# goldmansachs/gs-quant

Python toolkit for quantitative finance

Repository: https://github.com/goldmansachs/gs-quant
Canonical: https://ross.abutalabs.com/products/gs-quant
Homepage: https://developer.gs.com/discover/products/gs-quant/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: gs-quant, goldman-sachs, risk-management, trading-strategies, derivatives
Last push: 2026-08-26T13:29:05+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 2819, "days_push": 7, "days_rel": 7, "gap_med": 3, "n_releases_24m": 186}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 12672, forks 1696 (observed 2026-08-28T04:10:59.426401+00:00)

## What it is
GS Quant is a Python toolkit for quantitative finance built by Goldman Sachs quants on top of their risk transfer platform. It supports derivative pricing, trading strategy development, risk management, and statistical data analytics, with API access requiring Goldman Sachs Marquee credentials.

## Use cases
- price and analyze derivative products in python
- build quantitative trading strategies
- manage portfolio risk programmatically
- run risk analytics on swaps and options
- statistical analysis of market data
- structure derivatives via Goldman Sachs Marquee API

## When to choose
- you are a Goldman Sachs institutional client with Marquee API credentials
- you need production-grade derivatives pricing and risk analytics in Python
- you want to prototype and backtest quantitative trading strategies

## When to avoid
- you lack a Goldman Sachs client id and secret - core APIs are inaccessible
- you need an open data source or broker-agnostic toolkit
- you only need simple charting or basic pandas analytics

## Facets
- artifact type: library
- maturity: stable
- function: trading, data-science, sdk
- domain: fintech, data-science, analytics
- platform: python, cross-platform
- tags: quantitative-finance, derivatives, risk-management, trading-strategies, goldman-sachs, marquee-api

## Member repositories
- goldmansachs/gs-quant (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:59.426401+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:13:52.277366+00:00, confidence not recorded.
  - readme: https://github.com/goldmansachs/gs-quant (fetched 2026-08-28T04:10:59.426401+00:00, sha ed6f9c0dcb91)
- Data as of 2026-08-30T08:39:29.467469+00:00.