At Dysnix, we’ve spent years crafting robust infrastructures for large DEXes, blockchain analytics platforms, ZK-powered projects, and other applications. And in terms of underlayer, these companies demanded nothing less than by-design efficiency, high availability, best-fit scaling approach, and mature security. High-frequency trading (HFT) primarily demands the same, with a few distinctive features that we’ll surely mention below.
So, if you’re curious about what powers the trading at the speed of light, let us share a trick or two about the HFT underlayer.
The trading flow for those who want to buy and sell in nanoseconds hasn’t changed from centuries ago, wasting years for a single successful trade. It’s all the same in a core:
The distinctive feature of HFT is that all of these actions are based on ultra-low-latency networks and high-speed data transmission and analysis. The trading platform's algorithms are continually improving and self-educating to facilitate more efficient actions, and overflowing market data pools are vast oceans that can be traversed within nanoseconds.
HFT companies are the new F1 racing that bring more adrenaline to the players and audience, demonstrating billions of trades in the blink of an eye.
It uses algorithms to execute thousands of trades in milliseconds, profiting from diminutive price differences. It thrives in markets where speed is critical, like stocks, crypto, and commodities.
When we say small price differences, we mean even the most minor discrepancies. Margins are microscopic—but that’s the point. HFT doesn’t rely on big wins; it thrives on consistent, incremental gains over millions of trades. Think of it like mining cryptocurrency: individually, each mined block isn’t much, but scale it across a mining farm, and suddenly, you’re swimming in profit.
Here’s a real-world analogy from the blockchain: ever notice how validators on a proof-of-stake chain like Ethereum have been rewarded fractions of ETH per block? They’re not chasing a jackpot but building wealth one block at a time. HFT traders operate on the same principle.
HFT companies can trade across borders without centralized intermediaries slowing them down—all features of the free market in a nutshell.
TL;DR for the table
HFT systems are trying to conquer the speed of light by implementing the shortest tick-to-trade pipelines. Here’s an example.
If Exchange A releases a price update at 10:00:00.001000 (1 millisecond past 10:00), an ultrafast system might receive, process, and execute a trade by 10:00:00.001500 (within 0.5 milliseconds). A slower system, even lagging by 1 millisecond, misses the opportunity.
HFT can be applied by hedge funds, proprietary trading firms, and crypto traders globally. And they can do that successfully thanks to their infrastructure features described below.
Co-located servers
Housed directly in exchange data centers to minimize physical distance (because even light has travel time!).
Custom firmware
FPGA cards accelerate data processing to the nanosecond level.
For example, a specialized FPGA-based system processes raw tick data via Ethernet, bypasses operating system layers, and directly computes actionable decisions in microseconds. This ensures trades are executed before competitors relying on traditional software-based systems.
Another great technique we advise for NFT projects related to the hardware layer is the Kernel bypass procedure.
Kernel bypass allows applications to directly access network hardware, skipping the kernel’s network stack, thereby significantly reducing latency and overhead. This direct hardware access also provides greater control over networking, which is good for optimizing HFT performance. Most popular techniques of Kernel bypass include:
Low-latency networks
Dedicated fiber optics and low-latency switches that shave milliseconds off every hop. Techniques like UDP offloading directly decode incoming packets, such as order books or trades, into actionable data streams processed by trading logic embedded in hardware.
Algorithmic trading software
Complex algorithms analyze market data in real-time to identify profitable opportunities.
This software uses algorithms to identify tiny price differences and capitalize on them instantly, making thousands of trades per second. Algorithmic trading software removes emotion and human delay from the equation.
For DevOps engineers, this software is designed to ensure the entire pipeline—from data ingestion and analysis to order execution and risk management—operates with unparalleled efficiency and reliability.
Risk management systems
Automated tools manage exposure and compliance, thereby reducing risks associated with rapid market changes.
To manage risks effectively, HFT firms implement a DevOps-driven risk framework:
By embedding these strategies into an agile DevOps workflow, HFT projects achieve continuous risk assessment and robust operational stability.
Direct market access
Real-time data feeds via APIs allow traders to act on price changes instantly.
Efficient market data processing involves transforming high-throughput financial feeds into actionable signals. For instance, hardware accelerators such as FPGAs decode protocols like FAST (used by exchanges for real-time data) in parallel, avoiding OS-induced latency spikes.
Traders co-locate servers near exchanges to minimize latency, ensuring faster order execution. Key locations include financial hubs like New York, London, and Hong Kong. While controversial for potentially increasing market volatility, HFT boosts liquidity and narrows bid-ask spreads.
Let’s go behind the curtains of this flow to see what kind of high-frequency trading network architecture makes the best HFT.
So you see, we need a fast, highly reliable, always available, data-flashing custom engine. What hides inside the high-frequency trading infrastructure that makes it so unique?
On-the-go risk checks within FPGA architectures are critical. Using preprogrammed logic blocks, these systems evaluate trade sizes, portfolio limits, and market exposure in real-time without delaying order execution. This ensures regulatory compliance and safeguards against excessive losses.
Ultra-low latency describes the total delay from when a market event occurs to when a trader’s system reacts to it, typically measured in microseconds. It consists of:
To achieve nanosecond-level latency, modern high-frequency systems optimize data paths using pipelined designs in FPGAs, where each stage handles specific computational tasks without waiting for others. For instance, in one implementation, pipelined operations calculate correlation matrices and eigenvalues (eigenvalues are used to identify correlations or patterns in financial data) for portfolio analysis.
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Once computations identify opportunities, execution strategies like arbitrage or momentum trading are executed.
These strategies, programmed directly into FPGA logic, can be pre-tested against historical data to minimize execution risk while ensuring ultra-fast order placements.
So that’s what HFT has under the hood.
We believe that such powerful infrastructures aren’t for “chosen ones.” Even solo traders are leveraging their strategies with HFT techniques, and it’s because technical and algorithmic creativity still leaves plenty of room for investigation and invention.
We’ll be glad if you invite Dysnix to participate in your HFT story.
