Python is rapidly becoming the world’s most popular programming language. Its versatility and ease of use have promoted extensive adoption throughout the financial industry. And it’s safe to say that Python has become the multi-purpose tool of choice for quantitative analysts and other financial technologists worldwide.
So, why has Python seen such rapid adoption in the financial industry? Here are five key reasons.
1. Gain Versatility and Ease of Use
Python is known the world over as being one of the easiest (if not the easiest) coding language. And this is, of course, not merely by accident. Frustrated by the shortcomings of other programming languages, in late 1989 Guido van Rossum set out to create one that would be easy to read and have maximum flexibility. Thus, Python was born, offering syntax so easy to learn that even those who’ve never coded can follow the logic. In fact, Python is so massively appealing because it empowers users to write code faster than with other coding languages.
2. Access Powerful Libraries
Python offers a vast ecosystem of powerful math and science libraries, which can save time and shorten your development cycle. Many of these libraries are free to use and are ideal for the modeling, analysis and computation functions that are essential to derivatives finance. For example, 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 for user interface (UI) and visualization, Python becomes a very powerful data science and analytics tool. Standard libraries and tools enable quants to focus on creating a competitive advantage, rather than spending large amounts of resources on reinventing basic functionality.
3. Collaborate Better
Indeed, Python’s ease of use and setup means a range of roles within organizations are using Python rather than other programming languages. Thus, it is not just traditional developers that have a say in the development process. With Python, quants, traders and portfolio managers can get involved and shape the direction of new systems and applications. This leads to increased collaboration and allows for rapid development timeframes, saving time and cost.
4. Get Going Quickly
The large pool of resources available to those using Python makes getting up and running straightforward, meaning many can build their own custom analytics and bespoke reports without needing to go through an internal development team or wait for a software vendor’s next release. Introducing this level of speed into customizing functionality improves agility within your business.
5. Easily Enhance Existing Systems
Instead of building systems from scratch each time you need new functionality, Python can be used to extend and enhance larger systems without a lot of money and headache. In fact, this approach can diminish the margin for error and reduce overall operational risk. Python can also be used to quickly prototype new workflows and reports without engaging in drawn-out and costly development projects.
How FINCAD Uses Python
At FINCAD, we recognize the power and flexibility of developing with Python. That’s why we have equipped FINCAD with Python as a powerful API. Firms use FINCAD’s industry-leading analytics along with easy to use Python tools in order to accurately value derivative and fixed income instruments, as well as perform on-point pre-trade analysis. This approach offers our clients complete control and precision over managing every aspect of their portfolio.
Additionally, because FINCAD analytics library is designed to work brilliantly with Python, it is both quick and easy for users to get started developing analytics, and new applications. This is a big advantage for firms that are looking to add business value quickly and economically.
Check out our brief video to learn more about the many advantages of using Python in financial organizations.