How Python Makes Data Science More Accessible
The use of the infinitely flexible programming language, Python, is taking off within Finance. In fact, Python is increasingly being used by leading hedge funds and other financial institutions as their multi-purpose tool of choice. And now there is an extensive ecosystem with numerous Python libraries that make data science more accessible to these firms than ever before.
In FINCAD’s latest video, Christian Kahl, PhD, explains how quants, financial analysts and traders can leverage Python for better data analysis. Use of the many data science packages available is a good starting point. NumPy and SciPy can be used for numerical analysis such as VaR and time series; Pandas can be used for data analysis; and Plotly Dash for data visualization—just to name a few. Leveraging these tools in tandem helps firms perform their own on-point data analysis, which can in turn fuel improved decision-making.
Christian also remarks that many firms using an Excel-based framework for data analysis struggle with issues around scalability and accessibility. On the other side of it, once one harmonizes their infrastructure and processes, Python can offer the flexibility for firms to easily accomplish precisely what they want around data analysis, without restrictions.
With FINCAD F3, we give clients access to our technology infrastructure via Python. The F3 API allows users to access extensive pricing libraries with Python. For many of our clients, this approach helps to transform a reporting obligation like VaR into a powerful tool for decision-making. With F3, VaR can be calculated in real-time at the portfolio or sub-portfolio-level.