Python for FinTech — FinTech Projects and Use Cases

8 min read
Python for FinTech — FinTech Projects and Use Cases

FinTech is a combination of the terms "finance" and "technology." It refers to any business that leverages technology to improve or automate financial services and operations. Python comes in handy in a broad range of FinTech use cases. Its clear programming language syntax and amazing ecosystem of tools make it one of the best technologies.

Where is Python used in FinTech?

Python comes in handy in a broad range of applications. Here are the most popular uses of the language in the FinTech industry.

Analytics tools

Python is commonly used in quantitative finance to process and analyze massive datasets, such as financial data. Pandas is a library that simplifies the process of data visualization and allows for complex statistical analyses. Python-based solutions are equipped with powerful machine learning algorithms that enable predictive analytics, which is extremely important to all financial services providers, thanks to libraries like Scikit or PyBrain. Examples of such products: Iwoca, Holvi.

Stock Trading Platforms

The stock market generates enormous amounts of financial data, which necessitates extensive research. That's when Python comes in handy. It can be used by developers to build solutions that discover the best stock trading methods and provide practical, predictive analytical insights into the state of specific markets. Algorithmic trading in fintech products is an example of a use case.

Examples of such products: Quantopian, Quantconnect, Zipline, Backtrader

See our article on Companies Using Python - Stock Trading Platforms where to describe the usage of Python in Robinhood, Bank of America’s Quartz, and J.P. Morgan’s Athena.


Every business that sells cryptocurrencies requires tools to analyze cryptocurrency market data and make predictions. Anaconda, a Python data science platform, aids developers in obtaining cryptocurrency prices, analyzing them, and visualizing financial data. As a result, Python is used by the vast majority of web applications that deal with bitcoin analysis.

Examples of such products: Dash, Enigma, ZeroNet, koine, crypto-signal

Advantages of Python in FinTech?

Versatility & Scalability

Python roughly has over 137,000 python libraries and countless frameworks. All these libraries and frameworks are there to facilitate developers in many different ways. If someone is working on a Gaming application then they are probably going to use a dedicated Python library while a programmer working on Web Development has a different set of tools and libraries offered by Python.

These libraries and frameworks are pretty scalable and a developer has a lot of room to play around for developing their desired product. Also, new frameworks are also frequently introduced into the market to make the processes even much simpler and smoother.

Strong Community

According to SlashData, the Python community has expanded more than that of the Java community. There are roughly over 8.2 million Python developers who use this language for personal or professional purposes.

This clearly depicts the size of this huge community. These developers are spread all across different forums and platforms such as Python and they really help each other to grow by learning from their mistakes.

Easy to Read, Learn & Use

A normal person with basic computer knowledge can learn the basics of Python in a single month and this ease of learning comes down to the simple Syntax of this language.

Also, the coding at an initial level is pretty easy and a Python coder with basic knowledge can easily understand other programs written in the same language, just to get a context of what’s actually happening inside the code.

The best Python Libraries for FinTech


Numpy is an array processing package that can be used for a variety of tasks. It offers high-performance multidimensional array objects as well as array-related tools. The homogeneous multidimensional array is NumPy's core object. It's a table containing the same datatype elements or numbers, indexed by a tuple of positive integers.

What can you do with NumPy?

  • Basic array operations: add, multiply, slice, flatten, reshape, index arrays
  • Advanced array operations: stack arrays split into sections, broadcast arrays
  • Work with DateTime or Linear Algebra
  • Basic Slicing and Advanced Indexing in NumPy Python


Pandas is a Python library that provides high-performance, easy-to-use data structures, and data analysis tools for labeled data. Python Data Analysis Library is referred to as Pandas. Pandas take data from a CSV or TSV file or a SQL database and turn it into a data frame, a Python object with rows and columns.


PyAlgoTrade is a Python Algorithmic Trading Library with a focus on backtesting and support for paper trading and live trading. Let’s say you have an idea for a trading strategy and you’d like to evaluate it with historical data and see how it behaves. PyAlgoTrade allows you to do so with minimal effort.


