“Goldman Sachs slang is great. It’s like Python, but easier.”



Goldman Sachs uses a lot of slang. Also, at Goldman, slang means something very specific. It’s an in-house proprietary programming language (the Securities LANGuage) used by most of the front office. Slang is somewhat similar to Python, but in many ways easier to learn and use.

To understand slang, you have to understand its history. It was invented at Goldman more than 30 years before him and was decades ahead of its time. A group of engineers, including Armen Avanessians, Mike Dubno, Glenn Gribble, and Kevin Lundeen, created the language behind Goldman’s SecDB, the risk and pricing engine he was building at the time. However, unlike most other large banks and financial institutions that dreamed of their own internal programming language, when others moved to more universal languages ​​like C++ and Java, Goldman decided to create their own programming language. maintained.

That’s not to say Goldman only uses slang. – Java is also the core language used by most technical teams in the company. Python is commonly used for all machine learning efforts today. Other languages ​​are also used based on each team’s requirements. But there was a time, over a decade ago, when a philosophical mandate from management demanded that most things be included in Slang and SecDB.

However, slang isn’t for everyone. Over the years, people tried to impose it and work on things it wasn’t designed for, such as working with GUIs, but in favor of more modern web-based his UX frameworks like was soon deprecated. angle/reaction. Also, slang doesn’t handle massive parallelism well and can be very slow when used for something that wasn’t designed to be fast. Slang is known (and infamous) inside the company for its blue screen user interface. Experienced users love the slang, but new graduates and external hires find the learning curve frustrating.

There have been recent efforts to integrate Slang into more modern IDEs, but this has been slow to spread due to the limited feature set currently available outside of the traditional blue screen. Slang also uses CVS as its code commit repository, which is several generations behind Git.

A key feature of Slang is how it integrates with SecDB (SECurities DataBase). SecDB, and the dependency graph on which the risk modeling capabilities are built, were critical to Goldman’s ability to effectively manage risk across the company. SecDB enables bitemporal access to all trades, all positions, all product data, and all market data within a single integrated framework.

Slang was created for this purpose, and that’s why its core functionality is so great. The slang was designed for sensitivity analysis and execution of Goldman’s risk engine. If you have a financial instrument such as a swap or option and you want to see how its net present value (NVP) changes given changes in underlying parameters such as exchange rates and interest rates, Slang and SecDB can do it very quickly. can run. calculation. It has a very powerful graph technique that simply overrides some parameters in order to provide a new his NVP based on a specific market scenario. This allows PNL to get a very fast and up-to-date view.

When we’re at Goldman, the slang is also almost entirely open source in-house. Anyone with access to slang can see all slang ever created by all teams across the company (with the exception of the very few libraries responsible for data permissions). Slang has the advantage that most of it is done at client-side runtime. This means developers can quickly test their code locally before pushing it to production.

For decades, this has been Goldman’s competitive advantage. Slang and SecDB were written entirely in-house, and Goldman didn’t have the drawback of other banks merging with their rivals to rewrite their systems or mash things up. Instead, he had Slang and SecDB, and Slang made his SecDB development quick and lightweight. And because the system was open source internally, everyone in the market department had access to what someone else had already built. So instead of having to repeat the same work by rebuilding the same product over and over again by different teams, we were able to use what already existed directly. (reservation screen, tradeable, or downstream pipe, etc.).

This is why Slang and SecDB have long been Goldman’s secret to effectively risk managing all his positions in a consistent manner. But other banks (JPM, BofA, etc.) have hired Goldman talent over the years to implement their own versions of his SecDB in-house and are catching up fast. Currently, platforms like Slang/SecDB (, etc.), designed from the ground up to take advantage of more modern technology features and address some of SecDB’s core limitations.

Some people don’t want to join Goldman and work in slang. Because I fear that by doing so, I won’t be able to develop transferable skills that are relevant elsewhere. But a good developer can always learn a new language. Moreover, slang has a huge number of advantages. Having been coding for over 10 years, I’m still a huge advocate.

Slang has its place and isn’t going anywhere anytime soon. Yes, Goldman is trying to stay away from slang and uses more Python and Java, but the company has millions of lines of slang code and to phase them out It will take decades. In the meantime, developers joining Goldman will have to embrace the language. I may be biased, but slang is better in my opinion.

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