I have reorganized this batch of AI investment projects on GitHub. Whether you are a regular stock trader, value investor, or quantitative investor AI Agent， I still want to directly turn Claude Code/Codex into an investment assistant, and you can basically find the corresponding tools here. 1. OpenBB | Financial Data Base Integrate financial data such as stocks, financial reports, macro, and options into AI. It is suitable for building AI investment systems on your own, and those who do not want models to rely on memory for blind analysis should be given priority. 2. daily_stock_analysis | Daily automatic research investment Automatically capture A-share, Hong Kong stock, and US stock market trends, news, and fundamentals, and then have AI generate daily analysis reports and push them. Ordinary investors are most likely to use it directly. 3. ai hedge fund | AI hedge fund Let different AI agents play the roles of value investors, technical analysts, risk managers, etc., jointly researching stocks and making investment decisions. 4. Qlib | Microsoft Quantitative Research Platform We have a complete set of data, factors, machine learning models, backtesting, and portfolio management. If you want to play quantification seriously, this is basically unavoidable. five http://vn.py ｜ Domestic quantitative real inventory A very mature Python quantitative trading framework in China, which can be used for stocks, futures, etc., with a focus on strategy development and real trading execution. 6. FinceptTerminal | Open Source Financial Terminal It can be understood as an open-source Bloomberg for individual investors, where market trends, research, news, and analysis are centralized in one terminal. 7. Backtrader | Classic Strategy backtesting Write a trading strategy and directly test it against historical market trends to see if it makes money or not. Old brand, simple, suitable for quantitative entry. 8. Lean | Institutional level Quantitative Trading Engine The open-source engine behind QuantConnect can handle stocks, options, futures, forex, and cryptocurrency, making it suitable for complex strategies and multi asset trading. 9. Vibe Trading | AI Investment Skills Gift Pack Transform a large amount of financial research capabilities directly into AI callable skills. Claude Code/Codex can independently search for data, research companies, analyze strategies, and run investment tasks. 10. FinGPT | Financial Language Model A large-scale model project specifically focused on financial training and research, capable of conducting financial sentiment analysis, text understanding, prediction, etc., is more suitable for those who want to study "financial specific AI". 11. noFX | AI Automated Trading Agent Closer to real transaction execution, allowing AI to generate decisions based on market information and connect trading processes. The gameplay is exciting, but the funding authority and risk are also significantly higher. 12. ai Berkshire | AI Value Investment Researcher Hand over Buffett style research ideas to AI: read financial reports, look at valuations, study moats, management, and long-term investment logic. Long term investment parties are very suitable. 13. TradingAgents | AI Long Short Investment Committee Let multiple agents, including fundamentals, technical aspects, long positions, short positions, risk management, etc., study and debate first, and finally form trading viewpoints. 14. FinRobot | AI Financial Research Agent Automated financial analysis and research allow agents to handle tasks such as financial data, company research, financial analysis, and research reports. 15. go stock | Local AI disk monitoring tool More personal desktop investment assistant, it can run market monitoring and AI analysis locally, suitable for people who want to build their own monitoring system. 16. Mars | Microsoft Financial Market Simulation It is not intended for ordinary candidates in the stock market, but rather simulates market participants, trading behavior, and market microstructure. Quantitative research and academic directions are more valuable. 17. Finance Toolkit | AI Financial Calculator A large number of financial ratios, valuations, risks, and technical indicators have been encapsulated. The most valuable aspect is to enable AI to truly calculate data, rather than just making numbers in a serious manner. 18. RD Agent | AI Automated Quantitative Research and Development Microsoft's AI development agent can automatically generate hypotheses, write code, conduct experiments, and iterate models. After cooperating with Qlib, we have begun to approach allowing AI to research quantitative strategies on its own. If I were to categorize them based on usage thresholds: Ordinary investors: 1/2/9/12/13/17 Value investment: 12/17/1 AI Agent Investment: 3/9/13/14 Quantitative research: 4/7/8/18 Real programmatic trading: 5/8/11 Financial AI development: 1/10/17/18 No need to fully install. Choose a direction that you truly need, integrate data, research, and backtesting capabilities into Claude Code/Codex, and AI will truly begin to function as an investment assistant. The GitHub address can be found in the comments section.