ZhuLinsen/daily_stock_analysis
LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, and cost-free scheduled runs. observed · 2026-08-28
Health v2 · maintenance only
82/100
- Activity 99
- Release rhythm 99
- Longevity 16
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: 4.0
- age_days: 235
- days_rel: 10
- days_push: 8
- n_releases_24m: 35
Adoption not part of the score
64010 stars · 53727 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
An LLM-powered stock analysis application that aggregates multi-source market data, news, and fundamentals for A-share, HK, US, Japanese, Korean, and Taiwanese markets, generating daily AI decision reports with scores, trends, and risk alerts. It includes a FastAPI/WebUI workbench, agent-based strategy Q&A, backtesting, and automated push to WeChat Work, Feishu, Telegram, Discord, Slack, and email, with zero-cost scheduled runs via GitHub Actions.
Use cases
- analyze my watchlist stocks daily with AI and push a report
- generate LLM stock decision reports for A-shares and US stocks
- get daily market review and stock analysis on Telegram or Feishu
- run scheduled stock analysis without a server using GitHub Actions
- ask an AI agent strategy questions about a specific stock
- aggregate stock quotes, news, and fundamentals from multiple data sources
- backtest past AI stock reports and track accuracy
- self-host a stock analysis dashboard with Docker
When to choose
- you want automated daily AI-generated stock research reports pushed to chat or email
- you track stocks across Chinese, Hong Kong, and US markets and want multi-source data with fallbacks
- you want a zero-cost scheduled setup via GitHub Actions or a self-hosted Docker deployment
- you need a web workbench with history, backtesting, and strategy Q&A around your watchlist
When to avoid
- you need high-frequency or algorithmic trade execution rather than daily research reports
- you require guaranteed real-time market data feeds with strict SLAs
- you want a fully deterministic quant backtesting engine without LLM involvement
- you cannot provide any LLM API key or prefer not to send financial data to cloud AI services
Facets
application · maturity active
llm-inference agent-framework data-visualization analytics web-scraping workflow-automation scheduling webhook chatbot fintech artificial-intelligence large-language-models data-science self-hosted analytics python cli self-hosted cross-platform stock-analysis quantitative-finance a-stock watchlist market-data decision-dashboard github-actions fastapi news-sentiment backtesting notifications trading ai-agents automation docker web-server
7 sources
- readme: https://github.com/ZhuLinsen/daily_stock_analysis · fetched 2026-08-28 · 2a3babba226e
- homepage: https://dsa.zhulinsen.tech · fetched 2026-08-28 · 56d13ef3c2e3
- site_page: https://dsa.zhulinsen.tech/docs/ · fetched 2026-08-28 · d3b19f45a89a
- site_page: https://dsa.zhulinsen.tech/docs/quick-start/ · fetched 2026-08-28 · d77c54e2120c
- site_page: https://dsa.zhulinsen.tech/docs/github-actions/ · fetched 2026-08-28 · 3dd901509bdf
- site_page: https://dsa.zhulinsen.tech/docs/local/ · fetched 2026-08-28 · ab470bdc5e5a
- site_page: https://dsa.zhulinsen.tech/docs/docker/ · fetched 2026-08-28 · 936144fae9d0
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ZhuLinsen/daily_stock_analysis | main | 82 |
For agents
markdown · JSON · MCP: product_card(name="ZhuLinsen/daily_stock_analysis")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem