Financial data resampling for machine learning based trading : application to cryptocurrency markets
著者
書誌事項
Financial data resampling for machine learning based trading : application to cryptocurrency markets
（Springer briefs in applied sciences and technology, . Computational intelligence）
Springer, c2021
- : pbk
大学図書館所蔵 件 / 全2件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
"This Springer imprint is published by the registered company Springer Nature Switzerland AG ... Cham, Switzerland"--T.p. verso
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
Chapter 1 - Introduction
Chapter 2 - Related work
Chapter 3 - Implementation
Chapter 4 - Results
Chapter 5 - Conclusions and future work
「Nielsen BookData」 より
