Congressional Trades & Disclosure Analyzer

Turn public US congressional trade datasets into ticker concentration, disclosure-delay, buy/sell flow and new-activity intelligence.

Why use this Actor

Congressional disclosure data is available as rows, but research teams still need recurring portfolio-level intelligence about concentration, reporting delay, new activity and buy/sell pressure.

Key features

• Buyer-native inputs aligned to congressional trading.
• Predictable structured output in the default Apify Dataset.
• Pay Per Event pricing tied to measurable result rows or reports.
• Optional MCP connector delivery for downstream automation and AI agents.

Quick start

Start with this working example and replace the target values with your own:

{
  "currentItems": [
    {
      "txId": "T1",
      "politicianFullName": "Member Alpha",
      "ticker": "NVDA",
      "txType": "purchase",
      "txDate": "2026-08-01",
      "pubDate": "2026-08-20",
      "amountRange": "$15,001 - $50,000"
    },
    {
      "txId": "T2",
      "politicianFullName": "Member Beta",
      "ticker": "NVDA",
      "txType": "purchase",
      "txDate": "2026-08-02",
      "pubDate": "2026-09-25",
      "amountRange": "$50,001 - $100,000"
    },
    {
      "txId": "T3",
      "politicianFullName": "Member Alpha",
      "ticker": "AAPL",
      "txType": "sale",
      "txDate": "2026-08-05",
      "pubDate": "2026-08-15",
      "amountRange": "$1,001 - $15,000"
    }
  ],
  "previousItems": [
    {
      "txId": "T0",
      "politicianFullName": "Member Alpha",
      "ticker": "AAPL",
      "txType": "purchase",
      "txDate": "2026-07-01",
      "pubDate": "2026-07-15",
      "amountRange": "$1,001 - $15,000"
    }
  ],
  "watchTickers": [
    "NVDA"
  ],
  "delayThresholdDays": 45
}

The Actor writes structured results to the default Apify Dataset and can be used from the Store, API, schedules, Tasks, automations and MCP-compatible AI workflows.

Input

• currentItems — Current disclosure rows: Current rows from a Congress financial-disclosure or stock-trade dataset.
• currentDatasetId — Current Apify Dataset ID: Optional Apify Dataset containing current disclosure rows.
• previousDatasetId — Previous Apify Dataset ID: Optional prior Apify Dataset.
• watchTickers — Watch tickers: Optional ticker watchlist used to prioritize actions.
• watchMembers — Watch members: Optional member-name watchlist.
• delayThresholdDays — Disclosure delay threshold: Days used to flag delayed disclosure rows.

All integration and advanced analysis fields are optional. The default example is intentionally runnable without configuring MCP or a previous-run baseline.

Output

The Dataset exposes predictable machine-readable output. Representative fields include results, report.

Pricing

This Actor uses Pay Per Event. The current factory base price is $0.012000 per result event. The Apify Store remains the source of truth for the price and plan/tier details shown to the buyer.

Use cases

• Run the buyer-ready workflows exposed as Apify Tasks without preparing a custom integration first.
• Use the Actor from API or schedules for recurring collection, comparison or monitoring.
• Feed the structured Dataset output into spreadsheets, databases, automations or AI agents.
• Compare repeat runs when the product supports snapshots or previous-run inputs.

Automation and AI

Use the same Actor through Apify API, schedules, public Tasks and the Apify MCP server. Outputs are structured for downstream workflows and AI agents rather than requiring manual copy/paste.

Limitations

• Public websites and upstream APIs can change markup, access rules, rate limits or field availability without notice.
• Fields that are not publicly available are returned as unavailable or omitted rather than fabricated.
• Analytical outputs depend on the quality and coverage of the supplied or collected source data.
• Treat marketplace, reputation, workforce, safety or commercial signals as decision support and validate material decisions against the underlying source evidence.

Detailed documentation

Detailed documentation

A decision layer for congressional financial-disclosure datasets: normalize multiple schemas, compare snapshots, quantify ticker/member concentration, disclosure delays and buy/sell activity, and emit deterministic research actions.

Why use this Actor

Use this Actor when raw upstream records are not enough and you need a compact, repeatable intelligence layer with deterministic change signals.

Key features

The product normalizes public upstream data, compares optional previous snapshots, aggregates decision-relevant signals, writes one compact report to the Dataset, and stores a reusable snapshot in the Key-Value Store.

Example

Run the default example input from the Actor console. It uses a small synthetic current/previous sample so the output shows change detection, aggregated scores, and agentAction without requiring credentials.

Use cases

Scheduled monitoring, research dashboards, AI-agent tools, data pipelines, webhook workflows, and analyst review queues.

Pricing

Pay per primary intelligence result. One run produces one decision-ready report; pricing is tied to the analytical outcome rather than charging separately for every derived table inside the report.

Limitations

The Actor analyzes the supplied public-data sample. Coverage depends on upstream data completeness, and derived scores describe observed patterns rather than guaranteeing real-world outcomes.

Turn public U.S. congressional financial-disclosure datasets into a compact research report instead of another pile of transaction rows. This Actor is built as a downstream intelligence layer for datasets from Congress trade/disclosure scrapers or your own normalized records.

What it does

It normalizes common field names for member, ticker, transaction type, transaction date, disclosure date and disclosed amount range. It then compares an optional previous snapshot, detects newly observed disclosures, estimates reporting delay, aggregates activity by ticker and member, and calculates concentration and buy/sell-pressure indicators.

The output is one decision-ready report per run. That makes the Actor useful in scheduled monitoring, dashboards, research agents and downstream alert workflows without charging separately for every analytical table it creates.

Intelligence produced

• new and removed disclosure fingerprints between snapshots;
• ticker-level trade counts, unique-member counts and disclosed-value concentration;
• estimated buy versus sell value from disclosed ranges;
• member-level activity summaries;
• filing-delay flags using a configurable day threshold;
• watchlist prioritization for selected tickers or members;
• deterministic actions such as REVIEW_NEW_DISCLOSURES, REVIEW_LATE_DISCLOSURES, REVIEW_CONCENTRATED_ACTIVITY and MONITOR;
• a reusable normalized snapshot in the key-value store.

Inputs

Provide currentItems directly or select an Apify currentDatasetId. Add previousItems or previousDatasetId for period-over-period change detection. Optional watchTickers and watchMembers raise the priority of rows you explicitly monitor.

The Actor is source-agnostic by design. It recognizes common field aliases used by congressional transaction datasets rather than requiring one specific scraper.

Why this is different

Existing Congress disclosure Actors are valuable upstream sources. Their primary job is to retrieve searchable public transactions, parse House/Senate reports, or monitor newly disclosed trades. This Actor starts where extraction ends. It combines rows into portfolio-level concentration, reporting-delay, member and ticker activity intelligence and can compare datasets produced by different upstream sources.

Amount estimates

Congressional disclosures commonly report value ranges rather than exact transaction values. For aggregation only, this Actor uses the midpoint of a numeric range when available. estimatedValueUsd is therefore an analytical estimate, not a claim about the exact trade size. The original range is preserved in normalized disclosure rows.

Responsible use

The data describes public disclosures and reporting patterns. Scores and actions are deterministic research indicators, not investment recommendations, predictions of returns, or claims of wrongdoing. Preserve upstream source links when using the output for journalism, compliance or public reporting.

Automation

Use an Apify Task on a schedule, feed it the latest disclosure dataset, and pass the prior dataset or stored snapshot when you want change intelligence. The single-report output is compact for webhooks, AI agents, Sheets, databases and notification pipelines.