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Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets PDF

93 Pages·2021·3.5888 MB·other
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by Tomé Almeida Borges, Rui Neves| 2021| 93 pages| 3.5888| other

About 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.

Detailed Information

Author:Tomé Almeida Borges, Rui Neves
Publication Year:2021
ISBN:3030683788
Pages:93
Language:other
File Size:3.5888
Format:PDF
Price:FREE
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