Python for Algorithmic Trading

Appendix A. Python, NumPy, matplotlib, pandas

Talk is cheap. Show me the code.

Linus Torvalds

Python has become a powerful programming language and has developed a vast ecosystem of helpful packages over the last couple of years. This appendix provides a concise overview of Python and three of the major pillars of the so-called scientific or data science stack:

• NumPy (see https://numpy.org)

• matplotlib (see https://matplotlib.org)

• pandas (see https://pandas.pydata.org)

NumPy provides performant array operations on large, homogeneous numerical data sets while pandas is primarily designed to handle tabular data, such as financial time series data, efficiently.

Such an introductory appendix—only addressing selected topics relevant to the rest of the contents of this book—cannot, of course, replace a thorough introduction to Python and the packages covered. However, if you are rather new to Python or programming in general you might get a first overview and a feeling of what Python is all about. If you are already experienced in another language typically used in quantitative finance (such as Matlab, R, C++, or VBA), you see what typical data structures, programming paradigms, and idioms in Python look like.

For a comprehensive overview of Python applied to finance see, Hilpisch (2018). Other, more general introductions to the language with a scientific and data analysis focus are VanderPlas (2017) and McKinney (2017).

Python Basics

This section introduces basic Python data ...