When Your Stock Watchlist Hires a Committee Instead of an Intern
A self-hosted stock dashboard that runs a multi-agent AI committee to analyze your A-share, Hong Kong, and US holdings before pushing a verdict to your phone.

What it does
PanWatch is a self-hosted web dashboard for tracking A-share, Hong Kong, and US equities. It aggregates real-time quotes, portfolio positions across multiple accounts, and standard technical indicators such as MACD, RSI, and KDJ. The standout feature is an integration with the TradingAgents framework, which chains technical, sentiment, news, and fundamental analysts into a structured bull-versus-bear debate, followed by risk-control review and a final decision pushed to Telegram, WeChat, DingTalk, or other channels.
The interesting bit
Instead of a single LLM summary, PanWatch orchestrates a full research pipeline: four analyst personas argue, a risk manager vets the consensus, and a portfolio manager drafts the brief. The README says this takes three to five minutes and costs roughly $0.05 per run on the default DeepSeek model, making it either admirably transparent or slightly alarming depending on your patience.
Key highlights
- Self-hosted
FastAPIbackend withReactfrontend; the app runs locally, though AI analysis relies on external OpenAI-compatible APIs. - Covers A-shares, Hong Kong, and US markets with multi-account portfolio aggregation and a paper-trading simulator.
- PWA support means it installs like a native app on mobile, with dedicated mobile layouts shown in the repo screenshots.
- Alerting engine supports compound conditions—price, volume,
RSI, and more—with AND/OR logic, cooldown timers, and per-rule notification routing. - Scheduled agents deliver pre-market briefs, intraday monitoring, and post-market daily reports via
APScheduler.
Caveats
- The Docker image bundles Playwright, so the container downloads Chromium on first boot; expect a slow, network-dependent startup unless you disable browser features with an environment variable.
- Each deep-analysis run takes three to five minutes and consumes LLM tokens; the README cites roughly $0.05 per run with DeepSeek, so frequent checks are not free.
- AI features require configuring an external API key; there is no bundled local model for offline inference.
Verdict
Worth a look if you are a retail investor in Chinese or US markets who wants algorithmic commentary without subscribing to a cloud dashboard. Skip it if you need sub-second trading signals or a fully offline, zero-cost stack.
Frequently asked
- What is TNT-Likely/PanWatch?
- A self-hosted stock dashboard that runs a multi-agent AI committee to analyze your A-share, Hong Kong, and US holdings before pushing a verdict to your phone.
- Is PanWatch open source?
- Yes — TNT-Likely/PanWatch is open source, released under the MIT license.
- What language is PanWatch written in?
- TNT-Likely/PanWatch is primarily written in Python.
- How popular is PanWatch?
- TNT-Likely/PanWatch has 731 stars on GitHub.
- Where can I find PanWatch?
- TNT-Likely/PanWatch is on GitHub at https://github.com/TNT-Likely/PanWatch.