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Identifying Patterns in Financial Markets: New Approach Combining Rules Between PIPs and SAX: SpringerBriefs in Applied Sciences and Technology

Autor João Leitão, Rui Ferreira Neves, Nuno C. G. Horta
en Limba Engleză Paperback – 18 ian 2018
This book describes a new pattern discovery approach based on the combination among rules between Perceptually Important Points (PIPs) and the Symbolic Aggregate approximation (SAX) representation optimized by Genetic Algorithm (GA). The proposed approach was tested with real data from S&P500 index and all the results obtained outperform the Buy&Hold strategy. Three different case studies are presented by the authors.
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Specificații

ISBN-13: 9783319701592
ISBN-10: 3319701592
Pagini: 74
Ilustrații: XVII, 66 p. 69 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.13 kg
Ediția:1st ed. 2018
Editura: Springer International Publishing
Colecția Springer
Seriile SpringerBriefs in Applied Sciences and Technology, SpringerBriefs in Computational Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Related Work.- SIR/GA approach.- Case studies.

Notă biografică

João Maria Rodrigues Leitão is a software engineer at PASS, S.A., Portugal. His research activity focus on pattern recognition and evolutionary computation in financial markets.Rui Ferreira  Neves is a professor at Instituto Superior Técnico, Portugal, since 2005. His research activity focus on evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems and mixed signal integrated circuits. He uses both fundamental, technical and pattern matching indicators to find the evolution of the financial markets.
Nuno Horta is the Head of the Integrated Circuits Group at Instituto de Telecomunicações, Portugal. His research interests are mainly in analog and mixed-signal IC design, analog IC design automation, soft computing and data science.

Caracteristici

Proposes a new methodology that has been tested with real data Approach combining multiple exit/sell methods namely time, price and pattern Suggests the use of a GA adaptive approach able to automatically identify multiple patterns and generate trading rules Includes supplementary material: sn.pub/extras