It is quite remarkable how the financial sector has opened its hands to technology—which has seen it using Python for financial analysis. The growing use of programming in financial activities has improved the speed and efficiency with which financial activities are run.
Python is currently among the most powerful programming languages used in the financial sector. The object-oriented, open-source programming language has earned itself a good spot as it is used by some of the biggest companies in the world like IBM, Google, HP, Cisco, to mention but a few.
But, before we continue, let’s have a brief refresher on what python is so that it can form a solid premise upon which you can better appreciate Python’s uses in finance;
Table of Contents
- A Brief Refresher: What Is Python?
- Python for Financial Analysis—An Introduction
- Benefits of Python In Finance and Fintech
- Python’s Uses In Finance
- Conclusion
A Brief Refresher: What Is Python?
Python is the most acclaimed high-level and multipurpose programming language—popular for its readability, being similar to English in many ways, and using a little bit of Mathematics. It is accessible to all the major operating systems and platforms like Windows, Mac or Linux, etc.
According to a 2018 HackerRank report, it was the most beloved programming language among developers of all ages. It was also the most popular programming language that developers are willing to learn, with a good number of them already knowing it.
With its intuitive syntax and comprehensive community development and support modules, it is a must-learn for fresh programmers in 2021. Besides, a collaborative environment for professional and newbie programmers makes it a sure launchpad for your programming journey.
A very versatile programming language, it finds application in virtually every sector and can be used to introduce essential solutions in web services. Its several development frameworks like Pyramid, Turbo Gear, and Django are feature-packed and easy to learn.
Python further boasts admirable data analysis capabilities, which makes it a top programming language, invaluable for creating a wide range of tech solutions. It’s widely applied in Mathematics, Engineering, Computing, and most importantly, Finance because of how much it enhances computing.
It flaunts some advanced deep learning libraries that make it the primary language for AI. In addition, its versatile nature is the major reason why it’s very popular in the field of data science.
As of 2020, Python was the most popular programming language for data science. Aside from being easy to use and learn, it also provides the most important tools for problem-solving: collection, exploration, modeling, and visualization of data.
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Python for Financial Analysis—An Introduction
The financial sector processes huge loads of data, and the major aspect of Python’s uses in finance is in qualitative and quantitative data analysis. And, as such, Python is now among the most popular programming languages in the Fintech industry.
Python finance tools help analysts to analyze stock market predictions and for stock-related machine learning technologies. Its robust modeling features and simple nature make it a favorite tool for analysts, traders, and researchers.
Every successful Fintech company needs a programming language that’s high-performing, easy to use, mature, and has ready-made components and libraries, and Python finance resources perfectly fit into the description of a robust and comprehensive financial technology stack.
Benefits of Python In Finance and Fintech
The frameworks and languages you use for your Fintech greatly decide the durability and the general success of your venture. They also determine the duration of market validation and what your products would eventually look like.
Banking institutions, investment, and other financial institutions have checked out other alternatives and have come to realize that Python is the ideal programming language for them. The reason, as you might imagine, is the invaluable benefits they derive from Python finance resources.
Let’s take a look below at some of the benefits of using Python for Fintech solutions:
It Contains the Essential Libraries
Python flaunts a comprehensive library system that can be used in various applications. What’s interesting is that most of those libraries like SciPy, quantecon.py, PyAlgoTrade, etc., are splendid resources for finance and Fintech.
It Is A High-Level Programming Language
High-level programming languages remove the need to apply in-depth details of the computer’s inner workings in the writing and execution of a program.
Let’s take computer arithmetics and logic, for instance: a low-level language would require you to have a sound understanding of the computer’s arithmetic and logical unit and its major operations. It would also require you to reflect those workings in your lines of code.
However, Python as a high-high-level gauge saves you that time and energy—allowing you to commit more of your time and programming resources to the task you want to accomplish.
A Great Option for Fintech Startups
As a startup, there are many factors you consider while choosing the programming language to use for your development projects.
You consider your budget, the speed of development, the number and relevance of the libraries, and the availability of experienced developers.
Other factors include the scalability, security, stability, and sustainability of the programming language. And, looking at all the factors above—that make for seamless development, Python is the language to go with.
