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Senate Trades Dataset

Access trading activity from US Senators. Track stock purchases and sales disclosed under the STOCK Act for systematic trading strategies that follow political insiders.

What’s Included

Section titled “What’s Included”

The Senate Trading dataset provides:

  • Senator Name: Name of the Senate member
  • Transaction Type: Purchase or Sale
  • Amount Range: Transaction size bracket as reported
  • Transaction Date: When the trade occurred
  • Historical Data: Years of senate trading history

Coverage

Section titled “Coverage”
Filing TypeDescriptionUpdate Frequency
Periodic Transaction ReportStock trades by senatorsAs filed
Annual Financial DisclosureComprehensive holdingsAnnual

Amount Ranges

Section titled “Amount Ranges”

Senate disclosures report amounts in ranges:

RangeMinimumMaximum
$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$5,000,001N/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

Section titled “Quick Start”
from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


# Get senate trades as DataFrame
df = fb.senate_trades.ticker("S&P 500", "META", as_dataframe=True)
print(df.head())

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

Use Cases

Section titled “Use Cases”

Senate Trade Alert System

Section titled “Senate Trade Alert System”

Build alerts for significant senate purchases:

from finbrain import FinBrainClient


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 scan_large_senate_trades(tickers):
    """Find stocks with large senate purchases"""
    results = []


    for ticker in tickers:
        try:
            trades = fb.senate_trades.ticker("S&P 500", ticker)


            large_purchases = [
                t for t in trades.get("senateTrades", [])
                if t["type"] == "Purchase"
                and t["amount"] in LARGE_TRADES
            ]


            if large_purchases:
                results.append({
                    "ticker": ticker,
                    "trades": large_purchases
                })
        except Exception:
            continue


    return results


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


for alert in alerts:
    print(f"\n{alert['ticker']}:")
    for trade in alert['trades']:
        print(f"  {trade['senator']}: {trade['amount']}")

Follow Specific Senators

Section titled “Follow Specific Senators”

Track trading activity of specific senators:

from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


def get_senator_trades(senator_name, tickers):
    """Get all trades by a specific senator"""
    trades_list = []


    for ticker in tickers:
        try:
            trades = fb.senate_trades.ticker("S&P 500", ticker)


            sen_trades = [
                {**t, "ticker": ticker}
                for t in trades.get("senateTrades", [])
                if senator_name.lower() in t["senator"].lower()
            ]


            trades_list.extend(sen_trades)
        except Exception:
            continue


    return trades_list


# Track a specific senator's trades
senator_trades = get_senator_trades(
    "Tuberville",
    ["NVDA", "AAPL", "MSFT", "GOOGL", "AMZN", "META"]
)


for trade in senator_trades:
    print(f"{trade['date']}: {trade['ticker']} - {trade['type']} {trade['amount']}")

Trade Activity Analysis

Section titled “Trade Activity Analysis”

Analyze buying vs selling activity for a ticker:

from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


def analyze_senate_activity(market, ticker):
    """Analyze senate trading activity for a ticker"""
    trades = fb.senate_trades.ticker(market, ticker)


    trade_data = trades.get("senateTrades", [])


    purchases = [t for t in trade_data if t["type"] == "Purchase"]
    sales = [t for t in trade_data if t["type"] == "Sale"]


    return {
        "ticker": ticker,
        "total_trades": len(trade_data),
        "purchases": len(purchases),
        "sales": len(sales),
        "buy_sell_ratio": len(purchases) / len(sales) if sales else float('inf'),
        "senators": list(set(t["senator"] for t in trade_data))
    }


analysis = analyze_senate_activity("S&P 500", "META")
print(f"Total trades: {analysis['total_trades']}")
print(f"Purchases: {analysis['purchases']}, Sales: {analysis['sales']}")
print(f"Buy/Sell Ratio: {analysis['buy_sell_ratio']:.2f}")

Multi-Senator Buying Signal

Section titled “Multi-Senator Buying Signal”

Find stocks where multiple senators are buying:

from finbrain import FinBrainClient


fb = FinBrainClient(api_key="YOUR_API_KEY")


def find_cluster_buying(ticker, min_buyers=2):
    """Find if multiple senators are buying a stock"""
    trades = fb.senate_trades.ticker("S&P 500", ticker)


    purchases = [
        t for t in trades.get("senateTrades", [])
        if t["type"] == "Purchase"
    ]


    # Count unique senators making purchases
    buyers = set(p["senator"] for p in purchases)


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


    return None


# Check multiple tickers
for ticker in ["NVDA", "AAPL", "MSFT", "GOOGL", "META"]:
    result = find_cluster_buying(ticker)
    if result:
        print(f"{ticker}: {result['unique_buyers']} unique senator buyers")
Section titled “Related Resources”