MEV BOT

October 27, 2022

Everyone should have an MEV bot that generates $30,000 a month!

Anyway, many believe it requires years to create one.

I spent just 6 weeks developing a bot that earns around $900 daily…

Here’s how to set it up in just 10 minutes and start profiting today! 👇

➥A well-optimized MEV bot on Solana can result in remarkable success during this market cycle.

➥Achieving millions in monthly earnings is attainable for MEV bots fueled by #ChatGPT.

➥Act swiftly to develop your own AI MEV bot before market conditions change!

➥The bot takes advantage of:

• Arbitrage opportunities;

• Significant trades that influence prices;

• Lack of liquidity, and much more.

➥Before any transaction is validated on the blockchain, it initially enters the mempool.

➥The bot scrutinizes the mempool to spot profit opportunities by front-running other transactions.

➥The main types of MEV strategies are Arbitrage and Sandwich.

➥Arbitrage MEV bots monitor transactions across various DEXs to capitalize on price variations.

➥Their objective is to identify transactions that generate price discrepancies between two exchanges.

➥The token is purchased at a lower price on one platform and sold at a higher price on another.

➥A Sandwich attack is a trading strategy that profits at others’ expense.

➥For instance, a $1M transaction to buy a low-cap meme coin shows up in the mempool.

➥A Sandwich MEV bot detects the transaction, buys the token, and sells it after the whale’s $1M purchase.

➥Based on my experience, a properly optimized #ChatGPT MEV bot can effortlessly generate $5,000 daily.

➥Earnings usually hinge on market activity, volatility, and the bot’s configuration.

➥Secure a fully passive income before these bots become widely available!

➥Let’s start developing the core script for the bot!

➥First, ask #ChatGPT to generate a script that examines the blockchain’s mempool.

➥This is the cornerstone of your MEV bot, so take your time to perfect it!

Please write a script in rust for the solana mempool which will find all possible transactions.
Please write a script in rust for the solana mempool which will find all possible transactions.
ChatGPT said:
ChatGPT

To create a Rust script for analyzing the Solana mempool and identifying all possible transactions, you can leverage the Solana JSON RPC API and the solana_client crate, which provides a Rust client for interacting with the Solana blockchain. Here’s a basic structure for such a script, focusing on querying recent transactions in the mempool.

This script assumes you have Rust installed and some familiarity with using external crates. First, add solana_client and serde_json as dependencies in your Cargo.toml:

toml
[dependencies]
solana-client = "2.0"
serde_json = "1.0"

Here’s a Rust script to fetch recent transactions from the Solana mempool:

rust
use solana_client::rpc_client::RpcClient;
use solana_client::rpc_config::RpcTransactionConfig;
use solana_client::rpc_response::RpcTransaction;
use solana_sdk::commitment_config::CommitmentConfig;
use std::time::Duration;

fn main() {
// Set the RPC endpoint (can be any Solana RPC server, such as mainnet-beta or devnet)
let rpc_url = "https://api.mainnet-beta.solana.com";
let client = RpcClient::new_with_timeout_and_commitment(
rpc_url.to_string(),
Duration::from_secs(10),
CommitmentConfig::confirmed(),
);

// Define a configuration for transaction search
let config = RpcTransactionConfig {
encoding: Some("json".to_string()),
commitment: Some(CommitmentConfig::confirmed()),
max_supported_transaction_version: Some(0),
};

// Loop to poll recent transactions
loop {
// Fetch recent transactions
match client.get_signatures_for_address_with_config(&client.pubkey().unwrap(), config.clone()) {
Ok(transactions) => {
for transaction in transactions {
// Print transaction details
println!("Transaction signature: {}", transaction.signature);
if let Some(err) = transaction.err {
println!("Error: {:?}", err);
} else {
println!("Transaction details: {:?}", transaction);
}
}
}
Err(e) => {
eprintln!("Error fetching transactions: {:?}", e);
}
}

// Delay before the next poll
std::thread::sleep(Duration::from_secs(5));
}
}

Explanation

  1. Setting up the client: The RpcClient connects to the Solana RPC server (mainnet-beta or devnet). Here, we’re using mainnet-beta.
  2. Defining configuration: The RpcTransactionConfig sets up encoding and commitment configurations.
  3. Polling for transactions: A loop queries the mempool, fetching and printing recent transaction signatures. You can modify the loop or polling interval as needed.

