As a programming language, Python can be used in various industries and for different tasks. The finance industry in particular relies heavily on technology. Programming languages form the digital systems that contain and manipulate data.
Python is one of the most popular languages used in the finance industry. But what makes it such a good fit?
Benefits of Using Python in the Finance Industry
- Increases the speed of Minimum Viable Product (MVP) Creation
- A large, supportive community of developers
- Cleaner code makes it faster
- Simple to learn & versatile in application
- Combines data science elements & economics
- Useful for creating prototypes
- Contains a large library
- Free to use
Bank companies like to use Python in their systems, but Python is also good for startup businesses. Corporations such as Bank of America and CitiGroup use it as well. For startups, the many benefits of Python are the reason they prefer it over other languages. It even has become more popular in the finance industry than Java.
Additionally, Python aids developers in solving algorithmic issues quickly. Since the finance industry deals with large amounts of data and calculations, the programming language must be able to handle it efficiently. Python’s versatility and performance speed can keep up with that.
This programming language has a large number of libraries at its disposal. Python libraries such as Cython and Numba help Python compile code statistically and dynamically. This is what can make its performance speed fast, which subsequently leads to an easier development process.
A collaborative tool that can be used with Python to create prototypes is the Jupyter Notebook. It interprets Python through code, images, and even Markdown text. The establishment of financial statements, completing a Discounted Cash Flow (DCF) calculation, and data exporting are part of how Python helps develop prototypes in the finance industry. It also works with the Jupyter Notebook to polish the prototypes and get them to their final forms.
How Python is Applied in the Finance Industry
As mentioned, Python can be used for banking companies. It works with online banking sites like Venmo, as well as ATM software. Aiding in the trading of stocks and using a subsystem of Python called Anaconda to collect then predict data are more of Python’s implementations in the finance industry.
In terms of the combination of finance and technology, or Fintech, Python has libraries specifically designated to work with those kinds of companies. Some of the libraries are Pyfolio, QuantLib, SciPy, PyAlgoTrade, Zipline, and NumPy.
Similar to Java, Python is extremely popular due to its ability to handle enormous amounts of data, perform exceptionally fast, and is adaptable to various implementations. This in itself makes Python an excellent choice for industries like Finance and Technology. It also has a number of assisting tools and a solid community of developers that help keep Python at the forefront of the programming language world.