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＜電子ブック＞
Financial Data Resampling for Machine Learning Based Trading : Application to Cryptocurrency Markets / by Tomé Almeida Borges, Rui Neves
(SpringerBriefs in Computational Intelligence. ISSN:26253712)

版	1st ed. 2021.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2021
本文言語	英語
大きさ	XV, 93 p. 30 illus., 28 illus. in color : online resource
著者標目	*Borges, Tomé Almeida author Neves, Rui author SpringerLink (Online service)
件　名	LCSH:Mathematics -- Data processing  全ての件名で検索 FREE:Computational Mathematics and Numerical Analysis
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-030-68379-5

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