Designing a smart algorithm for determining stock exchange signals by data mining

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock exchange market. This research proposes a smart algorithm by means of valuable big data that is generated by stock exchange market and different kinds of methodology to present a smart model.In this paper, we investigate relationships between the data and access to their latent information with an enormous amount of data which has a significant impact on the investor’s decisions. First, extracting technical indicators from different point of the charts based on two groups of stock exchanges like petrochemical and automotive during 1387 to 1396, then analyzing clusters by means of k-means algorithm and data mining methodology. The contributions of this paper are: 1. To create a model with twenty technical indicators in different stock exchange companies and industries.2. To evaluate the proposed model and finally to predict the sales signals at the maximum points which has significant performance and can be predicted with acceptable accuracy.