23 Daily — According to the project's description, ZhuLinsen released the open-source repository daily_stock_analysis on November 9, 2026. It is a large language model (LLM)-powered multi-market intelligent stock analysis system that supports markets including A-shares, Hong Kong stocks, U.S. stocks, Japanese stocks, Korean stocks, and Taiwanese stocks. The system automatically aggregates multi-source market data and real-time news to generate decision dashboards, which are pushed to enterprise WeChat, Feishu, Telegram, Discord, Slack, email, and other channels.
System Features and Technical Architecture
daily_stock_analysis provides automated daily stock analysis producing AI decision reports that include core conclusions, scores, trends, buy/sell points, risk alerts, and catalyst factors. Reports can be pushed automatically via various instant messaging platforms and email. Users can also perform manual analysis, task management, backtesting, and portfolio management through web and desktop workstations.
The system supports 15 built-in strategies like moving averages, Chan theory, wave theory, bullish trends, event-driven, and more, enabling multi-turn querying for deep stock strategy interaction. Data sources include free and licensed APIs such as AkShare, Tushare, Pytdx, Baostock, YFinance, complemented by multiple news search and social sentiment services like Anspire and SerpAPI, enhancing real-time and comprehensive information.
Deployment is flexible, recommended through GitHub Actions for automated operation, or via Docker and local scheduled tasks for customized setups. It integrates with diverse AI providers including Modelflare, AIHubMix, OpenAI-compatible services, Google Gemini, and Claude, offering flexible intelligent analysis engines.
Configuration and Market Coverage Details
Users need to configure their stock watchlists (e.g., stock code lists) and set API keys and webhook URLs for push notification channels like enterprise WeChat bots and Telegram bots to enable daily scheduled analysis and notification. The system defaults to multiple free market data sources for easy startup, with options for paid data feeds to improve data stability and completeness for advanced users.
Market coverage spans major Chinese and overseas exchanges, supporting various financial instruments including ETFs. The system automatically skips non-trading days according to market rules, ensuring timeliness and reliability of data and analysis.
Editor's take: Opportunities in AI-powered financial integration and automated push services
daily_stock_analysis demonstrates how open-source solutions can provide cross-market and multi-data-source stock analysis empowered by large language models, offering deep interpretation and decision support relevant to financial investment. Entrepreneurs can leverage similar frameworks to create more client-centric smart investment tools, especially capitalizing on automation in information aggregation and multi-channel push capabilities.
To validate such a system's effectiveness, one can deploy it targeting specific markets or stocks, observe the alignment between its daily decision reports and actual market trends, and test different AI model and data source combinations to assess impact on analysis quality, iterating strategy and operational models accordingly.