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Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets
Tomé Almeida Borges
(Author)
·
Rui Neves
(Author)
·
Springer
· Paperback
Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets - Borges, Tomé Almeida ; Neves, Rui
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Origin: U.S.A.
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Synopsis "Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets"
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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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
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