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Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Computational Intelligence) - Softcover
Inhaltsangabe
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.
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Über die Autorin bzw. den Autor
Tomé Almeida Borges is a data scientist at Santander Portugal since December 2019. He received the master’s degree in Electrical and Computer Engineering from Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 2019. His research activity is focused on pattern recognition and data resampling methods of financial markets.
Rui Ferreira Neves is a professor at Instituto Superior Técnico since 2005. He received the Diploma in Engineering and the Ph.D. degrees in Electrical and Computer Engineering from the Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 1993 and 2001, respectively. In 2006, he joined Instituto de Telecomunicações (IT) as a research associate. His research activity deals with evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems and mixed signal integrated circuits. He uses both fundamental, technical and pattern matching indicators to find the evolutionof the financial markets.
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- VerlagSpringer
- Erscheinungsdatum2021
- ISBN 10 3030683788
- ISBN 13 9783030683788
- EinbandTapa blanda
- SpracheEnglisch
- Auflage1
- Anzahl der Seiten112
- Kontakt zum HerstellerNicht verfügbar
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Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Computational Intelligence)
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Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Applied Sciences and Technology)
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Financial Data Resampling for Machine Learning Based Trading
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Financial Data Resampling for Machine Learning Based Trading
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Financial Data Resampling for Machine Learning Based Trading
Verlag:
Springer International Publishing, 2021
ISBN 10: 3030683788
ISBN 13: 9783030683788
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Zustand: 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. Bestandsnummer des Verkäufers 448686601
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FINANCIAL DATA RESAMPLING FOR MACHINE LEARNING BASED TRADING
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Financial Data Resampling for Machine Learning Based Trading
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Financial Data Resampling for Machine Learning Based Trading
ISBN 10: 3030683788
ISBN 13: 9783030683788
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Taschenbuch. Zustand: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 112 pp. Englisch. Bestandsnummer des Verkäufers 9783030683788
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Financial Data Resampling for Machine Learning Based Trading : Application to Cryptocurrency Markets
Verlag:
Springer International Publishing, 2021
ISBN 10: 3030683788
ISBN 13: 9783030683788
Neu
Taschenbuch
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Bestandsnummer des Verkäufers 9783030683788
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Financial Data Resampling for Machine Learning Based Trading
Verlag:
Springer International Publishing Feb 2021, 2021
ISBN 10: 3030683788
ISBN 13: 9783030683788
Neu
Taschenbuch
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 112 pp. Englisch. Bestandsnummer des Verkäufers 9783030683788
Anzahl: 2 verfügbar