Returns insider transactions from a specified date, including the insider's name, title, company, transaction type (buy/sell), shares traded, price, and total cost.
Path Parameters
| Name | Type | Example | Description |
|---|---|---|---|
| date | string | 2025-01-01 | - |
Auth
headers: { 'Authorization': 'Replace with your API token' }
Examples
fetch('https://api.gurufocus.com/data/insiders/2025-01-01', { method: 'GET', headers: { 'Authorization': 'Replace with your API token' }})
Responses
Successful response. The exact payload fields depend on this endpoint.
Example Body (Field Reference)
[ { "id": 22057219, "name": "Barkin Michael Z", "position": "President", "cik_reporting": "0001571970", "stockid": "US2AXF", "symbol": "YOU", "exchange": "NYSE", "company": "Clear Secure Inc", "date": "2026-03-01", "type": "S", "trans_share": "29941", "price": "48.22", "final_share": "5669", "split_factor": "1", "cost": "1443760" }, { "id": 22040021, "name": "Hood Lewis Randolph Jr", "position": "Director", "cik_reporting": "0001691723", "stockid": "US2EX2", "symbol": "NA", "exchange": "NAS", "company": "Nano Labs Ltd", "date": "2026-03-01", "type": "P", "trans_share": "65189", "price": "7.67", "final_share": "", "split_factor": "1", "cost": "500000" }, { "id": 22014240, "name": "Wambeke David J.", "position": "Chief Business Officer", "cik_reporting": "0001971798", "stockid": "US0MRZ", "symbol": "NMTC", "exchange": "NAS", "company": "NeuroOne Medical Technologies Corp", "date": "2026-03-01", "type": "P", "trans_share": "1000000", "price": "0.67", "final_share": "1000000", "split_factor": "1", "cost": "670000" }]
Introcution
The GuruFocus Insider Trading Dataset offers a complete historical record of open market transactions by corporate insiders across all publicly traded companies. Spanning back to 2004 and updated daily, this dataset provides clear, structured visibility into executive-level buying and selling activity—making it a powerful input for investment research, compliance tools, and data-driven strategies.
Used by asset managers, quant teams, and research platforms, this dataset helps surface conviction signals, behavioral trends, and potential red flags.
What’s Included
This dataset captures key details of insider transactions with consistency and precision:
- Insider Identity & Role
Captures the insider’s name, title or relationship to the company (e.g., CEO, Director, 10% Owner), and mapped ticker symbol and company name. - Transaction Details
Includes trade type (buy/sell), number of shares traded, execution price, transaction date, and pre-/post-transaction ownership. All entries include relevant filing codes (Form 4, transaction classification, etc.). - Historical Depth & Market Coverage
Contains over 1 million transactions going back to 2003 (U.S.) and 2013 (Canada), with hourly or twice-daily updates depending on region. Covers more than 100,000 insiders and thousands of stocks across the U.S. and Canada.
All transactions are mapped to ticker symbols, enabling direct integration with stock-level analytics or monitoring dashboards.
Data Coverage
USA
| Total Panel Size | Monthly Panel Size | |
|---|---|---|
| Insiders | 100,000+ | 1,000 - 4,000 |
| Stocks | 9,000+ | 500 - 2,000 |
| Transactions | 1,000,000+ | 1,000 - 7,000 |
Canada
| Total Panel Size | Monthly Panel Size | |
|---|---|---|
| Insiders | 10,000+ | 300 - 1,000 |
| Stocks | 3,000+ | 200 - 700 |
| Transactions | 340,000+ | 1,000 - 5,000 |
Use Cases
This dataset supports a wide range of professional use cases:
- Behavioral finance models focused on executive conviction
- Research dashboards showing recent insider trades for covered stocks
- Screeners and alerts based on insider accumulation or distribution
- Compliance tools requiring clear, auditable insider transaction logs
- Longitudinal studies of insider activity relative to performance
Extend Your Capabilities
For broader insights, combine with:
- Ranking Dataset – GF Score, GF Value, and quality rankings
- Valuation Dataset – Intrinsic value estimates and price targets
- News Dataset – Real-time commentary contextualizing recent trades
All datasets are aligned by ticker, date, and role, making integration seamless.