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Mayuresh
Mayuresh

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What is HFT (High Frequency Trading) and how can we implement it in Rust.

#rust #python #fintech #performance

Table of Contents

The Microsecond Money Game

High-Frequency Trading (HFT) operates in a realm where time literally equals money. We're not talking about seconds or even milliseconds—we're talking about microseconds (millionths of a second).

Here's what defines the HFT landscape:

Speed: Round-trip latency from receiving market data to order acknowledgment must be under 10 microseconds.

Scale: Processing millions of messages per second across thousands of financial instruments simultaneously.

Edge erosion: Every 100 microseconds of additional latency can completely eliminate your competitive advantage.

In this environment, Python—despite its dominance in quantitative finance—hits a fundamental wall.

What Actually Happens in an HFT System

Let me break down a typical HFT pipeline and where every microsecond goes:

graph TD
    A[Market-data UDP multicast] -->|kernel-bypass| B(NIC → user-space DMA)
    B --> C[Decoder ring buffer]
    C --> D[Signal model]
    D -->|order| E[Risk pre-trade]
    E -->|pass| F[Order gateway TCP]
    F --> G[Exchange matching engine]
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The Latency Budget (Target: Single-Digit Microseconds)

Stage Time Budget What's Happening
Kernel bypass 0.5 µs NIC → user-space via DPDK/Solarflare
Decode book 1.0 µs Parse binary market data
Signal math 1.5 µs Run trading strategy logic
Risk checks 1.0 µs Validate order limits
Serialize 0.5 µs Build FIX/binary order message
TOTAL 4.5 µs Complete pipeline

Here's the kicker: Python can't even import pandas in 4.5 microseconds.

The Python Performance Wall

Don't get me wrong—Python revolutionized quantitative finance. Libraries like NumPy, pandas, and scikit-learn made complex mathematical operations accessible to traders who weren't hardcore systems programmers. Python enabled the "citizen quant" revolution.

But in production HFT systems, Python's architecture creates insurmountable bottlenecks:

The Hard Numbers

Metric CPython 3.11 Rust 1.73 Impact
Mean tick-to-signal latency 250 µs 2.3 µs 100x faster
P99 tail latency 3 ms 4.1 µs ~750x more consistent
Messages/sec (single core) 120k 10M 83x throughput
Memory per instrument 240 MB 12 MB 20x more efficient
Deployment venv + deps 3.8 MB static binary Dramatically simpler

Why Python Struggles

The Global Interpreter Lock (GIL): Python's GIL means only one thread executes Python bytecode at a time. In a world where you need true parallelism to process millions of messages, this is crippling.

Garbage Collection Pauses: Python's memory management creates unpredictable latency spikes. That P99 latency of 3ms? That's garbage collection deciding to run at the worst possible moment.

Interpreted Overhead: Even with JIT compilation (PyPy), interpreted languages carry runtime overhead that compiled languages simply don't have.

Memory Bloat: Python's object model is incredibly memory-hungry. Every integer is an object with reference counting overhead. DataFrames are convenient but wasteful for real-time processing.

Rust's Unfair Advantages

Rust wasn't designed for HFT specifically, but it's almost perfect for it. Here's why:

1. Zero-Cost Abstractions

You can write expressive, high-level code that compiles down to the same machine code as hand-optimized C. No runtime overhead for iterators, pattern matching, or closures.

// This iterator chain compiles to optimal assembly
let total: f64 = prices
    .iter()
    .filter(|&&p| p > threshold)
    .map(|&p| p * volume)
    .sum();
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2. Memory Safety Without Garbage Collection

Rust's ownership system achieves memory safety at compile time. No runtime garbage collector means:

  • Zero GC pauses (goodbye tail latency spikes)
  • Predictable performance (critical for P99 requirements)
  • Lower memory usage (cache-friendly data structures)

3. Fearless Concurrency

Rust's type system makes data races impossible at compile time. You can write lock-free algorithms with confidence:

use crossbeam::queue::ArrayQueue;

// Lock-free ring buffer between NIC and strategy threads
let market_data_queue = ArrayQueue::<MarketUpdate>::new(1024);
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4. SIMD and Hardware Control

Direct access to SIMD instructions and hardware capabilities while maintaining safety:

use std::arch::x86_64::*;

// SIMD-accelerated price comparison
unsafe {
    let prices = _mm256_loadu_ps(price_array.as_ptr());
    let threshold = _mm256_set1_ps(100.0);
    let mask = _mm256_cmp_ps(prices, threshold, _CMP_GT_OQ);
}
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5. Single Binary Deployment

Rust compiles to a single static binary. No virtual environments, no dependency conflicts, no Python interpreter to ship. Just one executable that runs anywhere.

