HFT

High-frequency trading (HFT) is a form of algorithmic trading that uses powerful computers to execute a large number of orders at extremely high speeds. HFT firms typically rely on co-located servers, direct data feeds, and sophisticated algorithms to respond to market data in milliseconds or microseconds. Positions are often held for very short periods, and profits come from small price differences, tight spreads, and liquidity rebates rather than long-term bets.

Key techniques include market making, latency arbitrage, and statistical or event-driven strategies. Market makers provide liquidity by posting bids and offers and rapidly updating them as prices move. Latency arbitrage seeks to exploit momentary price differences across venues. Statistical arbitrage and related approaches rely on rapid data analysis to forecast short-term moves. Firms invest in low-latency networks, private data feeds, and advanced order-routing capabilities.

Markets and participants: HFT activity is strongest in equities and futures, with presence in options and currencies. It is dominated by specialized trading firms and large banks that devote substantial resources to technology, connectivity, and data acquisition. The practice often involves short holding periods and frequent order cancellations, reflecting a focus on speed and precision.

Impact and regulation: HFT can improve liquidity and tighten spreads, but it has also raised concerns about market stability, fairness, and systemic risk. Critics point to uneven access to speed and potential amplification of volatility. Regulators monitor HFT practices for manipulation and systemic effects and have implemented rules related to market access, order types, and surveillance to address these concerns.

Notable incidents and context: Operational failures in automated trading have highlighted risks, leading to regulatory reviews and ongoing evolution of market structure rules.