FinmarketPy is a Python-based library that enables you to analyze market data and also to backtest trading strategies using a simple to use API, which has prebuilt templates for you to define backtest. Included in the library

  • Prebuilt templates for backtesting trading strategies
  • Display historical returns for trading strategies
  • Investigate seasonality of trading strategies
  • Conduct market event studies around data events
  • Builtin calculator for risk-weighting using volatility targeting
  • Written in an object-oriented way to make code more reusable


The SciPy library is one of the foundational packages that comprise the SciPy stack. There is now a distinction between SciPy Stack and SciPy, the library. SciPy is based on the NumPy array object and is part of the stack, which also includes tools like Matplotlib, Pandas, and SymPy.

The SciPy library includes modules for efficient mathematical routines such as linear algebra, interpolation, optimization, integration, and statistics. The SciPy library's main functionality is based on NumPy and its arrays. NumPy is extensively used in SciPy.

Python for FinTech Use Cases


Affirm is another credit card firm that issues cards to millions of their customers without any hidden fees. This company is also utilizing python for their mobile and web applications, they recently made a few changes to their Python codebase.

Unnecessary database reads were removed that included N+1 queries issues with SQLAlchemy. The further optimized the CPU-bound code making heavy use of Python’s cProfile.

Why Affirm use Python?

  • They integrated Python to prevent any regressions by optimizing the CPU-bound code.
  • They also used Gevent along with Python to scale up their architecture.


Robinhood is one of the biggest players in the Crypto and Stock trading market with millions of customers, especially from the U.S. This is an interactive trading platform with clean interfaces to make trading easier for everyone.

Jaren Glover also cleared in an interview with SoftwareDaily that they used Python for their market data services just like other trading platforms did. Also, they rewrote those services in GoLang for performance factors.

Why Robinhood use Python?

  • They wrote some trading services in Python but then rewrote them in Go Lang by taking performance-critical factors in sight to ensure low latency.
  • The most of the RobinHood APIs are either written in Python and GoLang.


Revolut is a popular money exchanger and credit card provider firm. They have over 15 million customers globally who have excess to Revolut’s marketplace for buying Crypto and Gold. Revolut also posted a job for a Python developer for their core development team.

The focus of Revolut was to use popular Python frameworks such as Django to develop an efficient system for better customer experiences by utilizing the data.

Why Revolut use Python?

  • Revolut used Flask or Django for the backend development of the application, they hired new developers for this purpose.
  • They also utilized SqlAlchemy for machine learning purposes using Python.


The Stripe payment gateway is used by companies and individuals to send and receive payments all across the world. They smoothly handle all the transactions happening over their system by controlling all the processes such as invoicing, managing subscriptions, and fraud prevention.

The FinTech company actually used Python to develop all its APIs for mobile applications and even web apps. Some big companies that use Stripe gateway include Salesforce, Lyft, and Amazon. FinTech companies prefer Python because of its data features.

Why Stripe use Python?

  • Python is perfect for Big Data systems and that’s exactly what FinTech companies need in their environment to make millions of calculations and collect data.
  • The financial experts also find Python easy to learn and use which made it a good choice for Stripe as well.


Zopa makes loaning easier by bringing investors and borrowers to their platforms. The Zopa aims to provide a smooth and easy experience to lenders and borrowers using an interactive platform with Python as its backbone.

They also hired Python engineers and programmers for Big data architecture of their company. They use a cloud-based system to store all their data securely.

Why Zopa use Python?

  • Zopa wanted to make use of their Big Data system to filter out some useful information that can help to make their platform much better.
  • They also used PySpark to support their Apache Spark server which can easily be integrated with Python.


Python can be utilized to create incredibly scalable and secure FinTech solutions. Python's clear programming syntax and amazing ecosystem of tools make it one of the best technologies to handle the development process of any FinTech.