gs-quant

GS Quant is a Python library published by Goldman Sachs that provides tools for quantitative finance work, particularly for analyzing and trading derivative products. Derivatives are financial contracts whose value depends on an underlying asset, such as a stock, interest rate, or commodity. The library is described as having been built on top of internal Goldman Sachs infrastructure developed over 25 years of market activity.

The library is intended for two main audiences. The first is quantitative developers at financial institutions who want to build trading strategies or structure derivative products. The second is analysts who need statistical tools for data analysis in financial contexts. The README specifically mentions derivative structuring, trading, and risk management as the primary use cases.

Installing the library itself is straightforward with a single pip command. However, access to the API features requires a client ID and secret credential that Goldman Sachs only provides to its institutional clients. The README does not explain how to obtain those credentials if you are not already a Goldman Sachs client, other than to suggest speaking with a sales contact.

The GitHub repository hosts Jupyter Notebooks with examples and tutorials, though the README points readers to Goldman Sachs's own developer documentation site for full guides rather than explaining much in the repository itself. The README is quite sparse, so most of the actual detail about what the library can do lives in the external documentation.