| 書目名稱 | Financial Data Resampling for Machine Learning Based Trading |
| 副標(biāo)題 | Application to Crypt |
| 編輯 | Tomé Almeida Borges,Rui Neves |
| 視頻video | http://file.papertrans.cn/343/342999/342999.mp4 |
| 概述 | 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 them |
| 叢書名稱 | SpringerBriefs in Applied Sciences and Technology |
| 圖書封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2021 |
| 關(guān)鍵詞 | Financial Data Resampling; Financial Markets; Cryptocurrencies; Technical Analysis; Machine Learning; Ens |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-68379-5 |
| isbn_softcover | 978-3-030-68378-8 |
| isbn_ebook | 978-3-030-68379-5Series ISSN 2191-530X Series E-ISSN 2191-5318 |
| issn_series | 2191-530X |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 |
|Archiver|手機(jī)版|小黑屋|
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