Combining Principal Component Analysis, Discrete Wavelet Transform and XGBoost to trade in the financial markets

作者：

Highlights：

• A system that uses PCA, DWT and an optimized XGBoost classifier is proposed.

• PCA is used to perform dimensionality reduction to the input data.

• DWT performs a noise reduction to the input data.

• The XGBoost classifier is optimized using a MOO-GA.

• The proposed system outperforms the B&H strategy in different markets.

摘要

•A system that uses PCA, DWT and an optimized XGBoost classifier is proposed.•PCA is used to perform dimensionality reduction to the input data.•DWT performs a noise reduction to the input data.•The XGBoost classifier is optimized using a MOO-GA.•The proposed system outperforms the B&H strategy in different markets.

论文关键词：Financial markets,Principal Component Analysis (PCA),Discrete Wavelet Transform (DWT),Extreme Gradient Boosting (XGBoost),Multi-Objective Optimization Genetic Algorithm (MOO-GA)

论文评审过程：Received 15 June 2018, Revised 14 December 2018, Accepted 31 January 2019, Available online 1 February 2019, Version of Record 8 February 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.01.083