Evolutionary Induction of Trading Models
Siddhartha Bhattacharyya, Kumar Mehta · Studies in fuzziness and soft computing · 2002
Financial markets data present a challenging opportunity for the learning of complex patterns not readily discernable. This paper investigates the use of genetic algorithms for the mining of financial time-series for patterns aimed at the provision of trading decision models. A simple yet flexible representation for trading rules is proposed, and issues pertaining to fitness evaluation examined. Two key issues in fitness evaluation, the design of a suitable fitness function reflecting desired trading characteristics and choice of appropriate training duration, are discussed and empirically examined. Two basic measures are also proposed for characterizing rules obtained with alternate fitness criteria. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.