如何使用GPT4构建自动化MEV机器人实现躺赚

概要：本文介绍了如何利用GPT4构建自动化MEV（最大可提取价值）机器人来实现被动收入。作者通过3周时间将1,390美元增长到93,739美元，并分享了构建过程和关键技术点，包括机器人的工作原理、技术实现步骤、以及使用Rust语言编写机器人的示例代码。

本文作者：Klarck@0xklarck。发文时间：上午12:55 · 2024年8月16日

我用 GPT4构建了一个自动化 MEV 机器人……在 3 周内，它将 1,390 美元变成了 93,739 美元。我尝试了超过 193 次提示，让它找到 100 次播放，下面演示如何通过 3 次提示词构建相同的机器人🧵👇

在我们开始之前，我花了几个小时研究这个问题，并免费分享一流的 alpha 内容。希望大家喜欢。

1、 在过去的一个月中，MEV 机器人的利润达到了历史最高水平

一些个人使用这个机器人，以最少的努力每月赚取高达 200,000 美元。只需花 30 分钟创建您的第一个机器人，即可永远忘记金钱问题！

一个gpt 4生成的Mev交易机器人

2、什么是 MEW 机器人以及如何通过它赚钱？

꩜ MEW 机器人是一种自动化机器人，通过抢先交易来获取利润

꩜ 使用这些机器人赚钱主要有两种方式：

– 清算无担保贷款

– 套利

3、 深入研究技术部分，这个机器人分析了来自内存池的数百万笔交易

꩜ 通过分析，你可以从抢先交易的鲸鱼/内幕交易中获利

꩜ 这些机器人越来越受欢迎，成为第一个从中获利的人之一……

4、 三明治机器人——一种自动分析 $SOL 区块链上新区块的脚本

꩜ 例如，如果检测到大量购买新代币的交易，机器人将发送高 gas 的交易

꩜ 交易将更快地完成，让您有机会赚钱！

5、 MEW-Arbitrage – 一款同时追踪有前途交易和抢先交易的机器人

꩜ 其主要思想是，机器人寻找单一资产的价格差异并从中获利

꩜ MEW-Arbitrage 的运行速度比常规套利机器人快得多，因为它具有更快的交易分析能力

6、 如果您缺乏编程知识 – @ChatGPT将完美地处理编写机器人

꩜ 在创建一个高质量的 AI 机器人后，您将轻松实现每天 1,000 美元的初始收入

꩜ 及时创建您的第一个 MEW 机器人以被动产生利润！

7、 首先，请访问 chatgpt.com并继续编写请求

您需要用 Rust 编写一个机器人来跟踪内存池交易（您的利润直接取决于这一步，所以花更多时间在这上面吧！）

这里的提示词是：

Develop Rust code to track the Mempool on the Solana network for constructing MEV bots.

GPT4回复如下：

Creating a Rust program to track the mempool on the Solana network for constructing MEV (Maximal Extractable Value) bots involves several steps. You will need to interact with Solana's blockchain to monitor transactions and identify opportunities for MEV. Here's a high-level overview and a basic example to get you started:

### High-Level Steps

1. **Set Up Your Environment**: Ensure you have Rust installed. You can install it using `rustup`.

2. **Connect to Solana RPC**: Use Solana's JSON RPC API to access the blockchain data.

3. **Monitor the Mempool**: Continuously fetch and analyze pending transactions.

4. **Identify MEV Opportunities**: Implement logic to detect profitable opportunities.

5. **Execute Transactions**: Submit transactions when a profitable opportunity is detected.

