Python is one of the most in-demand programming languages for those working in the financial services sector. The number of Python jobs in the financial sector has more than doubled over the past two years. Some banks even offer Python classes or training for their analysts and traders as a part of their onboarding and continuing education.
It’s true that Java used to be the go-to language for fintech applications, but Python is closing in fast on Java’s number one slot. Python is the perfect programming language for banking, fintech, and financial app development.
But why is Python the best programming language for the finance sector? Who even uses Python in fintech anyway? Read on to find out how researchers, analysts, and startups can all benefit from the power of Python.
What is Python used for in FinTech?
Python is used to create applications for the web and mobile devices that provide services for banking, portfolio management, cryptocurrency, stock trading, mobile payment services, and more. It’s used by organizations both large and small from massive banks like J.P. Morgan Chase to small startups like Tala.
Who is using Python for FinTech?
Python is being used by big banks, small startups, crypto companies, and more. Find out how each company uses Python and its packages to create successful fintech applications by clicking on their name below.Stripe Robinhood Zopa Clear Minds Iwoca Holvi Venmo Affirm Dash ZeroNet Koinim Crypto-Signal
Why use Python for finance projects?
Python is simple, versatile, scalable, and has a low margin for error. Typically finance projects and financial services applications are quite complex. Python simplifies this process with its syntax alone but also makes building a finance application efficient, secure, and manageable with its massive collection of frameworks and libraries.
There are Python libraries for trading algorithms, data analysis and visualization, user interface elements, authentication functions, cybersecurity, online payments, and mobile payments. Almost any financial task can benefit in some way from Python or one if its many packages.
The most popular Python libraries and frameworks for fintech include:PyAlgoTrade – an event-driven algorithmic trading library that supports back-testing, live-feed paper trading, and real-time trading on bitstamp. Pyfolio – a library for risk analytics and performance-related financial portfolios. This library can also be used to model tear sheets based on returns, perform bayesian analysis, and other transactions. Zipline – an algorithmic trading simulator written in python that is used to simulate realistic slippage, transaction costs, and order delays. Quantecon – modules in this library include game theory, Markov chains, random generation utilities, tools, and other utilities. Finmarketpy – a library that enables market analysis and backtests trading strategies using a simple to use API with inbuilt templates. Finmarketpy can be used to analyze the volatility of a target, conduct surveys around specific events corresponding to the data being analyzed, visualize trading strategies over a period of time, and to deduce the best times to use specific strategies.
With all of these libraries plus many more to choose from, a Python developer can build a fintech minimum viable product (MVP) that is flexible and scalable in record time. Over the long term, a Python application is easy to update and change when a fintech organization needs to quickly meet consumer demands.
Python is one of the most popular programming languages in the world which is great for any fintech organization. There is a great support network, detailed documentation, and thousands of open-source projects that were all built by the enthusiastic Python community.
Because it’s so popular, open-source Python libraries and frameworks like the ones we’ve listed here are worked on by a large group of expert developers. Because these packages are written by Python experts, Python libraries and frameworks are as secure as possible – which is a must for fintech applications.
How to use Python in the Finance Sector
Python can be used to create a variety of useful fintech applications. Most popularly it’s used to create analytics tools, banking software, cryptocurrency tools, trading strategies, and stock market apps.
For quantitative finance, Python is used to create solutions for processing and analyzing large datasets, data visualization, and statistical calculations. Libraries like pandas open doors to simple data visualization while Scikit-Learn or PyBrain provide machine learning algorithms for predictive analytics.
Python can be used to create payment solutions, online banking platforms, ATM software, and more. It’s secure, fast, lightweight, and poised to evolve. Consider an app like Venmo which combines Python’s powerful social media capabilities and secure structure using the Django framework.
Market analysis tools for cryptocurrency are a must for any cryptocurrency seller. The Python framework Anaconda is the perfect framework for this task because it assists developers in retrieving cryptocurrency pricing and creating visualizations.
Start learning Python for FinTech today!
Python is perfect for creating fintech applications, assisting with analytics, or processing data more efficiently. It’s adored by analysts, researchers, developers, banks, and startups for its simplicity, flexibility, and security. Learn Python today and boost your career or prepare to launch your very first MVP!