Financial data resampling for machine learning based trading : application to cryptocurrency markets
Author(s)
Bibliographic Information
Financial data resampling for machine learning based trading : application to cryptocurrency markets
（Springer briefs in applied sciences and technology, . Computational intelligence）
Springer, c2021
- : pbk
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"This Springer imprint is published by the registered company Springer Nature Switzerland AG ... Cham, Switzerland"--T.p. verso
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
Chapter 1 - Introduction
Chapter 2 - Related work
Chapter 3 - Implementation
Chapter 4 - Results
Chapter 5 - Conclusions and future work
by "Nielsen BookData"
