Financial Data Resampling for Machine Learning Based Trading - Application to Cryptocurrency Markets (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Applied Sciences and Technology
Release date:	February 2021
First published:	2021
Authors:	Tome Almeida Borges • Rui Neves
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	93
Edition:	1st ed. 2021
ISBN-13:	978-3-03-068378-8
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis
LSN:	3-03-068378-8
Barcode:	9783030683788

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