U.S. House Trading Pipeline

Nancy Pelosi files a $500k–$1M purchase of Nvidia options. Three days later it's on Reddit. Two weeks later it's on the news.

This pipeline delivers that filing — and every other House PTR — as clean JSON, within 24 hours of the official disclosure. No third-party aggregators. Direct from the Clerk of the House.

Sister project to senate-trading-pipeline. Same target schema, separate fetcher + PDF parser. Run either or both.

Who uses this

• Retail traders tracking which Congress members are buying/selling before major legislation — defense stocks before NDAA votes, pharma before drug pricing bills, tech before antitrust hearings
• Fintech developers building portfolio tools, alert systems, or dashboards on top of STOCK Act data
• Journalists and researchers monitoring congressional trading patterns — no account, no paywall, raw government data
• Quiver Quantitative / Capitol Trades users who want the raw feed instead of a third-party UI

Why this instead of Quiver or Capitol Trades? Both aggregate from the same source — the Clerk of the House. This pipeline pulls directly from the official ZIP archive. No middleman, no rate limits, no subscription. You own the pipeline.

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What it produces

One row per individual transaction reported in a House PTR:

{
  "id": "4d6016b44239f646476ffac6798f21ae3e32c8ed75ea6c5b50a0bbdf9e5d3296",
  "politician": "Mark Alford",
  "transaction_date": "2026-03-16",
  "filing_date": "2026-03-31",
  "ticker": "AMZN",
  "asset_name": "Amazon.com, Inc. - Common Stock",
  "asset_type": "Stock",
  "type": "sell",
  "amount_min": 1001,
  "amount_max": 15000,
  "owner": "self"
}

Field	Type	Notes
id	string	SHA-256 of politician|date|asset|amount_min|amount_max — stable dedup key
politician	string	Filer name as it appears on the PTR
transaction_date	YYYY-MM-DD	Trade execution date
filing_date	YYYY-MM-DD	Date the PTR was submitted to the House Clerk
ticker	string | null	null for bonds, municipals, structured notes
asset_name	string	Full asset description
asset_type	string	Stock, Stock Option, Mutual Fund, Corporate Bond, etc.
type	'buy' | 'sell'	Purchase → buy; Sale (Full)/Sale (Partial) → sell
amount_min	integer	Lower bound of reported amount range, USD
amount_max	integer | null	Upper bound. null for unbounded "Over $X" disclosures
owner	'self' | 'joint' | 'spouse' | 'child'	Account owner per STOCK Act categories

---

How it works

ZIP fetch         XML parse          PDF download       Text extract       Normalize
┌──────────────┐  ┌────────────────┐  ┌───────────────┐  ┌──────────────┐  ┌──────────┐
│ <YEAR>FD.zip │─▶│ <YEAR>FD.xml   │─▶│ /ptr-pdfs/    │─▶│  pdf-parse   │─▶│ buy/sell │
│ from         │  │ filter         │  │ <YEAR>/       │  │ + marker-    │  │ + amount │
│ disclosures- │  │ FilingType='P' │  │ <DocID>.pdf   │  │ anchored     │  │ ranges   │
│ clerk        │  │ + date window  │  │ (~600ms each) │  │ regex        │  │ + dates  │
└──────────────┘  └────────────────┘  └───────────────┘  └──────────────┘  └──────────┘
                                                                                 │
                                                                                 ▼
                                                                     ┌──────────────────┐
                                                                     │ Dedup (SHA-256)  │
                                                                     │ + Apify Dataset  │
                                                                     └──────────────────┘

1. ZIP fetch. A single HTTPS GET pulls the year-to-date ZIP from https://disclosures-clerk.house.gov/public_disc/financial-pdfs/<YEAR>FD.zip. No proxy needed — plain HTTPS, no Akamai, no terms gate.

2. XML index. Inside the ZIP is <YEAR>FD.xml listing every disclosure for the year. Filter to FilingType=P (Periodic Transaction Report) within the configured date window.

3. Per-PTR PDF fetch. Each XML entry has a DocID. Fetch https://disclosures-clerk.house.gov/public_disc/ptr-pdfs/<YEAR>/<DocID>.pdf for each one. Rate-limited to 600ms between requests.

