Abstract

Commodity futures are futures contracts based on the physical commodities. Unlike commodity stocks, which must be “bought first and then sold”, commodity futures can also be “sold first and then bought”. Therefore, it is not possible to directly use the formula of capital flow in the stock market to characterize the capital flow in futures contracts. In this paper, the principal component analysis method is used to construct the principal component factors based on the K-line basic market data and one based on the K-line index data. Then the factors mentioned above are cross-validated using the Holdout verification form to generate the training set and test of the support vector machine. Then, this paper applies genetic algorithm to optimize the penalty parameters and kernel functions of SVM, and obtains the parameters with the highest accuracy of classification and prediction of capital flow. Finally, this paper uses the traversal algorithm to find the time window with the highest accuracy of the SVM classification to predict the capital flow. The research results of this paper show that the SVM-based classification of capital flow in commodity futures market is highly accurate.

This work is licensed under a Creative Commons Attribution 4.0 License.