In the context of the growing popularity of blockchain technology, the cryptocurrency trading ecosystem is rapidly expanding its capabilities. Decentralized exchanges (DEXs) 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 also evolved into multiple types to adapt to different trading environments and strategy needs. Below are several common types of sandwich bots:
01. Sandwich Bots
This type of bot listens for large orders in the trading pool and submits trades with higher gas fees ahead of these orders before they are officially recorded on-chain, thus completing trades before the users. This strategy involves inserting trades before and after the target transaction (front-running and back-running) to manipulate prices and profit.
02. Brick-moving 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 exchange, thus making a profit. This strategy usually requires the bot to quickly identify price changes between different exchanges and execute trades rapidly.
03. New Token Release Bots
This type of bot focuses on price fluctuations during new token issuance. At the initial stage of a new token listing on a DEX, prices are usually unstable and highly volatile. Sandwich bots will quickly buy when the token is just listed and sell after the price rises to capture the price difference. This type of bot requires a high level of attention to the release dynamics of new projects and the ability to place orders quickly.
04. Liquidity Pool Arbitrage Bots
Liquidity pool arbitrage bots profit by transferring assets between different liquidity pools. They search for price differences between different pools and provide and withdraw liquidity to achieve profit. This requires the bot to efficiently manage liquidity and quickly respond to price changes within the pool.
05. Flash Loan Arbitrage Bots
Flash loan arbitrage bots utilize the characteristics of flash loans for trading. Flash loans allow users to borrow large amounts of funds in a single transaction without collateral. Bots can leverage these funds to manipulate market prices for arbitrage within a short period. For example, by using a flash loan to drive up prices in one pool and then profiting in another pool.
06. Triangular Arbitrage Bots
Triangular arbitrage involves trading between three different token pairs to exploit exchange rate differences for profit. For example, by trading A/B, B/C, and then trading C/A in a cycle to achieve profit. This type of bot requires complex calculations and rapid trade execution capabilities.
This article mainly analyzes sandwich bots with everyone.
1. Sandwich Bots
Sandwich bots are automated trading tools specifically designed to profit through front-running trades on decentralized exchanges. They quickly capture on-chain trading opportunities by trading either before or after the target transaction, thus earning the price difference. The core of sandwich bots lies in seizing trading opportunities with high efficiency and speed.
2. Principles of Sandwich Bots
The profit operation of sandwich bots is based on the following basic principles:
Front-running: The bot buys the target token at a lower price before other users submit buy orders but before they are packed into a block by miners. When the user's order is executed and pushes the price up, the bot quickly sells to take the price difference.
Back-running: The bot sells at a higher price before other users sell their tokens. When the user's sell order lowers the price, the bot then repurchases at a lower price, thus achieving profit.
The so-called sandwich refers to the trading users who earn the price difference. The success of sandwich bots depends on precise timing of trades and high priority in trade execution.
3. Implementation Thoughts
1. Real-time Monitoring of Transactions:
Use WebSocket to connect to blockchain nodes for real-time monitoring of pending transactions.
Filter target transactions by comparing
transaction.to
or
transaction.from
Field to identify trades related to the target DEX.
2. Filtering and Screening
Filter out trades unrelated to the strategy and trades from its own address to prevent self-trading from causing deadlocks.
3. Dynamically Adjust Gas Prices
Manually set higher gas prices to prioritize processing the bot's trades by miners to execute ahead of regular users.
4. Decoding Transaction Data
Use smart contract interfaces (such as Interface in ethers.js) to decode transaction data and determine the tokens and amounts involved in the trades.
Based on the decoded information, select the appropriate contract call method, such as
swapExactETHForTokens or swapTokensForExactTokens.
4. Code Thoughts
Selected the WSS provided by ZAN's node service; if you don't know how to create it, you can find the complete tutorial in this document (https://docs.zan.top/docs/quick-start-guide). The script uses ethers.js to implement it.
1. Create a listening WS service
const ZAN_WSS_URL = `wss://api.zan.top/node/ws/v1/eth/mainnet/${YOUR_KEY}` const ZANWssProvider = new ethers.providers.WebSocketProvider(ZAN_WSS_URL); ZANWssProvider.on('pending', (tx) => { // TODO })
2. Filter these trades
ZANWssProvider.on('pending', (tx) => { if (transaction && transaction.to && transaction.to.toLowerCase() === ROUTER.toLowerCase() && transaction.from !== blackAddress) { // TODO } })
3. A method to determine trade direction is also needed, manually setting gas prices
function calculate_gas_price(action, amount) { if (action === 'buy') { return amount.add(100000000) // 0.1 Gwei } else { return amount.sub(100000000) // 0.1 Gwei } }
4. Decode Transaction Methods, Call Functions
const iface = new ethers.utils.Interface(abi) const result = iface.decodeFunctionData('swapExactETHForTokens', transaction.data)
5. 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, and ZAN also provides support for independent nodes.
2. Gas Costs:
When competing for 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 helps execute large trades quickly without significantly affecting market prices. Insufficient liquidity may lead to increased slippage or failed trades.
4. Contract Security:
The security of the target contract directly relates to the risk of strategy operations. Bots need to have basic verification capabilities for contract code to avoid trades being exploited by malicious contracts, and can use ZAN's contract auditing capabilities for risk assessment of the target contract (https://zan.top/home/ai-scan).
5. Competitive Environment:
There may be multiple sandwich bots in the market competing for profit opportunities. In times of intense competition, the success rate of trades and profits may be affected.
Conclusion
MEV bots provide an efficient solution for arbitrage in decentralized exchanges. Through real-time analysis and rapid execution, they can gain an advantage in the market. However, sandwich bots also face challenges of high competition and high risk. Investors need to comprehensively consider technical implementation, risk control, and market strategies to stay competitive in the ever-changing cryptocurrency market. In the future, as technology progresses and the DeFi ecosystem expands, sandwich bots are expected to unleash their potential in more industries and create more value for users.
This article is written by KenLee from the ZAN Team (X account @zan_team), and the content is for technical sharing only and does not constitute any investment advice.
This article is reprinted with permission from: (PaNews)
Original author: ZAN Team
'Automate Crypto Trading! Understand MEV Bots in Five Minutes, and You Can Write One Too.' This article was first published in 'Crypto City.'