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 price per share
Ownership Changes: Post-transaction holdings
Filing Links: Direct links to SEC 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

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

df = fb.insider_transactions.ticker("AAPL", as_dataframe=True)
print(df)

import requests

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

response = requests.get(
    f"{BASE_URL}/insider-trading/AAPL",
    headers={"Authorization": f"Bearer {API_KEY}"}
)

data = response.json()
for txn in data["data"]["transactions"]:
    print(f"{txn['date']}: {txn['insider']} - {txn['transactionType']} ${txn['totalValue']:,.0f}")

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

Visualization

Plot insider transactions on a price chart with the built-in SDK chart. 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 chart with buy/sell markers on price
fb.plot.insider_transactions("NVDA", price_df)

Insider Transactions Chart — 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(symbols, days=30, min_value=100000):
    """Find symbols 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 symbol in symbols:
        try:
            df = fb.insider_transactions.ticker(
                symbol, date_from=date_from, date_to=date_to,
                as_dataframe=True
            )

            purchases = df[
                (df["transactionType"] == "Buy") &
                (df["totalValue"] >= min_value)
            ]

            if not purchases.empty:
                total_value = purchases["totalValue"].sum()
                results.append({
                    "symbol": symbol,
                    "purchases": len(purchases),
                    "total_value": total_value
                })
        except Exception:
            continue

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

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

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

Cluster Buying Detection

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

from finbrain import FinBrainClient

fb = FinBrainClient(api_key="YOUR_API_KEY")

def detect_cluster_buying(symbol):
    """Detect cluster buying (multiple insiders buying)"""
    df = fb.insider_transactions.ticker(symbol, as_dataframe=True)

    purchases = df[df["transactionType"] == "Buy"].copy()

    # Group by month
    purchases["month"] = purchases.index.to_series().str[:7]  # YYYY-MM

    clusters = []
    for month, group in purchases.groupby("month"):
        unique_insiders = group["insider"].unique()
        if len(unique_insiders) >= 2:
            clusters.append({
                "period": month,
                "insiders": list(unique_insiders),
                "total_value": group["totalValue"].sum()
            })

    return clusters

clusters = detect_cluster_buying("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(symbol):
    """Filter for C-suite and board member transactions"""
    df = fb.insider_transactions.ticker(symbol, as_dataframe=True)

    mask = df["relationship"].str.lower().apply(
        lambda r: any(title.lower() in r for title in C_SUITE_TITLES)
    )
    return df[mask]

csuite = get_csuite_transactions("AAPL")
for date, row in csuite.iterrows():
    print(f"{date}: {row['insider']} ({row['relationship']})")
    print(f"  {row['transactionType']} ${row['totalValue']:,.0f}")

Insider Transactions API Reference - Endpoint details, parameters, and response schema
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