@_zheergen: Wow! Goldman Sachs open-sourced their quantitative toolkit gs-quant. Goldman Sachs has open-sourced the Python quant library used by their internal quants, currently with 11.3K stars on GitHub. It offers a rich set of quantitative analysis tools for structured products…
Summary
Goldman Sachs open-sourced its internal quantitative trading toolkit gs-quant, providing institutional-grade derivatives pricing, risk management, and strategy development tools. It has received 11.3K GitHub stars.
View Cached Full Text
Cached at: 07/16/26, 10:16 AM
Wow! Goldman Sachs Open-Sourced Their Quant Library gs-quant
Goldman Sachs has open-sourced the Python quant library used by their internal quants, which has already gained 11.3K stars on GitHub.
It provides a rich set of quantitative analysis tools, suitable for structured products, risk modeling, strategy research, etc. While the full API requires Goldman Sachs institutional client access, the core library is open-sourced and can be installed by ordinary users (pip install gs-quant).
Key highlights:
Institutional-grade quant tools: Supports the full workflow including derivatives pricing, risk management, backtesting, and trading strategy development
Production-level code quality: Directly from Goldman Sachs’ real trading desk tool stack
Claude Skills support: Officially integrated Claude Skills, can be used directly within Claude
Wide asset class coverage: Strong capabilities for analyzing stocks, options, futures, forex, and other derivatives
Continuous maintenance and updates: Latest version 2.1.1 just released
Apache-2.0 open source license, can be used directly for research and development
For developers wanting to learn institutional-grade quantitative modeling, do derivatives pricing and risk management, or use AI for quantitative research, this is one of the highest-value open-source quant libraries available.
爱吃折耳根的Ace (@_zheergen):
🚨 Brothers! The fully automated Agent-native trading platform AI-Trader is hereHKUDS open-sourced AI-Trader is a 100% Agent-Native trading platform that allows AI agents to autonomously participate in stock, cryptocurrency, forex, options, and futures trading.
Key highlights:
Agent-native design: Any AI agent can quickly integrate and start trading by simply reading a SKILL.md link
Similar Articles
@WEB3_furture: What did the world's most expensive financial teams open source on GitHub? How can ordinary people learn about quantitative trading? Directly getting hands-on is the fastest way. Top quantitative and high-frequency trading institutions like Jane Street, Goldman Sachs, J.P. Morgan, etc., have released representative financial/engineering tools to help ordinary quant...
This tweet introduces three financial/engineering tools open-sourced by top quantitative institutions such as Jane Street, Goldman Sachs, and J.P. Morgan: magic-trace (high-precision process tracing), gs-quant (Python package for derivatives pricing and risk management), and Perspective (real-time data visualization tool), helping quant enthusiasts gain institutional-level technical capabilities for free.
@waveking1314: Someone compiled all the tools commonly used by quantitative funds into a free GitHub repository. Pricing engines, backtesting frameworks, order books, real-time quotes, risk models – almost a complete set. The projects included are absurdly numerous: Options pricing library for calculating option and derivative values, covering multiple pricing models and risk metrics. Complete backtesting framework…
A user curated a free GitHub repository aggregating numerous open-source quantitative finance tools, including pricing engines, backtesting frameworks, order book simulators, and risk models, making institutional-grade research tools accessible to individuals at minimal cost.
@KKaWSB: Going the extra mile, folks — quantitative trading projects on GitHub have reached a whole new level. There are plenty of ready-to-use strategies you can practice with (here's a curated list). What Wall Street teams earn millions for is now given to you for free by these open-source projects, complete with tutorials.
A curated list of open-source quantitative trading projects on GitHub, including AI-powered platforms like Qlib and FinGPT, multi-agent frameworks, and backtesting tools, all with tutorials and ready-to-use strategies.
@eastweb3eth: Github US Stock Quant Compilation - A Must-Use Tool for Smart People. Since Github came along, ordinary people can also do quant. But don't start by grinding away writing your own backtesting engine; really, most people's code is less robust than a three-year-old repo on Github. There are many repos, but I've already filtered them for you: these 4…
Recommends 4 open-source quantitative trading tools/frameworks (VeighNa, AI-Trader, StockSharp, QuantDinger), emphasizing that they are suitable for ordinary users to conduct US stock quantitative trading, helping to free your hands and let the model handle trading.
@gemchange_ltd: Someone put every tool quant funds use into a single free GitHub repo. Pricing engines. Backtesters. Order books. Live …
A GitHub repo aggregates free, open-source quantitative finance tools—pricing engines, backtesters, order books, and HFT simulators—typically guarded by firms, including Microsoft's AI quant platform.