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

•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. When investing in financial markets it is crucial to determine a trading signal that can provide the investor with the best entry and exit points of the financial market, however this is a difficult task and has become a very popular research topic in the financial area. This paper presents an expert system in the financial area that combines Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT), Extreme Gradient Boosting (XGBoost) and a Multi-Objective Optimization Genetic Algorithm (MOO-GA) in order to achieve high returns with a low level of risk. PCA is used to reduce the dimensionality of the financial input data set and the DWT is used to perform a noise reduction to every feature. The resultant data set is then fed to an XGBoost binary classifier that has its hyperparameters optimized by a MOO-GA. The importance of the PCA is analyzed and the results obtained show that it greatly improves the performance of the system. In order to improve even more the results obtained in the system using PCA, the PCA and the DWT are then applied together in one system and the results obtained show that this system is capable of outperforming the Buy and Hold (B&H) strategy in three of the five analyzed financial markets, achieving an average rate of return of 49.26% in the portfolio, while the B&H achieves on average 32.41%.