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.

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

Autorentext
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.

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