Congressional Trading Dataset

Access stock trading activity from both chambers of the US Congress. Track purchases and sales disclosed by House Representatives and Senators under the STOCK Act, collected directly from the official disclosure systems and updated daily.

What’s Included

The Congressional Trading dataset provides:

Politician Name: Name of the House or Senate member
Chamber: Whether the trade was filed by a House Representative or a Senator
Transaction Type: Purchase or Sale
Amount Range: Transaction size bracket as reported
Transaction Date: When the trade occurred
Historical Data: 10+ years of history (filings since 2016), 100,000+ disclosed transactions

Coverage

Chamber	Source	Update Frequency
House	House Clerk financial disclosures (Periodic Transaction Reports)	Daily
Senate	Senate eFD system (Periodic Transaction Reports)	Daily

Filings are collected directly from the official House and Senate disclosure systems — not resold third-party feeds — with the original filings archived for provenance. Coverage spans US-listed stocks and ETFs, with 10+ years of history (filings since 2016) available for backtesting.

Disclosure timing: the STOCK Act requires members of Congress to disclose trades within 45 days of the transaction. New filings appear in the dataset the day they are collected, but the reporting lag is inherent to the disclosure regime — factor it into any signal research. Members occasionally disclose trades late, so transaction dates older than the filing window do appear.

Amount Ranges

Congressional disclosures report amounts in ranges:

Range	Minimum	Maximum
$1,001 - $15,000	$1,001	$15,000
$15,001 - $50,000	$15,001	$50,000
$50,001 - $100,000	$50,001	$100,000
$100,001 - $250,000	$100,001	$250,000
$250,001 - $500,000	$250,001	$500,000
$500,001 - $1,000,000	$500,001	$1,000,000
$1,000,001 - $5,000,000	$1,000,001	$5,000,000
Over $5,000,000	$5,000,001	N/A

Note: The amount field can be an exact value (e.g., "$360.00") or a range (e.g., "$15,001 - $50,000").

Quick Start

House and Senate trades are served by separate endpoints with an identical schema:

Python SDK
Python (requests)

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

house_df = fb.house_trades.ticker("NVDA", as_dataframe=True)
senate_df = fb.senate_trades.ticker("NVDA", as_dataframe=True)
print(house_df)
print(senate_df)

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.finbrain.tech/v2"

for chamber in ["house", "senate"]:
    response = requests.get(
        f"{BASE_URL}/congress/{chamber}/NVDA",
        headers={"Authorization": f"Bearer {API_KEY}"}
    )

    data = response.json()
    for trade in data["data"]["trades"]:
        print(f"{trade['date']}: {trade['politician']} ({chamber}) - "
              f"{trade['transactionType']} {trade['amount']}")

For complete code examples in Python, JavaScript, C++, Rust, and cURL, see the API Reference.

Visualization

Plot congressional trades on a price chart with the built-in SDK charts. You must supply your own price data:

from finbrain import FinBrainClient
import yfinance as yf

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Provide your own price data
price_df = yf.download("NVDA", start="2024-01-01", end="2025-01-01")

# One-line interactive charts with buy/sell markers on price
fb.plot.house_trades("NVDA", price_df)
fb.plot.senate_trades("NVDA", price_df)

House Trades Chart — NVDA House Representative trades overlaid on price chart

Senate Trades Chart — NVDA Senate trades overlaid on price chart

Use Cases

Congressional Trade Alert System

Build alerts for significant congressional purchases across both chambers:

from finbrain import FinBrainClient
import pandas as pd

fb = FinBrainClient(api_key="YOUR_API_KEY")

LARGE_TRADES = [
    "$500,001 - $1,000,000",
    "$1,000,001 - $5,000,000",
    "Over $5,000,000"
]

def get_congress_trades(symbol):
    """Combine House and Senate trades for a symbol"""
    frames = []
    for chamber, api in [("house", fb.house_trades), ("senate", fb.senate_trades)]:
        try:
            df = api.ticker(symbol, as_dataframe=True)
            df["chamber"] = chamber
            frames.append(df)
        except Exception:
            continue
    return pd.concat(frames) if frames else pd.DataFrame()

def scan_large_congressional_trades(symbols):
    """Find stocks with large congressional purchases"""
    results = []

    for symbol in symbols:
        df = get_congress_trades(symbol)
        if df.empty:
            continue

        large_purchases = df[
            (df["transactionType"] == "Purchase") &
            (df["amount"].isin(LARGE_TRADES))
        ]

        if not large_purchases.empty:
            results.append({
                "symbol": symbol,
                "trades": large_purchases
            })

    return results

# Scan popular stocks
symbols = ["NVDA", "AAPL", "MSFT", "GOOGL", "AMZN", "META", "TSLA"]
alerts = scan_large_congressional_trades(symbols)

for alert in alerts:
    print(f"\n{alert['symbol']}:")
    for _, trade in alert['trades'].iterrows():
        print(f"  {trade['politician']} ({trade['chamber']}): {trade['amount']}")

Follow Specific Politicians

Track trading activity of specific members of Congress:

from finbrain import FinBrainClient
import pandas as pd

fb = FinBrainClient(api_key="YOUR_API_KEY")

def get_politician_trades(politician_name, symbols):
    """Get all trades by a specific member across both chambers"""
    all_trades = []

    for symbol in symbols:
        for api in [fb.house_trades, fb.senate_trades]:
            try:
                df = api.ticker(symbol, as_dataframe=True)
                mask = df["politician"].str.lower().str.contains(politician_name.lower())
                matched = df[mask].copy()
                matched["symbol"] = symbol
                all_trades.append(matched)
            except Exception:
                continue

    return pd.concat(all_trades) if all_trades else pd.DataFrame()

# Track a specific politician's trades
trades = get_politician_trades(
    "Pelosi",
    ["NVDA", "AAPL", "MSFT", "GOOGL", "AMZN", "CRM", "RBLX"]
)

for date, row in trades.iterrows():
    print(f"{date}: {row['symbol']} - {row['transactionType']} {row['amount']}")

Cluster Buying Signal

Find stocks where multiple members of Congress are buying:

from finbrain import FinBrainClient
import pandas as pd

fb = FinBrainClient(api_key="YOUR_API_KEY")

def find_cluster_buying(symbol, min_buyers=3):
    """Find if multiple members of Congress are buying a stock"""
    frames = []
    for api in [fb.house_trades, fb.senate_trades]:
        try:
            frames.append(api.ticker(symbol, as_dataframe=True))
        except Exception:
            continue

    if not frames:
        return None

    df = pd.concat(frames)
    purchases = df[df["transactionType"] == "Purchase"]

    # Count unique members making purchases
    buyers = purchases["politician"].unique()

    if len(buyers) >= min_buyers:
        return {
            "symbol": symbol,
            "unique_buyers": len(buyers),
            "total_purchases": len(purchases),
            "politicians": list(buyers)
        }

    return None

# Check multiple symbols
for symbol in ["NVDA", "AAPL", "MSFT", "GOOGL", "META"]:
    result = find_cluster_buying(symbol)
    if result:
        print(f"{symbol}: {result['unique_buyers']} unique buyers")

Congressional Trading API Reference - Endpoint details, parameters, and response schema
Insider Transactions - Corporate insider trades
Corporate Lobbying - Lobbying disclosure filings