Rust for Low-Latency Trading Systems

Why Rust Is Showing Up on Trading Desks

For decades, C++ has been the default language for performance-critical financial systems — matching engines, market data handlers, risk calculators. It delivers the speed, but at a cost: memory bugs, undefined behaviour, and security vulnerabilities that even experienced developers struggle to prevent entirely.

Rust offers the same performance characteristics as C++ but with a fundamentally different approach to safety. The compiler catches entire categories of bugs — null pointer dereferences, data races, use-after-free — at compile time. Code that compiles is guaranteed to be free of these issues. For financial systems where a memory corruption bug in production could mean corrupted trade data, this guarantee is genuinely valuable.

Several trading firms and exchanges have started adopting Rust for new systems, and the trend is accelerating.

---

The Ownership Model

Rust's defining feature is its ownership system. Every value has exactly one owner. When the owner goes out of scope, the value is dropped (memory is freed). No garbage collector, no manual memory management — the compiler figures it all out.

fn process_order(order: Order) {
    // 'order' is owned by this function
    validate(&order);      // Borrow: temporary read access
    let result = execute(order);  // Move: ownership transfers to execute()
    // Cannot use 'order' here — it has been moved
    println!("Result: {:?}", result);
}

fn validate(order: &Order) {
    // Immutable borrow: can read but not modify
    assert!(order.quantity > 0);
    assert!(order.price > 0.0);
}

fn execute(order: Order) -> ExecutionResult {
    // Now owns 'order', can modify and drop it
    // ...
}

This might feel restrictive at first, but it eliminates an entire class of bugs. You cannot have two parts of your code modifying the same data simultaneously (no data races). You cannot use memory after it has been freed (no use-after-free). You cannot forget to free memory (no leaks).

---

Performance Without Compromise

Rust compiles to native machine code with no runtime overhead. No garbage collector means no GC pauses — a critical concern for latency-sensitive systems where a 10ms GC pause during order processing is unacceptable.

use std::collections::HashMap;

struct OrderBook {
    bids: Vec<PriceLevel>,
    asks: Vec<PriceLevel>,
    order_map: HashMap<u64, usize>, // order_id -> index
}

impl OrderBook {
    fn add_order(&mut self, order: Order) {
        let level = self.find_or_create_level(order.price, order.side);
        level.add(order);
    }

    fn match_orders(&mut self) -> Vec<Trade> {
        let mut trades = Vec::new();
        while !self.bids.is_empty() && !self.asks.is_empty() {
            let best_bid = &self.bids[0];
            let best_ask = &self.asks[0];
            if best_bid.price >= best_ask.price {
                let trade = self.execute_match();
                trades.push(trade);
            } else {
                break;
            }
        }
        trades
    }
}

Zero-Cost Abstractions

Rust's iterators, closures, and generics compile down to the same machine code you would write by hand. High-level code does not mean slow code:

// This high-level code...
let total_notional: f64 = trades.iter()
    .filter(|t| t.symbol == "AAPL")
    .map(|t| t.quantity as f64 * t.price)
    .sum();

// ...compiles to the same assembly as a hand-written loop

---

Concurrency Without Fear

Rust's type system makes concurrent programming dramatically safer. The compiler prevents data races at compile time — not through runtime checks, but by making it structurally impossible to write code that has them.

use std::sync::{Arc, Mutex};
use std::thread;

let order_book = Arc::new(Mutex::new(OrderBook::new()));

let handles: Vec<_> = (0..4).map(|i| {
    let book = Arc::clone(&order_book);
    thread::spawn(move || {
        let mut book = book.lock().unwrap();
        book.add_order(Order::new(i));
        // Mutex automatically released when 'book' goes out of scope
    })
}).collect();

for handle in handles {
    handle.join().unwrap();
}

For multi-threaded trading engines processing market data from multiple feeds simultaneously, this safety guarantee is a significant advantage over C++ where data races are a constant concern.

---

The Ecosystem for Finance

Rust's ecosystem is maturing rapidly:

• tokio — async runtime for high-concurrency network applications
• serde — serialisation/deserialisation (JSON, binary formats)
• tonic — gRPC framework for inter-service communication
• sqlx — async SQL with compile-time query checking
• criterion — benchmarking framework

Interoperability with Python

Rust integrates well with Python through PyO3, letting you write performance-critical inner loops in Rust while keeping the flexibility of Python for orchestration:

use pyo3::prelude::*;

#[pyfunction]
fn calculate_vwap(prices: Vec<f64>, volumes: Vec<f64>) -> PyResult<f64> {
    let total_volume: f64 = volumes.iter().sum();
    if total_volume == 0.0 {
        return Ok(0.0);
    }
    let weighted_sum: f64 = prices.iter()
        .zip(volumes.iter())
        .map(|(p, v)| p * v)
        .sum();
    Ok(weighted_sum / total_volume)
}

#[pymodule]
fn fast_calcs(_py: Python, m: &PyModule) -> PyResult<()> {
    m.add_function(wrap_pyfunction!(calculate_vwap, m)?)?;
    Ok(())
}

# From Python
from fast_calcs import calculate_vwap
vwap = calculate_vwap(prices, volumes)  # Rust speed, Python convenience

---

When to Choose Rust

Rust is not the right tool for every job. Use it when:

• Latency matters — matching engines, market data handlers, signal processing
• Reliability matters — systems that run continuously with zero tolerance for crashes
• Concurrency is complex — multi-threaded systems with shared state
• You would otherwise use C++ — Rust offers comparable performance with better safety

Keep using Python for data analysis, prototyping, and glue code. The best trading infrastructure often combines both — Python for strategy research and Rust (or C++) for the execution layer. For understanding where hardware limits matter, see our guide on hardware acceleration.