In the context of the increasing popularity of blockchain technology, the cryptocurrency trading ecosystem is also rapidly expanding. Decentralized exchanges (DEX) have become important platforms for digital asset trading due to their advantages of disintermediation and transparency. As the market matures, various automated trading tools have emerged. MEV (Maximal Extractable Value) bots are automated programs used to execute strategies and other trading tactics on blockchain networks. They extract maximum value by rearranging, inserting, or delaying blockchain transactions. This article will delve into the definition, principles, implementation methods, determining factors, and optimization directions of sandwich bots.
With the development of technology and market demand, sandwich bots have evolved into various types to adapt to different trading environments and strategy needs.
Here are several common types of sandwich bots:
1. Sandwich Bots
This type of bot listens for large orders in the transaction pool and submits transactions with a higher gas fee before these orders are officially on-chain, completing transactions ahead of users. This strategy involves inserting transactions before and after the target transaction (front-running and back-running) to manipulate prices and profit from it.
2. Arbitrage Type Sandwich Bots
This type of sandwich bot focuses on profiting from price differences between DEXs. It buys assets at a low price on one exchange and sells them at a high price on another, thus obtaining profits. This strategy typically requires the bot to quickly identify price changes between different exchanges and swiftly execute trades.
3. New Token Release Bots
This type of bot focuses on price fluctuations during the issuance of new tokens. In the early days of a new token's launch on a DEX, prices are usually unstable and highly volatile. Sandwich bots buy rapidly when the token goes live and sell after the price rises to capture the price difference. This type of bot requires close attention to the release dynamics of new projects and quick ordering capabilities.
4. Liquidity Pool Arbitrage Bots
Liquidity pool arbitrage bots conduct arbitrage by transferring assets between different liquidity pools. They seek price differences between different pools, providing and withdrawing liquidity to realize profits. This requires the bot to efficiently manage liquidity and quickly respond to price changes within the pool.
5. Flash Loan Arbitrage Bots
Flash loan arbitrage bots utilize the characteristics of flash loans to conduct trades. Flash loans allow users to borrow large amounts of funds in a single transaction without collateral. Bots can use these funds to manipulate market prices in a short time for arbitrage. For example, they can use a flash loan to push prices up in one pool and then profit in another.
6. Triangular Arbitrage Bots
Triangular arbitrage involves trading between three different token pairs to take advantage of exchange rate differences for arbitrage. For example, profits can be realized by trading A/B, B/C, and then trading C/A in a loop. This type of bot requires complex calculations and rapid trade execution capabilities.
This article mainly analyzes sandwich bots.
I. Sandwich Bots
Sandwich bots are automated trading tools specifically designed to profit from front-running transactions in decentralized exchanges. They quickly capture on-chain trading opportunities and trade before or after the target transaction to earn the price difference. The core of sandwich bots lies in seizing trading opportunities with high efficiency and speed.
II. Principles of Sandwich Bots
The profit operations of sandwich bots are based on the following fundamental principles:
Front-running: Before other users submit buy orders, the bot purchases the target token at a lower price. When the user's order is executed and drives the price up, the bot quickly sells to capture the price difference.
Back-running: Before other users sell tokens, the bot sells at a higher price. When the user's sell order lowers the price, the bot repurchases at a lower price to realize profits. The term 'sandwich' refers to the trading users being sandwiched, earning the price difference. The success of sandwich bots relies on precise timing and high-priority trade execution.
III. Implementation Ideas
1. Real-time Monitoring of Transactions:
● Use WebSocket to connect to blockchain nodes and monitor pending transactions in real-time.
● Screen target transactions by comparing the transaction.to or transaction.from fields to identify transactions related to the target DEX.
2. Screening and Filtering
● Filter out transactions unrelated to the strategy and transactions involving its own address to prevent self-trading from causing a deadlock.
3. Dynamically Adjust Gas Prices
● Manually set a higher Gas price to prioritize the bot's transactions for execution before ordinary users.
4. Decode Transaction Data
● Use smart contract interfaces (like Interface in ethers.js) to decode transaction data and determine the tokens and amounts involved in the transaction.
● Choose the appropriate contract calling method based on the decoded information, such as swapExactETHForTokens or swapTokensForExactTokens.
IV. Code Ideas
Selected the wss provided by the node service of ZAN. If you're unable to create it, you can find the complete tutorial in this document (https://docs.zan.top/docs/quick-start-guide). The script is implemented using ethers.js:
1. Create a Listening WebSocket Service
2. Filter These Transactions
3. A method is also needed to determine the transaction direction, manually set gas price
4. Decode Transaction Methods, Call Functions
V. Determining Factors
The effectiveness and success of sandwich bots are closely related to various factors:
1. Transaction Speed
Network latency and node response speed directly affect the bot's reaction time. Using high-performance node services (such as ZAN, Infura, Alchemy) can reduce latency. ZAN also offers support for independent nodes.
2. Gas Fees
When seizing transaction priority, gas fees are an important consideration. Excessively high gas fees can eat into profits, so a balance between speed and cost must be found.
3. Market Liquidity
High liquidity facilitates the quick execution of large trades without significantly impacting market prices. Insufficient liquidity may lead to increased slippage or transaction failures.
4. Contract Security
The security of the target contract directly relates to the risk of strategy operations. The bot needs to have basic verification capabilities of the contract code to avoid transactions being exploited by malicious contracts. ZAN’s contract auditing capabilities can be used to assess the risk of the target contract (https://zan.top/home/ai-scan).
5. Competitive Environment
There may be multiple sandwich bots in the market simultaneously competing for profitable opportunities. When competition is fierce, success rates and profits may be affected.
Conclusion
MEV bots provide an efficient solution for arbitrage in decentralized exchanges. By analyzing in real-time and executing quickly, they can gain an advantage in the market. However, sandwich bots also face high competition and high-risk challenges. Investors need to thoroughly consider technical implementation, risk control, and market strategies to remain competitive in the ever-changing cryptocurrency market. In the future, as technology advances and the DeFi ecosystem expands, sandwich bots are expected to unleash their potential in more areas, creating more value for users.
This article is written by KenLee from the ZAN Team (X account @zan_team). The content is for technical sharing only and does not constitute any investment advice.