The fintech industry is powered by a venerable tech stack behind the scenes. These technologies are responsible for the billions of dollars flowing into fintech companies in the past 3-4 years.
Python is currently one of the most loved programming languages according to the Stack Overflow Developer Survey 2022. It also makes for a versatile fintech development tool owing to its vast ecosystem of financial libraries. Plus, it’s simple syntax and high-level language tenets make it easy to learn for non-technical people.
Read on to learn how to leverage Python in finance and the libraries to help you do it.
Why Is Python Widely Used by the Financial Sector?
The finance sector and fintech in particular operate in a fast, competitive, and heavily-regulated environment. Time-to-market is key and efficiency and ease of use are highly desired in any technological tool.
Python lends itself well to fintechs by scoring high on the above metrics and more. The most significant advantages it brings to the fintech sector are:
Accelerated Time-To-Market
The fintech space is marked by hundreds, if not thousands of players vying to crack the next big solution and get funded. There really is no place for laggards or technologically hesitant companies. Python’s flexible and scalable code enables developers to quickly build a minimum viable product (MVP) and test its efficacy on an existing market problem.
Companies no longer need months of work to test the waters with a new idea. Once they’ve validated their MVP, they can easily modify and refine it, thanks to Python’s performant code.
Increased Simplicity
Python was designed with readability in mind. Its syntax resembles plain English and is fairly easy to learn and master which is especially useful for designing complicated financial models. It’s also known for its concise code and is hence perfect for deploying large financial applications.
Its simplicity also minimizes potential errors and can save organizations time and money by avoiding penalties in a heavily regulated industry.
Unprecedented Functionality
Fintech developers don’t need to design their applications from the ground up. Python makes the job easy with its rich collection of standard and third-party libraries and tools.
Financial software involves analysis, algorithms, models, and other complex mathematical concepts. By providing these solutions built-in using various libraries and third-party integrations, Python surpasses other programming languages.
Rising Popularity
In terms of popularity, Python sits well above Java and C++ which are also commonly used by the financial sector.
Python provides stellar support to fintech developers, due to its open-source codebase and excellent support from a passionate community. These factors naturally provide fintechs with ample Python talent to choose from.
Better Versatility
Economists and other finance professionals don’t need to get their MATLAB models translated into Python code. Python supports end-to-end development of financial models and solutions in a single environment. It’s a versatile tool for prototyping and development for both small and large applications.
4 Applications of Python in Fintech
Fintech applications encompass a wide range of sub-verticals and mathematical disciplines. The beauty of Python is that its multiple libraries give developers access to countless pre-built instances of financial models and solutions.
Here are four major applications of Python in finance.
1. Data Analysis & Visualization
Making sense of large and complex datasets and visualizing them for further predictive analytics is at the core of many fintech solutions. Python libraries are equipped with robust data visualization, statistical analysis, and machine learning capabilities. Pandas, PyBrain, and Scikit are some examples of such libraries.
2. Banking Software
Banking applications, both traditional and modern, make use of Python’s simplicity and scalability. ATM software uses Python. So does Venmo for its payment services. Many other fintech companies like the payment processor Stripe and the trading platform Robinhood utilize Python for their platforms as well.
3. Cryptocurrency
With new players entering the cryptocurrency space almost every day, the demand for market analysis programs is also increasing. Companies dependent on analyzing cryptocurrency prices and offering predictive insights need these programs on a daily basis.
Data analysis tools like Anaconda and many decentralized platforms on the blockchain utilize Python and its libraries to empower their code.
4. Stock Trading
Python’s impressive data analysis capabilities are a great fit for designing stock trading strategies and offering predictive insights into the markets. Algorithmic trading is no longer limited to large corporations. Anyone can use Python’s libraries to design their own predictive algorithms for stock trading.
What Are the Advantages of Python Over Excel?
Microsoft Excel is one of the most preferred tools for data analysis and visualization. It’s easy to pick up and offers a decent degree of functionality. But Python leaves Excel in the dust when it comes to working with data. Here’s why:
- Better Data Import & Processing: Cleaning multiple large datasets in Excel isn’t really what you’d call simple. It’s tedious and time-consuming. Python can easily recognize and clean both structured and unstructured data much faster than Excel.
Plus, performing tasks like merging or recoding data are also much simpler in Python.
