Gone are the days when portfolio managers picked stocks based on price-earnings ratios and instinct. Today’s portfolio managers have a computer science degree and a preference for using computer algorithms. Recently we have seen a shift in preference for writing code using open-source programming languages such as Python. Python's clear programming syntax and amazing ecosystem of tools make it one of the best technologies for developing a financial services application. Python actually just took over Java as the most often used language over the last 12 months. Over the last couple of years, with the advent of Quantopian and other quant finance platforms, the Python language has become the defacto language for analyzing large data sets, building machine learning and deep learning models to analyze time series and other types of data, and to make predictions.

The increased use cases for Python in financial services applications is the driving force behind Xignite’s decision to offer Python sample code for our entire suite of cloud-native financial data APIs. Python makes data science accessible to quants, financial analysts and traders with the help of several data science packages such as NumPy and SciPy for numerical analysis, Pandas for data analysis and Plotly Dash for data visualization. 

We at Xignite use Python extensively for several of our use-cases around data quality and integrity. We leverage several of these packages to scan through large data sets to proactively detect any data gaps and quality issues for millions of securities. On our website Xignite provides sample code specific to each API and now, in addition to ASP .NET , C#, Java, Perl, PHP, Ruby and VB .NET, we offer a Python 3 test environment in our API test forms. It is available within the Sample Code section for each API under Python 3.