Research And Application Of Futures Data Mining Algorithm

With the gradual deepening of reform and opening up,the rapid development of the financial market,some related systems have been gradually improved,the futures market is facing good development opportunities,and more and more companies,individuals and institutional investors are joining the futures market.Nowadays,the relationship between the Chinese futures market and the world futures market is getting closer and closer,and economic globalization has had a huge impact on the competition of the futures markets of various countries.Therefore,it is necessary to establish the futures market to avoid the risks of the spot market and strengthen the theoretical research on China's futures market.The use of data mining to explore the value information of futures trading data has become the most important problem.In this paper,the futures data mining algorithm is researched from the aspects of dividing consumer customer categories and predicting product prices.On the basis of focusing on classification,prediction,big data,front-end technology and data preprocessing,the FCM clustering and BP prediction algorithm under serial and parallel comparison is obtained,which realizes the distributed operation of the algorithm under the Spark computing framework,and simultaneously The improved algorithm is applied to the futures management platform.First,the classification of futures trading customers is combined with K-means and fuzzy clustering FCM and other classification algorithms,and the customer portraits are described in the form of labels.Based on BP neural network,genetic algorithm is used to improve the algorithm,and the two algorithms are used to compare the prediction results of multiple product prices;then,the verification of feasible customer clustering and product prediction algorithms are combined with big data technology,Optimize the design of the algorithm to meet the application effect in the big data environment,and parallelize the two algorithms in the built Spark platform,which improves the algorithm's performance and data processing ability under the big data;Finally,combined with customers According to the demand,the futures platform and data visualization page have been designed from the perspective of customers,products,and early warnings to provide data support for enterprise managers to understand the dynamic information of customers and products in real time,so as to avoid the risk of customer churn and formulate accurate customer marketing programs.In this paper,the serial and parallel design of futures data mining algorithms is applied to the enterprise's intelligent platform,which verifies the rationality and feasibility of the algorithm,and provides a theoretical basis and reference basis for the future development of futures algorithm research under big data.