This book presents a new computational finance approach combining a Symbolic Aggregate approximation (SAX) technique with an optimization kernel based on genetic algorithms (GA). While the SAX representation is used to describe the financial time series, the evolutionary optimization kernel is used in order to identify the most relevant patterns and generate investment rules. The proposed approach considers several different chromosomes structures in order to achieve better results on the trading platform The methodology presented in this book has great potential on investment markets.

Presents a new computational finance approach combining SAX and GA Shows soft computing and computational intelligence as solutions for financial markets Case studies presented help identifying the investment strategy to apply in different situations Includes supplementary material: sn.pub/extras

Inhalt
Introduction.- Market Analysis Background and Related Work.- SAX-GA Approach.- Results.- Conclusions and Future Work.