Overview
- Presents a framework consisting of several supervised machine learning procedures to trade in the Cryptocurrencies Market
- Compares the performance of 5 different forecasting trading signals among themselves and with a Buy and Hold strategy as baseline
- Proposes a new method for resampling financial data
Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)
Part of the book sub series: SpringerBriefs in Computational Intelligence (BRIEFSINTELL)
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Table of contents (5 chapters)
Keywords
About this book
This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk. The performance of the algorithm with the new resampling method and the classical time sampled data are compared and the advantages of using the system developed in this work are highlighted.
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Authors and Affiliations
About the authors
Tomé Almeida Borges is a data scientist at Santander Portugal since December 2019. He received the master’s degree in Electrical and Computer Engineering from Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 2019. His research activity is focused on pattern recognition and data resampling methods of financial markets.
Rui Ferreira Neves is a professor at Instituto Superior Técnico since 2005. He received the Diploma in Engineering and the Ph.D. degrees in Electrical and Computer Engineering from the Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 1993 and 2001, respectively. In 2006, he joined Instituto de Telecomunicações (IT) as a research associate. His research activity deals with 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 evolutionof the financial markets.
Bibliographic Information
Book Title: Financial Data Resampling for Machine Learning Based Trading
Book Subtitle: Application to Cryptocurrency Markets
Authors: Tomé Almeida Borges, Rui Neves
Series Title: SpringerBriefs in Applied Sciences and Technology
DOI: https://doi.org/10.1007/978-3-030-68379-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-68378-8Published: 23 February 2021
eBook ISBN: 978-3-030-68379-5Published: 22 February 2021
Series ISSN: 2191-530X
Series E-ISSN: 2191-5318
Edition Number: 1
Number of Pages: XV, 93
Number of Illustrations: 2 b/w illustrations, 28 illustrations in colour