Evolutionary building of stock trading experts in a real-time system

This paper addresses the problem of constructing real-time stock trading expertise for financial time series. The expertise is arrived at via an evolutionary algorithm on the basis of a set of specified trading rules. As in most real-time expert systems, one of the main bottlenecks is the time constraint. In this paper, two approaches were compared using our system, Bourse-Expert, the first based on 350 trading rules, and the second based on 150 particular linear combinations of these 350 rules. Experiments carried out on real data from the Paris Stock Exchange showed that focusing on only 150 rules highly reduced the computation time without significantly reducing the quality of the expertise.