### Basic Rust Example

Below is a simplified example to get you started with connecting to Solana's RPC and fetching transaction data. This example focuses on setting up the environment and making RPC calls. For a full MEV bot, you would need to expand upon this to include transaction analysis and execution logic.

```rust
use reqwest::Client;
use serde_json::json;
use std::error::Error;

const SOLANA_RPC_URL: &str = "https://api.mainnet-beta.solana.com";

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
let client = Client::new();

// Example RPC request to get recent blockhash
let request_body = json!({
"jsonrpc": "2.0",
"id": 1,
"method": "getRecentBlockhash",
"params": []
});

let response = client
.post(SOLANA_RPC_URL)
.json(&request_body)
.send()
.await?;

let response_text = response.text().await?;
println!("Response: {}", response_text);

// You would typically parse the response and implement logic to analyze transactions here

Ok(())
}
```

### Additional Steps

1. **Parse and Analyze Transactions**: Use the `serde_json` crate to parse JSON responses and implement logic to analyze transactions for MEV opportunities.

2. **Implement MEV Logic**: This could involve strategies like frontrunning, backrunning, or sandwich attacks, depending on your objectives and ethical considerations.

3. **Transaction Execution**: Use Solana's SDK or RPC calls to submit transactions when an opportunity is detected.

4. **Testing and Optimization**: Test your bot on a testnet and optimize for performance and profitability.

### Considerations

- **Ethical and Legal**: MEV strategies can be controversial. Ensure your activities comply with legal regulations and ethical standards.

- **Network Costs**: Consider transaction fees and network latency, as they can impact profitability.

- **Security**: Ensure your bot is secure to prevent exploits or losses.

This example provides a foundation, but building a fully functional MEV bot requires significant development and testing. You may also want to explore existing Rust libraries or frameworks that can simplify interactions with Solana.

8、 接下来，转到分析内存池的脚本

根据此分析，机器人将做出资产购买决策，如果可能的话，你可以添加分析区块的人工智能

这里的提示词是：

Create code that analyzes transactions in the Solana blockchain
mempool and uses Chat GPT Alt to determine whether to execute
transactions

GPT4回复如下：

Developing a system to analyze transactions in the Solana blockchain mempool and using an AI model like ChatGPT to determine whether to execute transactions involves several components. Below, I'll outline a high-level approach and provide a code snippet to illustrate the basic structure. Please note that integrating with AI models requires careful consideration of latency, cost, and the specific use case.

### High-Level Approach

1. **Connect to Solana's Network**: Use RPC or WebSocket connections to receive real-time transaction data from the Solana mempool.

2. **Analyze Transactions**: Implement logic to parse and analyze transactions for potential MEV opportunities.

3. **Integrate AI Model**: Use an AI model like ChatGPT to assist in decision-making by providing context or recommendations based on transaction data.

4. **Execute Transactions**: Based on the AI model's output and your predefined criteria, decide whether to execute transactions.

### Implementation Steps

1. **Set Up Environment**: Ensure you have Rust and necessary dependencies installed. Use `reqwest` for HTTP requests and `tokio` for async operations.

2. **Fetch and Analyze Transactions**: Continuously fetch transactions and analyze them for MEV opportunities.

3. **Integrate with ChatGPT**: Use OpenAI's API to send transaction data to ChatGPT and receive recommendations.

4. **Decision Logic**: Implement logic to execute transactions based on AI recommendations and other criteria.

