Financial Data Resampling for Machine Learning Based Trading
Application to Cryptocurrency Markets
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Beschreibung
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.
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 themselves and with a Buy and Hold strategy as baseline Proposes a new method for resampling financial data
Autor*in
Tomé Almeida Borges
Themen in »Financial Data Resampling for Machine Learning Based Trading«
Financial Data Resampling Financial Markets Cryptocurrencies Technical Analysis Machine Learning Ensemble Classification
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“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)
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