# Activist Stake Catalyst: 13D/13G Plus Insider Buying (`scrapemint/activist-stake-catalyst-pipeline`) Actor For each ticker, finds recent Schedule 13D and 13G filings on EDGAR and correlates them with insider open-market buying around the filing, scores conviction 0 to 100, and tiers each as activist\_confirmed, accumulation, or watch. An activist filing while insiders buy is the signal. - **URL**: https://apify.com/scrapemint/activist-stake-catalyst-pipeline.md - **Developed by:** [Ken M](https://apify.com/scrapemint) (community) - **Categories:** Business, News - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Activist Stake Catalyst: 13D/13G Plus Insider Buying Find where an activist or large holder just filed on a company **and** the company's own insiders are buying their stock in the open market. One filing alone is a lead. A 13D landing while insiders add to their own position is conviction. For each ticker you pass, this pipeline: 1. **Pulls activist filings from EDGAR.** It resolves the ticker to its SEC CIK, then runs an EDGAR full text search for Schedule 13D and 13G filings where that company is the subject. Filtering on the subject CIK (not a name match) means you get real filings on the company, not filings that merely mention it. A 13D signals intent to influence; a 13G is a passive holder above 5%. 2. **Pulls insider open-market activity.** It calls `scrapemint/sec-form4-insider-tracker` for Form 4 open-market buys and sells (transaction codes P and S) on the same tickers. 3. **Joins, scores, and tiers.** For each filing it counts insider buys and sells within a window around the filing date, scores conviction 0 to 100 from the filing type, recency, and insider dollar flow, then tiers the result. Both data sources are plain EDGAR HTTP and JSON. No browser, no residential proxy, no API keys. ### Output One row per filing: ```json { "ticker": "BHC", "company": "Bausch Health Companies Inc.", "filingType": "SC 13D/A", "isAmendment": true, "filerName": "Icahn Carl C", "intent": "activist (intent to influence)", "fileDate": "2026-06-12", "daysAgo": 8, "filingUrl": "https://www.sec.gov/Archives/edgar/data/.../...-index.htm", "edgeScore": 72, "tier": "activist_confirmed", "insider": { "windowDays": 21, "buyCount": 2, "sellCount": 0, "buyValueUsd": 410000, "netValueUsd": 410000, "topBuys": [{ "insider": "...", "role": "Director", "valueUsd": 250000, "date": "2026-06-10" }] } } ```` ### Tiers and pricing Pay per event. The first `activist_confirmed` row per run is free so you can validate output. | Tier | Meaning | Price | | --- | --- | --- | | `watch` | Passive 13G holder, no insider buying in the window | $0.05 | | `accumulation` | A 13D activist filing, or any filing with insider buying in the window | $0.10 | | `activist_confirmed` | A recent 13D, optionally with net insider buying. The strongest setup | $0.18 | **Combined cost note.** This pipeline calls `sec-form4-insider-tracker`, which bills its own per-event charges to you in the same run. Both stages are EDGAR HTTP, so total compute stays small. ### Input | Field | Default | Notes | | --- | --- | --- | | `tickers` | `[]` | One or more tickers to scan as 13D/13G targets. Required. | | `formTypes` | `["SC 13D","SC 13G"]` | Limit to activist 13D only, or passive 13G only. | | `maxAgeDays` | `90` | Ignore filings older than this. | | `insiderWindowDays` | `21` | Days on either side of the filing to count insider activity. | | `includeInsider` | `true` | Turn off for filings only. | | `minScore` | `0` | Drop and never charge filings below this 0 to 100 score. | | `userAgent` | generic | EDGAR asks for a descriptive User-Agent with an email. | ### Good use cases - Watch a basket of potential break-up or turnaround names for activists building a position. - Confirm an activist headline against whether insiders are buying alongside. - Feed an event-driven or special-situations screen with one clean signal per filing. # Actor input Schema ## `tickers` (type: `array`): Stock tickers of the companies to scan as 13D/13G targets, e.g. \["DIS", "BHC", "MSFT"]. ## `formTypes` (type: `array`): Which schedules to include. SC 13D = activist intent to influence. SC 13G = passive >5% holder. ## `maxAgeDays` (type: `integer`): Ignore filings older than this many days. 90 = last quarter. ## `insiderWindowDays` (type: `integer`): Count insider buys and sells within this many days on either side of the filing date. ## `includeInsider` (type: `boolean`): Attach insider open-market buys and sells around each filing. Turn off for filings only. ## `minScore` (type: `integer`): Drop and never charge filings scoring below this 0 to 100 threshold. 