4. Text extraction. pdf-parse reads the PDF and returns text. House PTRs are machine-generated so the text is clean — but the layout has quirks (header null bytes, glued fields, comment-block bleed).

5. Marker-anchored parsing. Each transaction row in the PDF includes a (TICKER) [TYPE] marker. The parser anchors on these markers, walks backward for the asset name, forward for the transaction details, and emits one record per marker.

6. Normalize + dedup + push. Map source codes (P/S/S (partial), SP/DC/JT) to the canonical schema, hash the natural key for dedup, push to the default Apify dataset.

Older filings filed on paper produce scanned-image PDFs that pdf-parse can't extract from. The parser logs them as unparseable and continues — about 5% of historical PTRs. OCR fallback is on the Phase 2 list.

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Apify deployment

The actor lives at apify.com/seralifatih/congress-trading-pipeline-1.

To run it via API:

# Trigger a run
curl -X POST "https://api.apify.com/v2/acts/seralifatih~congress-trading-pipeline-1/runs?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{ "fetchDaysBack": 30 }'

# Read the dataset
curl "https://api.apify.com/v2/datasets/<dataset-id>/items?token=YOUR_TOKEN&format=json"

Input schema

Field	Type	Default	Description
fetchDaysBack	integer	90	Rolling window of PTRs to fetch (1-365)
fromDate	string (YYYY-MM-DD)	—	Explicit start date. Overrides fetchDaysBack
toDate	string (YYYY-MM-DD)	today	Explicit end date
debugPtrLimit	integer	0	Diagnostic — fetch only first N PTRs
debugPdfText	boolean	false	Log first 2KB of any PDF where regex finds 0 rows

---

Self-hosting

If you'd rather run it yourself:

git clone https://github.com/seralifatih/house-trading-pipeline
cd house-trading-pipeline
npm install
cp .env.example .env
npm run build
node dist/apify.js   # or wire your own runner around runPipeline()

The pipeline's main export is in src/scheduler/pipeline.ts:

import { runPipeline } from './scheduler/pipeline.js';
import { SqliteStore } from './store/sqliteStore.js';

const stats = await runPipeline(SqliteStore.getInstance(), {
  fromDate: '2026-01-01',
  toDate: '2026-04-30',
});

console.log(stats); // { inserted, skipped, errors }

Storage is pluggable — StoreAdapter interface in src/types/index.ts. The repo ships with a SQLite implementation for local runs and an Apify Dataset implementation for cloud runs. Add Postgres or whatever else by implementing the same interface.

---

Project layout

src/
├── apify.ts                  Actor entry point — wires runPipeline + ApifyStore
├── fetcher/
│   └── houseFetcher.ts       ZIP download + XML index + per-PDF fetch
├── parser/
│   └── housePdfParser.ts     Marker-anchored regex extractor
├── transformer/
│   └── normalize.ts          Source codes → canonical schema
├── store/
│   ├── sqliteStore.ts        Local SQLite via better-sqlite3
│   └── apifyStore.ts         Apify Dataset via Apify SDK
├── scheduler/
│   └── pipeline.ts           Fetch → parse → normalize → dedup → save
├── utils/
│   ├── config.ts             Zod-validated env vars
│   ├── dedup.ts              SHA-256 ID generation
│   ├── retry.ts              Exponential backoff with jitter
│   └── logger.ts             JSON-lines structured logger
└── types/
    └── index.ts              RawTransaction, Transaction, StoreAdapter, schemas

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Data source

Clerk of the U.S. House — Financial Disclosure Reports

Public domain government records published under the STOCK Act of 2012. The Clerk publishes a fresh ZIP daily containing every disclosure filed that year.

This pipeline does not scrape third-party aggregators. It pulls only from the official source.

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Phase 2

• OCR fallback for scanned PDFs (older paper filings)
• Ticker enrichment for bond/muni rows where the source omits the ticker
• Cross-chamber merge actor that consumes both Senate + House datasets and emits a single Congress-wide stream

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License

MIT. Use the actor or the source however you want.