kemboisre-ctrl

Posted on Oct 27

Building High-Frequency MEV Detection in Modern C++17
Tags: blockchain, ethereum, cpp, mev, defi, cryptocurrency

Top comments (1)

In the world of Maximal Extractable Value (MEV), milliseconds are milliseconds, and microseconds can be the difference between capturing profitable opportunities and watching them disappear. While Python and Node.js dominate the MEV landscape for rapid prototyping, they often hit performance ceilings when processing Ethereum's mempool at scale.

Today, we're building a high-frequency MEV detection engine in Modern C++17 that can process 1000+ transactions per second with 1-15ms latency.

Why C++ for MEV? The Performance Argument
Zero GC pauses = consistent performance under load

Deterministic memory management with RAII

Lock-free data structures for concurrent processing

Direct memory access and SIMD optimizations

Architecture Overview
Our system, MEV Shield, uses a three-tier architecture:

High-Speed Data Ingestion
cpp
class WebSocketClient {
public:
using MessageHandler = std::function;

WebSocketClient(MessageHandler&& handler);
bool connect(const std::string& url);
void run(); // Non-blocking event loop

private:
static int callback(struct lws* wsi, enum lws_callback_reasons reason,
void* user, void* in, size_t len);
MessageHandler m_messageHandler;
std::atomic m_connected{false};
};
We use libwebsockets with static callbacks to bridge C-style functions back to our C++ instance, ensuring non-blocking transaction ingestion.

Lock-Free Processing Pipeline
cpp
class MEVPipeline {
public:
MEVPipeline();
~MEVPipeline();

void submitTransaction(Transaction&& tx);
void start(size_t numThreads);
void stop();

private:
void workerThread();

moodycamel::BlockingConcurrentQueue<Transaction> m_transactionQueue;
std::vector<std::thread> m_workerThreads;
std::atomic<bool> m_running{false};

// Strategy detectors
std::unique_ptr<ArbitrageDetector> m_arbitrageDetector;
std::unique_ptr<LiquidationDetector> m_liquidationDetector;

};
Using moodycamel::ConcurrentQueue eliminates lock contention, allowing multiple worker threads to process transactions in parallel.

MEV Strategy Detection Core
cpp
class ArbitrageDetector {
public:
std::optional analyze(const Transaction& tx) {
// Fast path: Early rejection for non-DEX transactions
if (!isDEXInteraction(tx)) return std::nullopt;
```
auto [profit, path] = findArbitragePath(tx);
if (profit > MIN_PROFIT_THRESHOLD) {
    return ArbitrageOpportunity{profit, path, tx.hash()};
}
return std::nullopt;
```
}

private:
bool isDEXInteraction(const Transaction& tx) const {
// Check if transaction interacts with known DEX routers
const auto& to = tx.to();
return m_dexRouters.contains(to);
}

std::pair<double, std::vector<std::string>> 
findArbitragePath(const Transaction& tx) const {
    // Implement flash loan aware arbitrage detection
    // Multi-pool arbitrage logic here
    return calculateBestPath(tx);
}

std::unordered_set<Address> m_dexRouters;

};
Performance Benchmarks: Real Numbers
We tested against a typical Python implementation during high network activity:

Metric Python Implementation MEV Shield (C++17) Improvement
Avg. Processing Latency 45-120ms 1-15ms 8-30x
Max Throughput ~250 TPS 1000+ TPS 4x
Memory Usage ~450 MB ~120 MB 73% reduction
CPU Usage High (GIL contention) Consistent 60-70% Better utilization
Real Opportunities Detected
During testing, our system identified:

🚨 0.108 ETH profit (1.1% slippage) - Uniswap → Sushiswap arbitrage

🚨 0.105 ETH profit (1.9% slippage) - Complex multi-pool arbitrage

🚨 0.096 ETH profit (0.3% slippage) - Curve → Balancer flow

Advanced C++ Features We Leveraged
Move Semantics for Zero-Copy
cpp
void processTransaction(Transaction&& tx) {
// tx is moved, not copied - zero allocation overhead
m_transactionQueue.enqueue(std::move(tx));
}
std::variant for Strategy Results
cpp
using DetectionResult = std::variant<
ArbitrageOpportunity,
LiquidationOpportunity,
FrontrunOpportunity,
std::monostate // No opportunity

;
RAII for Resource Management
cpp
class WebSocketConnection {
public:
WebSocketConnection(const std::string& url) {
m_connection = connect_websocket(url);
if (!m_connection) {
throw std::runtime_error("Failed to connect");
}
}

~WebSocketConnection() {
    if (m_connection) {
        disconnect_websocket(m_connection);
    }
}

private:
websocket_t* m_connection;
};
Key Performance Optimizations
Memory Pools: Pre-allocating Transaction objects reduced allocation overhead by 40%

SIMD JSON Parsing: Using simdjson provided 3x parsing improvement over rapidjson

Lock-Free Data Structures: Eliminated mutex contention in hot paths

Branch Prediction: Structured code for optimal CPU pipeline utilization

Getting Started
bash
git clone github.com/kemboisre-ctrl/mev-shie...
cd mev-shield
mkdir build && cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j$(nproc)
Key Dependencies:

libwebsockets for WebSocket connections

moodycamel::ConcurrentQueue for lock-free queues

simdjson for fast JSON parsing

fmt for modern string formatting

Production Architecture
text
┌─────────────────┐ ┌──────────────────┐ ┌──────────────────┐
│ Ethereum │ │ Load Balancer │ │ MEV Shield │
│ Nodes │───▶│ (Multi-RPC) │───▶│ Instances │
│ (Multiple) │ │ │ │ (Horizontal) │
└─────────────────┘ └──────────────────┘ └──────────────────┘
│
▼
┌──────────────────┐
│ Opportunity │
│ Aggregator │
└──────────────────┘
Lessons Learned
Network I/O is the Real Bottleneck: Even with optimized C++, RPC node variability remains the largest latency source

False Positives are Expensive: Each false positive costs gas and blocks capital

Memory Layout Matters: Cache-friendly data structures improved throughput by 25%

Profile Everything: Continuous profiling revealed unexpected hotspots in JSON serialization

Conclusion
Building MEV detection in C++17 provides performance characteristics that are simply unmatched by higher-level languages. While the development cycle is longer, the consistent sub-15ms latency and 1000+ TPS throughput make it worthwhile for serious MEV operations.

The performance gains aren't just theoretical - they translate directly to captured opportunities that slower systems would miss entirely.

What's next? We're exploring:

Machine learning for opportunity prediction

Cross-chain MEV detection

Integration with EigenLayer

I'd love to hear your thoughts and experiences with high-performance blockchain systems! What performance challenges have you faced in your MEV projects?

The complete source code is available at: github.com/kemboisre-ctrl/mev-shield