Applications for Python in Finance and FinTech
Updated: Feb 25
The demand for data management and analytics is growing exponentially. To put it to scale, in 2019, the global data system accounted for roughly 45 zettabytes. By 2025, we are expected to process and maintain up to 175 zettabytes worth of data. In an industry comprised of trading, cryptocurrencies, and financial modeling, programming is increasing in demand for financial professionals. For those unfamiliar with coding, there is not a better place to start than with Python.
Python is a general-purpose programming language used for coding and technology integration. Coding can seem like an intimidating skill to develop, but Python is known for its accessibility; any individual who wants to learn has the ability to. As an open-source program, Python developers provide several free resources for anyone to learn the language. Further, Python script is easy to read and understand. For all these reasons, Python is growing in popularity worldwide and financial institutions are investing in talented developers who provide value through Python-developed projects.
To give some background on Python’s applicability within finance, the program allows developers a broad range of quantitative functions. From data visualization to statistical calculations, Python’s machine learning algorithms provide predictive analytics for financial service providers. On a smaller scale, the individual trader can build their own trading strategy by processing data for stock predictions. The scale of Python is whatever the individual developer is capable of.
If you are interested in learning Python, here are some useful resources to get started.
- Install Anaconda (Python 3.8 version) is free for download and provides the toolkit for script writing
- Stack Overflow is an online public platform for developers to collaborate. Majority of the time when a Python question is searched in Google, a Stack Overflow link will be the first to come up. Imagine Reddit for developers.
It is never too late to learn.