Financial Data Resampling for Machine Learning Based Trading

Application to Cryptocurrency Markets

von Tomé Almeida Borges, Rui Neves

93 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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.

• Reihe: SpringerBriefs in Computational Intelligence
• Themen: Mathematik und Naturwissenschaften Mathematik Mathematische Analysis, allgemein Numerische Mathematik
• ISBN: 9783030683788
• Sprache: Englisch
• Erscheinungsdatum: 23.02.2021
• Größe: 23.5 x 15.5 cm
• Verlag: Springer International Publishing