Parallel Genetic Algorithms for Financial Pattern Discovery Using GPUs

  • João Baúto
  • Rui Neves
  • Nuno 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-xiv
  2. João Baúto, Rui Neves, Nuno Horta
    Pages 1-3
  3. João Baúto, Rui Neves, Nuno Horta
    Pages 5-20
  4. João Baúto, Rui Neves, Nuno Horta
    Pages 21-32
  5. João Baúto, Rui Neves, Nuno Horta
    Pages 33-44
  6. João Baúto, Rui Neves, Nuno Horta
    Pages 45-66
  7. João Baúto, Rui Neves, Nuno Horta
    Pages 67-88
  8. João Baúto, Rui Neves, Nuno Horta
    Pages 89-91

About this book


This Brief presents a study of SAX/GA, an algorithm to optimize market trading strategies, to understand how the sequential implementation of SAX/GA and genetic operators work to optimize possible solutions. This study is later used as the baseline for the development of parallel techniques capable of exploring the identified points of parallelism that simply focus on accelerating the heavy duty fitness function to a full GPU accelerated GA. 


computational finance pattern recognition techniques high performance computing data science SAX/GA algorithm market trading strategies

Authors and affiliations

  • João Baúto
    • 1
  • Rui Neves
    • 2
  • Nuno Horta
    • 3
  1. 1.Instituto Superior TécnicoInstituto de TelecomunicaçõesLisbonPortugal
  2. 2.Instituto Superior TécnicoInstituto de TelecomunicaçõesLisbonPortugal
  3. 3.Instituto Superior TécnicoInstituto de TelecomunicaçõesLisbonPortugal

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-73328-9
  • Online ISBN 978-3-319-73329-6
  • Series Print ISSN 2191-530X
  • Series Online ISSN 2191-5318
  • Buy this book on publisher's site
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