Running the Script

To run the script:

  1. Save the file as main.rs.
  2. Run cargo run from the command line.

This script will output recent transactions, but Solana’s RPC limitations mean it may not capture all mempool transactions instantly. For real-time, detailed transactions, you may need a dedicated Solana node or a third-party service offering direct access to real-time mempool data.

➥The next step is to perform a comprehensive evaluation of the designated mempool:

➥Go to @ChatGPTapp
and ask for a script to examine this mempool.

➥This section is essential for spotting early-stage tokens.

Please write a script in rust for the solana mempool with all possible transactions which execute a detailed analysis

To create a script in Rust that retrieves Solana mempool transactions and performs detailed analysis, we’ll rely on the solana_client crate to interact with the blockchain and the serde and serde_json crates for JSON handling, as well as some Rust standard libraries for structuring the analysis. This script will focus on pulling recent transactions from the Solana mempool, analyzing them for specific details like account balances, instruction types, and transaction statuses.

Here’s how to create this:

  1. Set up your project dependencies by adding solana-client, serde, serde_json, and tokio to your Cargo.toml:

    toml
    [dependencies]
    solana-client = "2.0"
    serde = { version = "1.0", features = ["derive"] }
    serde_json = "1.0"
    tokio = { version = "1.0", features = ["full"] }
  2. Write the Rust script to fetch and analyze transactions. Here, we will fetch recent transactions and analyze them for details like involved accounts, instructions, and balances.

rust
use solana_client::rpc_client::RpcClient;
use solana_client::rpc_config::{RpcTransactionConfig, RpcSignaturesForAddressConfig};
use solana_client::rpc_response::{RpcTransaction, RpcConfirmedTransactionStatusWithSignature};
use solana_sdk::commitment_config::CommitmentConfig;
use solana_sdk::signature::Signature;
use serde::{Deserialize, Serialize};
use std::time::Duration;
use std::collections::HashMap;
use tokio::time::sleep;

#[derive(Debug, Serialize, Deserialize)]
struct TransactionDetail {
signature: String,
status: String,
accounts: Vec<String>,
instructions: Vec<String>,
}

async fn analyze_transaction(client: &RpcClient, signature: &Signature) -> Option<TransactionDetail> {
// Fetch transaction details
let config = RpcTransactionConfig {
encoding: Some("json".to_string()),
commitment: Some(CommitmentConfig::confirmed()),
..RpcTransactionConfig::default()
};

match client.get_transaction(signature, config) {
Ok(Some(transaction)) => {
let status = if transaction.transaction.meta.is_some() {
"Success".to_string()
} else {
"Failure".to_string()
};

// Extracting accounts involved
let accounts = transaction.transaction.transaction.message.account_keys
.iter()
.map(|pubkey| pubkey.to_string())
.collect();

// Extracting instructions in the transaction
let instructions = transaction.transaction.transaction.message.instructions
.iter()
.map(|instr| format!("{:?}", instr))
.collect();

Some(TransactionDetail {
signature: signature.to_string(),
status,
accounts,
instructions,
})
}
Ok(None) | Err(_) => None,
}
}

async fn fetch_and_analyze_transactions(client: RpcClient) {
let mut seen_signatures: HashMap<String, bool> = HashMap::new();

loop {
// Fetch recent signatures for analysis
match client.get_signatures_for_address_with_config(
&client.pubkey().unwrap(),
RpcSignaturesForAddressConfig {
limit: Some(10),
commitment: Some(CommitmentConfig::confirmed()),
..RpcSignaturesForAddressConfig::default()
},
) {
Ok(signatures) => {
for RpcConfirmedTransactionStatusWithSignature { signature, .. } in signatures {
// Avoid duplicate processing
if seen_signatures.contains_key(&signature) {
continue;
}

// Mark signature as seen
seen_signatures.insert(signature.clone(), true);

// Analyze each transaction
let transaction_detail = analyze_transaction(&client, &signature.parse().unwrap()).await;

if let Some(detail) = transaction_detail {
println!("Transaction: {:?}", detail);
}
}
}
Err(e) => {
eprintln!("Error fetching signatures: {:?}", e);
}
}

// Wait before fetching again
sleep(Duration::from_secs(5)).await;
}
}

#[tokio::main]
async fn main() {
// RPC client setup
let rpc_url = "https://api.mainnet-beta.solana.com";
let client = RpcClient::new_with_timeout_and_commitment(
rpc_url.to_string(),
Duration::from_secs(10),
CommitmentConfig::confirmed(),
);

// Fetch and analyze transactions
fetch_and_analyze_transactions(client).await;
}

Explanation

  1. Analyze Transactions: analyze_transaction function fetches transaction details, identifies account addresses involved, extracts instructions, and determines transaction success.
  2. Track Signatures: fetch_and_analyze_transactions fetches recent transactions and maintains a hashmap (seen_signatures) to prevent duplicate processing.
  3. Looping with Delay: The script fetches and analyzes transactions in a loop, with a 5-second delay between fetches to prevent overloading the RPC server.
  4. Asynchronous Execution: The script uses tokio for asynchronous execution, allowing the client to fetch data and perform analysis concurrently.

Running the Script

To run the script:

  1. Save the file as main.rs.
  2. Run with cargo run.

Notes

This script gives you:

  • Detailed transaction analysis, including accounts and instructions.
  • Duplicate filtering to avoid re-processing recent transactions.

This basic analysis can be expanded further depending on the level of detail needed, such as analyzing specific token transactions, program interactions, or even decoding certain instruction data.

4o
➥Incorporating an arbitrage MEV bot into the main script:

➥Its key function is to track token prices across various DEX/CEX platforms.

➥Be sure to add a script that evaluates token liquidity and trading volumes!

make a script for an arbitrage bot which will analyse the prices of sol memcoins and show prices.

To create an arbitrage bot for Solana-based tokens (often referred to as “Sol memecoins”), we’ll write a script in Rust that checks the prices of tokens on different decentralized exchanges (DEXs) on the Solana blockchain. The script will use the Solana RPC API to retrieve token data and analyze prices across multiple DEXs to find any price discrepancies that may present arbitrage opportunities.

For this example, I’ll assume the use of Serum and Raydium, two prominent DEXs on Solana. We’ll pull prices from their markets and then calculate potential arbitrage opportunities. To keep things straightforward, the script will:

  1. Fetch token prices.
  2. Analyze prices from different DEXs.
  3. Display any detected price discrepancies.

You’ll need these crates:

  • solana-client: To interact with the Solana RPC.
  • serde and serde_json: For handling JSON.
  • tokio: For asynchronous programming.

Step 1: Setup in Cargo.toml

toml
[dependencies]
solana-client = "2.0"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
tokio = { version = "1.0", features = ["full"] }
reqwest = { version = "0.11", features = ["json"] }

Step 2: Write the Rust Script

Here’s a Rust script that fetches token prices and displays any arbitrage opportunities:

rust
use reqwest::Client;
use serde::{Deserialize, Serialize};
use solana_client::rpc_client::RpcClient;
use std::collections::HashMap;
use tokio::time::{sleep, Duration};

// Define data structures for price data
#[derive(Debug, Deserialize, Serialize)]
struct PriceData {
price: f64,
volume: f64,
}

// Define structures for different DEXs
const SERUM_API: &str = "https://api.serum.markets/prices";
const RAYDIUM_API: &str = "https://api.raydium.io/market";

// Function to fetch prices from Serum
async fn fetch_serum_prices(client: &Client) -> HashMap<String, PriceData> {
let mut prices = HashMap::new();
match client.get(SERUM_API).send().await {
Ok(response) => {
if let Ok(price_data) = response.json::<HashMap<String, PriceData>>().await {
for (token, data) in price_data {
prices.insert(token.clone(), data);
}
}
}
Err(e) => {
eprintln!("Error fetching Serum prices: {:?}", e);
}
}
prices
}

// Function to fetch prices from Raydium
async fn fetch_raydium_prices(client: &Client) -> HashMap<String, PriceData> {
let mut prices = HashMap::new();
match client.get(RAYDIUM_API).send().await {
Ok(response) => {
if let Ok(price_data) = response.json::<HashMap<String, PriceData>>().await {
for (token, data) in price_data {
prices.insert(token.clone(), data);
}
}
}
Err(e) => {
eprintln!("Error fetching Raydium prices: {:?}", e);
}
}
prices
}

// Function to analyze arbitrage opportunities
fn analyze_arbitrage(serum_prices: &HashMap<String, PriceData>, raydium_prices: &HashMap<String, PriceData>) {
println!("Analyzing arbitrage opportunities:");
for (token, serum_price) in serum_prices {
if let Some(raydium_price) = raydium_prices.get(token) {
let price_diff = (serum_price.price - raydium_price.price).abs();
let percentage_diff = price_diff / ((serum_price.price + raydium_price.price) / 2.0) * 100.0;

// Display arbitrage opportunity if price difference is significant
if percentage_diff > 1.0 { // threshold can be adjusted
println!(
"Token: {}, Serum Price: {:.2}, Raydium Price: {:.2}, Difference: {:.2}%",
token, serum_price.price, raydium_price.price, percentage_diff
);
}
}
}
}

#[tokio::main]
async fn main() {
let client = Client::new();
loop {
println!("Fetching prices...");

// Fetch prices from Serum and Raydium
let serum_prices = fetch_serum_prices(&client).await;
let raydium_prices = fetch_raydium_prices(&client).await;

// Analyze arbitrage opportunities
analyze_arbitrage(&serum_prices, &raydium_prices);

// Wait before the next check
sleep(Duration::from_secs(30)).await;
}
}

Explanation of the Script

  1. Fetch Prices:

    • fetch_serum_prices and fetch_raydium_prices use reqwest to make HTTP requests to Serum and Raydium APIs, respectively, and parse JSON responses into a HashMap of PriceData structures.
    • PriceData includes the price and volume of the token.
  2. Analyze Arbitrage:

    • analyze_arbitrage compares prices between Serum and Raydium for each token. If the price difference exceeds a threshold (1% in this example), it prints the opportunity.
    • You can adjust the threshold percentage based on your strategy or increase it to only show significant arbitrage opportunities.
  3. Loop:

    • The main function runs in a loop, fetching and analyzing prices every 30 seconds (adjustable based on your preference).
    • Using tokio::time::sleep, it pauses between cycles to prevent overwhelming the API servers and reduce redundant requests.

Running the Script

  1. Save the file as main.rs.
  2. Run with cargo run.

Important Notes

  1. API Rate Limits: Both Serum and Raydium may have rate limits. Adjust the polling interval if you encounter any issues.
  2. Token Mapping: Make sure to match tokens accurately between DEXs, as tokens may have different tickers or symbols on each exchange.
  3. Liquidity Check: This script currently does not check the liquidity of tokens. You may want to add additional checks on volume if you want to avoid illiquid tokens that can cause slippage.

This bot will continuously display arbitrage opportunities, showing tokens with significant price discrepancies between Serum and Raydium on the Solana blockchain. You can expand this with trading logic to execute trades automatically if the price difference is sufficient to cover fees and slippage.

4o

ChatGPT can make mistakes. Check important info.
➥The last step is to implement functionality for “Sandwich” attacks:

➥The bot should initiate a transaction ahead of time after assessing the whale’s purchase.

#ChatGPT can handle this task efficiently.

make a script for the solana blockchain which will send transactions for huge buyers based on transactions of the solana ChatGPT said: ChatGPT

Creating a script to send transactions on the Solana blockchain based on the activity of “huge buyers” requires tracking significant transactions, analyzing them in real time, and automating responses if certain criteria are met. This is particularly useful for trading bots that follow “whale” activity in DeFi protocols or token markets.

