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Daily Stock Analysis

Free

LLM驱动的 A/H/美股智能分析器：多数据源行情 + 实时新闻 + LLM决策仪表盘 + 多渠道推送，零成本定时运行，纯白嫖. LLM-powered stock analysis system for A/H/US markets.

FreeFree tier
Type
Open Source
Visit Website GitHub (30.5k stars)

About Daily Stock Analysis

An open-source, LLM-powered multi-market stock analysis system that covers A-shares, Hong Kong, US, Japan, Korea, and Taiwan stocks along with ETFs. It aggregates data from multiple sources (market quotes, K-line charts, technical indicators, news, announcements, fundamentals) and generates AI decision reports with conclusions, scores, trends, buy/sell points, risk alerts, catalysts, and operation checklists. The system provides a web/desktop dashboard for manual analysis, task progress tracking, history, backtesting, portfolio management, and configuration. It supports 15 built-in agent strategies (e.g., moving average, chan theory, wave, trend, hot topics, events, growth) for multi-turn Q&A. Smart import allows loading stocks from images, CSV/Excel, or clipboard with auto-completion of codes/names. Automation is achieved via GitHub Actions (zero-cost scheduled runs), Docker, local cron, or FastAPI service, with push notifications to WeChat Work, Feishu (Lark), Telegram, Discord, Slack, and Email. The project integrates with many AI models (Anthropic, Gemini, OpenAI, DeepSeek, Qwen, Claude, Ollama local) and multiple market data and news sources.

Key Features

AI decision report with core conclusions, scores, trends, buy/sell points, risk alerts, catalysts, and operation checklists
Multi-market data aggregation covering A-shares, Hong Kong, US, Japan, Korea, Taiwan stocks and ETFs
Web/desktop dashboard for manual analysis, task progress, history, backtesting, portfolio management, and theme switching
Agent strategy Q&A supporting 15 built-in strategies like moving average, chan theory, wave, trend, hot topics, events, growth, etc.
Smart import from images, CSV/Excel, clipboard with auto-completion of stock codes, names, pinyin, and aliases
Automation and push via GitHub Actions, Docker, local cron, FastAPI, and notifications to WeChat Work, Feishu, Telegram, Discord, Slack, and Email

Pros & Cons

Pros
  • Fully open-source and free to use with zero-cost scheduled runs via GitHub Actions
  • Supports a wide range of markets (A, H, US, Japan, Korea, Taiwan) and ETFs
  • Integrates with numerous AI models and data sources for flexibility
  • Rich notification channels including enterprise messaging apps and email
  • Smart import features reduce manual entry effort
  • Built-in agent strategies provide diverse analytical perspectives
Cons
  • Free built-in data sources (AkShare, Baostock, YFinance) are subject to rate limits, interface changes, and network instability
  • Requires configuration of API keys for reliable long-term usage and premium features
  • Primarily targeted at individual investors rather than institutional-grade analysis
  • Not a substitute for professional financial advice; decisions remain user responsibility

Best For

Daily stock analysis for personal investors across multiple marketsAutomated decision report generation and push to team communication channelsBacktesting and evaluating trading strategies using historical dataPortfolio tracking and risk monitoring with AI-driven insights

FAQ

How do I deploy this system?
The recommended method is GitHub Actions: fork the repository, add required secrets (AI model API key, notification channel webhook, stock list), and the system will run automatically on schedule with zero cost.
Which markets are supported?
The system covers A-shares (China), Hong Kong stocks (hk), US stocks (US), Japan stocks (JP), Korea stocks (KR), Taiwan stocks (TW), and ETFs. Different data sources have varying support boundaries.
Is Daily Stock Analysis completely free?
Yes, the project is open-source (MIT license) and can be run for free using GitHub Actions free tier. However, for stable long-term and batch analysis, you may need to configure token-based data sources like TickFlow, Tushare, or Longbridge, which may have their own pricing.
Can I use local AI models?
Yes, the system supports Ollama for local model deployment, which is more suitable for local or Docker environments. For GitHub Actions, cloud APIs are recommended.