Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Computational Intelligence)
Borges, Tomé Almeida; Neves, Rui
ISBN 10: 3030683788 ISBN 13: 9783030683788
Published by Springer, 2021
New
Soft cover
From
Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
AbeBooks Seller since March 25, 2015
This specific item is no longer available.
About this Item
Description:
In. Seller Inventory # ria9783030683788_new
Synopsis:
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...
About the Author:
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.
"About this title" may belong to another edition of this title.
Bibliographic Details
Title: Financial Data Resampling for Machine ...
Publisher: Springer
Publication Date: 2021
Binding: Soft cover
Condition: New
Top Search Results from the AbeBooks Marketplace
Stock Image
Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Applied Sciences and Technology)
Seller: Chiron Media, Wallingford, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: New. Seller Inventory # 6666-IUK-9783030683788
Quantity: 10 available
Stock Image
Financial Data Resampling for Machine Learning Based Trading
Seller: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.
Seller rating 5 out of 5 stars
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. Seller Inventory # ABNR-279923
Quantity: 1 available
Stock Image
Financial Data Resampling for Machine Learning Based Trading
Seller: Basi6 International, Irving, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEJUNE24-346750
Quantity: 3 available
Stock Image
Financial Data Resampling for Machine Learning Based Trading
Seller: Majestic Books, Hounslow, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 394199647
Quantity: 1 available
Stock Image
FINANCIAL DATA RESAMPLING FOR MACHINE LEARNING BASED TRADING
Seller: Basi6 International, Irving, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service. Seller Inventory # ABEJUNE24-325331
Quantity: 2 available
Stock Image
Financial Data Resampling for Machine Learning Based Trading
Seller: Books Puddle, New York, NY, U.S.A.
Seller rating 4 out of 5 stars
Condition: New. Seller Inventory # 26386448768
Quantity: 1 available
Seller Image
Financial Data Resampling for Machine Learning Based Trading
Published by
Springer International Publishing, 2021
ISBN 10: 3030683788
ISBN 13: 9783030683788
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
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. Seller Inventory # 448686601
Quantity: Over 20 available
Stock Image
Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets (SpringerBriefs in Computational Intelligence)
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # ABLIING23Mar3113020026590
Quantity: Over 20 available
Stock Image
Financial Data Resampling for Machine Learning Based Trading
Seller: Biblios, Frankfurt am main, HESSE, Germany
Seller rating 5 out of 5 stars
Condition: New. Seller Inventory # 18386448778
Quantity: 1 available
Seller Image
Financial Data Resampling for Machine Learning Based Trading : Application to Cryptocurrency Markets
Published by
Springer International Publishing, 2021
ISBN 10: 3030683788
ISBN 13: 9783030683788
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9783030683788
Quantity: 1 available