High-frequency forecasting of the crude oil futures price with multiple timeframe predictions fusion

Abstract

In the abundant literature about crude oil futures price forecasting, researchers generally predicted the crude oil price movements from the perspective of only a single timeframe. In addition, the trading strategies of their trading models were generally designed to be less sophisticated, and their prediction models lacked interpretability. To fill these gaps, a price direction fused prediction and trading approach has been proposed for high-frequency prediction of the Chinese crude oil futures. In the proposed approach, the MTXGBoost (Multiple Timeframes eXtreme Gradient Boosting) is developed and utilized for predictions fusion under multiple timeframes, and the NSGA-II (Non-dominated Sorting Genetic Algorithm-II) is integrated for trading strategy optimization. Moreover, the SHAP (Shapley Additive exPlanation) approach is also employed to interpret how the proposed approach made predictions. Experimental results show that the approach proposed in this research averagely produced a direction prediction accuracy of 78.69%, an accumulated return of 23.17%, and a maximum drawdown of 1.00%, demonstrating that it can produce an excellent profit with small trading risks. Therefore, the proposed approach can be employed as an intelligent, efficient, and reliable decision support system for market investors, energy-related companies, and government departments to make crude oil related decisions.