According to the TIOBE’s Programming Language index, Python made a positive move in 2020, with its overall rating increase, representing the largest gain in popularity in one year in the index. We too are seeing a similar trend echoed in the financial industry as finance professionals look for simple, fast and efficient ways to serve their customers.
Over the last year, we have directed our focus on this very business challenge, to simplify and make efficient financial professionals in their roles in creating value for their customers. Derivatives are often associated with complexity, time consuming processing, and individuals with doctorates in physics, mathematics or engineering.
Addressing this business challenge head on, we just released our next-generation analytics, which is cloud-enabled and powered by Python. Financial professionals now have on their desktop the analytical capabilities historically reserved for large sell-side industry leading firms, as well as on demand access to horizontally scalable native cloud analytics. FINCAD’s high-level, Python-enabled API, enables traders, portfolio managers and risk managers alike to easily and transparently work within the vast Python ecosystem.
A large portion of a financial professional’s work is data analysis. Coupling Python’s native data handling and FINCAD’s analytics, one can compute values, sensitivities and cash flows of a derivative, such as a zero-coupon inflation swap, by writing just a few lines of code. The results can be conveniently returned as pandas data structures, making further analysis and aggregation simpler and straightforward.
Instead of reading about it, experience it yourself, you won’t be disappointed. Check out our new two-minute clip on FINCAD Python Analytics. I am confident you will want to learn more about how your organization can gain a competitive advantage and new levels of efficiency in serving your customers’ financial goals. If so, please contact us.
