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Home/Blog/High-Frequency Trading Explained: Speed, Strategy & Firms

High-Frequency Trading Explained: Speed, Strategy & Firms

ER
Dr. Elena Rostova• Principal Machine Learning & Alternative Data Researcher
•2026-01-31•Peer-Reviewed

What Is High-Frequency Trading?

High-frequency trading (HFT) is a subset of algorithmic trading characterized by extremely high speeds, high turnover rates, and very short holding periods. HFT firms use powerful computers, co-located servers, and specialized network infrastructure to execute trades in microseconds, capitalizing on tiny price discrepancies that exist for fractions of a second.

HFT is not a single strategy but rather a collection of strategies that share the common trait of requiring ultra-low latency. The firms that practice HFT invest heavily in technology infrastructure, often spending tens of millions of dollars annually on hardware and network optimization.

How HFT Works

At its core, HFT is about information processing speed. The faster a firm can receive market data, analyze it, and send orders, the more opportunities it can capture. This speed advantage is measured in microseconds (millionths of a second) and sometimes nanoseconds.

  • Co-location: HFT firms place their servers physically next to exchange matching engines to minimize transmission delay
  • FPGA and ASIC hardware: Custom chips process market data faster than general-purpose CPUs
  • Direct market access: Bypassing intermediaries to send orders as quickly as possible
  • Microwave and laser networks: Some firms use line-of-sight wireless links between exchanges for faster data transmission than fiber optic cables

Common HFT Strategies

Market making: The most common HFT strategy involves continuously quoting bid and ask prices, earning the spread on each transaction. HFT market makers provide liquidity and generally narrow spreads, benefiting other market participants. They manage inventory risk by rapidly adjusting quotes based on order flow and market conditions.

Statistical arbitrage at high frequency: These strategies exploit short-lived statistical relationships between related instruments. For example, an ETF and its underlying components should move in tandem, but brief deviations occur that HFT firms can capture.

Latency arbitrage: When the same asset trades on multiple venues, price updates propagate with slight delays. Firms with faster infrastructure can trade against stale quotes on slower venues, though this strategy has become increasingly competitive.

Major HFT Firms

The HFT industry is dominated by a relatively small number of well-capitalized firms. These include both pure-play HFT shops and divisions of larger quantitative trading firms.

  • Citadel Securities: One of the largest market makers globally, handling a significant share of US equity volume
  • Virtu Financial: A publicly traded firm known for consistent profitability in market making
  • Jump Trading: A technology-focused firm with deep investments in network infrastructure
  • Tower Research Capital: A major player in global electronic trading
  • Optiver and IMC: Amsterdam-origin firms with strong options market-making operations

Controversy and Regulation

HFT has been the subject of significant debate. Critics argue that it creates an unfair speed advantage, while proponents point to tighter spreads and improved market liquidity. Regulators worldwide have responded with measures including minimum resting times for orders, transaction taxes, and speed bumps on certain exchanges.

Careers in HFT

Working at an HFT firm requires exceptional skills in low-latency systems programming (C++, FPGA design), quantitative modeling, or network engineering. Compensation is among the highest in finance, reflecting the specialized skill set required. Explore current HFT opportunities on our job board and learn more about the firms through our company profiles.

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ER
Dr. Elena RostovaPhD in Machine Learning & Statistical Inference
Principal Machine Learning & Alternative Data Researcher

Focuses on deep reinforcement learning in execution algorithms, double machine learning for causal inference, LLM reasoning architectures, and NLP factor extraction for hedge funds.

View full bio & editorial board profile →

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