### Example Code

Below is a simplified example demonstrating how you might structure the code. Note that you'll need an OpenAI API key to interact with ChatGPT.

```rust
use reqwest::Client;
use serde_json::{json, Value};
use std::error::Error;
use tokio::task;
use std::env;

const SOLANA_RPC_URL: &str = "https://api.mainnet-beta.solana.com";
const OPENAI_API_URL: &str = "https://api.openai.com/v1/engines/davinci-codex/completions";

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
let client = Client::new();
let openai_api_key = env::var("OPENAI_API_KEY")?;

// Spawn a task to continuously fetch and analyze transactions
let handle = task::spawn(async move {
loop {
if let Err(e) = fetch_analyze_and_decide(&client, &openai_api_key).await {
eprintln!("Error in processing: {}", e);
}
}
});

handle.await?;
Ok(())
}

async fn fetch_analyze_and_decide(client: &Client, openai_api_key: &str) -> Result<(), Box<dyn Error>> {
// Fetch recent transactions from Solana
let request_body = json!({
"jsonrpc": "2.0",
"id": 1,
"method": "getRecentBlockhash",
"params": []
});

let response = client.post(SOLANA_RPC_URL).json(&request_body).send().await?;
let response_text = response.text().await?;
let json_response: Value = serde_json::from_str(&response_text)?;

// Example: Extract transactions (this would be more detailed in a real implementation)
if let Some(blockhash) = json_response["result"]["value"]["blockhash"].as_str() {
println!("Recent Blockhash: {}", blockhash);

// Here you would fetch and analyze transactions for this blockhash
let transactions = vec![]; // Placeholder for transaction data

// Send transaction data to ChatGPT for analysis
let decision = analyze_with_chatgpt(client, openai_api_key, transactions).await?;

// Decide whether to execute based on ChatGPT's response
if decision {
println!("Executing transaction based on AI recommendation");
// Execute transaction logic here
}
}

Ok(())
}

async fn analyze_with_chatgpt(client: &Client, api_key: &str, transactions: Vec<Value>) -> Result<bool, Box<dyn Error>> {
let prompt = format!("Analyze the following transactions and recommend if any should be executed for MEV opportunities: {:?}", transactions);

let request_body = json!({
"prompt": prompt,
"max_tokens": 100,
"temperature": 0.5
});

let response = client.post(OPENAI_API_URL)
.bearer_auth(api_key)
.json(&request_body)
.send()
.await?;

let response_json: Value = response.json().await?;
let ai_decision = response_json["choices"][0]["text"].as_str().unwrap_or("").trim();

// Simple logic to decide based on AI response
Ok(ai_decision.contains("execute"))
}
```

### Considerations

- **API Costs and Latency**: Using OpenAI's API can incur costs and introduce latency. Ensure that the benefits outweigh these factors.
- **Security**: Protect your OpenAI API key and ensure secure communication with both Solana and OpenAI APIs.
- **Ethical and Legal Compliance**: Ensure that your activities comply with legal regulations and ethical standards.
- **Scalability**: Consider how the system will scale with increased transaction volume and complexity.

This example provides a basic framework, and you'll need to expand it with more detailed transaction analysis, error handling, and decision-making logic to build a robust MEV bot.

9、 要编写 MEW-Arbitrage，还需要使用 AI 提出编写请求

机器人应该跟踪 Dexscreener 和 Tier-1-4 加密货币交易所的价格值。 此外，还要添加流动性池、合约和交易量的分析！

这里的提示词是：

Develop Rust code for a MEV Arbitrage bot on the Solana Network

GPT4输出：

Developing a Maximal Extractable Value (MEV) arbitrage bot on the Solana network involves several key components, including fetching transaction data, identifying arbitrage opportunities, and executing trades. Below, I'll provide a high-level overview and a basic Rust code example to get you started.

### Key Components

1. **Connect to Solana Network**: Use Solana's RPC API to connect to the network and fetch transaction data.

2. **Identify Arbitrage Opportunities**: Implement logic to detect price discrepancies across different decentralized exchanges (DEXs) on Solana.

3. **Execute Trades**: Use Solana's SDK or RPC calls to execute trades when an arbitrage opportunity is detected.

4. **Concurrency and Performance**: Utilize Rust's concurrency features to handle real-time data efficiently.

### Prerequisites

- **Rust Environment**: Ensure you have Rust installed. You can install it using `rustup`.
- **Solana Client Libraries**: Use Solana's Rust client libraries to interact with the blockchain.

