See what members of the US House are trading, parsed, scored and ranked. Provide one or more House member last names and get their recent stock trades from the official House Clerk disclosures: each transaction with ticker, buy or sell, estimated size and date, plus buys-vs-sells and an estimated total per member. A curated trading signal, not a raw PDF dump.
What it does
- Parsed transactions: ticker, buy/sell, estimated amount range and date, straight from the official PTR.
- Buys vs sells: the member's recent net direction.
- Estimated value: midpoint of each disclosed amount range, summed per member.
- Impact score 0-100 from trade size plus recency.
- Reads the official US House Clerk source, not a scraped aggregator.
Who uses congressional trading data
- Traders and retail quants tracking political trading as a signal.
- Fintech and research apps embedding congressional-trade data.
- Journalists and researchers monitoring disclosures without parsing PDFs.
How it works
The tool reads the official US House Clerk financial-disclosure feed (free, public, no key), filters to each member's recent Periodic Transaction Reports, and parses the trades from the e-filed PDFs. Clean and official.
Example
Nancy Pelosi in live data: a recent disclosure parsed into 17 trades worth an estimated tens of millions, including large GOOGL, AMZN and NVDA positions (buys and sells). Roger Williams: CVX, FANG, JPM, RTX.
Scope and limitation
US House only. The Senate eFD portal blocks automated access and prohibits commercial use, so it is intentionally excluded. Only e-filed (digital) reports are machine-readable; older scanned paper filings are skipped (no OCR).
FAQ
Where does the data come from?
The official US House Clerk financial-disclosure feed, published under the STOCK Act.
Why only the House?
The Senate portal is anti-bot protected and its terms prohibit commercial use.
Is this investment advice?
No. It is data for research and monitoring.
Use with AI agents and automation
This runs as an Apify Actor, so it drops into your stack with no scraping or glue code: - AI agents and LLMs: call it as a live tool from LangChain, LlamaIndex or Flowise, or over MCP. - No-code automation: wire it to Zapier or Make (new disclosed trade -> Slack, Google Sheets or alert). - Webhooks and pipelines: fire a webhook on each run, or chain it into another DataSignals Lab product. - API and schedule: JSON output, on demand or on a daily schedule.