gs-quant
Goldman Sachs Quant
Decision gist · record as of 2026-08-14
Yes, if you are an institutional client of Goldman Sachs with API credentials and need to build quantitative models or trading strategies. The toolkit is actively maintained, has low install friction, and provides a comprehensive quantitative finance stack. No, if you lack Goldman Sachs credentials or are looking for an open-source alternative—the core value requires institutional access.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or greater.
- API access requires a client ID and secret, available only to institutional clients of Goldman Sachs.
- Low install friction with a pure-wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute the package freely provided you include a copy of the license and state significant changes.
last release 2026-08-07 (7 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 113,537 downloads/mo, #12,340 on PyPI
Alternatives
Verify before relying
pip install gs-quant
import gs_quant
# Requires Goldman Sachs client credentials to access APIs
# See https://developer.gs.com/docs/gsquant/ for examples- Whether the package works offline or requires persistent connection to Goldman Sachs servers for core functionality.
- Performance characteristics and scalability limits for large derivative portfolios or high-frequency data.
- Whether statistical/analytics features (numpy, scipy, statsmodels) are usable without Goldman Sachs API credentials.
What it is and what it does
GS Quant is a quantitative finance toolkit maintained by Goldman Sachs that combines access to their risk transfer platform with statistical and data-analysis packages. It is designed to accelerate development of trading strategies and risk management solutions by providing APIs for derivative structuring, trading, and analysis, alongside standard Python scientific libraries for statistical modeling and data analytics.
The package targets institutional users—primarily those with existing Goldman Sachs relationships—who need to build quantitative models, backtest strategies, or analyze derivative products. It bundles numpy, pandas, scipy, and statsmodels for numerical and statistical work, plus utilities like lmfit for curve fitting, tqdm for progress tracking, and websockets for real-time data. Access to the core APIs requires credentials (client ID and secret) issued only to Goldman Sachs institutional clients.
Use it for
- Develop and backtest quantitative trading strategies using Goldman Sachs market data and risk models.
- Price and analyze derivative products using built-in financial models and statistical tools.
- Build risk management dashboards and reports for institutional portfolios.
- Perform statistical analysis and data science tasks on financial datasets using numpy, pandas, and scipy.
- Structure and evaluate complex financial instruments with access to Goldman Sachs' platform.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are an institutional client of Goldman Sachs with API credentials and need to build quantitative models or trading strategies.
The toolkit is actively maintained, has low install friction, and provides a comprehensive quantitative finance stack. No, if you lack Goldman Sachs credentials or are looking for an open-source alternative—the core value requires institutional access.
Install
gs-quant on PyPI
Before you install
Low install friction with a pure-wheel distribution. Active maintenance with a release within the last week. Requires Python 3.10 or greater and depends on 24 runtime packages including numpy, pandas, scipy, and statsmodels—a substantial but standard quantitative finance stack.
Requires Python 3.10 or greater. API access requires a client ID and secret, available only to institutional clients of Goldman Sachs.
License in practice
Licensed under Apache-2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute the package freely provided you include a copy of the license and state significant changes.
Quickstart
pip install gs-quant
import gs_quant
# Requires Goldman Sachs client credentials to access APIs
# See https://developer.gs.com/docs/gsquant/ for examplesVerify before relying
- Whether the package works offline or requires persistent connection to Goldman Sachs servers for core functionality.
- Performance characteristics and scalability limits for large derivative portfolios or high-frequency data.
- Whether statistical/analytics features (numpy, scipy, statsmodels) are usable without Goldman Sachs API credentials.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 24 packagesaenumbackoffcachetoolscertifidataclasses_jsondeprecationinflectionlmfitmore_itertoolsmsgpacknest-asyncionumpyopentelemetry-apiopentelemetry-sdkpandaspydashpython-dateutilrequestshttpxscipystatsmodelspyyamltqdmwebsockets |
| Maintenance | Actively maintained 7 days since the last release |
| First released | |
| Downloads | 113,537 / month, #12,340 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13 |
Evidence: gs_quant-2.1.3-py3-none-any.whl
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