Python JSON logging has become the standard for generating readable structured data from logs. While logging in JSON is definitely much better than using the standard…
Python has staked its claim as the most popular programming language among developers worldwide. Accessible via Windows, Linux, and Mac, it’s intuitive and easy to read, and its use of maths lends itself perfectly to Python for finance and data analysis.
A popular and intuitive programming language means good availability of programmers, so it’s little surprise that recent years have seen rapid growth in Python at big banks, financial services providers, and emerging financial markets like cryptocurrency.
Lean Python programming allows for efficient automated financial services, faster transaction processing, and quicker decision-making, bolstering processing speeds in critical pipelines.
There are many reasons to choose Python for finance applications, including platform versatility, intuitive programming, high levels of efficiency, and so on.
Python can be implemented effectively for quantitative and qualitative data analysis, which is ideal for the vast volumes of data generated by the financial services and fintech sectors. Finance and banking data can be processed alongside discrete demographic data without difficulty.
Python’s analysis of financial data and its ability to support machine learning are ideal for analysts and traders. While no forecasts are fool-proof, Python’s predictions can help to guide informed decision-making in changing market conditions.
Python is classed as a high-level programming language, which means the programmer doesn’t need to write code for basic functions like logic and arithmetic. The availability of Python libraries can extend this further, as they contain functions ideal for financial data analysis, further streamlining development.
The syntax of Python is unusually accessible, based on the concept that simple is better than complex. New programmers can gain competency faster because learning Python is not like trying to master the grammar of a foreign language. Even intermediate programmers can gain a relatively high level of fluency quickly.
Scalability makes Python a suitable programming language for businesses — and budgets — of all sizes and stages. Fintech startups embrace Python as an affordable way to implement initial code, but its massive scalability and extensibility mean the largest financial firms can use it equally well.
One of the most powerful applications of Python in finance data analysis is the creation of stock market trading strategies. The continual generation of unfathomable data during a typical trading day means that no human alone could plot emerging trends fast enough to capitalize on them.
Using Python, stock market financial data analysis is automated and streamlined, providing fast insights to respond before market opportunities vanish. This analysis is then returned to the human trader in bite-sized, understandable instructions that can be acted upon without delay.
A more recent application of Python in finance data analysis is cryptocurrency, and the Python-based finance data science tool Anaconda is aimed at this turbulent financial market.
Anaconda allows cryptocurrency developers to collect, analyze and report on real-time pricing data, which allows for a more rapid response to changing market conditions in the fast-moving cryptocurrency sector.
Newly developed Python libraries for cryptocurrency applications make the language easier for crypto-focused fintech startups, allowing new entrants to compete in the maturing market.
While the fintech and financial services sectors are necessarily data-driven, the same is only sometimes true of those working there. Python can process structured and unstructured data, making it more understandable to personnel with less expertise in computers and programming.
The predictions, forecasts, and insights made by Python can be applied across a wide range of decisions, ranging from stock purchases and investments to credit ratings, to qualitative decisions, without any programming knowledge from the end user.
Libraries make it even easier to set this up, with the ability to pull in data from multiple sources, merge it, and create all manner of outputs, often with just a few lines of code.
The Matplotlib package for Python is a valuable tool for plotting financial data and creating understandable visualizations to help decision-making among personnel who deal better with graphs and charts than with raw numerical data tables.
Matplotlib is well documented but is only a host of Python data visualization packages that can be used in isolation or together to create customized reports with compelling visuals. But because many other Python fintech analysis packages depend on Matplotlib, it is considered all but essential by many programmers in the financial services sector.
Again, simple syntax means that even complex finance data visualizations can be created quickly by anyone familiar with Python. Financial models can be set up and used on an ongoing basis, giving finance firms a stable reporting platform that can grow as needed in the future.
The financial services sector — especially the fintech segment — continues to evolve rapidly, with the emergence of cryptocurrencies, instant cash transfer apps, and the nearly universal use of contactless technology in gift cards, loyalty cards, travel cards, and more.
Python’s massive versatility, and the availability of code libraries with pre-written functions for specific niches, make it the perfect fit for a fintech-dominated financial sector. At the same time, it can work behind the scenes to make technical data understandable to those working in non-tech financial disciplines.
It’s no surprise that Python is used extensively by some of the biggest brands in the business, including e-wallet platforms like PayPal and Venmo. With this in mind, the next big name in the fintech segment will likely provide financial services powered by Python financial data analysis, visualization, and reporting.