Financial Data Resampling for Machine Learning Based Trading Application to Cryptocurrency Markets
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Combines four machine learning algorithms for robust trading strategies
Introduces a closing value threshold resampling method for improved signal quality
Compares new resampling approach with classical time-sampled data
Provides actionable insights for cryptocurrency trading
Highlights advantages of the proposed system for risk-adjusted returns
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SpringerBriefs in Applied Sciences and Technology SpringerBriefs in Computational Intelligence Financial Data Resampling for Machine Learning Based Trading Application to Cryptocurrency Markets Tomé Almeida Borges | Rui Neves Mathematics / Numerical Analysis
This book presents a system that combines the expertise of four algorithms, namely Gr