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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.
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)
"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)