# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 1 Introduction
Nevertheless, when using the machine-learning-based methods, training and testing data often have a problem of data imbalance in the model learning process, resulting in unsatisfied classification performance of the minority class samples [25; 26]. Therefore, classification tasks with imbalanced data categories in machine learning have received increasing attention in academic and practical applications. To solve this problem, the Synthetic Minority Oversampling Technique (SMOTE) [27], which is an improved random oversampling method, has been extensively adopted. For the class-imbalanced data, the training dataset of minority categories could be oversampled by the SMOTE, which aims to solve the unbalanced class distribution problem of the studied data [28]. In academics, the efficiency of the SMOTE technique for enhancing classification accuracy of minority categories has been successfully applied and proved by many scholars [29; 30]. Therefore, in this research, the SMOTE technique is integrated with the XGBoost model to predict the price-movement direction of rebar futures. For the simulation trading in financial markets, how to design trading strategies and determine various trading parameters has drawn much attention from market participants. However, because of the extraordinary high leverage and risk of futures trading, other than a great return, trading risk should also be an essential indicator in the futures market, while it is generally overlooked by researchers. Therefore, different from the literature reports that considered only the return as the objective function, we propose to implement a multiobjective optimization approach for optimizing the pre-designed trading rule by considering both the trading return and the trading risk. Recent optimization studies have shown that the application of the Nondominated Sorting Genetic Algorithm II (NSGA-II)algorithm [31], which is one of the well-known multiobjective optimization algorithms, has been successfully expanded in many application fields. For example, [PERSON] et al. optimized the design of an urban water distribution system using an NSGA-II-based method, intending to find a tradeoff between system cost and reliability [32]. [PERSON] et al. focused on optimizing spatial multiobjective land use, and they applied an NSGA-II-based method to generate the best compromise solutions among minimum conversion costs, maximum accessibility, and maximum compatibilities among land uses [33]. [PERSON] et al. developed a multiobjective optimization algorithm based on the NSGA-II algorithm to find a reasonable allocation of medical resources [34]. To improve the trading performance of the proposed approach, the objective functions, including the accumulated return and maximum drawdown, are both optimized using NSGA-II to search for the best parameter combination of the pre-designed trading rule. Additionally, in recent years, hybrid methods have been extensively applied widely in research, and their empirical results demonstrated that those hybrid algorithms generally performed better than one part of the single model [35; 36; 37; 38]. It provides us with an idea to forecast the movement and to perform trading simulation by a hybrid approach that integrates the XGBoost, SMOTE, and NSGA-II. In summary, the main procedures of the method proposed in this research are as follows. First, the proposed approach collects and pre-processes the historical high-frequency data of the Chinese rebar futures. Second, the whole dataset is divided into several consecutive in-sample and out-of-sample periods, with the predicted labels divided into multiple categories of magnitude. Third, the training samples with smaller ratios are oversampled using the SMOTE for producing the balanced training datasets. Next, an XGBoost-based multiclass direction prediction model is trained for price direction change forecasting. Meanwhile, NSGA-II is employed to optimize the simulation trading rule.