---
id: gs-quant
version: "2.1.3"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# gs-quant — Goldman Sachs Quant

License: permissive · Maintenance: active · Downloads: 113.5K/mo

## 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 above — verify before relying.

GS Quant is a Python toolkit for quantitative finance that provides APIs and statistical packages for developing trading strategies, analyzing derivatives, and managing risk.

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

pip install gs-quant
uv add gs-quant
poetry add gs-quant

## Installing gs-quant

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.

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 examples

Requires Python 3.10 or greater. API access requires a client ID and secret, available only to institutional clients of Goldman Sachs.

Verify 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_current
- Install friction: low
- Maintenance: active
- Downloads: 113.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

quantitative finance python, trading strategy development, derivative pricing analysis, risk management toolkit, financial data analytics, quant trading library, goldman sachs quant api, quantitative-finance, derivatives-trading, risk-management

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