# High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method
## 6 Conclusions  
In this research, a novel integration approach based on a combination of XGBoost, SMOTE, and NSGA-II is developed to predict and trade on rebar futures. In the proposed model, the training datasets of minority classes are oversampled by the SMOTE technique to overcome the problem of the class imbalance, and XGBoost is adopted for the rebar futures price direction prediction using the oversampled training data. Then, NSGA-II is employed to search for the Pareto optimal solution of the trading rule parameters by efficiently allocating accumulated returns and maximum drawdown. The proposed approach produced an average hit ratio of 53.98% and an average return of 34.9% over the four testing periods. The proposed method also performed well in terms of the accumulated return of the entire one-year simulation trading with the value of 1.395. Furthermore, the maximum drawdown and Sharpe ratio of the proposed method reached 0.135 and 3.6574, respectively, which were superior to all the benchmark methods. Based on the experimental results comparison of the proposed method and benchmarks, it demonstrates that the proposed method XGBoost-SMOTE-NSGA-II successfully enhances the direction prediction accuracy and profitability, and generates the best return-risk ratio results among all the investigated methods. In summary, according to the outstanding direction prediction and simulation trading performances, it demonstrates that the proposed XGBoost-SMOTE-NSGA-II method can be utilized as an effective decision-making system for both investors and regulators engaged in the rebar futures market. In this paper, we propose a high-frequency direction prediction and simulation trading model for rebar futures that combines the XGBoost, SMOTE and NSGA-II algorithms. The LIME algorithm is adopted to explain the prediction results of the proposed method. There are some research directions to be further expanded. For instance, in this research, historical transaction data from ten periods are utilized for five periods-ahead movement prediction, different time frames of historical data could be further considered as input features for prediction performance comparison. Moreover, this research develops an integrated approach based on the historical price data of rebar futures for direction forecasting. However, the sentiment and behavior of investors in the futures market could have an impact on the movement of futures prices. Therefore, other researchers may take into account the influence of investors' sentiment features to improve the model prediction and trading accuracy. Moreover, scholars could employ other machine-learning algorithms as the multiclassification models, such as Random Forest [53], CatBoost, and LightGBM. Moreover, scholars can also try other methods to solve the imbalance sample problem, such as the Generative Adversarial Networks (GAN) [54], LoRAS [55], or the improved algorithms of SMOTE [56]. **Author Contributions:** Conceptualization, S.D. (Shanghai Deng); methodology, S.D. (Shanghai Deng); software, X.H. and Y.Z. ; validation, S.D. (Shanghai Deng) and Y.Z. ; formal analysis, S.D. (Shanghai Deng) and X.H. ; investigation, Y.Z. ; resources, S.D. (Shanghai Deng) and Z.F. ; data curation, Y.Z. ; writing--original draft preparation, Y.Z. and S.D. (Shanghai Deng); writing--review and editing, S.D. (Shanghai Deng) and S.D. (Shuangyang Duan); visualization, X.H. ; supervision, S.D. ; project administration, S.D. (Shanghai Deng) and Z.F. ; funding acquisition, S.D. (Shanghai Deng) and X.H. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was sponsored by the Hubei Provincial Natural Science Foundation of China (grant number 2021 CFB175) and the Philosophy and Social Science Research Project of the Department of Education of Hubei Province (grant number 21Q035). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Publicly available datasets were analyzed in this study.