### Basic Rust Code Example

Below is a simplified example to illustrate the basic structure of an MEV arbitrage bot. This example focuses on setting up the environment, fetching data, and outlining where you would implement arbitrage logic.

```rust
use reqwest::Client;
use serde_json::Value;
use std::error::Error;
use tokio::task;

const SOLANA_RPC_URL: &str = "https://api.mainnet-beta.solana.com";

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
let client = Client::new();

// Spawn a task to continuously monitor and execute arbitrage
let handle = task::spawn(async move {
loop {
if let Err(e) = find_and_execute_arbitrage(&client).await {
eprintln!("Error in processing: {}", e);
}
}
});

handle.await?;
Ok(())
}

async fn find_and_execute_arbitrage(client: &Client) -> Result<(), Box<dyn Error>> {
// Fetch recent transactions from Solana
let request_body = serde_json::json!({
"jsonrpc": "2.0",
"id": 1,
"method": "getRecentBlockhash",
"params": []
});

let response = client.post(SOLANA_RPC_URL).json(&request_body).send().await?;
let response_text = response.text().await?;
let json_response: Value = serde_json::from_str(&response_text)?;

// Example: Extract transactions (this would be more detailed in a real implementation)
if let Some(blockhash) = json_response["result"]["value"]["blockhash"].as_str() {
println!("Recent Blockhash: {}", blockhash);

// Here you would fetch and analyze transactions for arbitrage opportunities
let transactions = vec![]; // Placeholder for transaction data

// Analyze transactions for arbitrage opportunities
if let Some(opportunity) = detect_arbitrage(transactions) {
// Execute trades if an opportunity is found
execute_trade(opportunity).await?;
}
}

Ok(())
}

fn detect_arbitrage(transactions: Vec<Value>) -> Option<ArbitrageOpportunity> {
// Implement logic to detect arbitrage opportunities
// This could involve comparing prices across different DEXs
None
}

async fn execute_trade(opportunity: ArbitrageOpportunity) -> Result<(), Box<dyn Error>> {
// Implement logic to execute trades on Solana
// This would involve constructing and sending transactions to the network
println!("Executing trade for opportunity: {:?}", opportunity);
Ok(())
}

#[derive(Debug)]
struct ArbitrageOpportunity {
// Define fields relevant to an arbitrage opportunity
}
```

### Considerations

- **Arbitrage Detection**: Implement sophisticated algorithms to detect price discrepancies across different markets. This might involve integrating with multiple DEX APIs to fetch order book data.

- **Transaction Execution**: Use Solana's transaction capabilities to construct and send transactions. Consider using the Solana SDK for Rust to facilitate this process.

- **Concurrency and Performance**: Use Rust's async features and the `tokio` runtime to handle multiple tasks concurrently, such as fetching data and executing trades.

- **Network Costs and Latency**: Consider transaction fees and network latency, as they can impact profitability.

- **Risk Management**: Implement risk management strategies to avoid unprofitable trades.

- **Testing and Optimization**: Test your bot on a testnet and optimize for performance and profitability.

This example provides a basic framework to get you started. Building a fully functional MEV arbitrage bot requires significant additional work, including integrating with DEXs, implementing robust arbitrage detection algorithms, and handling edge cases and errors effectively.

10、 最后一部分应该包括针对三明治攻击的功能

根据内存池分析，机器人应该在鲸鱼大额购买之前发送交易，然后出售

这部分提示词：

Develop Rust code for an MEV sandwich bot that monitors the
mempool and sends transactions. The bot should initially execute a
purchase transaction based on large whale trades and then follow up
with a selling transaction

GPT4输出：

Building a Maximal Extractable Value (MEV) sandwich bot on the Solana network involves several steps, including monitoring the mempool for large transactions, executing a buy transaction just before the large trade (front-running), and then executing a sell transaction immediately after (back-running). This strategy attempts to profit from the price impact of the large trade.

Below is a simplified example of how you might set up such a bot in Rust. This example focuses on the structure and logic, rather than a complete, production-ready implementation. It assumes you have some familiarity with Solana's transaction model and the APIs available for interacting with the blockchain.

### Key Components

1. **Monitor Mempool**: Continuously monitor the Solana mempool for large transactions that might create profitable opportunities.

2. **Execute Front-running Transaction**: Buy tokens just before the large transaction is confirmed to benefit from the price movement.