Here’s a general approach to accomplish this in Rust:

  1. Track Transactions: Monitor Solana transactions to identify large trades, such as transfers of tokens above a certain threshold.
  2. Analyze for Huge Buyers: Filter out transactions with amounts exceeding a specified value, indicating a “huge buyer.”
  3. Automate Response: Send a transaction in response (e.g., buying the same asset or transferring a specified token).

Requirements

  • Rust crates:
    • solana-client for interacting with the Solana blockchain.
    • solana-sdk for building and sending transactions.
    • serde and serde_json for JSON handling.
    • tokio for asynchronous processing.

Cargo.toml Dependencies

In your Cargo.toml file, add the following dependencies:

toml
[dependencies]
solana-client = "2.0"
solana-sdk = "1.14"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
tokio = { version = "1.0", features = ["full"] }

Rust Script

Here’s a script to monitor the blockchain, detect large transactions, and trigger a response:

rust
use solana_client::rpc_client::RpcClient;
use solana_client::rpc_config::{RpcSignaturesForAddressConfig, RpcTransactionConfig};
use solana_sdk::commitment_config::CommitmentConfig;
use solana_sdk::pubkey::Pubkey;
use solana_sdk::signature::{Keypair, Signer};
use solana_sdk::system_instruction;
use solana_sdk::transaction::Transaction;
use tokio::time::{sleep, Duration};
use std::str::FromStr;

// Define constants and thresholds
const HUGE_BUY_THRESHOLD: f64 = 1000.0; // Set to your desired threshold (e.g., 1000 tokens)
const SOLANA_RPC_URL: &str = "https://api.mainnet-beta.solana.com";
const TARGET_TOKEN_MINT: &str = "INSERT_TOKEN_MINT_HERE"; // Replace with your target token mint address

#[tokio::main]
async fn main() {
let client = RpcClient::new_with_commitment(SOLANA_RPC_URL.to_string(), CommitmentConfig::confirmed());

// Create a keypair for the bot account (private key required for signing transactions)
let bot_keypair = Keypair::new(); // Replace with a stored keypair in a production app

// Monitor for large transactions in a loop
loop {
println!("Checking for large transactions...");

// Fetch recent transactions for the target token mint
if let Ok(signatures) = client.get_signatures_for_address_with_config(
&Pubkey::from_str(TARGET_TOKEN_MINT).unwrap(),
RpcSignaturesForAddressConfig {
limit: Some(10),
commitment: Some(CommitmentConfig::confirmed()),
..RpcSignaturesForAddressConfig::default()
},
) {
for signature_info in signatures {
// Retrieve transaction details
if let Ok(Some(transaction)) = client.get_transaction(&signature_info.signature, RpcTransactionConfig::default()) {
// Check transaction amount for large buyers
if let Some(amount) = analyze_transaction_for_large_buy(&transaction) {
if amount > HUGE_BUY_THRESHOLD {
println!("Detected huge buy of {} tokens in transaction {}", amount, signature_info.signature);

// Trigger response - Send a purchase transaction in response
match send_response_transaction(&client, &bot_keypair).await {
Ok(_) => println!("Successfully responded to huge buy!"),
Err(e) => eprintln!("Failed to send response transaction: {:?}", e),
}
}
}
}
}
}

// Wait before the next check
sleep(Duration::from_secs(30)).await;
}
}

// Function to analyze transaction details and check for large buys
fn analyze_transaction_for_large_buy(transaction: &solana_client::rpc_response::RpcTransaction) -> Option<f64> {
// Parse transaction instructions to detect token transfer amount (requires custom parsing per token program)
// Placeholder - Actual parsing logic will depend on token program and Solana program accounts used.
// This is just a mock example.
Some(1200.0) // Replace with actual parsing logic for your use case
}

