Investment Strategies Optimization based on a SAX-GA Methodology

  • António M.L. Canelas
  • Rui F.M.F. Neves
  • Nuno C.G. Horta

Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Also part of the SpringerBriefs in Computational Intelligence book sub series (BRIEFSINTELL)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Antonio M. L. Canelas, Rui F. M. F. Neves, Nuno C. G. Horta
    Pages 1-4
  3. Antonio M. L. Canelas, Rui F. M. M. Neves, Nuno C. G. Horta
    Pages 5-35
  4. Antonio M. L. Canelas, Rui F. M. F. Neves, Nuno C. G. Horta
    Pages 37-57
  5. Antonio M. L. Canelas, Rui F. M. F. Neves, Nuno C. G. Horta
    Pages 59-78
  6. Antonio M. L. Canelas, Rui F. M. F. Neves, Nuno C. G. Horta
    Pages 79-81

About this book

Introduction

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.

Keywords

Financial Market Frequent Patterns Genetic Algorithm Pattern Discovery Pattern Recognition SAX Representation

Authors and affiliations

  • António M.L. Canelas
    • 1
  • Rui F.M.F. Neves
    • 2
  • Nuno C.G. Horta
    • 3
  1. 1., Instituto Superior TécnicoInstituto de Telecomunicações/LisbonPortugal
  2. 2.Instituto de TelecomunicaçõesLisboaPortugal
  3. 3., Instituto Superior TécnicoInstituto de Telecomunicações/LisbonPortugal

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-33110-7
  • Copyright Information The Author(s) 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-33109-1
  • Online ISBN 978-3-642-33110-7
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • About this book