The global stock market is expansive and volatile. With thousands of stocks whose values change constantly due to (macro) economic events, it’s almost impossible for investors to keep track. Furthermore, reliable and current data are not available to everyone. If traders were able to collect real-time stock information, they would be able to not only monitor these stocks of interest but also predict changes in value over time and react accordingly.
Data scientists build a workflow in KNIME Analytics Platform to estimate the percentage change in stock value for the following day. The workflow uses the native KNIME Python Integration to collect stock information via the pandas-datareader library. The workflow is then deployed on KNIME Server and, using the built-in scheduling feature, is executed at the start of each working day. A report is generated, which is delivered to the trader’s inbox as soon as it’s finished executing and is ready for them to read the minute they open their mail.
KNIME Analytics Platform enables data scientists to seamlessly integrate other technologies within one familiar environment. In this case, data is read in from Yahoo Finance using the native KNIME Python Integration. KNIME Server makes it possible to schedule and execute the workflow daily, making important stock information available to traders and other decision makers.
This Success Story can be downloaded here as a PDF.