@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...

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, and J.P. Morgan have released representative financial/engineering tools to help ordinary quant enthusiasts learn institutional-level pricing models, real-time data visualization, and high-precision performance debugging capabilities for free. 1. Jane Street magic-trace (5.4k stars) https://github.com/janestreet/magic-trace… A high-precision process tracing tool based on Intel Processor Trace. When ordinary profilers can't see the call stack clearly, it records the complete execution process of every CPU instruction with nanosecond-level resolution. If you want to deeply debug performance and figure out where your program is stuck, this is highly recommended. 2. Goldman Sachs gs-quant (10.2k stars) https://github.com/goldmansachs/gs-quant… A Python toolkit for derivatives pricing and risk management used daily by Goldman Sachs traders. It includes complete pricing models and risk calculation modules for common derivatives like options and swaps. You can install it directly with pip, making it suitable for those who want to systematically learn institutional-level quantitative pricing. Very practical. 3. Perspective (originally a J.P. Morgan project, 10.5k stars) https://github.com/perspective-dev/perspective… A real-time data visualization tool open-sourced by J.P. Morgan, especially good at handling massive streaming market data. It allows you to quickly build beautiful interactive dashboards and real-time monitoring interfaces, supports Jupyter, and is more flexible than many paid terminals. Very friendly for those doing data analysis and market visualization. These three open-source projects allow you to directly use institutional-level pricing models, real-time market dashboards, and high-precision performance debugging tools, helping ordinary developers improve quantitative analysis, data visualization, and code optimization capabilities — completely free.