A Neat Syntax
In software and web development and even while using Python in financial analysis, its codes are easy to read and understand since it is similar to the real English language, as we stated earlier. This shortens the learning period for programmers and makes them competent in it for a vast application of financial activities.
One of the foundational concepts of Python is that “simple is better than complex,” little wonder it has a clean syntax. And while other programming languages are written majorly using punctuation keywords, it uses English, only with a few exceptions.
Speeds Up Financial/Fintech Activities
As a dynamically typed language, Python makes development faster, unlike what’s obtainable in statically typed languages like C++, Java, C, etc.
A dynamically typed programming language is such that the programmer can write without necessarily having to specify variable types every time as opposed to a statically typed language.
In addition, writing programs with Python helps to minimize deployment time as it allows you to use less code to write powerful programs. This enables companies to market their products faster without interfering so much with their employees’ time.
Python’s well-known frameworks—a group of modules or packages used to write applications—enhance speed. Such frameworks include Flask: a lightweight framework with useful tools for creating web applications, Pyramid (necessary for scaling up small web applications into big ones), the infamous Django (for rapid and seamless development), etc.
Admirable Collaborative Ability
Using Python for financial analysis has a massive advantage because of how well it collaborates with other programming languages. For example, Python works well with R—a powerful programming language that’s popularly used in high-frequency trading funds.
R is used by those trading funds for running predictive analysis and simulations—which explains why many companies in need of Python programmers list a good knowledge of R as one of the job criteria.
A Favorite Of Wall Street’s Big Banks
Big financial companies like JP Morgan are not left out in the long list of influential companies using Python in financial analysis and other operations. According to Misha Tselman, J.P. Morgan Chase’s executive director, JP Morgan’s Athena trading platform used over 150,000 Python modules, 500+ open source packages, and a walloping 35 million lines of Python codes written by over 1500 developers.
Interestingly, the Bank of America also uses Python for financial analysis and the development of Quartz—its trading and risk management forum. It is considered a wonderfully flexible language by its Quartz developers as it allows them to work smoothly in a big development community.
Another reason that makes using Python for financial analysis a viable option is that it offers different aspects of many other languages, making it quite comprehensive. Consequently, Python programmers have a massive demand among the above-mentioned financial institutions, including some other big names like Citigroup, Goldman Sachs, etc.
An Improved Standard
Python’s invaluable features—extensive libraries, quick tools, etc., are the reason it’s replacing other long-reigning languages like Java in the financial sector.
Financial brokerages use Python’s APIs (Application Programming Interfaces), and its libraries have the essential data, analytic, and compliance tools. Similarly, It is used for solving quantitative problems in pricing and trade and risk management.
Suitable For Non-Programming Bankers Too
With the recent transitions in some U.S, banks—as they begin to integrate more technology into their activities and brand themselves as tech firms, traders, and banking analysts have fallen headlong into programming, with Python being the most relevant and easiest to write in the financial sector.
The current Fintech trends make it very easy for experienced Python programmers to land great jobs. Meanwhile, reports from the Wall Street Journal and Financial Times show that some big banks like Citigroup, JP Morgan, and Goldman Sachs have begun taking their bankers on Python classes, seeing that online lending institutions and AI (Artificial Intelligence) are taking over the industry.
Banking is gently taking a new dimension, gradually bringing the traditional banking system where the average banker mostly runs and tracks financial deals on the telephone to an end. Professionals are picking up relevant programming skills to create and monitor algorithms that analyze and regulate their banks’ positions, automate transactions, and create risk alerts.
However, this spreading acceptance of automation in the banking sector hasn’t taken the place of traders. It has only broadened their roles—making them more tech-oriented bankers—well-equipped to satisfy their banks’ tech demands instead of just being traditional bankers.
With that said, let’s jump right into some of the applications of Python’s uses in finance:
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Uses Of Python In Finance/Fintech
Used For Banking software Development
Python has an easy-to-write and simple syntax that makes it a highly flexible language. Banking institutions use Python to create technologies that enhance online banking and payments. Moreso, using Python in financial analysis simplifies math-intensive and calculative operations for analysts.
It is also used by financial institutions for developing banking applications and payment solutions. A very good example is Kolinx—the smart money app that Innuy built.
