Insider Transactions Dataset

Track executive purchases, sales, option exercises, and ownership changes from SEC Form 4 filings. Insider transactions are among the most predictive alternative data signals for equity returns.

What’s Included

The Insider Transactions dataset provides:

• Transaction Details: Buy, sell, option exercise, and gift transactions
• Insider Information: Name, title, and relationship to company
• Share Data: Number of shares and transaction price
• Ownership Changes: Post-transaction holdings
• Filing Dates: Transaction date and SEC filing date
• Direct SEC Links: Links to original Form 4 filings

Coverage

Filing Type	Description	Update Frequency
Form 4	Changes in beneficial ownership	Real-time
Form 3	Initial statement of ownership	Real-time
Form 144	Notice of proposed sale	Real-time

Transaction Types & Signals

Type	Description	Signal
Purchase	Open market buy	Bullish - insiders buying with their own money
Sale	Open market sell	May be neutral (planned sales) or bearish
Option Exercise	Stock option converted to shares	Neutral - compensation related
Gift	Shares donated	Neutral
Automatic	10b5-1 plan execution	Neutral - pre-planned

Quick Start

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Get insider transactions as DataFrame
df = fb.insider_transactions.ticker("S&P 500", "AAPL", as_dataframe=True)
print(df.head())

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

Visualization

Plot insider transactions on a price chart using the Python SDK. Since FinBrain doesn’t provide historical prices, you must supply your own price data:

from finbrain import FinBrainClient
import yfinance as yf

fb = FinBrainClient(api_key="YOUR_API_KEY")

# Load price data (example using yfinance)
price_df = yf.download("NVDA", start="2025-10-01", end="2026-01-30")

# Plot insider transactions overlaid on price chart
fb.plot.insider_transactions("S&P 500", "NVDA", price_data=price_df)

NVDA insider transactions overlaid on price chart

Use Cases

Insider Purchase Scanner

Build a scanner to find stocks with significant insider buying:

from finbrain import FinBrainClient
from datetime import datetime, timedelta

fb = FinBrainClient(api_key="YOUR_API_KEY")

def scan_insider_purchases(tickers, days=30, min_value=100000):
    """Find tickers with significant insider purchases"""
    date_from = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
    date_to = datetime.now().strftime("%Y-%m-%d")

    results = []

    for ticker in tickers:
        try:
            data = fb.insider_transactions.ticker(
                "S&P 500",
                ticker,
                date_from=date_from,
                date_to=date_to
            )

            purchases = [
                t for t in data.get("insiderTransactions", [])
                if t["transaction"] == "Buy"
                and t["USDValue"] >= min_value
            ]

            if purchases:
                total_value = sum(p["USDValue"] for p in purchases)
                results.append({
                    "ticker": ticker,
                    "purchases": len(purchases),
                    "total_value": total_value
                })
        except Exception as e:
            continue

    return sorted(results, key=lambda x: x["total_value"], reverse=True)

# Scan tech stocks
tech_tickers = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"]
purchases = scan_insider_purchases(tech_tickers)

for p in purchases:
    print(f"{p['ticker']}: {p['purchases']} purchases, ${p['total_value']:,.0f}")

Cluster Buying Detection

Detect when multiple insiders are buying - a stronger bullish signal:

from finbrain import FinBrainClient
from collections import defaultdict

fb = FinBrainClient(api_key="YOUR_API_KEY")

def detect_cluster_buying(market, ticker):
    """Detect cluster buying (multiple insiders buying)"""
    data = fb.insider_transactions.ticker(market, ticker)

    purchases = [
        t for t in data.get("insiderTransactions", [])
        if t["transaction"] == "Buy"
    ]

    # Group by month
    monthly = defaultdict(list)
    for p in purchases:
        month = p["date"][:7]  # YYYY-MM
        monthly[month].append(p)

    # Find months with multiple buyers
    clusters = []
    for month, txns in monthly.items():
        unique_insiders = set(t["insiderTradings"] for t in txns)
        if len(unique_insiders) >= 2:
            clusters.append({
                "period": month,
                "insiders": list(unique_insiders),
                "total_value": sum(t["USDValue"] for t in txns)
            })

    return clusters

clusters = detect_cluster_buying("S&P 500", "AAPL")
for c in clusters:
    print(f"{c['period']}: {len(c['insiders'])} insiders, ${c['total_value']:,.0f}")

C-Suite Tracking

Focus on CEO, CFO, and board member transactions - often the most informed insiders:

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

C_SUITE_TITLES = [
    "Chief Executive Officer",
    "CEO",
    "Chief Financial Officer",
    "CFO",
    "Chief Operating Officer",
    "COO",
    "President",
    "Director",
    "Chairman"
]

def get_csuite_transactions(market, ticker):
    """Filter for C-suite and board member transactions"""
    data = fb.insider_transactions.ticker(market, ticker)

    csuite = [
        t for t in data.get("insiderTransactions", [])
        if any(title.lower() in t["relationship"].lower() for title in C_SUITE_TITLES)
    ]

    return csuite

csuite = get_csuite_transactions("S&P 500", "AAPL")
for t in csuite:
    print(f"{t['date']}: {t['insiderTradings']} ({t['relationship']})")
    print(f"  {t['transaction']} ${t['USDValue']:,.0f}")

Related Resources

• Insider Transactions API Reference - Endpoint details, parameters, and response schema
• Building an Insider Trading Scanner - Step-by-step guide
• Congressional Trading Data - Track politician trades
• Python SDK Documentation