Financial Data Resampling for Machine Learning Based Trading
Springer International Publishing (Verlag)
978-3-030-68378-8 (ISBN)
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.
lt;p>
Tomé Almeida Borges is a data scientist at Santander Portugal since December 2019. He received the master's degree in Electrical and Computer Engineering from Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 2019. His research activity is focused on pattern recognition and data resampling methods of financial markets.
Rui Ferreira Neves is a professor at Instituto Superior Técnico since 2005. He received the Diploma in Engineering and the Ph.D. degrees in Electrical and Computer Engineering from the Instituto Superior Técnico, Technical University of Lisbon, Portugal, in 1993 and 2001, respectively. In 2006, he joined Instituto de Telecomunicações (IT) as a research associate. His research activity deals with evolutionary computation and pattern matching applied to the financial markets, sensor networks, embedded systems and mixed signal integrated circuits. He uses both fundamental, technical and pattern matching indicators to find the evolution of the financial markets.
lt;p>Chapter 1 - Introduction
Chapter 2 - Related work
Chapter 3 - Implementation
Chapter 4 - Results
Chapter 5 - Conclusions and future work
"The book contains little theory and presents mostly detailed numerical experiments, it reads very engagingly and inspires with many ideas. It is certainly not a reference book but rather a short monograph on a very clearly defined topic. It will be interesting to see whether the trading strategies presented can be transferred from the crypto markets to the presumably more efficient standard stock markets ... as published strategies tend to make markets more efficient." (Volker H. Schulz, SIAM Review, Vol. 64 (3), September, 2022)
“The book contains little theory and presents mostly detailed numerical experiments, it reads very engagingly and inspires with many ideas. It is certainly not a reference book but rather a short monograph on a very clearly defined topic. It will be interesting to see whether the trading strategies presented can be transferred from the crypto markets to the presumably more efficient standard stock markets … as published strategies tend to make markets more efficient.” (Volker H. Schulz, SIAM Review, Vol. 64 (3), September, 2022)
| Erscheinungsdatum | 01.03.2021 |
|---|---|
| Reihe/Serie | SpringerBriefs in Applied Sciences and Technology | SpringerBriefs in Computational Intelligence |
| Zusatzinfo | XV, 93 p. 30 illus., 28 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 185 g |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Analysis |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Schlagworte | cryptocurrencies • ensemble classification • Financial Data Resampling • Financial Markets • machine learning • Technical Analysis |
| ISBN-10 | 3-030-68378-8 / 3030683788 |
| ISBN-13 | 978-3-030-68378-8 / 9783030683788 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
