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Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Computational Intelligence)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 30.69
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Paperback. Condition: Brand New. 112 pages. 9.25x6.10x0.27 inches. In Stock.
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Taschenbuch. Condition: Neu. Financial Data Resampling for Machine Learning Based Trading | Application to Cryptocurrency Markets | Tomé Almeida Borges (u. a.) | Taschenbuch | SpringerBriefs in Applied Sciences and Technology | xv | Englisch | 2021 | Springer | EAN 9783030683788 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: new. Questo è un articolo print on demand.
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Financial Data Resampling for Machine Learning Based Trading
Language: English
Published by Springer International Publishing, 2021
ISBN 10: 3030683788 ISBN 13: 9783030683788
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents a framework consisting of several supervised machine learning procedures to trade in the Cryptocurrencies Market Compares the performance of 5 different forecasting trading signals among themselves and with a Buy and Hold stra.
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Financial Data Resampling for Machine Learning Based Trading
Language: English
Published by Springer, Palgrave Macmillan Feb 2021, 2021
ISBN 10: 3030683788 ISBN 13: 9783030683788
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 112 pp. Englisch.
