These 8 GitHub projects in the US stock market are very suitable for use with Codex From market data, SEC financial reports, strategy backtesting to AI quantitative research, it basically covers a set of US stock investment research workflows After encapsulating them into skills, Codex will not only help you write analysis, but also participate in the entire research process 1. OpenBB Financial data platform covering stocks ETF、 Macro, fundamental, options and other scenarios Suitable for Codex to automatically pull data, organize metrics, and generate company research summaries after entering tickets https://((((((((github.com))))))))/OpenBB-finance/OpenBB 2. yfinance An introductory tool for analyzing US stock data, which can obtain market trends, historical prices, dividends, stock splits, and some financial data Suitable for allowing Codex to automatically write scripts, draw trend charts, calculate maximum drawdown, and compare the performance of multiple stocks https://((((((((github.com))))))))/ranaroussi/yfinance 3. edgartools Used for reading and analyzing SEC EDGAR files, including 10-K, 10-Q, 8-K, Form 4, 13F, etc Suitable for Codex to directly read the original company disclosure instead of just reading second-hand news https://((((((((github.com))))))))/dgunning/edgartools 4. sec-edgar-downloader Suitable for bulk downloading SEC files For example, downloading the company's 10-K, 10-Q, and 8-K from the past few years, and then having Codex extract risk factors, management discussions, and changes in revenue structure https://((((((((github.com))))))))/jadchaar/sec-edgar-downloader 5. vectorbt Strategy backtesting tool, suitable for testing different parameters, stocks, and cycles For example, breaking through the moving average, RSI reversal, and holding for N days after reaching a new high can all be written as backtesting scripts for Codex https://((((((((github.com))))))))/polakowo/vectorbt 6. QuantStats Backtesting performance analysis tool that can generate indicators such as Sharpe, maximum drawdown, volatility, win rate, monthly returns, etc Suitable for connecting after vectorbt, allowing Codex to output a complete strategy health check report https://((((((((github.com))))))))/ranaroussi/quantstats 7. Microsoft Qlib Microsoft's open-source AI quantification research platform covers processes such as data, models, training, and backtesting Suitable for Codex to help you dismantle frameworks, modify models, conduct experiments, and reproduce quantitative paper ideas https://((((((((github.com))))))))/microsoft/qlib 8. FinRL Financial reinforcement learning open-source project, suitable for researching automated trading, portfolio configuration, and reinforcement learning strategies Suitable for Codex to help you understand the trading environment, modify reward functions, and adjust strategy logic https://((((((((github.com))))))))/AI4Finance-Foundation/FinRL The most valuable aspect of Codex in the US stock market is not asking it 'what do you think of this stock?' Instead, it encapsulates data sources, financial report sources, backtesting frameworks, and AI quantification projects into its own research workflow Ordinary people use Codex to write prompts Master uses Codex to build workflow Non investment advice, just sharing of AI investment research tool flow