Introduction
According to Matt Davies Stockton, despite being around four decades old, Python has managed to stay relevant and popular over its long cycle of evolution. While its application and use are seen across industries, the world of finance has benefited most from it. Let’s check out why python is so huge in finance.
The Details
- Flexibility and simplicity – Both writing and deploying python are easy. That makes the programming language a perfect fit for handling financial applications that can be very complex and resource intensive. The clear and simple syntax of python allows for quick development speed that lets businesses push new products and build essential tools when necessary.
The simplicity and ease of the programming language also reduces the error rate. This is vital for the finance industry since it is heavily regulated, scrutinized, and punished when people lose money due to an error in poorly developed apps. The low error rate allows organizations in this industry to reduce their liability.
- Vast libraries and endless tools – This is a benefit that every industry, including the finance sector, gets from using Python. Due to its long existence and wide adoption, you’ll find a vast ecosystem of libraries and tools that have been created by individual users. This allows you to take the short route and make development faster and easier compared to other languages.
Moreover, finch products require integration with various third parties. The existing Python libraries and tools usually help a great deal with that integration. This allows for a competitive advantage since fintech companies can quickly adapt to changing customer needs and modify their product accordingly. It’s all about efficiency and speed.
- Great for data science – Data scientists usually use Matlab, R and other such data specialized languages. However, they have a steep learning curve and that becomes a bar for widespread adoption. That’s where Python comes in with its practicality and simplicity for creating formulas and algorithms.
The language has tools like numpy, scipy, and matplotlib that allow fintech professionals to compute advanced financial calculations and show the result in a digestible manner. This allows them to share the results with other departments and less tech-savvy people in the organization.
- Analytics – Quantitative finance can’t get enough of python due to a very good reason. The language allows for the processing of large datasets and allows professionals to analyze them with big financial data to identify trends and predict them.
There are popular libraries like Pandas that allow easy visualization of more advanced statistical computations. Apart from that, there are libraries like PyBrain and Scikit that are armed with powerful machine learning algorithms for predictive analytics. Those analytics allow large organizations to build and modify their investment strategies.
Conclusion
Matt Davies Stockton suggests that you increase your mastery and proficiency in python if you intend to develop back-end architecture or apps for the finance world. It’s open-source, flexible, and allows you to build scalable systems with ease.
