Using IBM Watson Studio and Watson Machine Learning, this code pattern provides an example of data science workflow which attempts to predict the end-of-day value of S&P 500 stocks based on historical data. This pattern includes the data mining process that uses the Quandl API – a marketplace for financial, economic, and alternative data delivered in modern formats for today’s analysts.

Use Jupyter Notebooks in Watson Studio to mine financial data using public APIs.

Use specialized Watson Studio tools like Data Refinery to prepare data for model training.

Build, train, and save a time series model from extracted data, using open-source Python libraries or the built-in graphical Modeler Flow in Watson Studio.

Interact with IBM Cloud Object Storage to store and access mined and modeled data.

Store a model created with Modeler Flow and interact with the Watson Machine Learning service using the Python API.

Generate graphical visualizations of time series data using Pandas and Bokeh.

Mridul Bhandari, IBM Developer Advocate, https://developer.ibm.com/profiles/mridul.bhandari/

Anchal Bhalla, Data and AI Technical Specialist, https://developer.ibm.com/profiles/anchal.bhalla/

Log in or sign up for your cloud account: http://ibm.biz/StockMarketWatsonStudio

Hands-on - https://ibm.biz/StockMarketWatsonStudioLab

GitHub Repository - https://github.com/IBM/watson-stock-market-predictor

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Forecasting the Stock Market with Watson Studio