TY  - GEN
N2  - 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.
DO  - 10.1007/978-3-030-68379-5
DO  - doi
AB  - 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.
T1  - Financial data resampling for machine learning based trading :application to cryptocurrency markets /
AU  - Almeida Borges, Tomé,
AU  - Neves, Rui,
CN  - HG1710.3
ID  - 1434323
KW  - Cryptocurrencies
KW  - Investments
KW  - Resampling (Statistics)
KW  - Cryptomonnaie
KW  - Investissements
KW  - Rééchantillonnage (Statistique)
SN  - 9783030683795
SN  - 3030683796
TI  - Financial data resampling for machine learning based trading :application to cryptocurrency markets /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-68379-5
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-68379-5
ER  -