Evaluating machine learning models for predictive accuracy in cryptocurrency price forecasting.

ABSTRACT: Our research investigates the predictive performance and robustness of machine learning classification models and technical indicators for algorithmic trading in the volatile cryptocurrency market. The main aim is to identify reliable approaches for informed decision-making and profitable strategy development. With the increasing global adoption of cryptocurrency, robust trading models are essential for navigating its unique challenges and seizing investment opportunities. This study contributes to the field by offering a novel comparison of models, including logistic regression, random forest, and gradient boosting, under different data configurations and resampling techniques to address class imbalance. Historical data from cryptocurrency exchanges and data aggregators is collected, prep

SUBMITTER: Qureshi SM 

PROVIDER: S-EPMC12571449 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature