SpringerBriefs in Applied Sciences and Technology: Application to Cryptocurrency Markets

Borges, Tomé Almeida; Neves, Rui

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.

Details

Published by: Springer

Publication Date: 2021-02-23

Format: Paperback

ISBN-13: 9783030683788

DOI: 10.1007/978-3-030-68379-5

Dimensions: 235cm x155cm

Pages: 93