0 keeps every filing. ## `maxFilingsPerCompany` (type: `integer`): Cap on filings returned per ticker, most recent first. ## `maxFilingsTotal` (type: `integer`): Cap on filings scored across all tickers. ## `userAgent` (type: `string`): EDGAR asks for a descriptive User-Agent that includes an email. Defaults to a generic scrapemint value if left blank. ## `proxyConfiguration` (type: `object`): Optional. EDGAR does not need a proxy, so the default is no proxy. ## Actor input object example ```json { "tickers": [ "DIS", "PARA", "BHC" ], "formTypes": [ "SC 13D", "SC 13G" ], "maxAgeDays": 90, "insiderWindowDays": 21, "includeInsider": true, "minScore": 0, "maxFilingsPerCompany": 25, "maxFilingsTotal": 200, "userAgent": "", "proxyConfiguration": { "useApifyProxy": false } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "tickers": [ "DIS", "PARA", "BHC" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/activist-stake-catalyst-pipeline").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "tickers": [ "DIS", "PARA", "BHC", ] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/activist-stake-catalyst-pipeline").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "tickers": [ "DIS", "PARA", "BHC" ] }' | apify call scrapemint/activist-stake-catalyst-pipeline --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/activist-stake-catalyst-pipeline", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Activist Stake Catalyst: 13D/13G Plus Insider Buying", "description": "For each ticker, finds recent Schedule 13D and 13G filings on EDGAR and correlates them with insider open-market buying around the filing, scores conviction 0 to 100, and tiers each as activist_confirmed, accumulation, or watch. An activist filing while insiders buy is the signal.", "version": "0.1", "x-build-id": "eZ7UM0cFd8lP9OqiS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~activist-stake-catalyst-pipeline/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-activist-stake-catalyst-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapemint~activist-stake-catalyst-pipeline/runs": { "post": { "operationId": "runs-sync-scrapemint-activist-stake-catalyst-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapemint~activist-stake-catalyst-pipeline/run-sync": { "post": { "operationId": "run-sync-scrapemint-activist-stake-catalyst-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "tickers": { "title": "Tickers", "type": "array", "description": "Stock tickers of the companies to scan as 13D/13G targets, e.g. [\"DIS\", \"BHC\", \"MSFT\"].", "default": [], "items": { "type": "string" } }, "formTypes": { "title": "Filing types", "type": "array", "description": "Which schedules to include. SC 13D = activist intent to influence. SC 13G = passive >5% holder.", "items": { "type": "string", "enum": [ "SC 13D", "SC 13G" ] }, "default": [ "SC 13D", "SC 13G" ] }, "maxAgeDays": { "title": "Max age (days)", "minimum": 1, "maximum": 730, "type": "integer", "description": "Ignore filings older than this many days. 90 = last quarter.", "default": 90 }, "insiderWindowDays": { "title": "Insider window (days)", "minimum": 1, "maximum": 90, "type": "integer", "description": "Count insider buys and sells within this many days on either side of the filing date.", "default": 21 }, "includeInsider": { "title": "Include insider buying", "type": "boolean", "description": "Attach insider open-market buys and sells around each filing. Turn off for filings only.", "default": true }, "minScore": { "title": "Minimum conviction score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Drop and never charge filings scoring below this 0 to 100 threshold. 0 keeps every filing.", "default": 0 }, "maxFilingsPerCompany": { "title": "Max filings per company", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cap on filings returned per ticker, most recent first.", "default": 25 }, "maxFilingsTotal": { "title": "Max filings total", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Cap on filings scored across all tickers.", "default": 200 }, "userAgent": { "title": "SEC User-Agent (optional)", "type": "string", "description": "EDGAR asks for a descriptive User-Agent that includes an email. Defaults to a generic scrapemint value if left blank.", "default": "" }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Optional. EDGAR does not need a proxy, so the default is no proxy.", "default": { "useApifyProxy": false } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```