Methodological Basis and Experience of Using Data Mining Methods in Trade

Abstract

The article explores data mining methods that allow us to get helpful information from the data. The possibility of using these methods in practice in the financial sector was considered. Since financial activity is closely related to our social life, the use of data mining methods plays an essential role in the analysis and forecasting of the financial market in the modern era of big data. However, due to differences in the experience of researchers in different disciplines, it is not easy to use data mining methods when analyzing financial data. Therefore, creating a methodological basis for the practical application of data mining methods in the analysis of financial data is an urgent issue. The purpose of this article is to create a methodological basis for using data mining methods for efficient trading. When processing product data, a priori methods and visualization methods were used, and their implementation in practice was described. As a result, scenarios of computer applications were created as a sample of the practical implementation of the algorithms of these methods. Building a quantitative trading strategy requires first statistical analysis of the information in the market and then testing the quantitative model on the collected data. This study developed a quantitative trading system based on data mining methods. The primary development tool used is the Jupyter web platform, and three cores have been developed: quantitative data selection, strategy testing on data, time series analysis, and visualization. The developed system supports modules for making simple trading decisions.