Over the next few decades, Python continued to grow and become a programming giant. Widely considered to be one of the three most popular programming languages in use today (along with Java and JavaScript), Python powers software and hardware that runs in nearly every major industry in the world. With use cases everywhere from traditional web development to cutting-edge fields like machine learning and AI, Python is poised to become an essential part of technology for decades to come.
The area where Python has proven most useful is finance, where the language has led to innovations that have revolutionized modern business and hundreds of different investments. The versatility of the language, the powerful set of tools and libraries developed for it, and the growing developer community that drives it forward make the language a cornerstone to a great extent.
However, there are also reasons unique to the financial industry that make Python a popular choice for developing the software and hardware required for the industry to operate. But why is Python so big in finance? Let's take a look at some of the most popular use cases, benefits, and tools Python offers to finance professionals, as well as some of the ways Python promises to continue to drive finance in the future.
Why has Python become such a popular programming language?
If you're new to Python or just starting to learn coding, you might not know that Python is a great language to learn, along with other beginner versions like Ruby. Here are some reasons why Python is so popular in the financial world and in general:
It is open source
Most of the libraries and support tools available with Python are open source and often come with flexible (often free!) licenses.
The reference CPython implementation is a language interpreter that converts Python code to executable bytecode at runtime. multi-paradigm
Python supports a variety of programming and implementation paradigms, such as object-oriented and imperative, functional, or procedural programming.
Multifunction
You can use Python to develop interactive code quickly and create large-scale applications. It can also be used for low-level system tasks and high-level analysis tasks.
Transversal adaptation
Python is available for major operating systems such as Windows, Linux, and Mac OS. Python is widely used for building desktop and web applications. Python can be used on the largest clusters and most powerful servers, as well as on small devices like the Raspberry Pi.
Dynamic Typing
Types in Python are usually created at runtime and are not declared static compared to most other compiled languages.
Know Indentation
Unlike many other programming languages, Python uses indentation to indicate blocks of code, rather than brackets, parentheses, or semicolons.
Why Is Python So Useful For Finance?
Simplicity and flexibility
Python is helpful for finance because it has a clear and concise syntax, making it easy to write code that is both readable and efficient. Additionally, Python offers powerful libraries that can be used for Simplicity and flexibility are two important qualities in any system. Python's syntax is efficient and reliable, making it an ideal language for developing software that is essential to an organization. Meanwhile, reducing the potential error rate can be very important in industries like finance. The reduction of liability is important in an industry where the bottom line is all that matters.
An MVP can be built quickly if the organization has a clear understanding of the problem and the proposed solution. The financial services sector demands incredible adaptability and flexibility in order to meet the ever-changing demands of customers and clients. This allows for customized experiences and extra services that can add value to a product. That's why Python is a great language for finance organizations and fin-techs, which is why it has proven to be both flexible and scalable. Developers can quickly take an idea from wireframing to a working prototype to stay ahead of the competition and get software facing customers as soon as possible. After QA'ing the minimum viable product, businesses can easily make adjustments to parts of the code or incorporate new ones to create a user-friendly finished product.
The Universe of Python Tools and Libraries is second to none.
One of the major benefits of Python is the vast ecosystem of user-created tools and libraries that can shortcut coding and make development easier. In addition, fintech products often require third-party integrations, and there are several Python libraries to facilitate this. Python's speed of development provides a competitive advantage to organizations that want to bring products to market quickly with a wide range of tools and libraries to meet changing customer needs. Again, speed and efficiency are paramount in fintech, and Python delivers both.
Python is also ideal for data science applications in finance.
Data scientists often use specialized languages, such as R or Mat-lab, designed specifically for this purpose, but their steep learning curve can limit their wider use. Enter Python, which is much more popular among financial professionals due to its simplicity and ease of use for creating algorithms and formulas. Tools such as scipy , numpy , or mat-plotlib allow finance professors to generate complex financial calculations and display the results in an easy-to-understand way, ideal for communicating results with other teams, especially those who are not tech-savvy.
Python's analytical features are well-suited for analytics-based careers
Python is particularly popular in the field of quantitative finance, providing solutions for processing and analyzing large data sets and large financial data for trend identification and forecasting. Popular libraries such as Pandas simplify the data visualization process and support more complex statistical calculations. Also, thanks to libraries like Pybrain , Python-based solutions equipped with powerful machine learning algorithms help in the predictive analysis that forms the basis of modern financial and investment strategies.
Python Provides Tools For Emerging Fintech And Crypto Needs.
The uses of analytics mentioned above also apply to more recent developments in fintech, both in terms of software and in new areas like cryptocurrencies. Developers can use Anaconda, a Python data science ecosystem, to retrieve cryptocurrency prices and analyze or visualize financial data, for instance.