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Financial data resampling for machin...

Borges, Tome Almeida.

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Financial data resampling for machine learning based trading = application to cryptocurrency markets /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Financial data resampling for machine learning based trading/ by Tome Almeida Borges, Rui Neves.
Reminder of title:	application to cryptocurrency markets /
Author:	Borges, Tome Almeida.
other author:	Neves, Rui.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xv, 93 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Cryptocurrencies - Statistical methods. -
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, Tome Almeida.

Financial data resampling for machine learning based tradingapplication to cryptocurrency markets /[electronic resource] :by Tome Almeida Borges, Rui Neves. - Cham :Springer International Publishing :2021. - xv, 93 p. :ill., digital ;24 cm. - SpringerBriefs in applied sciences and technology, Computational intelligence. - SpringerBriefs in applied sciences and technology.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:

3491609
Cryptocurrencies
--Statistical methods.

LC Class. No.: HG1710.3

Dewey Class. No.: 332.63

Financial data resampling for machine learning based trading = application to cryptocurrency markets /
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245 1 0 $a Financial data resampling for machine learning based trading $h [electronic resource] : $b application to cryptocurrency markets / $c by Tome Almeida Borges, Rui Neves.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xv, 93 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in applied sciences and technology, Computational intelligence
520 $a 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.
650 0 $a Cryptocurrencies $x Statistical methods. $3 3491609
650 0 $a Investments $x Statistical methods. $3 773734
650 0 $a Resampling (Statistics) $3 647644
650 1 4 $a Computational Mathematics and Numerical Analysis. $3 891040
700 1 $a Neves, Rui. $3 3301933
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