Summary

In this chapter, we discussed how Python might be suitable for certain areas of finance and also discussed its advantages for our software applications. We also considered the functional programming paradigm and the object-oriented programming paradigm that are supported in Python, and saw how we can achieve brevity in our applications. There is no clear rule as to how one approach may be favored over the other. Ultimately, Python gives programmers the flexibility to structure their code to the best interests of the project at hand.

We were introduced to IPython, the interactive computing shell for Python, and explored its usefulness in scientific computing and rich media presentation. We worked on simple exercises on our web browser with the IPython Notebook, and learned how to create a new notebook document, insert text with the Markdown language, performed simple calculations, plotted graphs, displayed mathematical equations, inserted images and videos, rendered HTML, and learned how to use pandas to fetch the stock market data from Yahoo! Finance as a DataFrame object before presenting its content as an HTML table. This will help us visualize data and perform rich media presentations to our audience.

Python is just one of the many powerful programing languages that can be considered in quantitative finance studies, not limited to Julia, R, MATLAB, and Java. You should be able to present key concepts more effectively in the Python language. These concepts, once mastered, can easily be applied to any language you choose when creating your next financial application.

In the next chapter, we will explore linear models in finance and techniques used in portfolio management.