Kolinx wanted a secure application with robust functionalities and features that would create a good user experience while addressing the possible security challenges that could arise in a finance-handling system—and Innuy delivered a slick Python-built application.
Another example is Venmo—a PayPal-owned digital wallet that has grown into a massive social network of over 70 million users, handling over $159 billion in transactions only within the first quarter of 2018.
Python also comes in handy for payment-enhancing ATM software development, thanks to its flexibility and general simplicity. Other financial solutions and applications that are based in Python include the popular Stripe, Affirm, Robinhood, and Zopa.
Python Is Used For Creating Stock Trading Strategies
The stock market, being one of the most dynamic financial marketplaces, generates tons of financial data that often require more than a sound head to analyze. Using Python for financial analysis in the stock market becomes very necessary to help analysts and traders to determine the most powerful stock trading strategies to employ.
When it comes to the application of Python for financial analysis in the stock market, its Django framework comes to mind as it is the most prominent Python tool used for stock trading. Python for financial analysis is an almost indispensable tool in today’s stock institutions as it converts mind-boggling stock data into simpler easy-to-read bits.
Examples of Python in such applications include PyAlgoTrade, Backtrader, Quantconnect, Zipline, IBPy, Quantopian, etc. Python developers can create financial solutions that give accurate insight into the trends in different markets. They can also create algorithms for spot-on stock market prediction and Fintech solutions.
Python In Cryptocurrency
Just like in the stock market, all cryptocurrency firms also need tech solutions for market data analysis and for making smart predictions that will keep them on top of market situations.
Python has deployed a data science tool called Anaconda for the highly volatile market. The tool enables developers to fetch, analyze, and visualize cryptocurrency pricing data in real-time—which explains why Python is a common tool with most crypto-related web applications.
If you’re a cryptocurrency user, you must have imagined how big its databases must be to accommodate users’ data. This Python finance technology helps crypto firms to ensure secure transactions and record cryptocurrency users’ respective data in a massive database.
Some of the important Python-based cryptocurrency resources include Pybitcointools, finmatketpy, crypto-signal, ZeroNet, Dash, joining, enigma, etc. More Fintech resources are expected to emerge in the future as Python finance advances, and swarms of Python development services will also emerge to satisfy the raging population of crypto users.
Python For Data Analysis
The basis of using Python for financial analysis is its strong and efficient data analysis capability. Python has some of the most powerful built-in libraries that enhance the speed and efficiency of financial transactions, mostly as regards mathematical calculations and analysis.
Analysts that are skilled in using Python for financial analysis can transform large chunks of both structured and unstructured data into something simple, thereby making it easy for financial novices, so to speak—to understand. Also, since Python is regarded as a machine learning language, you can easily make data predictions via its PyBrain and Sci-kit libraries.
The Panda library is currently the most popular library used in Python for financial analysis, and through Python programming, financial institutions can make smarter predictions and be more profitable in their transactions.
Some of Panda’s most common manipulations like joining, merging, grouping, filling, imputing, and replacing null values can all be implemented in a single line of code. Additionally, the Python finance package creates functions for importing data from different standard formats, for rapid plotting, and for outputting data or extracting basic statistics.
As important as the use of Python for financial analysis is its use for data visualization. Python uses its package, Matplotlib, for mathematical modeling and data visualization. Aside from being very essential for analyzing financial data trends, visualization is also very important for giving insight to less- or non-technical personnel.
While there are few other data visualization packages in Python with their respective pros and cons, Matplotlib is the easiest to use for Python finance modeling and stands out as the best. It is also a big favorite because of its rich documentation, its straightforward syntax, and the reliance of virtually every other package on it.
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Conclusion
The financial sector is fast transitioning into a more tech-oriented ecosystem. As a result, Python has become the leading language for creating trade, pricing, and risk management solutions for banks and other financial institutions.
With its clean and simple syntax, collaborative features, library-packed environment, and high-Level advantage, there is no doubt that Python is unarguably the best language for developing financial applications.
The application of Python in financial analysis has become so popular that some of the biggest financial/fintech companies, such as Paypal, Stripe, Croodle, Vyze, Venmo, etc., employ mostly Python finance resources for their projects.