gs-quant: Build Quantitative Finance Tools in Python

Overview

GS Quant is a Python toolkit for quantitative finance, maintained by Goldman Sachs quantitative developers. It provides tools for building trading strategies and analyzing derivative products, with applications in derivative structuring, trading, and risk management.

It is most relevant to quants and institutional developers working with Goldman Sachs APIs. It can also be used as a collection of statistical packages for data analytics, but access to the APIs requires institutional client credentials.

Key Features

• Supports development of quantitative trading strategies.
• Provides tools for derivative product analysis.
• Facilitates derivative structuring and trading workflows.
• Supports risk management solutions.
• Includes statistical packages for data analytics applications.
• Offers a Python package installable through pip.

Use Cases

• A quant develops and analyzes a trading strategy using Python.
• A derivatives developer evaluates products during structuring or trading workflows.
• A risk team builds analysis for derivative-related risk management.
• A data analyst uses the statistical packages for finance-related analytics.

Getting Started

Install the package:

pip install gs-quant

See the repository README and Goldman Sachs Developer documentation for details and examples.

Alternatives

• quantstats: QuantStats focuses on performance and risk analysis of investment return series, while gs-quant also supports derivatives analytics and trading strategies.

Considerations

• Requires Python 3.9 or greater and access to pip.
• API access requires a client ID and secret, available to institutional clients of Goldman Sachs.
• The README directs users to Goldman Sachs Developer documentation for examples, guides, and tutorials.