Python is a spectacular programming language released in 1991 by Guido van Rossum. It came into prominence in the late 2000s (being named TIOBE Index’s Programming Language of the Year for the first time in 2007) and only got bigger and more relevant ever since.
Its simplicity and flexibility made it extremely popular among professional developers, other specialists, and enthusiasts. Additionally, Python is considered a reliable choice when it comes to security. It should come as no surprise that this trendy language got the attention of the fintech industry too.
Let’s answer some popular questions about how Python is used in finance, and then we’ll explore five reasons why Robinhood, Stripe, Venmo, and many others chose Python for their fintech products and why you should consider it as well. If you’d like to read more about Python first, you can start by reading this blog post.
Probably the first significant Fintech/Python project was Bank of America Merrill Lynch’s Quartz, trading and risk management platform, released in 2010. After that, new interesting projects began to appear routinely. To name some of the biggest, Python was used in ThoughtMachine’s Vault OS, JPMorgan’s Athena, Figo’s “banking as a service” platform, Revolut’s alternative banking solution, and more.
Python is a general-purpose programming language, which means it was designed to enable the creation of a variety of software. With a vast number of libraries and frameworks at developers’ disposal, as well as advanced security features, Python proved to be an effective tool in scientific computing, data analytics, artificial intelligence projects, and web applications.
There are also many uses for this language in the world of fintech. Python was successfully used to build digital payment solutions (Stripe), financial analytics software (Kensho), banking platforms (Revolut), as well as cryptocurrency and stock marketplaces (Robinhood).
There are many reasons to love Python: it’s simple to write and read, it’s fun to learn and use, and a lot of people know it, making it a lot easier to overcome any obstacles. An abundance of open-source libraries makes it easier for everyone to quickly build foundations of their software, from pre-made components, rather than creating everything from scratch.
Robust frameworks, like popular and powerful Django, open even more ways to create reliable software fast. Easy integrations with third-party APIs are probably among the reasons too. But probably the best reason for this preference is the prevalence of Python knowledge among mathematicians and economists, making Python a sort of programming lingua franca for finance industry professionals. On the other hand, some developers say that Python is slow – but this claim is arguable.
Python is an open-source, high-level, general-purpose programming language that’s much easier to understand and use than it is to explain the first part of this sentence. But since explaining it will prove why it’s so simple to create code in Python, let’s try to do this anyway.
These characteristics make Python more accessible and easier to learn, compared to low-level programming languages and more specialized high-level ones.
In modern software development, it’s the people behind the programming language who make it genuinely great. When a language reaches a certain level of popularity, it becomes much easier to code in by virtue of the sheer amount of knowledge and support the community shares for free.
Python's user base is vast and diverse. Its most dedicated enthusiasts are so committed, they have a name for themselves: “Pythonistas”. There are 215,000+ members on Python’s official Discord server, and 1,744,000+ questions tagged [python] on Stack Overflow as of June 2021. With that many developers actively sharing their work and solutions to encountered issues, there is a big chance that their documentation and knowledge will help in resolving potential hiccups with your app quickly.
As you can probably guess by now, the talent pool is one of the most obvious advantages of Python. As of June 2021, Python places at close second place in the TIOBE Index (which measures the popularity of programming languages). It’s inevitably about to take the first place soon, a feat that only C and Java achieved before. According to Stack Overflow's annual Developer Survey in 2020, Python is the third most loved language among developers, with TypeScript and Rust at second and first places, respectively. Additionally, they found that 30% of developers who are not working in Python would be interested in starting.
But that’s not all: Python is also popular among those not working as professional developers. The simplicity of its code and plenitude of usages make it popular with analysts, researchers, and economists.
Creating software with Python is surprisingly fast. Frameworks like Django and libraries like NumPy (for scientific computing) or Pandas (for data analysis and manipulation) enable developers to build from already existing blocks of code, rather than creating everything from scratch. Writing new code that will make those blocks work together is also pretty straightforward, as already mentioned above. These conditions result in a quicker development process, with MVP typically being ready in 2-4 months, assuming we’re talking about a low- to mid-sized project. As a general rule, Python applications are also highly scalable, especially when building upon frameworks.
For the purposes of this text, you should know that machine learning is a form of artificial intelligence project that uses algorithms and statistical models to make predictions based on continually received new sets of data. It’s commonly used in fintech products in areas of automated trading, cyber-security (including fraud detection), personal finance, customer service, and risk management. There are several reasons to choose Python for any machine learning project (e.g., code readability, speed of execution, and supportive communities), and they were discussed on our blog at length before.
For us at Monterail, Python ticks all the necessary boxes to be our number one choice for fintech projects: it’s secure, fast to write, easy to collaborate in, full of helpful add-ons, and fun to use. So if you’re on board, we’d love to help you in building your fintech product.