Dynamic-Radius Species-Conserving Genetic Algorithm for the Financial Forecasting of Cryptocurrencies

【Author】 Brown, Michael Scott

【Source】2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021)

【Abstract】This research uses a Niche Genetic Algorithm (NGA) called Dynamic-radius Species-conserving Genetic Algorithm (DSGA) to select cryptocurrencies to purchase from a publicly available dataset. DSGA uses a set of training data to produce a set of rules. These rules are then used to predict cryptocurrency prices in test data by constructing weekly portfolios. DSGA is an NGA that uses a clustering algorithm enhanced by a tabu list and radius variations. DSGA also uses a shared fitness algorithm to investigate different areas of the domain. The DSGA algorithm did very well in predicting cryptocurrency movements. The weekly portfolios generated by DSGA produced a return nearly double that of investing in an equal number of cryptocurrencies.

【Keywords】Niche Genetic Algorithm; Genetic Algorithm; cryptocurrency forecasting; financial forecasting; classification; black-box investing

【发表时间】2021

【收录时间】2022-08-02

【文献类型】Proceedings Paper

【论文大主题】加密货币

【论文小主题】市场分析与预测

【翻译者】林定康