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The Simplicity of Using Python to Unlock FINCAD Analytics
By Michiel Laleman | April 6, 2021
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Lately we have been talking a lot about how Python is used to help our clients get quick and simplified access to FINCAD’s powerful analytics. Today we thought we’d show you how the magic happens.

In our recently released video, a concrete example demonstrates just how easy it is to set up a trade, in this case an interest rate swap, and value that trade using FINCAD’s Python Analytics. It literally takes a couple lines of code to access our powerful analytics library, start pricing, and understand exposure through various risk metrics — even under multiple different scenarios.

Watch this two-minute run through to see some of the core Python features of our FINCAD offering below:

Why is instilling simplicity into the use of our analytics so important to us? Derivatives are often associated with complex and time-consuming processes, an idea perpetuated by many of the available derivative software packages, which use rather opaque or even black-box pricing algorithms. However, FINCAD’s next-generation analytics, cloud-enabled and powered by Python, aims to change this paradigm.

Financial professionals can now access, on their desktop, the analytical capabilities historically reserved for large industry-leading firms, as well as on demand, horizontal scaling through the cloud. FINCAD’s high-level, Python-enabled API empowers traders, portfolio managers, and risk managers alike to natively integrate analytics and pricing within the vast Python ecosystem — both easily and transparently.

But, don’t take my word for it. In the video, you can check out how one notebook summarizes everything you need to get started with FINCAD Python Analytics. You’ll see that setting up the swap trade is done in two lines of code, and valuing it is an additional one-liner. Furthermore, we catch a glimpse of the comprehensive documentation, containing details on use, mathematics and modeling, and more.

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Lastly, note the possibilities for seamless integration of FINCAD’s solution in the rest of the Python universe. For example, Matplotlib is used to plot cashflows, and for visualization of the different rate curves in the models. Pretty awesome, if you ask me.

Learn more

Watch the full video for additional specifics on how we use Python to help clients unlock our best-in-class analytics. To learn more about how your organization can gain a competitive advantage and new levels of efficiency in serving your customers, please contact us directly.

About the author
Michiel_Laleman
Michiel Laleman
Associate Quantitative Analyst | FINCAD

Michiel is an Associate Quantitative Analyst with FINCAD. In his role, he helps clients tackle qualitative and quantitative questions, and implement solutions, with a focus on modeling and pricing. Before joining FINCAD, he worked as a quant, analysing statistical trading models. Michiel holds a PhD in Statistical Physics from KU Leuven, and an Advanced Master’s degree in Quantitative Finance from Solvay Brussels School.