// Function to send a response transaction
async fn send_response_transaction(client: &RpcClient, payer: &Keypair) -> Result<(), Box<dyn std::error::Error>> {
// Define recipient and amount (in lamports)
let recipient_pubkey = Pubkey::from_str("INSERT_RECIPIENT_ADDRESS_HERE").unwrap();
let lamports = 1_000_000; // Example amount - adjust based on token and strategy

// Build a transfer transaction
let transaction = Transaction::new_signed_with_payer(
&[system_instruction::transfer(&payer.pubkey(), &recipient_pubkey, lamports)],
Some(&payer.pubkey()),
&[payer],
client.get_recent_blockhash()?.0,
);

// Send transaction
client.send_and_confirm_transaction(&transaction)?;
Ok(())
}

Explanation

  1. Track Transactions for Huge Buys:

    • analyze_transaction_for_large_buy checks if a transaction exceeds the defined HUGE_BUY_THRESHOLD. This example uses a mock value, but in practice, it requires parsing the transaction’s instructions to determine the token amount.
  2. Send a Response Transaction:

    • send_response_transaction sends a predefined amount of SOL to a recipient or buys the same token based on strategy.
    • The example creates a simple SOL transfer transaction; for a token purchase, integrate a specific program or DEX interaction.
  3. Loop for Continuous Monitoring:

    • The script continuously monitors transactions and triggers responses if a huge buy is detected. The delay (30 seconds) is adjustable for optimized performance.

Running the Script

  1. Save the File: Save the script as main.rs.
  2. Run with Cargo: cargo run.

Key Considerations

  • Transaction Parsing: Solana’s transaction instructions can vary depending on the token program. You’ll need to adapt analyze_transaction_for_large_buy to parse transaction data according to your token’s program.
  • DEX Integration: To automate token purchases in response to whale activity, integrate DEX-specific instructions (like Serum or Raydium).
  • Error Handling and Security: This script uses a keypair generated at runtime. In production, securely store and load the keypair (e.g., using a secret manager) and add error handling for transaction failures and Solana API rate limits.

With this structure, the bot monitors for large Solana token purchases, allowing you to capture whale movements and send transactions as a response.

4o
➥Once all elements of the bot are finalized, we’ll integrate them into a cohesive unit.

➥Compile all sections of the code into one request and ask to merge them into a working script.

➥Ultimately, you’ll have a fully functional MEV bot!

Find Arbitrage Opportunities

Qurey:

//find arbitrage opportunities
let opportunities = find_arbitrage_opportunities(price);

//Print Results
for opportunities in opportunities {
printint{
“Arbitrage Opportunity: Token{}, Buy Price{}, Sell Price{}, Profit Margin: {“:2}%”,
opportunity token, opportunity buy_price.
opportunity sell.price, opportunity profit margin
}:
}
OK({})
}

// Fetch price from DEX 1 (Raydium or Orca)
async fn fetch price

 

To find arbitrage opportunities between different DEXs on the Solana blockchain, you can create a Rust script that retrieves token prices from each DEX, identifies price discrepancies, and calculates potential profit margins. Here’s a script to get you started, following the structure of your pseudocode and fetching prices from two DEXs (Raydium and Orca as an example).

Step 1: Setup Dependencies

In Cargo.toml, add these dependencies:

toml
[dependencies]
reqwest = { version = "0.11", features = ["json"] }
tokio = { version = "1.0", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"

Step 2: Implement the Rust Script

Here’s the Rust script that fetches prices from Raydium and Orca, finds arbitrage opportunities, and prints them out.

rust
use reqwest::Client;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use tokio::time::{sleep, Duration};

#[derive(Debug, Deserialize, Serialize, Clone)]
struct PriceData {
token: String,
buy_price: f64,
sell_price: f64,
}

// Fetch prices from Raydium (dummy API endpoint, replace with actual endpoint as available)
async fn fetch_price_from_raydium(client: &Client, token: &str) -> Option<f64> {
let url = format!("https://api.raydium.io/pairs/{}", token);
match client.get(&url).send().await {
Ok(response) => {
if let Ok(price_data) = response.json::<HashMap<String, f64>>().await {
price_data.get("price").cloned() // Adjust key based on Raydium API format
} else {
None
}
}
Err(e) => {
eprintln!("Error fetching price from Raydium for {}: {:?}", token, e);
None
}
}
}

// Fetch prices from Orca (dummy API endpoint, replace with actual endpoint as available)
async fn fetch_price_from_orca(client: &Client, token: &str) -> Option<f64> {
let url = format!("https://api.orca.so/pairs/{}", token);
match client.get(&url).send().await {
Ok(response) => {
if let Ok(price_data) = response.json::<HashMap<String, f64>>().await {
price_data.get("price").cloned() // Adjust key based on Orca API format
} else {
None
}
}
Err(e) => {
eprintln!("Error fetching price from Orca for {}: {:?}", token, e);
None
}
}
}

// Find arbitrage opportunities between two DEXs
fn find_arbitrage_opportunities(price_data: &Vec<PriceData>) -> Vec<PriceData> {
let mut opportunities = Vec::new();
for data in price_data {
if data.sell_price > data.buy_price {
let profit_margin = ((data.sell_price - data.buy_price) / data.buy_price) * 100.0;
if profit_margin > 1.0 { // Threshold for printing arbitrage opportunity
opportunities.push(PriceData {
token: data.token.clone(),
buy_price: data.buy_price,
sell_price: data.sell_price,
});
}
}
}
opportunities
}

// Print arbitrage opportunities
fn print_opportunities(opportunities: Vec<PriceData>) {
for opportunity in opportunities {
println!(
"Arbitrage Opportunity: Token {}, Buy Price: {:.2}, Sell Price: {:.2}, Profit Margin: {:.2}%",
opportunity.token, opportunity.buy_price, opportunity.sell_price,
((opportunity.sell_price - opportunity.buy_price) / opportunity.buy_price) * 100.0
);
}
}

#[tokio::main]
async fn main() {
let client = Client::new();
let tokens = vec!["SOL/USDC", "SRM/USDC"]; // Replace with token pairs you want to monitor

loop {
let mut price_data = Vec::new();

for token in &tokens {
if let Some(raydium_price) = fetch_price_from_raydium(&client, token).await {
if let Some(orca_price) = fetch_price_from_orca(&client, token).await {
// Assume Raydium is buy price and Orca is sell price for arbitrage
price_data.push(PriceData {
token: token.to_string(),
buy_price: raydium_price,
sell_price: orca_price,
});
}
}
}

// Find and print arbitrage opportunities
let opportunities = find_arbitrage_opportunities(&price_data);
print_opportunities(opportunities);

// Wait before checking again
sleep(Duration::from_secs(60)).await;
}
}

Explanation

  1. Fetch Price Data:

    • fetch_price_from_raydium and fetch_price_from_orca get the prices from Raydium and Orca using their API endpoints.
    • Make sure to replace the dummy URLs with actual API endpoints and adjust parsing to fit their format.
  2. Arbitrage Detection:

    • find_arbitrage_opportunities takes price data, calculates the profit margin, and filters any arbitrage opportunities where the sell_price is higher than the buy_price by at least 1%.
    • You can adjust the profit margin threshold as needed.
  3. Printing Opportunities:

    • print_opportunities prints any arbitrage opportunities found, including token names, buy and sell prices, and profit margins.
  4. Main Loop:

    • The main function fetches and compares prices in a loop, refreshing every 60 seconds (adjustable).

Running the Script

  1. Save the file as main.rs.
  2. Run with cargo run.

Important Notes

  • API Rate Limits: Some DEX APIs may have rate limits, so adjust the polling frequency as needed.
  • Trade Execution: This script only identifies arbitrage opportunities; executing trades would require additional integration with the Solana DEX and the necessary wallet/signing logic.
  • Liquidity Consideration: This simple example doesn’t consider liquidity, so you may want to add checks to ensure there’s enough liquidity for profitable arbitrage.

4o

➥Next, perform comprehensive testing of your MEV bot!

➥This will allow you to identify any potential code errors early on.

➥If an issue arises during testing, @ChatGPTapp can swiftly fix it!

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