Combined ensemble multi-class SVM and fuzzy NSGA-II for trend forecasting and trading in Forex markets

作者：

Highlights：

• A combined computational intelligence technique for trend classification and trading in Forex markets.

• An Ensemble multi-class SVM for efficient trend forecasting into uptrend, sideway, and downtrend.

• A fuzzy-based trading system comprising multiple AND-OR Buy/Sell fuzzy rules.

• Utilizing NSGA-II to optimize the hyperparameters of the fuzzy trading system.

摘要

•A combined computational intelligence technique for trend classification and trading in Forex markets.•An Ensemble multi-class SVM for efficient trend forecasting into uptrend, sideway, and downtrend.•A fuzzy-based trading system comprising multiple AND-OR Buy/Sell fuzzy rules.•Utilizing NSGA-II to optimize the hyperparameters of the fuzzy trading system.

论文关键词：Forex market,Trend classification,Technical analysis,Ensemble learning,Fuzzy logic,NSGA-II

论文评审过程：Received 16 October 2020, Revised 27 May 2021, Accepted 4 July 2021, Available online 8 July 2021, Version of Record 6 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115566