SEC Form 144 Insider Selling Monitor: Planned Insider Sales, Scored

See which insiders are filing to sell, and how much, before the sale happens. This Actor turns SEC Form 144 filings into a structured insider selling feed: give it one or more companies and it returns the recent planned insider sales per company, with the named seller, their relationship to the issuer, and the proposed dollar value, parsed from official SEC EDGAR data and scored by impact. It is built as a leading-indicator companion to Form 4 insider buying, so you can watch distribution and accumulation side by side.

Why this is different

Most EDGAR tools hand you raw filing rows and leave the work to you. This insider sell monitor reads the structured Form 144 XML and builds the signal layer on top:

• Structured seller, role, and value. Every notice carries the named insider, their relationship to the issuer (officer, director, affiliate, or 10 percent owner), the securities class, the units, and the aggregate market value of the planned sale, pulled directly from the filing XML.
• Scored, not just listed. Each notice gets a transparent impact score from 0 to 100 based on proposed dollar size, seller role, and recency, so the largest and most recent planned sales rank first.
• Per-company aggregate. You get a total proposed-sell value and a high-impact count across all recent notices for each company, not just a flat list.
• A companion to Form 4 buying. Form 144 is a notice of intent to sell restricted or control stock and tends to precede the executed sale that later appears on Form 4. Pairing planned sales (this Actor) with executed buys (Form 4) gives you both sides of insider flow.
• Verifiable. Every notice links straight back to its filing on SEC.gov.

Who it's for

• Traders and quants who want a ready-made insider-selling feed to screen watchlists and feed models, complementary to insider buying.
• Fintech and data apps that need clean, structured planned-sale signals to drop into a product or dashboard.
• Research and risk teams tracking insider distribution around earnings, lock-up expirations, and price highs.

Use cases

• Watchlist distribution scan. Run your watchlist daily and surface companies where insiders are filing large, recent planned sales.
• Pair with insider buying. Cross-reference Form 144 planned sells against Form 4 executed buys to see where insiders are accumulating versus distributing.
• Event-driven monitoring. Schedule a run around earnings or lock-up expiration to catch a cluster of officer and director sell notices.
• Risk overlay. Flag positions where a high-impact insider sell notice has just been filed and route it into your risk review.
• Feed for models and agents. Pull scored planned-sale data on demand into a quant pipeline, notebook, or AI agent workflow.

Input

Field	Type	Required	Description
tickers	array	yes	One or more US stock tickers (e.g. TSLA, NVDA, AAPL) or 10-digit SEC CIK numbers. Each company is scanned for recent Form 144 insider-sale notices.
maxFilings	integer	no	How many of the company's most recent Form 144 notices to parse and rank. Default 25, range 1 to 60.
minImpact	integer	no	Only return notices at or above this impact score, 0 to 100. For example 70 returns large or recent planned sells. Default 0 returns all.

Output

The Actor pushes one item per company, with the ticker, company name, notices found, total proposed-sell value, a high-impact count, and a ranked list of notices. Each notice includes the seller, relationship, securities class, units, aggregate market value, approximate sale date, exchange, filing date, a plain-language catalyst description, the impact score, the accession number, and a direct SEC.gov URL.

{
  "type": "insider_sell_notices",
  "ticker": "TSLA",
  "company": "Tesla, Inc.",
  "notices_found": 12,
  "total_proposed_sell_value": 26034000.0,
  "high_impact_count": 2,
  "notices": [
    {
      "issuer": "Tesla, Inc.",
      "ticker": "TSLA",
      "seller": "Kimbal Musk",
      "relationship": "Director",
      "securities_class": "Common Stock",
      "units": 75000,
      "aggregate_value": 24980000.0,
      "approx_sale_date": "2026-06-02",
      "exchange": "NASDAQ",
      "filing_date": "2026-06-01",
      "catalyst": "Kimbal Musk (Director) filed to sell $24,980,000 of Common Stock, approx 2026-06-02",
      "impact": 100,
      "accession": "0000000000-26-000000",
      "url": "https://www.sec.gov/Archives/edgar/data/1318605/000000000026000000/"
    }
  ]
}

The example values above illustrate the shape and field types. Live values come straight from the filings.

How the score works

The impact score is transparent and computed directly in code:

1. Proposed dollar size sets the base. A planned sale of 10M or more starts at 90, 1M or more at 70, 100k or more at 50, any disclosed value above zero at 35, and an undisclosed value at 25.
2. Seller role adds a bonus. If the relationship contains officer or director, add 5, because executives and directors tend to weigh more than passive large holders.
3. Recency adds a bonus. A filing within 14 days adds 12, and within 45 days adds 6.

The total is capped at 100. Notices are ranked by impact first, then by aggregate value, so a large, recent, officer-filed planned sale rises to the top.

Use with AI agents and automation

This Actor is built to be called by code and by AI agents, not just clicked in the UI.

• AI frameworks. Call it from LangChain or LlamaIndex as a tool inside an agent pipeline.
• Apify MCP server. Expose it over the Apify MCP server so assistants such as Claude, ChatGPT, and Cursor can invoke it directly and read back the scored insider-selling data.
• No-code automation. Wire it into Zapier or Make to trigger downstream steps when new high-impact notices appear.
• Webhooks and schedules. Run it on demand for fresh JSON, or on a schedule, and use Apify webhooks to push results into your own systems when a run finishes.

Pricing

Pay per event: one charge per company analyzed. There is no subscription. Scanning a 10-ticker watchlist is 10 charges, so you pay only for the companies you scan.

Data source and compliance

Data comes from the official SEC EDGAR submissions API at data.sec.gov, which is free, public, and requires no API key. The Actor maps each ticker to its CIK, filters for Form 144 filings, then reads each filing's structured primary-document XML to extract the seller, relationship, and proposed dollar value. This is public regulatory data, and the Actor surfaces only what the filing itself discloses. It does not collect or add personal data beyond the filer information published in the filing.

FAQ

What is a Form 144? It is a notice of intent. An insider files Form 144 to declare a proposed sale of restricted or control securities. It signals that a sale is planned, ahead of the actual transaction, which later appears on Form 4.

Does a Form 144 mean the shares were sold? No. It is a notice of intent to sell, not a confirmation that the sale occurred. Treat it as a leading indicator.

Can an AI agent call this automatically? Yes. Expose the Actor through the Apify MCP server and assistants like Claude, ChatGPT, and Cursor can call it on their own, or invoke it from LangChain or LlamaIndex inside an agent workflow.

How current is the data? SEC EDGAR is the official US filing registry and is updated continuously. Each run reads the latest filings available at that moment.

How does it relate to insider buying? Form 144 captures planned insider selling, while Form 4 captures executed insider transactions including buys. Use them together to see both distribution and accumulation.

Can I filter out small notices? Yes. Set minImpact to return only notices at or above a given score, for example 70 for large or recent planned sells.

---

Keywords: insider selling, SEC Form 144, planned insider sales, insider sell monitor, insider sell data, proposed sale, restricted stock, SEC EDGAR API, insider transactions, Form 4 companion, executive stock sales, insider distribution.

---

Note: This Actor is provided as data for research, screening, and monitoring. It is not investment advice. Historical patterns do not guarantee future results.