Algorithmic Trading, HFT, and News Trading: History and Market Structure
Summary
The article distinguishes algorithmic trading, high-frequency trading (HFT), and news-based trading by their aims, time horizons, speeds, and data sources. It describes algorithmic systems as rule-based automation across varied horizons, HFT as speed-focused trading using real-time market and order-book data, and news strategies as analysis of updates and sentiment. It also sketches how exchanges, electronic order routing, communication networks, and co-location helped enable automated and faster trading.
A historical timeline connects early share trading and information transmission with electronic execution, the growth of algorithmic methods, and HFT. The article also mentions regulatory changes and lists firms active in the field. Its account is introductory and descriptive: examples illustrate the approaches, but it does not provide strategy specifications, comparative performance tests, or evidence that any method is profitable. Some historical and market claims are presented without detailed sourcing, and the discussion of rules and regulations is incomplete in the supplied text.
Key ideas
- Algorithmic trading automates decisions using predefined rules and can operate across different time horizons.
- HFT relies on speed, frequent trades, and real-time market data to pursue fleeting price differences.
- News-based trading uses timely information and sentiment to anticipate market reactions.
- Electronic execution, communications technology, and exchange co-location supported the growth of automated trading.
- The article provides historical context and examples but no empirical comparison of strategy performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.