程序。在 30 天期间（12 月 7 日至 1 月 5 日），该程序执行了 155 万笔三明治交易，盈利 65,880 SOL（约 1343 万美元），每笔三明治交易平均利润为 0.0425 SOL（约 8.67 美元）。每年，该计划将产生超过 801,500 SOL 的利润。

MEV Mitigation Mechanisms

MEV 缓解机制

Finally, let’s explore some strategies and mechanisms that are being considered to reduce or eliminate harmful forms of MEV.

最后，让我们探讨一些正在考虑减少或消除有害形式 MEV 的策略和机制。

Whitelisting

白名单

Whitelisting is a common approach to combating bad actors, especially those engaging in harmful MEV extraction practices. This approach involves identifying and whitelisting a set of trusted validators or transactions, while blocking or delaying transactions from other parties.

白名单是打击不良行为者的常用方法，尤其是那些从事有害 MEV 提取行为的行为者。这种方法涉及识别一组可信验证器或交易并将其列入白名单，同时阻止或延迟来自其他方的交易。

However, whitelisting can introduce several problems. Firstly, it may lead to a semi-permissioned and censored environment, which goes against the decentralized philosophy of the blockchain industry. Secondly, whitelisting can delay transaction processing, affecting the user experience, especially during periods of high network activity.

然而，白名单可能会带来一些问题。首先，它可能会导致半许可和审查的环境，这违背了区块链行业的去中心化理念。其次，白名单可能会延迟交易处理，影响用户体验，尤其是在网络活动频繁期间。

Multiple Concurrent Leaders (MCL)

多个并发领导者 (MCL)

The Multiple Concurrent Leaders (MCL) system offers a promising long-term solution to the pernicious MEV problem by allowing users to choose between leaders without incurring delays. If leader A acts maliciously, users can redirect their transactions to honest leader B.

多个并发领导者 (MCL) 系统允许用户在领导者之间进行选择而不会造成延迟，从而为有害的 MEV 问题提供了一个有前景的长期解决方案。如果领导者A有恶意行为，用户可以将他们的交易重定向到诚实的领导者B。

However,

然而，