ZhuLinsen/daily_stock_analysis
Some data may be outdated — see provenance labels.
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 · 2026-10-05
Health v2 · maintenance only
81/100
- Activity 98
- Release rhythm 93
- Longevity 20
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 4
- age_days: 283
- days_rel: 44
- days_push: 16
- n_releases_24m: 36
Adoption not part of the score
65897 stars · 54970 forks observed · 2026-10-05
What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-21, confidence 0.97
Daily Stock Analysis (DSA) is an LLM‑powered system that aggregates market data, news, and fundamentals across multiple stock exchanges and generates structured decision reports. It can run as a CLI tool, a FastAPI server with a web UI, or a Docker‑compose service for automated daily execution and multi‑channel notifications.
Use cases
- Generate daily AI‑driven analysis reports for selected stocks across A‑share, Hong‑Kong, US, Japanese and other markets.
- Automate multi‑source market data collection, news aggregation, and technical indicator calculation.
- Receive daily decision dashboards via WeChat, Feishu, Telegram, Discord, Slack or email.
- Run scheduled stock analysis tasks on GitHub Actions, local cron, or Docker containers.
- Interact with an LLM agent to ask strategy‑specific questions about a stock in real time.
- Backtest historical reports and view portfolio performance through the built‑in web UI.
When to choose
- You need an end‑to‑end, LLM‑enhanced stock research pipeline that works across many global exchanges.
- You want automated daily reports with multi‑channel push notifications without maintaining a server.
- You prefer a flexible deployment: CLI, FastAPI service, or Docker‑compose for long‑running analysis.
When to avoid
- You require a lightweight, pure‑Python library without external services or Docker dependencies.
- Your workflow does not involve LLMs or you need only raw market data without AI‑generated insights.
- You need real‑time high‑frequency trading where the latency of LLM inference is unacceptable.
Facets
application · maturity active
api-framework http-server cli agent-framework artificial-intelligence data-science python windows cli multi-market stock-analysis llm agent scheduled notification fastapi webui docker-compose docker linux macos web-server
10 sources
- readme: https://github.com/ZhuLinsen/daily_stock_analysis · fetched 2026-10-05 · b85347fa66aa
- homepage: https://dsa.zhulinsen.tech · fetched 2026-09-07 · 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
- site_page: https://dsa.zhulinsen.tech/docs · fetched 2026-09-07 · 9cfedad7e3c4
- site_page: https://dsa.zhulinsen.tech/docs/quick-start · fetched 2026-09-07 · ee46263aa010
- site_page: https://dsa.zhulinsen.tech/docs/github-actions · fetched 2026-09-07 · f30e341f289b
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| ZhuLinsen/daily_stock_analysis | main | 81 |
For agents
markdown · JSON · MCP: product_card(name="ZhuLinsen/daily_stock_analysis")
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem