Investment Strategies Optimization based on a SAX-GA Methodology

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.

Sommario

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

Riassunto

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.

Testo aggiuntivo

From the reviews:
“The book is accessible by anyone with a broad knowledge of statistics and algorithms, and an interest in finance. The nicely done, comprehensive illustrations make this complicated subject easy to understand, and compensate for the often-clumsy sentence structure. I recommend the book … .” (Martin Gfeller, Computing Reviews, May, 2013)

Relazione

From the reviews:
"The book is accessible by anyone with a broad knowledge of statistics and algorithms, and an interest in finance. The nicely done, comprehensive illustrations make this complicated subject easy to understand, and compensate for the often-clumsy sentence structure. I recommend the book ... ." (Martin Gfeller, Computing Reviews, May, 2013)

Dettagli sul prodotto

• Autori: Antonio Canelas, António M Canelas, António M L Canelas, António M. L. Canelas, António M.L. Canelas, Nuno Horta, Nuno C. G. Horta, Nuno C.G. Horta, Rui Neves, Rui F M Neves, Rui F M F Neves, Rui F. M. F. Neves, Rui F.M.F. Neves
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Tascabile
• Pubblicazione: 01.08.2012
• EAN: 9783642331091
• ISBN: 978-3-642-33109-1
• Pagine: 81
• Peso: 168 g
• Illustrazioni: XII, 81 p. 81 illus., 19 illus. in color.
• Serie: Computational Intelligence; SpringerBriefs in Applied Sciences and Technology; SpringerBriefs in Applied Sciences and Technology / SpringerBriefs in Computational Intelligence; SpringerBriefs in Computational Intelligence; SpringerBriefs in Applied Sciences and Technology; Computational Intelligence; SpringerBriefs in Computational Intelligence
• Categoria: Scienze naturali, medicina, informatica, tecnica > Tecnica > Tematiche generali, enciclopedie