Overview

High-Frequency Trading represents the cutting edge of financial technology, as trades are made in milliseconds. By leveraging the power of parallel computing and sophisticated algorithms HFT firms are able to execute thousands of trades a second. They capitalise on split second price discrepancies in markets around the world allowing them to have as much uptime as possible maximising profits. A key goal to HFT firms is latency optimisation where they attempt to maximise the speed of data processing order execution and market access. High Frequency Trading in quantitive finance rely on powerful and reliable infrastructure to do analysis and execute trades. This technologies include Field-Programable-Gate-Arrays and colocation services. This article will explore the technical backbone of HFT and it quantitive strategies as well as offering an insight into how latency optimisation is changing market microstructure. Also addressing the implementations of HTFs in top firms such as Citadel, Virtu and Jane Street who push the boundaries of speed, and question the fairness, stability and sustainability of this technology. With advances in AI, quantum computing, and decentralised finance (DeFi), HFT’s which promises both opportunities and challenges for fintech.

The Mechanics of High-Frequency Trading

High-Frequency Trading (HFT) is a subset of algorithmic trading which focuses of high speed, high turnover and high order-to-trade ratios. But unlike traditional trading, HFT relies on powerful computes often Clusters to analyse market data and execute trades in microseconds. Often holding positions for seconds. In 2025 HFTs accounted for 55% of U.S. equity trading volume, with firms such as Virtu Financial, Citadel Securities and Jane Street leading the charge. These firms act as market makers, providing liquidity by narrowing bid-ask spreads, which benefits retail and institutional investors. However HTFs reliance on speed creates a latency race where reductions in nanoseconds can yield significant profits.

HFT Strategies

The strategies used by High-Frequency trading systems include:

  • Statistical Arbitrage
  • Market making
  • Liquidity Provision

These strategies are executes across equities, forex and cryptocurrencies. The 2010 Flash Crash, where the Dow Jones dropped 600 points in minutes due to HTFs executing an estimated 75,000 sell orders, highlighted both its power and risks, Today HFT firms operate globally with exchanges such as the Tokyo Stock Exchange’s Arrowhead platform which enhances speed and security which attracted HFT activity.

Latency Optimisation: The Technical Core of HFTs

The latency is the time it takes to process and execute a trade and is the core of HFTs. Firms invest heavily into infrastructure to minimise delays. Often measured in nanoseconds which is 1 × 10⁻³ µs (microseconds).

Hardware:

Field-programmable-Gate-Arrays (FPGAs) are key to HFTs offering sub-microseconds data processing by executing tasks in parallel. AMD’s Alveo UL3422 FinTech accelerator, built on a 16 nm silicon process. This chip reduces latency by up to 7x compared to previous generations by integrating ultra-low latency Ethernet MAC for fast market access. GPUs and custom ASICs (Application Specific Integrated Circuits) also accelerate computations, while overclocked servers from providers like Blackcore Technologies cater to smaller HTF firms such as Quedex and Allston Trading.

Network Infrastructure:

  • Co-Location is where firms place servers in exchange data centres helps them to shave microseconds off trade execution.
  • Microwave networks and low-latency protocols like Solarflare’s kernel bypass reduce data transmission times.
  • Modern HFT platforms handle over 1 million orders per second with connection speeds of 10-40 Gbps.

Software Optimisation:

HFT platforms are written in C++ for speed with Rust and Java gaining traction in mid-frequency trading. Kernel bypass techniques eliminate operating system overhead, enabling direct hardware access.

  • A sample lua order book processor illustrates this:
#!/usr/bin/env lua
local bids, asks = {}, {}   -- arrays act as heaps for brevity

-- Keep best bid at index 1 (highest price first)
local function sort_bids() table.sort(bids, function(a, b) return a.price > b.price end) end
-- Keep best ask at index 1 (lowest price first)
local function sort_asks() table.sort(asks, function(a, b) return a.price < b.price end) end

-- Core matching engine
local function match_orders()
    while #bids > 0 and #asks > 0 do
        local bid, ask = bids[1], asks[1]
        if bid.price >= ask.price then
            print(string.format("Trade executed – Buy %.2f | Sell %.2f", bid.price, ask.price))
            table.remove(bids, 1)
            table.remove(asks, 1)
        else
            break
        end
    end
end

-- API: add an order and try to match

local clock = os.clock  -- CPU time; replace with higher‑res timer if you need ns accuracy

local function add_order(price, qty, side)
    local t0 = clock()
    local order = {price = price, quantity = qty, side = side}
    if side == "buy" then
        bids[#bids + 1] = order
        sort_bids()
    else
        asks[#asks + 1] = order
        sort_asks()
    end
    local latency_ns = (clock() - t0) * 1e9
    print(string.format("Order added (%s): %.2f x %d — latency: %.0f ns", side, price, qty, latency_ns))
    match_orders()
end

-- Demo sequence – tweak as you like

add_order(164.92, 100, "buy")
add_order(164.45,  50, "sell")
add_order(163.98,  75, "buy")
add_order(165.42, 100, "sell")

Order added (buy): 164.92 x 100 — latency: 6000 ns
Order added (sell): 164.45 x 50 — latency: 3000 ns
Trade executed – Buy 164.92 | Sell 164.45
Order added (buy): 163.98 x 75 — latency: 2000 ns
Order added (sell): 165.42 x 100 — latency: 2000 ns

This code simulates a low-latency order book on NVDA stock, tracking execution time in nanoseconds which as a simplified version of HFT systems that firms like Jane Street optimise such systems for real-time market-making.