# Congressional Trades & Disclosure Analyzer (`quanmatrix/congressional-trades-disclosure-intelligence`) Actor

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

- **URL**: https://apify.com/quanmatrix/congressional-trades-disclosure-intelligence.md
- **Developed by:** [Rafael Barreto Haddad](https://apify.com/quanmatrix) (community)
- **Categories:** News, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.40 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 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:

```json
{
  "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.

# Changelog

This Actor's version history is a separate document: https://apify.com/quanmatrix/congressional-trades-disclosure-intelligence/changelog.md

# Actor input Schema

## `currentItems` (type: `array`):

Current rows from a Congress financial-disclosure or stock-trade dataset.

## `currentDatasetId` (type: `string`):

Optional Apify Dataset containing current disclosure rows.

## `previousItems` (type: `array`):

Optional prior rows for change detection.

## `previousDatasetId` (type: `string`):

Optional prior Apify Dataset.

## `watchTickers` (type: `array`):

Optional ticker watchlist used to prioritize actions.

## `watchMembers` (type: `array`):

Optional member-name watchlist.

## `delayThresholdDays` (type: `integer`):

Days used to flag delayed disclosure rows.

## `maxItems` (type: `integer`):

Maximum number of input rows loaded and analyzed in this run.

## `mcpConnectors` (type: `array`):

Optional MCP connectors authorized in your Apify account. Use them to send or write this Actor result to tools such as Slack, Notion, GitHub, Sentry, Supabase, or another compatible MCP service.

## `mcpToolName` (type: `string`):

Optional exact MCP tool name. Leave blank to let the selected MCP action preset discover a compatible tool automatically.

## `mcpToolArguments` (type: `object`):

JSON object passed to the selected MCP tool. String values may use {{actor_title}}, {{result_summary}}, or {{result_json}} placeholders.

## `mcpFailOnError` (type: `boolean`):

When enabled, an MCP delivery error fails the Actor run. Disabled by default so data extraction and intelligence results remain available even if the external destination is unavailable.

## `mcpActionPreset` (type: `string`):

Choose a safe action pattern. AUTO_SAFE_WRITE discovers a compatible non-destructive write tool automatically; use a specific preset for Slack, GitHub, Notion, or database delivery.

## Actor input object example

```json
{
  "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": [],
  "watchMembers": [],
  "delayThresholdDays": 45,
  "maxItems": 10000,
  "mcpToolName": "",
  "mcpToolArguments": {},
  "mcpFailOnError": false,
  "mcpActionPreset": "AUTO_SAFE_WRITE"
}
```

# Actor output Schema

## `results` (type: `string`):

No description

## `report` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "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"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quanmatrix/congressional-trades-disclosure-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "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",
        }],
}

# Run the Actor and wait for it to finish
run = client.actor("quanmatrix/congressional-trades-disclosure-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "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"
    }
  ]
}' |
apify call quanmatrix/congressional-trades-disclosure-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quanmatrix/congressional-trades-disclosure-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/ekToo6F7W5chlDtmM/builds/WDtbWkVyNnRekddM7/openapi.json