- Powerful Automation: Excel is many things but it isn’t an automation powerhouse. If you wanted to run the same analysis week after week with different data, you’d have to do it manually in Excel.
But in Python, you can write a simple script that imports the new data every week and performs the analysis automatically, while also outputting the results in your desired format.
- Easier Debugging: When something doesn’t go according to plan in Excel, you’re given an error notation and left to fend for yourselves. In Python, however, you’ll get an error message explaining what exactly went wrong.
Moreover, if you’ve left comments in your code, you’ll have additional information to help you fix the source of the error.
- Open-Source Accessibility: Excel might help you do a lot with your data, but you’re ultimately dependent on Microsoft for feature updates and support. Whereas Python is a free, open-source language which can anyone can update and add functionality to.
- Superior Statistics & ML capabilities: Excel isn’t suited to building advanced statistical and machine learning models. Python, with its extensive third-party libraries, has superior tools to easily create advanced data analysis models.
- Advanced Data Visualization: Data visualization is essential for effective communication with stakeholders. Although Excel has a robust data visualization interface with its charts and graphs, Python lets you do much more with your data.
You can create more customizable graphs and charts and use techniques that are simply much more flexible as compared to Excel.
- Cross-Platform Portability: Python’s scripts are portable across platforms like Windows, MacOS, Linux, and more. This isn’t common with other preferred technologies in the fintech industry, including Excel.
The 10 Most Useful Python Packages for Fintech
Now that you’re aware of Python’s incredible functionality for building fintech applications, it’s time to learn the most useful packages or libraries that will actually help you do it.
1. NumPy
NumPy (pronounced num-pi) is one of the most basic yet essential libraries in Python. It brings several mathematical and scientific computing capabilities to the language, which other libraries on this list build on.
It introduces concepts like n-dimensional arrays and matrices into Python and contains basic functions to manipulate such data structures.
2. SciPy
SciPy introduces the tools to build sophisticated data models from the basic mathematical structures provided by NumPy. Any statistical model requires algorithms for tasks like clustering, interpolation, transformation, integration, and more.
Hence, SciPy is the library providing developers with the advanced techniques to build predictive data models, a mainstay of many fintech applications.
3. Pandas
Pandas is a popular Python library known for its ‘DataFrame’ and ‘Series’ structures that are specially designed for data analysis and model building. Pandas is optimized for multiple kinds of data like tabular, multidimensional, and heterogeneous. You can also create basic visualization plots using this library.
It also provides concise and powerful functions for importing and manipulating data.
4. statsmodels
While SciPy provides advanced statistical tools and Pandas helps implement them, statsmodels introduces more thorough testing of different statistical models. Diagnostics and results statistics for every model are available and the results are tested against statistical packages to ensure accuracy.
5. Quandl
Quandl allows you to access financial datasets from central banks, government websites, multinational companies, and stock markets to help you build the financial model you desire. You simply need an API key to access most of the raw datasets for free, with more in-depth ones available for a fee.
6. Zipline
Zipline brings many of the above libraries together to become an algorithmic trading library. Quantopian, a popular platform for building trading strategies, is powered by Zipline.
It imports data from Quandl, and helps you design and implement custom trading algorithms. It also includes backtesting of algorithms and live trading.
7. Pyfolio
Pyfolio enables fintech developers to generate tearsheets containing performance statistics pertaining to the algorithms they designed with Zipline. Stats like annual returns, Sharpe ratios, portfolio turnover, and more are easily available through Pyfolio.
8. TA-Lib
Technical Analysis Library or TA-Lib can be used as an alternative to Zipline and Pyflio. It’s a C++ library with a Python wrapper available. TA-Lib provides common financial tools like momentum indicators, volatility indicators, cycle indicators, pattern recognition, and pure statistical functions.
9. QuantLib
QuantLib is another alternative to Zipline and Pyflio. It is also written in C++ and then exported to Python. Quantlib is optimized for building tools related to modeling, trading, and risk management.
Its algorithms include features like yield curve models, solvers, Monte Carlo analysis, market conventions, and more.
10. Matplotlib
While Pandas offers some basic visualization tools, it’s not a data visualization library like Matplotlib. It is one of the easiest packages to implement for financial modeling. It has a simple syntax and extensive documentation.
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