{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"GS Quant is a Python toolkit for quantitative finance that provides APIs and statistical packages for developing trading strategies, analyzing derivatives, and managing risk.","skillfed_tags":["quantitative-finance","derivatives-trading","risk-management"],"use_cases":["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."],"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.\n\nThe package targets institutional users\u2014primarily those with existing Goldman Sachs relationships\u2014who 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.","worth_installing":"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\u2014the core value requires institutional access."},"id":"gs-quant","links":{"html":"https://skillfed.io/packages/gs-quant","md":"https://skillfed.io/packages/gs-quant.md","pypi":"https://pypi.org/project/gs-quant/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"permissive","name":"gs-quant","python_support":"supports_current","summary":"Goldman Sachs Quant"},"popularity":{"monthly_downloads":113537,"position":12340,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.1.3"}