gs-quant vs quantstats
Python quantitative finance tools compared
gs-quant provides tools for developing trading strategies and analyzing derivatives, with workflows that can support structuring, trading, and risk management. quantstats analyzes periodic investment returns through metrics, visualizations, reports, and benchmark comparisons.
gs-quant: Build Quantitative Finance Tools in Python
GS Quant is a Python toolkit for developing quantitative trading strategies, analyzing derivatives, and supporting risk management. It suits developers and quants with access to Goldman Sachs APIs who need finance-focused tools and analytics.
quantstats: Analyze Portfolio Performance and Risk
QuantStats is a Python library for analyzing investment return series with performance metrics, risk statistics, visualizations, and reports. It suits quants and portfolio managers who want to profile and compare strategies from periodic returns.
| gs-quant | quantstats | |
|---|---|---|
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Stars | 13k | 7.7k |
| Forks | 1.8k | 1.2k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- gs-quant focuses on strategy development, derivatives, and risk workflows, while quantstats focuses on profiling portfolio return series.
- gs-quant can use Goldman Sachs APIs, which require institutional client credentials; quantstats works with daily, weekly, or monthly returns rather than trade records.
- quantstats includes performance and risk metrics, visualizations, HTML tear sheets, benchmark comparisons, and Monte Carlo simulations; gs-quant provides statistical packages for data analytics.
- gs-quant requires Python 3.9 or greater, while quantstats lists Python 3.10 or newer and minimum versions for several dependencies.
- Both projects use Python and the Apache-2.0 license. gs-quant has 13k stars and 1.8k forks, compared with quantstats at 7.7k stars and 1.2k forks.
Choose gs-quant if you…
- develop quantitative trading strategies or analyze derivatives in Python.
- support derivative structuring, trading, or risk management workflows.
- use Goldman Sachs APIs and have the required institutional client credentials.
Choose quantstats if you…
- analyze periodic investment returns with performance and risk metrics.
- visualize return patterns or produce reports such as HTML tear sheets.
- compare return series with a benchmark or run Monte Carlo simulations on return data.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.