Financial forecasting by improved fragmentation algorithm of Granular Fragment based mining

Many algorithms of the existing methods are used for stock predictions. Most of stock predictions use static stored data for their analysis of algorithms. JStock framework is implemented as was proposed earlier to analyze improved fragmentation algorithm using dynamic online live data. Open source software is used for implementation which uses yahoo finance and Google finance for extraction of data and displaying the results. In this paper the implementation of JStock framework is discussed. The improved Fragmentation algorithm and its usage removed all previous disadvantages of using static data. JStock is open source software which has a legal way of extracting data from yahoo finance. The extracted data then has been processed by fragmentation algorithm of granular based inter-transactions associations and comparisons of various indicators are also done. The data is then successfully shown in Bar Charts and Pie charts to check the maximum profit in Stock Exchange of specific company. This implementation can be used for any companies all over the world. Different Indian sector companies are used for results and analysis. The results are very simple and can be used very easily for every newbie in Stock Market. The framework can be further used for newly proposed algorithms for more accurate and precise results for Stock predictions, which is otherwise very much fluctuating and highly unpredictable. The stream of data can be used for big data analytics too for all financial companies.