Early warning in online stock trading systems

Abstract

In this paper, a new functionality of early warning for an online stock trading system is presented. The warning functionality helps to focus traders' attention on specific situations on the stock market. The specific situations relate to the rare circumstances where a trader should be alerted by exceptional raises or drops of share prices, volatilities and market index changes. Usually, these alerts force a trader to make a decision either to buy or sell a share. To discover the warning rules and events, an evolution-based model is proposed. This model also introduces a new function that stores the experimental knowledge by keeping track of all historical alert events-solutions and actions taken by a trader. This model is composed of the three following components, which are integrated with each other: alert rules, pattern clustering and genetic engine. This approach has been tested on real data extracted from the Internet Bourse Expert System and Paris Stock Exchange.