Disclosure Alpha

Deterministic SEC filing analytics — parse, score, diff. No LLM required.
Extract sections, measure tone and boilerplate, detect year-over-year changes, and screen peers.

• Quick start
• What it is
• Integration surfaces
• OpenBB Workspace
• Capabilities
• Example output
• Research-backed
• Documentation

Quick start

Requires Python 3.11+.

1. Install from PyPI

pip install "disclosure-alpha"

For HTTP API and MCP: pip install "disclosure-alpha[api,mcp]". Full options: Installation.

2. Set your SEC User-Agent (required for --ticker / EDGAR only; skip for local --html scoring)

export SEC_USER_AGENT="YourName [email protected]"

See SEC EDGAR setup.

3. Score a filing

disclosure-alpha score --ticker AAPL --fiscal-year 2025 --form 10-K \
  | jq '.scores.overall_disclosure_risk_score'

from disclosure_alpha import score_filing_ticker
result = score_filing_ticker("AAPL", 2025, form_type="10-K")
print(result.scores.overall_disclosure_risk_score)

What it is

Open-source, deterministic SEC filing analytics for 10-K and 10-Q HTML. Reproducible JSON scores from text metrics, boolean risk flags, and section diffs — one pipeline across CLI, Python SDK, HTTP API, OpenBB Workspace, and MCP. 8-K is supported via local --html or the MCP Builder bundle only (not --ticker, EDGAR, or HTTP ticker routes).

What it is not:

• Not investment advice or a trading signal
• Not a substitute for reading the filing

Full scope and limits: Scope and claims.

Integration surfaces

Six entry points, one deterministic pipeline. Not sure which to pick? See Choose your surface.

You are…	Entry	Install extra
Terminal / scripts	disclosure-alpha	(base)
Notebooks / apps	import disclosure_alpha	(base)
REST screener or dashboard	disclosure-alpha-api	[api]
OpenBB Workspace analyst	disclosure-alpha-api + OpenBB guide	[api,mcp]
AI agent (ticker scoring)	disclosure-alpha-mcp-analyst	[mcp]
Agent with raw HTML	disclosure-alpha-mcp-builder	[mcp]

HTTP matrix tiers apply to single-ticker GET GET /v1/company/{ticker}/disclosure-matrix only: tier=lite (headline score), tier=standard (components + metrics), tier=analyst (provenance for audit). Panel POST /v1/panel/disclosure-matrix has no tier param — use include / fields. See HTTP guides.

disclosure-alpha-api              # HTTP + OpenBB Workspace backend on :8000
disclosure-alpha-mcp-analyst      # MCP analyst bundle

Guides, Postman collections, and MCP reference: Guides.

OpenBB Workspace

Score filings in OpenBB Workspace with a self-hosted backend — overall score, components, active flags, and section changes in one widget.

pip install "disclosure-alpha[api,mcp]"
export SEC_USER_AGENT="YourName [email protected]"
disclosure-alpha-api

In Workspace: Apps → Connect backend → http://127.0.0.1:8000 → My Apps → Disclosure Alpha → Company → Run. Connect Disclosure Alpha Analyst MCP from the app page for Copilot scoring tools.

OpenBB Copilot can summarize the widget; that is an OpenBB feature, not part of Disclosure Alpha.

Quickstart: OpenBB Workspace · Full guide: OpenBB guide

Capabilities

Deterministic scores — ten computed components (nine headline-weighted, 0–100), section extraction, year-over-year change detection. Canonical component list: Score catalog. Score scale: Understanding scores.

Task	How
Score one company	disclosure-alpha score --ticker AAPL --fiscal-year 2025 --form 10-K
Score in OpenBB Workspace	disclosure-alpha-api + Workspace connect → OpenBB quickstart
Screen up to 25 tickers	HTTP POST /v1/panel/disclosure-matrix (no tier; use include / fields)
Compare year-over-year	--prior-html prior.html or HTTP compare=prior
Work offline (no EDGAR)	disclosure-alpha score --html filing.html --form 10-K
Inspect raw signals	disclosure-alpha metrics … or GET /disclosure-metrics
Pull boolean risk flags	GET /disclosure-flags

# Screen a peer set (start disclosure-alpha-api first)
curl -s -X POST "http://localhost:8000/v1/panel/disclosure-matrix" \
  -H "Content-Type: application/json" \
  -d '{"tickers": ["AAPL", "MSFT", "GOOGL"], "fiscal_year": 2025, "form_type": "10-K"}'

# Year-over-year from local HTML (no network)
disclosure-alpha score --html current.html --form 10-K --prior-html prior.html

Copy-paste recipes: Workflows. Pipeline overview: Methodology.

Example output

Single filing score (synthetic 10-K):

{
  "scores": {
    "overall_disclosure_risk_score": 17.84,
    "score_coverage_ratio": 0.7778,
    "components": {
      "risk_factor_intensity_score": 8.62,
      "boilerplate_risk_score": 42.53,
      "legal_regulatory_risk_score": 25.34
    }
  },
  "versions": {
    "parser_version": "section_extractor_v1",
    "metrics_engine_version": "text_metrics_v4",
    "dictionary_version": "built_in_dictionaries_v3",
    "scoring_model_version": "deterministic_scoring_v2",
    "analytics_config_id": "builtin_default"
  }
}

More examples: Examples gallery and Workflows.

Research-backed

S&P 500 FY2025 Item 1A · deterministic_scoring_v2 · full evidence →

Check	Result
Analysis cohort	478 firms (503-name universe)
Specificity vs NER	Spearman ρ ≈ 0.87 (n=478)
Boilerplate vs LS4-gram proxy	Spearman ρ ≈ 0.92 (n=478)
Post-filing vol (90d)	Q5/Q1 ≈ 1.15 (n=435)

Construct checks use independent references. Vol association is descriptive only — not investment advice or alpha. Scope: Scope and claims.

Documentation

I want to…	Start here
Prove it works in five minutes	First successful run
Evaluate whether to trust this	Evidence and validation
Understand the numbers	Understanding scores
Score in terminal	Quickstart CLI
Build a screener	HTTP guides → Workflows
Use in Python	Quickstart Python
Score in OpenBB Workspace	Quickstart OpenBB
Copy-paste examples	Examples gallery

License

Apache-2.0. See LICENSE. Changelog · Releases

Contributors

See CONTRIBUTING.md for development setup, tests, and docs build. Report security issues via SECURITY.md.