PanWatch Packs Multi-Agent Stock Research Into One Open-Source Docker Image
A self-hosted AI stock watcher wires the TradingAgents multi-agent framework into a portfolio dashboard for A-shares, HK, and US markets.
- PanWatch is a self-hosted AI stock dashboard integrating the TradingAgents multi-agent framework.
- Nine agents debate bull vs bear, risk, and PM decisions in 3-5 minutes per run.
- Covers A-share, Hong Kong, and US markets with multi-account portfolio aggregation.
- Deploys via a single Docker command, default model deepseek-chat at ~$0.05 per deep analysis.
- Push notifications to Telegram, WeChat Work, DingTalk, Lark, Bark, or custom webhooks.
- Optional OpenTelemetry exporter maps LLM calls to standard GenAI spans for Jaeger or Langfuse.
PanWatch packages portfolio agents in one Docker image
PanWatch, a Chinese open-source portfolio dashboard, gained hundreds of GitHub stars over one week by combining market monitoring, technical analysis, LLM research, and notifications in a self-hosted application. It covers A-share, Hong Kong, and US equities and integrates the TradingAgents framework to generate structured investment reports.
The project consolidates several tools that retail investors often run separately. PanWatch stores watchlists, positions, alert rules, and configuration in a local Docker volume, then connects to external market-data sources, an OpenAI-compatible model endpoint, and messaging services. Its agents recommend actions but do not place live trades.
| Layer | Included capabilities |
|---|---|
| Markets | A-share, Hong Kong, and US equities |
| Analysis | Technical signals, fundamentals, news, sentiment, and multi-agent debate |
| Delivery | Telegram, WeChat Work, DingTalk, Lark, Bark, and custom webhooks |
| Trading | Paper trading only |
The portfolio becomes an agent graph
TradingAgents supplies four analyst roles focused on technicals, sentiment, news, and fundamentals. Their findings pass through a bull-versus-bear debate, a risk review, and a portfolio-manager node that proposes an action. Clicking the brain icon on the portfolio page starts this workflow and exposes the intermediate role outputs alongside the final report.
The project estimates that a deep analysis takes 3 to 5 minutes. Its default model is deepseek-chat, with an estimated cost of about $0.05 per run. Actual latency and expense depend on the selected provider, prompt size, model pricing, and retry behavior.
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