Python Stack for Design and Visualization in Financial Engineering

The practical problems in financial engineering are highly interdisciplinary, requiring as much facility with applied mathematics, statistics and programming as with finance. Solving mathematically challenging problems and writing efficient computer programs to price complex structured products is just one part of the puzzle, however. Given the number of design elements involved in creating such products, a front end that combines visualization and interactivity is as important as speed and efficiency of computations. In this note we highlight the power of the Python stack for designing graphical user interfaces for engineering structured product solutions by visualizing their payoffs and prices in a web browser. Object-oriented programming in Python combined with the power of NumPy, Matplotlib and Jupyter fits the bill perfectly for design and visualization in financial engineering. We find that Python combined with Jupyter is not only very well suited for designing and visualizing structured products and examining the impact on pricing as different design elements are tweaked, but it is also amenable to a variety of extensions and integration with other open-source computational finance libraries.