The Smart Migration Strategy

Here's the crucial insight: You don't need to rewrite everything in Rust overnight.

The winning strategy leverages each language's strengths:

Phase 1: Research in Python

Keep your alpha discovery pipeline in Python:

  • Exploratory data analysis: Jupyter notebooks with pandas
  • Feature engineering: NumPy, scikit-learn
  • Model training: PyTorch, TensorFlow
  • Backtesting: Zipline, Backtrader

Why? Because productivity matters here. You're exploring ideas, not optimizing latency. Python's ecosystem is unmatched.

Phase 2: Export Model Weights

Once you have a winning strategy:

  • Save model weights as dense f32 arrays or ONNX format
  • Export decision boundaries, coefficients, or neural network parameters
  • Document the inference logic clearly

Phase 3: Implement Hot Path in Rust

Rewrite only the latency-critical production execution path:

Market data ingestion:

use tokio::net::UdpSocket;

#[tokio::main]
async fn main() {
    let socket = UdpSocket::bind("0.0.0.0:9000").await.unwrap();
    let mut buf = [0u8; 1500];

    loop {
        let (len, _) = socket.recv_from(&mut buf).await.unwrap();
        process_market_data(&buf[..len]);
    }
}
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Key techniques:

  • Use tokio for async I/O but pin critical threads to isolated CPU cores
  • Implement lock-free ring buffers (via crossbeam) between threads
  • Serialize orders with zerocopy or bincode, never JSON
  • Use #[repr(C)] for zero-copy message parsing

Phase 4: (Optional) Embed Python for Housekeeping

Use pyo3 to call Python from Rust for non-critical tasks:

  • End-of-day portfolio reconciliation
  • Risk reporting
  • Performance analytics 
use pyo3::prelude::*;

fn generate_daily_report() -> PyResult<()> {
    Python::with_gil(|py| {
        let report_module = py.import("reporting")?;
        report_module.call_method0("generate_pnl_report")?;
        Ok(())
    })
}
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Quick Reference Guide

When should you use which language?

Task Language Reason
Exploratory data analysis Python Jupyter + pandas = productivity
ML model training Python Best ecosystem (PyTorch, scikit-learn)
Strategy backtesting Python Rapid iteration matters
Real-time market data parsing Rust 10M+ msg/s throughput needed
Microsecond order execution Rust Latency budget too tight for Python
Risk checks (pre-trade) Rust Must be deterministic, no GC pauses
Overnight reconciliation Python Speed less critical, code clarity matters
Portfolio reporting Python Rich visualization libraries

Further Learning

Essential Reading

  • "Lock-Free Programming for HFT" – CME Group white-paper (CME Group)
  • "Systems Performance" by Brendan Gregg – Master profiling and optimization
  • "Rust for Rustaceans" by Jon Gjengset – Advanced Rust patterns

Key Crates for HFT

  • tokio – Async runtime (but use with CPU pinning)
  • crossbeam – Lock-free data structures
  • zerocopy – Zero-copy parsing
  • rayon – Data parallelism
  • serde – Serialization (use binary formats)

The Bottom Line

Python democratized quantitative finance—it made sophisticated trading strategies accessible to traders who weren't systems programmers. That's revolutionary and valuable.

But HFT operates in a different reality. When your entire latency budget is measured in single-digit microseconds, Python's architectural choices become fundamental limitations, not just optimization opportunities.

Rust provides:

  • 100x lower latency (median)
  • 750x more consistent performance (P99)
  • 83x higher throughput (single core)
  • 20x lower memory footprint

The smart approach isn't "Rust vs Python"—it's "Python AND Rust." Research in Python, execute in Rust. Use each tool where it excels.

The future of trading infrastructure is being written in Rust, one microsecond at a time.

About the Author: I'm Mayuresh, CTO at AmbiCube, where we build high-performance systems for hospitality and fintech. Currently working on edge AI architectures and distributed compliance systems. Connect with me on LinkedIn or check out my other technical deep-dives.

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