5 of the Best Python Libraries for Derivatives Finance
Python, the amazingly versatile programming language, is quickly becoming a preferred tool in the realm of derivatives finance. In addition to its ease of use and ability to help you speed up the development lifecycle, Python also offers a vast ecosystem of powerful math and science libraries.
Many of these libraries are free to use and are well-suited to the modeling, analysis and computation functions that are essential to derivatives finance. Below I’ll discuss five exceptionally useful Python libraries that are becoming increasingly popular with quants and other financial technologists in the industry.
A highly popular mathematics Python library, NumPy, is often used for scientific computation. A large benefit of NumPy is that it can be quickly and easily integrated with a variety of different databases. Additionally, NumPy helps to simplify code, enabling users to code in less steps. Fewer lines of code typically equates to fewer bugs in production.
Similar to NumPy, SciPy is a Python-based library designed for math, science and engineering. SciPy is very good for numerical problem solving in that it offers an extensive collection of useful algorithms. Plus, SciPy provides many user-friendly and efficient numerical routines such as routines for numerical integration, interpolation, optimization, linear algebra and statistics.
Where Python was once less performant in areas like data analysis and modeling, pandas has bridged this gap. Indeed, pandas is great for managing data frames and offers excellent data representation. This powerful python library provides easy-to-use data structures and tools for high-performance data analysis and can efficiently handle large amounts of data. As a side note, pandas is best suited for fundamental data types rather than sophisticated statistical analysis.
Jupyter is becoming a highly productive environment for collaboration and sharing ideas across teams on a web-based platform. In particular, Jupyter can enable you to create and share notebooks that have code, equations, visualizations and narrative text. An added benefit of Jupyter is that it offers support for dozens of programming languages, enabling users to code in their language of choice.
Powerful Libraries, Better Productivity
As I’ve hopefully clarified in today’s post, the extensive selection of powerful libraries is a primary reason why financial technologists are turning to Python for their development projects. Python tools available today are flexible, easy to use and, in many cases, very interactive.
As we’ve seen, mathematics and statistics libraries such as NumPy and SciPy are well suited to financial analytics, and when users add tools such as Jupyter notebooks for interactive development, pandas for managing data frames and Plotly Dash for UI and visualization, Python becomes a formidable data science and analytics tool.
In fact, this vast ecosystem is a significant factor in the dramatic productivity gains organizations see with Python. Standard libraries and tools enable quants to focus on creating a competitive advantage, rather than spending resources reinventing basic functionality.
For more information on the benefits of developing with Python, check out: 5 of Our Top Python Resources.