3. **Execute Back-running Transaction**: Sell tokens immediately after the large transaction to capture the profit.

### Prerequisites

- **Rust Environment**: Ensure you have Rust installed.
- **Solana Client Libraries**: Use Solana's Rust client libraries to interact with the blockchain.
- **Private Key Management**: Securely manage your private keys for signing transactions.

### Example Code

Here's a basic structure for an MEV sandwich bot:

```rust
use reqwest::Client;
use serde_json::Value;
use std::error::Error;
use tokio::time::{sleep, Duration};

const SOLANA_RPC_URL: &str = "https://api.mainnet-beta.solana.com";

#[tokio::main]
async fn main() -> Result<(), Box<dyn Error>> {
let client = Client::new();

// Continuously monitor the mempool
loop {
if let Err(e) = monitor_mempool(&client).await {
eprintln!("Error monitoring mempool: {}", e);
}
sleep(Duration::from_secs(1)).await; // Adjust the frequency as needed
}
}

async fn monitor_mempool(client: &Client) -> Result<(), Box<dyn Error>> {
// Fetch recent transactions from the mempool
let transactions = get_recent_transactions(client).await?;

// Analyze transactions for large trades
for transaction in transactions {
if is_large_trade(&transaction) {
// Execute front-running transaction
execute_front_run_transaction(&transaction).await?;

// Execute back-running transaction
execute_back_run_transaction(&transaction).await?;
}
}

Ok(())
}

async fn get_recent_transactions(client: &Client) -> Result<Vec<Value>, Box<dyn Error>> {
// Example: Fetch transactions from the latest block
let request_body = serde_json::json!({
"jsonrpc": "2.0",
"id": 1,
"method": "getRecentPerformanceSamples",
"params": [1]
});

let response = client.post(SOLANA_RPC_URL).json(&request_body).send().await?;
let response_json: Value = response.json().await?;
let transactions = response_json["result"]
.as_array()
.ok_or("Failed to get transactions")?
.clone();

Ok(transactions)
}

fn is_large_trade(transaction: &Value) -> bool {
// Implement logic to determine if a transaction is a large trade
// For example, check the transaction amount or specific addresses
true // Placeholder
}

async fn execute_front_run_transaction(transaction: &Value) -> Result<(), Box<dyn Error>> {
// Implement logic to execute a buy transaction before the large trade
println!("Executing front-run transaction for: {:?}", transaction);
Ok(())
}

async fn execute_back_run_transaction(transaction: &Value) -> Result<(), Box<dyn Error>> {
// Implement logic to execute a sell transaction after the large trade
println!("Executing back-run transaction for: {:?}", transaction);
Ok(())
}
```

### Considerations

- **Transaction Timing**: Accurately timing the front-run and back-run transactions is crucial. Network latency and transaction confirmation times can affect profitability.

- **Gas Fees**: Consider transaction fees when calculating potential profits, as these can impact the net gain from a sandwich strategy.

- **Security**: Securely manage private keys and other sensitive information. Consider using hardware wallets or secure key management systems.

- **Ethical and Legal Implications**: MEV strategies can be controversial and may have ethical and legal implications. Ensure compliance with applicable laws and regulations.

- **Testing**: Thoroughly test your bot on a testnet before deploying it on the mainnet to ensure it behaves as expected and handles edge cases effectively.

This example provides a basic framework to get you started. Building a fully functional MEV sandwich bot requires detailed knowledge of Solana's transaction mechanics, network behavior, and specific trading strategies.

11、 当 AI 编写完代码的所有部分后，请求创建一个包含所有先前创建的部分的脚本

编写完成后，请务必分析机器人。建议首先在 Solana 测试网进行测试代码，通过后再部署到主链上。

12、 如果代码不起作用，请向 AI 发出更正请求

如果出错，可以直接指出并把所有代码粘贴给GPT4，多次调整修正错误。这样最后，您将收到一个完全可用的脚本……