ZhuLinsen/daily_stock_analysis is an LLM-powered stock analysis system for A/H/US markets, providing multi-source data, real-time news, decision dashboards, and multi-channel notifications.
Source: README View on GitHub →The project is gaining attention due to its comprehensive stock analysis capabilities, integration of AI and LLM technologies, and ease of deployment through GitHub Actions and Docker. Its unique feature set, including multi-dimensional analysis, global market support, and automated notifications, addresses the pain points of manual stock analysis and decision-making.
Source: Synthesis of README and project traitsThe system provides a decision dashboard with core conclusions, ratings, buy/sell points, risk alerts, and operational checklists, powered by AI and LLMs.
Source: READMEThe system offers technical analysis, real-time market data, chart patterns, news sentiment, corporate announcements, and fundamental analysis aggregation.
Source: READMESupports A-share, HK-share, US-share, US index, and common ETFs, providing a comprehensive view of global markets.
Source: READMEBuilt-in strategies such as A-share review, US Regime, moving average,缠论,波浪理论, and sentiment cycle analysis.
Source: READMESupports notifications via WeChat, Feishu, Telegram, Discord, Slack, and email, with automated scheduling and multi-channel support.
Source: READMEThe architecture is modular, with clear separation of concerns. Key modules include AI analysis, data processing, market analysis, and notification systems. The system uses a combination of design patterns like MVC for the web interface and dependency injection for the backend. Data flows through a series of interconnected modules, with a focus on scalability and maintainability.
Source: Code tree + dependency filesinfra: Docker, GitHub Actions, and local environments. | key_deps: python-dotenv, tenacity, sqlalchemy, schedule, exchange-calendars, efinance, akshare, tushare, pytdx, baostock, yfinance, longbridge, lark-oapi, pandas, pypinyin, openpyxl, numpy, json-repair, litellm, tiktoken, openai, PyYAML, tavily-python, google-search-results, requests, markdown2, imgkit, fake-useragent, httpx | language: Python | framework: FastAPI for web services, SQLAlchemy for ORM, Pandas for data analysis, and Litellm for LLM integration.
Source: Dependency files + code treeThe project is suitable for individual investors, financial analysts, and institutional investors who need automated stock analysis, market strategy, and real-time notifications. It is useful for daily market analysis, long-term investment planning, and risk management.
Source: READMEv3.15.0 (2026-05-05): Added Anspire OpenAI-compatible gateway integration, improved market review, and other enhancements.
Source: GitHub ReleasesZhuLinsen/daily_stock_analysis is a valuable tool for investors and analysts looking for an automated, AI-powered stock analysis system. Its comprehensive feature set, ease of deployment, and support for global markets make it a strong candidate for those seeking to enhance their investment decision-making process.