Best Python Libraries/Packages for Finance and Financial Data Scientists

Finance professionals involved in data analytics and data science make use of R, Python and other programming languages to perform analysis on a variety of data sets. Python has been gathering a lot of interest and is becoming a language of choice for data analysis. Python also has a very active community which doesn’t shy from contributing to the growth of python libraries. If you search on Github, a popular code hosting platform, you will see that there is a python package to do almost anything you want.

This article provides a list of the best python packages and libraries used by finance professionals, quants, and financial data scientists.

Numerical, Statistical & Data Structures

• numpy - NumPy is the fundamental package for scientific computing with Python. It is a first-rate library for numerical programming and is widely used in academia, finance, and industry. NumPy specializes in basic array operations.
• scipy - SciPy supplements the popular Numeric module, Numpy. It is a Python-based ecosystem of open-source software for mathematics, science, and engineering. It is also used intensively for scientific and financial computation based on Python
• pandas - The pandas library provides high-performance, easy-to-use data structures and data analysis tools for the Python programming language. Pandas focus is on the fundamental data types and their methods, leaving other packages to add more sophisticated statistical functionality
• quantdsl - Quand DSL is domain specific language for quantitative analytics in finance and trading. Quant DSL is a functional programming language for modeling derivative instruments.
• statistics - This is a built-in Python library for all basic statistical calculations

Financial Instruments

• pyfin - Pyfin is a python library for performing basic options pricing in python