daily_stock_analysis vs vibe-trading
Two of the top projects in Agent platforms, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
daily_stock_analysis
Daily LLM stock analysis for A-share, HK, US and other markets, pushed to chat apps
vibe-trading
Finance research agent for backtests, market data and broker connectors
Verdict
daily_stock_analysis ranks higher, 80 to 77. Of the 5 score parts we measure for both, daily_stock_analysis leads on 2, vibe-trading on 2 and 1 is even.
- Adoptiondaily_stock_analysis leads by 15 points, 85 to 70: a wide gap.
- FreshnessEven at 100.
- Maintenancevibe-trading leads by 4 points, 99 to 95: close to even.
- Easy to runvibe-trading leads by 17 points, 67 to 50: a wide gap.
- Agent-readydaily_stock_analysis leads by 85 points, 85 to 0: a wide gap.
Pick daily_stock_analysis if you are
Retail investors and self-hosters wanting scheduled LLM watchlist reports
- Runs on a GitHub Actions fork with no server; default schedule is weekdays 18:00 Beijing time
- Free data sources (AkShare, Baostock, YFinance) work with zero configuration
Pick vibe-trading if you are
Quant researchers and traders who want an LLM agent for backtesting
- Grounding gate checks each reported figure against session tool evidence and cuts failures
- Backtest run cards cite metrics only where saved CSV output agrees
| What we compare | daily_stock_analysis | vibe-trading |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 85, widely used | 70, popular |
| Freshness | 100, active | 100, active |
| Maintenance | 95, healthy | 99, healthy |
| Easy to run | 50, easy | 67, easy |
| Agent-ready | 85, ready | 0, none |
| Facts from GitHub and the README | ||
| Stars | 66.8k | 36.1k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Sep 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Not needed | Not needed |
| arm64 or Apple Silicon | Mentioned | Mentioned |
daily_stock_analysis
Python app that pulls quotes, K-lines, news and fundamentals for a watchlist across A-share, HK, US, Japan, Korea and Taiwan markets. It has an LLM write a decision report with score, trend, buy/sell levels and risks, then pushes it to WeCom, Feishu, Telegram, Discord, Slack or email. It runs on GitHub Actions, Docker or locally, with a FastAPI web workbench for history, backtests and strategy chat.
Who it is for: Retail investors and self-hosters wanting scheduled LLM watchlist reports
Strengths
- Runs on a GitHub Actions fork with no server; default schedule is weekdays 18:00 Beijing time
- Free data sources (AkShare, Baostock, YFinance) work with zero configuration
- Supports Gemini, Claude, OpenAI-compatible APIs, DeepSeek, Qwen and local Ollama models
- Six push channels plus a web UI with history, backtest, portfolio and 15 built-in chat strategies
Weaknesses
- README is mainly Chinese; English docs are in a separate file
- Free data sources are subject to rate limits and upstream changes; stability not guaranteed
- Report quality depends on a paid news-search API key; a search service is recommended
- Output is for study only; the disclaimer excludes investment advice and liability
- no GPU
- Docker + Compose
- Needs LLM API key or Ollama, News search API (Anspire, SerpAPI, Tavily, Bocha, Brave, MiniMax or SearXNG), Market data sources (AkShare, Baostock, YFinance; optional TickFlow, Tushare, Longbridge), Notification channel
- Models: Gemini, OpenAI-compatible, DeepSeek, Qwen, Claude
- port 8000
vibe-trading
Vibe-Trading is a Python agent (FastAPI backend, React 19 web UI, CLI, MCP) for trading research. It pulls market data across many exchanges, runs backtests and factor analysis, and checks figures in generated reports against tool evidence. It also connects to 18 brokers and sends scheduled reports through channels such as Email, Slack, Discord and Feishu.
Who it is for: Quant researchers and traders who want an LLM agent for backtesting
Strengths
- Grounding gate checks each reported figure against session tool evidence and cuts failures
- Backtest run cards cite metrics only where saved CSV output agrees
- 18 broker connectors, several read-only, with paper/live permissions documented
- Active: v0.1.16 shipped 116 merged PRs, with near-daily fixes afterward
Weaknesses
- Narrow domain: finance research only, not a general agent builder
- Some broker features are unavailable, e.g. Robinhood options execution
- Data quality depends on public sources such as Stooq, which can block requests
- Frequent backtest-correctness fixes in the changelog; verify results independently
- no GPU
- Docker + Compose
- Needs LLM provider (OpenRouter is the default), Market data sources, Broker APIs (optional)
- Models: OpenRouter, Codex, Copilot, custom model endpoints
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