1. Understanding CLMM Pools and Their Importance

Concentrated Liquidity Market Maker (CLMM) pools represent the evolution of automated market makers (AMMs). Unlike traditional constant product AMMs, CLMM pools allow liquidity providers to concentrate their capital within specific price ranges, dramatically improving capital efficiency.

Real-time analysis of 500+ CLMM pools across multiple DEXs

2. Rust Project Architecture and Setup

Building a high-performance DEX scanner requires careful architecture design. Here's how I structured the Rust application for maximum efficiency and maintainability:

src/
├── main.rs      # Main application entry point
├── common.rs    # Shared utilities and token definitions
├── raydium.rs   # Raydium CLMM integration
├── orca.rs      # Orca Whirlpool integration
├── meteor.rs    # Meteor DLMM integration
└── lib.rs       # Library exports

Cargo.toml       # Dependencies and project configuration

[dependencies]
reqwest = { version = "0.11", features = ["json"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
tokio = { version = "1.0", features = ["full"] }
anyhow = "1.0"
chrono = { version = "0.4", features = ["serde"] }
clap = { version = "4.0", features = ["derive"] }

[dev-dependencies]
tokio-test = "0.4"

3. Multi-DEX Integration Implementation

Each DEX has its own API structure and data format. Here's how I implemented a unified interface for analyzing pools across Raydium, Orca, and Meteor:

// common.rs - Unified pool structure
use serde::{Deserialize, Serialize};

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Pool {
    pub id: String,
    pub dex: String,
    pub token_a: String,
    pub token_b: String,
    pub pair_name: String,
    pub tvl: f64,
    pub volume_24h: f64,
    pub apr_day: f64,
    pub fee_rate: f64,
    pub score: f64,
}

impl Pool {
    pub fn calculate_score(&mut self) {
        // Composite scoring algorithm
        let tvl_score = if self.tvl > 0.0 { 
            (self.tvl.ln() / 15.0).max(0.1) 
        } else { 0.1 };
        
        let volume_score = if self.volume_24h > 0.0 { 
            (self.volume_24h.ln() / 15.0).max(0.1) 
        } else { 0.1 };
        
        self.score = self.apr_day * tvl_score * volume_score;
    }
}

// Known token addresses for filtering
pub const KNOWN_TOKENS: &[(&str, &str)] = &[
    ("So11111111111111111111111111111111111111112", "SOL"),
    ("EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v", "USDC"),
    ("Es9vMFrzaCERmJfrF4H2FYD4KCoNkY11McCe8BenwNYB", "USDT"),
    ("DezXAZ8z7PnrnRJjz3wXBoRgixCa6xjnB7YaB1pPB263", "BONK"),
    ("EKpQGSJtjMFqKZ9KQanSqYXRcF8fBopzLHYxdM65zcjm", "WIF"),
    // ... more tokens
];

// raydium.rs - Raydium API integration
use reqwest::Client;
use serde_json::Value;
use crate::common::{Pool, KNOWN_TOKENS};

pub async fn fetch_raydium_pools() -> anyhow::Result<Vec<Pool>> {
    let client = Client::new();
    let url = "https://api-v3.raydium.io/pools/info/list";
    
    let response = client
        .get(url)
        .header("User-Agent", "DEX-Scanner/1.0")
        .send()
        .await?;
    
    let data: Value = response.json().await?;
    let mut pools = Vec::new();
    
    if let Some(pool_list) = data["data"]["data"].as_array() {
        for pool_data in pool_list {
            if let Some(pool) = parse_raydium_pool(pool_data) {
                if is_known_token_pair(&pool.token_a, &pool.token_b) {
                    pools.push(pool);
                }
            }
        }
    }
    
    Ok(pools)
}

fn parse_raydium_pool(data: &Value) -> Option<Pool> {
    let pool_id = data["id"].as_str()?;
    let mint_a = data["mintA"]["address"].as_str()?;
    let mint_b = data["mintB"]["address"].as_str()?;
    
    // Extract financial metrics
    let tvl = data["tvl"].as_f64().unwrap_or(0.0);
    let volume_24h = data["day"]["volume"].as_f64().unwrap_or(0.0);
    let apr_day = data["day"]["apr"].as_f64().unwrap_or(0.0);
    let fee_rate = data["feeRate"].as_f64().unwrap_or(0.0) / 10000.0;
    
    let token_a_symbol = get_token_symbol(mint_a);
    let token_b_symbol = get_token_symbol(mint_b);
    let pair_name = format!("{}/{}", token_a_symbol, token_b_symbol);
    
    let mut pool = Pool {
        id: pool_id.to_string(),
        dex: "Raydium".to_string(),
        token_a: mint_a.to_string(),
        token_b: mint_b.to_string(),
        pair_name,
        tvl,
        volume_24h,
        apr_day,
        fee_rate,
        score: 0.0,
    };
    
    pool.calculate_score();
    Some(pool)
}

4. Advanced Pool Scoring Algorithm

The key to finding profitable opportunities lies in the scoring algorithm. I developed a composite scoring system that balances APR, TVL, and volume to identify the most attractive pools:

impl Pool {
    pub fn calculate_score(&mut self) {
        // Logarithmic scaling for TVL (reduces impact of mega pools)
        let tvl_score = if self.tvl > 0.0 {
            (self.tvl.ln() / 15.0).max(0.1)
        } else {
            0.1
        };
        
        // Logarithmic scaling for volume (rewards consistent trading)
        let volume_score = if self.volume_24h > 0.0 {
            (self.volume_24h.ln() / 15.0).max(0.1)
        } else {
            0.1
        };
        
        // APR is the primary driver (linear scaling)
        let apr_score = self.apr_day.max(0.0);
        
        // Composite score: APR * TVL_factor * Volume_factor
        self.score = apr_score * tvl_score * volume_score;
        
        // Apply penalty for extremely high APRs (likely unsustainable)
        if self.apr_day > 1000.0 {
            self.score *= 0.5; // 50% penalty for suspicious APRs
        }
        
        // Bonus for balanced pools (good TVL + Volume combination)
        if self.tvl > 1_000_000.0 && self.volume_24h > 100_000.0 {
            self.score *= 1.2; // 20% bonus for established pools
        }
    }
    
    pub fn risk_assessment(&self) -> RiskLevel {
        match (self.tvl, self.apr_day, self.volume_24h) {
            (tvl, apr, vol) if tvl > 10_000_000.0 && apr < 50.0 && vol > 1_000_000.0 => {
                RiskLevel::Low
            },
            (tvl, apr, vol) if tvl > 1_000_000.0 && apr < 100.0 && vol > 100_000.0 => {
                RiskLevel::Medium
            },
            _ => RiskLevel::High,
        }
    }
}

#[derive(Debug, Clone)]
pub enum RiskLevel {
    Low,
    Medium,
    High,
}

5. Performance Optimization and Results

Building a production-ready DEX scanner requires careful attention to performance, error handling, and resource management:

// main.rs - Concurrent DEX processing
use tokio::time::{timeout, Duration};
use futures::future::join_all;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    println!("🚀 Starting CLMM pools analysis...");
    
    // Create concurrent tasks for each DEX
    let tasks = vec![
        tokio::spawn(async {
            println!("📊 Analyzing Raydium CLMM pools...");
            timeout(Duration::from_secs(10), raydium::fetch_raydium_pools()).await
        }),
        tokio::spawn(async {
            println!("🌊 Analyzing Orca CLMM pools...");
            timeout(Duration::from_secs(10), orca::fetch_orca_pools()).await
        }),
        tokio::spawn(async {
            println!("☄️ Analyzing Meteor DLMM pools...");
            timeout(Duration::from_secs(10), meteor::fetch_meteor_pools()).await
        }),
    ];
    
    // Wait for all tasks to complete
    let results = join_all(tasks).await;
    let mut all_pools = Vec::new();
    
    // Process results with error handling
    for (dex_name, result) in ["Raydium", "Orca", "Meteor"].iter().zip(results) {
        match result {
            Ok(Ok(Ok(pools))) => {
                println!("✅ {} - Found {} pools", dex_name, pools.len());
                all_pools.extend(pools);
            },
            Ok(Ok(Err(e))) => {
                eprintln!("⚠️ {} API error: {}", dex_name, e);
            },
            Ok(Err(_)) => {
                eprintln!("⏰ {} timeout after 10 seconds", dex_name);
            },
            Err(e) => {
                eprintln!("💥 {} task failed: {}", dex_name, e);
            }
        }
    }
    
    // Sort pools by score and display results
    all_pools.sort_by(|a, b| b.score.partial_cmp(&a.score).unwrap());
    display_results(&all_pools);
    
    Ok(())
}

🚀 Starting CLMM pools analysis...
📊 Analyzing Raydium CLMM pools...
🌊 Analyzing Orca CLMM pools...
☄️ Analyzing Meteor DLMM pools...

✅ Raydium - Found 11 pools
✅ Orca - Found 487 pools  
✅ Meteor - Found 26 pools

📈 Total pools found: 524

🏆 TOP 3 BEST POOLS BY SCORE:
Rank  Source     Pair            TVL ($)      APR Day%   Fee%     Vol 24h ($) 
================================================================================
1     Raydium    WSOL/USDC       15372864     55.32      0.040    56929921    
2     Raydium    WSOL/RAY        7729195      39.20      0.050    15971892    
3     Raydium    WSOL/USDC       2491163      29.17      0.020    9167029     

📊 SUMMARY BY DEX:
Raydium - 11 pools, TVL: $146569529, average APR: 17.08%
Orca - 487 pools, TVL: $2847291847, average APR: 8.45%
Meteor - 26 pools, TVL: $89472638, average APR: 12.33%

💎 BEST OPPORTUNITY:
   DEX: Raydium
   Pair: WSOL/USDC
   Daily APR: 55.32%
   TVL: $15372864
   24h Volume: $56929921
   Risk Level: Medium
   Pool ID: 3ucNos4NbumPLZNWztqGHNFFgkHeRMBQAVemeeomsUxv

Conclusion

Building a DEX pool scanner demonstrates the power of combining Rust's performance with DeFi's innovation. The ability to analyze hundreds of pools across multiple DEXs in seconds opens up new possibilities for yield farming optimization, arbitrage detection, and risk management.

The CLMM pool landscape is rapidly evolving, with new protocols and features being launched regularly. Tools like this scanner become essential for navigating the complexity and finding profitable opportunities in an increasingly competitive market.