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Disclosure Alpha
Transform complex regulatory disclosure into structured data
Disclosure Alpha is an open-source Python tool that parses, scores, and diffs 10-K, 10-Q, and 8-K filings locally and deterministically. Stripping out expensive LLM dependencies, it extracts 10 native, headline-weighted language scores to flag immediate text shifts at zero cost. Built for pragmatic quantitative and developer workflows, it runs seamlessly using local HTML pipelines or connects instantly to your AI environment via an integrated MCP server.
I built Disclosure Alpha out of a simple frustration: processing raw SEC regulatory filings (10-Ks, 10-Qs, 8-Ks) usually requires burning endless API credits on LLMs or wrestling with heavy, over-engineered enterprise datasets just to extract basic textual shifts.
I wanted something lightweight, fast, and entirely deterministic.
What Disclosure Alpha does out of the box:
No LLMs Required: It parses, scores, and diffs corporate filings locally without relying on external APIs or risking hallucinations.
Granular Text Scoring: It computes 10 distinct language scores (including 9 headline-weighted metrics) so you can instantly pinpoint exactly where and how a company’s tone or disclosure details changed quarter-over-quarter.
Native MCP Support: It features built-in Model Context Protocol (MCP) server capabilities. If you use AI assistants or local agents, they can connect directly to parse and explore these disclosures programmatically.
The project is entirely open source and built for developers, quantitative analysts, or anyone trying to extract clean data from messy financial disclosures without the bloat.
I’d love to hear your thoughts, answer any questions about the scoring logic, or get feedback on what features you'd like to see next!
About Disclosure Alpha on Product Hunt
“Transform complex regulatory disclosure into structured data”
Disclosure Alpha was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #123 on the daily leaderboard. Disclosure Alpha is an open-source Python tool that parses, scores, and diffs 10-K, 10-Q, and 8-K filings locally and deterministically. Stripping out expensive LLM dependencies, it extracts 10 native, headline-weighted language scores to flag immediate text shifts at zero cost. Built for pragmatic quantitative and developer workflows, it runs seamlessly using local HTML pipelines or connects instantly to your AI environment via an integrated MCP server.
On the analytics side, Disclosure Alpha competes within Analytics, Investing, GitHub and Development — topics that collectively have 246.7k followers on Product Hunt. The dashboard above tracks how Disclosure Alpha performed against the three products that launched closest to it on the same day.
Who hunted Disclosure Alpha?
Disclosure Alpha was hunted by Alwan Alkautsar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Disclosure Alpha including community comment highlights and product details, visit the product overview.
Alwan Alkautsar
Hey Product Hunt! 👋
I built Disclosure Alpha out of a simple frustration: processing raw SEC regulatory filings (10-Ks, 10-Qs, 8-Ks) usually requires burning endless API credits on LLMs or wrestling with heavy, over-engineered enterprise datasets just to extract basic textual shifts.
I wanted something lightweight, fast, and entirely deterministic.
What Disclosure Alpha does out of the box:
No LLMs Required: It parses, scores, and diffs corporate filings locally without relying on external APIs or risking hallucinations.
Granular Text Scoring: It computes 10 distinct language scores (including 9 headline-weighted metrics) so you can instantly pinpoint exactly where and how a company’s tone or disclosure details changed quarter-over-quarter.
Native MCP Support: It features built-in Model Context Protocol (MCP) server capabilities. If you use AI assistants or local agents, they can connect directly to parse and explore these disclosures programmatically.
The project is entirely open source and built for developers, quantitative analysts, or anyone trying to extract clean data from messy financial disclosures without the bloat.
Check out the project website here: https://disclosurealpha.com
I’d love to hear your thoughts, answer any questions about the scoring logic, or get feedback on what features you'd like to see next!