What is Algorithmic Trading
Background
High-frequency trading (HFT) is a form of algorithmic trading characterized by high speeds, highturnover rates, and high order-to-trade ratios. HFT uses sophisticated algorithms and high-speed data networks to execute large numbers of orders at extremely high speeds. Here are some key aspects of HFT:
Speed and Latency:
Low Latency: HFT firms strive to achieve the lowest possible latency, which is the delay between receiving market data and executing trades. Latency is often measured in microseconds (millionths of a second).
Co-location: To reduce latency, HFT firms often place their trading servers in the same data centers as the exchanges' servers. This practice is known as co-location.
Algorithmic Trading:
Sophisticated Algorithms: HFT relies on complex algorithms that can make decisions and execute trades based on predefined criteria. These algorithms analyze market data, identify trading opportunities, and execute orders at high speeds.
Automation: HFT systems are fully automated, allowing them to execute a large number of trades without human intervention.
Order Types and Strategies:
Market Making: HFT firms often act as market makers, providing liquidity by continuously buying and selling securities. They profit from the bid-ask spread.
Arbitrage: HFT firms exploit price discrepancies between related securities or different markets. Examples include statistical arbitrage, index arbitrage, and latency arbitrage.
Event-driven Trading: HFT algorithms can react to news events, earnings reports, and other market-moving events almost instantaneously.
High-frequency trading (HFT) represents a significant portion of trading activity in the United States. While exact numbers can fluctuate due to market conditions and regulatory changes, several studies and industry reports provide estimates of HFT's prevalence in the U.S. markets:
Equities Markets:
HFT accounts for approximately 50-60% of equity trading volume in the U.S.