Building a High-Performance Trading Engine in Rust

When we built Venbot.io, we needed a trading engine that could handle 100,000+ trades reliably. After evaluating Node.js and Go, we chose Rust with Tokio. Here’s why and what we learned.

Why Rust for Trading

Trading engines have strict requirements:

• Low latency — milliseconds matter for order matching
• Memory safety — no GC pauses, no null pointer crashes at 3 AM
• Concurrency — thousands of concurrent wallet operations

Rust gives you all three without compromise. The borrow checker catches data races at compile time, and Tokio’s async runtime handles massive concurrency with minimal overhead.

Async Architecture with Tokio

Our engine used a multi-layered async architecture:

#[tokio::main]
async fn main() {
    let (tx, rx) = mpsc::channel(10_000);

    // Spawn order ingestion
    tokio::spawn(async move {
        ingest_orders(tx).await;
    });

    // Spawn matching engine
    tokio::spawn(async move {
        match_orders(rx).await;
    });

    // Spawn wallet monitors for BTC, ETH, USDT
    for chain in [Chain::Btc, Chain::Eth, Chain::Trc20] {
        tokio::spawn(monitor_wallets(chain));
    }
}

Tokio’s channels gave us backpressure control — if the matching engine fell behind, order ingestion would naturally slow down rather than consuming unbounded memory.

Wallet Management

Managing wallets across BTC, ETH, and USDT (ERC-20 and TRC-20) was the hardest part. Each chain has different confirmation times, fee models, and failure modes:

• BTC — UTXO model, variable fees, slow confirmations
• ETH/ERC-20 — nonce management, gas estimation, reorg handling
• TRC-20 — energy/bandwidth model, different API patterns

We abstracted these behind a common trait:

#[async_trait]
trait WalletManager {
    async fn create_wallet(&self) -> Result<Address>;
    async fn send(&self, to: Address, amount: Amount) -> Result<TxHash>;
    async fn confirm(&self, tx: TxHash) -> Result<Confirmation>;
}

Scaling to 3,000 MAU

The microservices architecture let us scale individual components independently. The matching engine ran on a single beefy instance (CPU-bound), while wallet monitors scaled horizontally across chains.

Docker on Digital Ocean kept infrastructure costs manageable during the startup phase. We only moved to more sophisticated orchestration when the traffic justified it.

Key Takeaway

Rust’s upfront investment in fighting the compiler pays dividends in production. In two years of operation, we had zero memory-related crashes and zero data races — try saying that about a Node.js trading engine.