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US Crowding Radar

Local-first, explainable US equity crowding-risk monitor

What is US Crowding Radar?

US Crowding Radar is a local-first, explainable tool that monitors crowding risk in US equities, helping traders and analysts identify crowded positions before they lead to sharp reversals. It supports both a local web dashboard and CLI automation, leveraging the Clawby API to fetch crowding scores and component evidence. The tool is read-only and does not execute trades, making it safe for research. Key features include watchlist management, cached or fresh scanning, optional notifications, and a 'doctor' command to verify API key configuration. The dashboard validates the API key via Clawby's /api/account endpoint and stores it locally with file mode 600 for security. The skill's documentation emphasizes safety, explaining that the crowding score is not a price forecast or investment advice. It runs tests with unittest and compiles Python modules to ensure correctness.

Works with~Claude Code~Codex CLI~Cursor
npx skills add lionman888/us-crowding-radar

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Documentation

What does US Crowding Radar do?

Use the tested Python CLI in this repository. Do not reimplement scoring or call undocumented interfaces directly.

Safety

  • This is read-only market intelligence, not trade execution.
  • Never request, print, log, or place CLAWBY_API_KEY in command arguments.
  • The dashboard accepts the key through its local UI, validates it against Clawby's official /api/account endpoint, and may save it to the Git-ignored local credential file with mode 600.
  • CLI automation reads the key only from the environment or the ignored .env file.
  • Never describe the crowding score as a price forecast or investment advice.

Commands

From the repository root:

python3 -m us_crowding_radar doctor
python3 -m us_crowding_radar watchlist list
python3 -m us_crowding_radar watchlist add TICKER --label "Company"
python3 -m us_crowding_radar watchlist remove TICKER
python3 -m us_crowding_radar scan
python3 -m us_crowding_radar scan --refresh
python3 -m us_crowding_radar scan --notify
python3 -m us_crowding_radar serve --port 8786

Workflow

  1. For the dashboard, ask the user to enter their own Key in the local UI; never repeat or log it. A successful validation can be restored from the protected local credential file.
  2. For CLI automation, run doctor and report only whether the environment key is configured.
  3. Confirm the watchlist before a live scan.
  4. Use scan for a cached authorized read-only run or scan --refresh for fresh remote data.
  5. Report score, direction, coverage, component evidence, source timestamps, and failed dimensions together.
  6. Only use --notify when the user has configured and authorized the notification target.

Validation

python3 -m unittest discover -s tests -v
node --check us_crowding_radar/web/app.js
python3 -m compileall -q us_crowding_radar

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