ZhuLinsen/daily_stock_analysis is an LLM-powered stock analysis system for A/H/US markets, providing automated analysis and multi-channel notifications.
Source: README View on GitHub →The project is gaining attention due to its integration of AI-driven analysis, multi-source data aggregation, and real-time news, offering a comprehensive solution for stock analysis. Its unique technical choices, such as support for various AI models and multiple notification channels, stand out.
Source: Synthesis of README and project traitsGenerates detailed analysis reports with core conclusions, ratings, trends, trading points, risk alerts, catalysts, and operational checklists.
Source: READMEAggregates data from A-share, Hong Kong stocks, US stocks, and ETFs, including market data, K-line charts, technical indicators, fund flow, holding patterns, news, announcements, and fundamentals.
Source: READMEFeatures manual analysis, task progress tracking, historical reports, full Markdown reports, backtesting, portfolio management, configuration management, and light/dark theme options.
Source: READMESupports multi-round questioning and 15 built-in strategies, covering Web/Bot/API, including moving averages,缠论, wave theory, trend, hot topics, events, growth, and expectations.
Source: READMESupports image, CSV/Excel, and clipboard import, as well as stock code/name/pinyin/alias completion.
Source: READMESupports GitHub Actions, Docker, local scheduled tasks, and notifications via WeChat, Feishu, Telegram, Discord, Slack, and email.
Source: READMEThe architecture inferred from the code structure and dependencies suggests a modular design with clear separation of concerns. Key technical decisions include the use of ORM for database operations, scheduled tasks for automation, and integration with various AI models and data sources.
Source: Code tree + dependency filesCenter: project; inner ring: core feature modules; outer ring: key dependencies. Auto-generated from core_features and tech_stack.key_deps.
python-dotenvtenacitysqlalchemyscheduleexchange-calendarsefinanceaksharetusharepytdxbaostockyfinancelongbridgelark-oapipandaspypinyinopenpyxlnumpyjson-repairlitellmtiktokenopenaiPyYAMLtavily-pythongoogle-search-resultsrequestsmarkdown2imgkitfake-useragenthttpxThe project is suitable for individual investors, financial analysts, and institutional investors who need automated stock analysis and multi-channel notifications. It is useful in scenarios such as daily market analysis, portfolio management, and investment decision-making.
Source: READMEv3.17.1 (2026-05-16): Added Alert API MVP, supporting alert rule CRUD, enable/disable, one-time test, and trigger/notification result query. The first version covers 'price_cross', 'price_change_percent', and 'volume_spike', while maintaining legacy configuration compatibility and response desensitization.
Source: GitHub ReleasesZhuLinsen/daily_stock_analysis is a promising project for those interested in automated stock analysis and AI-driven insights. It is particularly suitable for individuals and institutions looking for a comprehensive solution to manage their portfolios and make informed investment decisions.
ZhuLinsen/daily_stock_analysis is an LLM-powered stock analysis system for A/H/US markets, providing automated analysis and multi-channel notifications.
daily_stock_analysis's core features include: AI Decision Reports, Multi-Market Data Aggregation, Web/Desktop Dashboard, Agent Strategy Questioning, Automated Import and Completion.
The project is gaining attention due to its integration of AI-driven analysis, multi-source data aggregation, and real-time news, offering a comprehensive solution for stock analysis.
The project is suitable for individual investors, financial analysts, and institutional investors who need automated stock analysis and multi-channel notifications.