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goldmansachs/gs-quant

★ 10,204Jupyter NotebookAudience · dataComplexity · 4/5Setup · hard

TLDR

A Python library from Goldman Sachs for quantitative finance, structuring and pricing derivatives, building trading strategies, and risk analysis, but full API access requires institutional Goldman Sachs client credentials.

Mindmap

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    What it does
      Derivative pricing
      Risk management
      Trading strategy tools
    Audience
      Quant developers
      Financial analysts
      GS institutional clients
    Tech
      Python library
      Jupyter Notebooks
      Marquee API
    Access
      pip install easy
      API credentials required
      GS clients only
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Things people build with this

USE CASE 1

Build a Python script to structure and analyze derivative products using the Goldman Sachs Marquee platform API.

USE CASE 2

Run statistical analysis on financial market data for quantitative trading strategy research.

USE CASE 3

Perform risk management calculations on a portfolio of derivative contracts using the library's built-in tools.

Tech stack

PythonJupyter Notebook

Getting it running

Difficulty · hard Time to first run · 1day+

Full API access requires Goldman Sachs institutional client credentials, contact GS sales to obtain them.

In plain English

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.

Copy-paste prompts

Prompt 1
Using gs-quant, show me how to price a vanilla interest rate swap and compute its DV01 risk metric.
Prompt 2
How do I authenticate with the Goldman Sachs Marquee API using gs-quant client credentials and run my first data query?
Prompt 3
Build a backtesting script in gs-quant for an equity options strategy using historical price data.
Prompt 4
Show me how to use gs-quant portfolio tools to analyze the risk and return profile of a multi-leg derivative structure.
Prompt 5
Which gs-quant features work without Marquee API credentials, and how do I explore the bundled Jupyter Notebook examples offline?
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