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NoiseFloor
Offline diagnostics for series, headlines, and forecasts
What it is
An offline Python toolkit that checks market observations, groups repeated headlines, exposes coverage gaps, and grades forecasts. The same functions run through a Python API, a JSON command line, REST, and an MCP server, and they also cover correlation spectra, shared-factor concentration, and change detection on panels the caller already holds. Research desks and monitoring agents that need to keep private observations local can use it with no API key and no model call. It does not fetch market feeds, retain a portfolio, or send orders, and retrieving public financial sources is a separate workflow from these checks.
At a glance
Research onlyOur rating, based on popularity, maintenance and how ready it is for real use.
| Best for | Developers |
|---|---|
| Used for | Data analysis |
| Markets | Multi-market |
| Stack | Python |
| Learning curve | Moderate learning curve |
| Practical value | Medium practical value |
| Cost | Free and open source |
| Hardware | No GPU needed |
| Maintenance | Last commit 9 days ago |
GitHub stars, last 30 days
Daily snapshots since 2026-09-28 (up to 30 days): 0 over the period, now 0. Gaps mean no snapshot was taken that day.
Offline financial time-series diagnostics, correlation spectra, change detection, and forecast evaluation with explicit coverage and input-data rights checks.
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