大額賣單的影響。

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.

大宗交易的價格影響。後繼者在受害者交易之後下訂單，但仍在同一區塊內，以從受害者交易引發的價格變動中受益。

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.

反向交易是一種經常與搶先交易結合使用的策略。它涉及觀察記憶體池中的大量交易，並在同一方向但稍後在區塊中下訂單。該策略受益於受害者交易引起的價格變動，但沒有被搶先交易的風險。

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.

反向交易的利潤通常低於搶先交易，因為交易的價格影響會隨著時間的推移而減弱。不過，這仍然是一個可行的策略，尤其是當受害者的交易量特別大的時候。

Sandwich Attack

三明治攻擊

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.

三明治攻擊是 MEV 策略的一種，涉及圍繞受害者的交易進行兩筆交易，以從價格影響中獲利。搜尋者首先在記憶體池中識別受害者的交易，並在其之前下達買入或賣出訂單，然後在受害者交易後以相反方向下達另一個訂單。

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.

三明治攻擊是一種更先進、更有利可圖的 MEV 策略形式。它們涉及識別內存池中的一筆大額交易，並圍繞該交易下兩個訂單，一個在之前，一個在之後。目標是捕捉受害者交易對雙方的價格影響。

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.

在此範例中，搜尋者觀察到 100 SOL 的待售交易，這可能會壓低 SOL 的價格。為了利用這一點，搜尋者在受害者交易之前進行了 50 SOL 的買入交易，然後在受害者交易之後進行了 50 SOL 的賣出交易。結果，搜尋者能夠以較低的價格買入並以較高的價格賣出，每筆三明治交易獲利 0.0002 SOL（約 0.04 美元）。

Solana MEV Data

Solana MEV 數據

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

以下是相關、可量化和上下文數據的集合，用於說明 MEV 在 Solana 中的當前範圍和影響。

Jito Bundles

吉托捆綁包

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捆綁是尋求者確保有利可圖的交易訂購的主要方法。然而，Jito 數據並未涵蓋 MEV 活動的全部範圍；特別是，它不會透過替代記憶體池來獲取尋求者的利潤或活動。此外，許多應用程式將 Jito 用於非 MEV 目的，例如繞過優先費以確保及時納入交易。

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 在過去一年中處理了超過 30 億筆交易捆綁，總共產生了 375 萬個 SOL 提示。該活動呈現出明顯的上升趨勢，從 2024 年 1 月 11 日的低點 781 SOL 到 11 月 19 日的 60,801 SOL。

Jito Arbitrage Detection

Jito套利檢測

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 的套利偵測演算法會分析所有 Solana 交易，包括 Jito 套裝以外的交易，並在過去一年中識別出超過 9,044 萬筆成功的套利交易。每次套利的平均利潤為 1.58 美元，而單筆利潤最高的套利交易產生了 370 萬美元的收益，這些套利交易總共產生了 1.428 億美元的利潤。

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 開頭的地址上運行三明治交易機器人。 Jito 的內部分析顯示，針對 Solana 的三明治攻擊幾乎有一半可歸因於該程式。在30 天期間（12 月7 日至1 月5 日），該程序執行了155 萬筆三明治交易，盈利65,880 SOL（約1343 萬美元），每筆三明治交易平均利潤為0.0425 SOL（約8.67 美元） 。每年，該計劃將產生超過 801,500 SOL 的利潤。

MEV Mitigation Mechanisms

MEV 緩解機制

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

最後，讓我們探討一些正在考慮減少或消除有害形式 MEV 的策略和機制。

Whitelisting

白名單

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.

白名單是打擊不良行為者的常用方法，尤其是那些從事有害 MEV 提取行為的行為者。這種方法涉及識別一組可信任驗證器或交易並將其列入白名單，同時阻止或延遲來自其他方的交易。

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，白名單可能會帶來一些問題。首先，它可能會導致半許可和審查的環境，這違背了區塊鏈行業的去中心化概念。其次，白名單可能會延遲交易處理，影響使用者體驗，尤其是在網路活動頻繁期間。

Multiple Concurrent Leaders (MCL)

多個並發領導者 (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.

多個並發領導者 (MCL) 系統允許用戶在領導者之間進行選擇而不會造成延遲，從而為有害的 MEV 問題提供了一個有前景的長期解決方案。如果領導者A有惡意行為，使用者可以將他們的交易重新導向到誠實的領導者B。

However,

然而，