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Notes on implementation

A comprehensive list of technologies, libraries and tools enabled Built Stock Explorer to be brought to the users. It is being developed primarily in Python with some pieces of HTML and CSS scripts and the extensive use of TeX and Markdown. The components at the interface level are produced with Dash, Plotly and Matplotlib.

Functional capabilities of the application rely on a number of Python libraries:

  • Numpy for numerous algebraic operations;
  • Pandas for data manipulations;
  • Scipy for density estimation and computing sample statistics in Distplot;
  • Sklearn for the implementation of clustering and regression modelling in Datacube.

Additionally:

  • IPython and Jupyter are used as the environments for interactive development and testing of solutions;
  • GitLab infrastructure made the collaborative workflow and version control possible;
  • Docker facilitated many procedures related to the development and deployment;
  • MkDocs enabled to build this documentation page.

Many thanks to all the talented teams who contributed to make them available. And enabled to develop Built Stock Explorer.