Bloomberg’s Derived Engine

Source: Bloomberg L.P.

Deriving data can be done simply in excel or via an in-house calculator written in a programming language such as java or python. However, another easy way to derive an output is by leveraging industry standard calculations from Bloomberg. The next time you sit in a meeting and consider building vs. buying it would be prudent to think about whether your in-house calculation will ultimately add that much value? 

For example, a large fixed-income asset manager told us that they spent 2 years rolling out an in-house fixed-income calculation engine to compliment their existing order management system. All the portfolio managers were referencing the in-house outputs against their Bloomberg terminal and often overriding the output with a Bloomberg computation. What was even worse was the fixed-income asset manager had spent hundreds of thousands of pounds developing and implementing the in-house calculators into their PMS/OMS.  

Typically, a firm’s USP will not come from standard financial calculations, so why do so many firms and software vendors re-invent the wheel when it comes to these derived outputs?

One observation is that Bloomberg doesn’t advertise the robustness of their derived calculation engine, nor do they educate firms on the fact that it’s available not only via terminal functions but also via the API, across all API solutions.

FLDS<GO> becomes your best friend when considering using Bloomberg’s derived engine because it has a field mnemonic hierarchy which show you the input fields available to manipulate the output of the derived field. Typically, terminal users are familiar with this concept within a function calculator like SWPM<GO> or YAS<GO> as “amber” input fields and “grey” output fields.

One FinTech OMS provider recently informed us they were able to improve the performance of their portfolio management view by leveraging Bloomberg’s derived engine and display a fund view 300% faster than when their software was previously supporting the calculations themselves. Furthermore, that same OMS provider was able to reduce the market data inputs needed for those calculations which in turn saved their users on market data fees from Bloomberg data license.

The most common use case for leveraging Bloomberg’s derived engine is when a front office has a high percentage of Bloomberg terminals and the need for derived data outputs. In this scenario it would be wise to consider evaluating whether you can outsource the derived calculations to Bloomberg.

For more insights or help with maximizing your Bloomberg solutions, contact us at Sherpa Consultancy LTD.