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Financial Data Resampling for Machine Learning Based Trading = Application to Cryptocurrency Markets /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Financial Data Resampling for Machine Learning Based Trading/ by Tomé Almeida Borges, Rui Neves.
Reminder of title:	Application to Cryptocurrency Markets /
Author:	Borges, Tomé Almeida.
other author:	Neves, Rui.
Description:	XV, 93 p. 30 illus., 28 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer mathematics. -
Online resource:	https://doi.org/10.1007/978-3-030-68379-5
ISBN:	9783030683795

Financial Data Resampling for Machine Learning Based Trading = Application to Cryptocurrency Markets /
Borges, Tomé Almeida.

Financial Data Resampling for Machine Learning Based TradingApplication to Cryptocurrency Markets /[electronic resource] :by Tomé Almeida Borges, Rui Neves. - 1st ed. 2021. - XV, 93 p. 30 illus., 28 illus. in color.online resource. - SpringerBriefs in Computational Intelligence,2625-3712. - SpringerBriefs in Computational Intelligence,.

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.

ISBN: 9783030683795

Standard No.: 10.1007/978-3-030-68379-5doiSubjects--Topical Terms:

1199796
Computer mathematics.

LC Class. No.: QA71-90

Dewey Class. No.: 518

Financial Data Resampling for Machine Learning Based Trading = Application to Cryptocurrency Markets /
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