# Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys (`scrapemint/buyback-insider-conviction-pipeline`) Actor For each ticker, finds 8-K share repurchase announcements and correlates them with insider open-market buying around the filing, scores conviction 0 to 100, and tiers each as conviction\_buyback, accumulation, or watch. A buyback plus insiders buying is the signal. Pure EDGAR data, no keys. - **URL**: https://apify.com/scrapemint/buyback-insider-conviction-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 ## Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys A company announcing a share repurchase is talking. The company's own insiders buying their stock in the open market at the same time is conviction. This pipeline finds where both happen on the same name. For each ticker you pass, it: 1. **Finds buyback announcements on EDGAR.** It resolves the ticker to its SEC CIK, then runs an EDGAR full text search for 8-K filings that mention a buyback keyword (default "repurchase" or "buyback") and whose subject CIK is the company. Matching on the subject CIK means the filing is the company's own 8-K, not one that merely mentions it. 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 announcement it counts insider buys and sells within a window around the filing date, scores conviction 0 to 100 from recency and the strength of the insider buying cluster, 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 buyback 8-K: ```json { "ticker": "OLN", "company": "Olin Corp", "eventType": "8-K buyback announcement", "fileDate": "2026-05-12", "daysAgo": 6, "filingUrl": "https://www.sec.gov/Archives/edgar/data/.../...-index.htm", "edgeScore": 71, "tier": "conviction_buyback", "insider": { "windowDays": 21, "buyCount": 3, "uniqueBuyers": 2, "sellCount": 0, "buyValueUsd": 520000, "netValueUsd": 520000, "topBuys": [{ "insider": "...", "role": "Director", "valueUsd": 300000, "date": "2026-05-10" }] } } ```` ### Tiers and pricing Pay per event. The first `conviction_buyback` row per run is free so you can validate output. | Tier | Meaning | Price | | --- | --- | --- | | `watch` | Buyback announced, no insider buying in the window | $0.05 | | `accumulation` | Buyback with insider open-market buying in the window | $0.10 | | `conviction_buyback` | Buyback with a net insider buying cluster (two or more insiders) | $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 for buyback announcements. Required. | | `buybackKeywords` | `["repurchase","buyback"]` | Words an 8-K must contain to count as a buyback. | | `maxAgeDays` | `120` | Ignore filings older than this. | | `insiderWindowDays` | `21` | Days on either side of the filing to count insider activity. | | `includeInsider` | `true` | Turn off for announcements 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 - Screen a basket of names for buybacks that management is personally backing with open-market buys. - Separate real capital-return conviction from routine repurchase boilerplate. - Feed a value or special-situations screen with one clean signal per announcement. # Actor input Schema ## `tickers` (type: `array`): Stock tickers to scan for buyback 8-K announcements, e.g. \["AAPL", "OLN", "EQT"]. ## `buybackKeywords` (type: `array`): Words an 8-K must contain to count as a buyback announcement. EDGAR full-text matches these in the filing body. ## `maxAgeDays` (type: `integer`): Ignore filings older than this many days. 120 = about a quarter. ## `insiderWindowDays` (type: `integer`): Count insider buys and sells within this many days on either side of the announcement. ## `includeInsider` (type: `boolean`): Attach insider open-market buys and sells around each announcement. Turn off for announcements only. ## `minScore` (type: `integer`): Drop and never charge filings scoring below this 0 to 100 threshold. 0 keeps every buyback announcement. ## `maxFilingsPerCompany` (type: `integer`): Cap on buyback 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": [ "AAPL", "OLN", "EQT" ], "buybackKeywords": [ "repurchase", "buyback" ], "maxAgeDays": 120, "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": [ "AAPL", "OLN", "EQT" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/buyback-insider-conviction-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": [ "AAPL", "OLN", "EQT", ] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/buyback-insider-conviction-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": [ "AAPL", "OLN", "EQT" ] }' | apify call scrapemint/buyback-insider-conviction-pipeline --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/buyback-insider-conviction-pipeline", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys", "description": "For each ticker, finds 8-K share repurchase announcements and correlates them with insider open-market buying around the filing, scores conviction 0 to 100, and tiers each as conviction_buyback, accumulation, or watch. A buyback plus insiders buying is the signal. Pure EDGAR data, no keys.", "version": "0.1", "x-build-id": "K7yJQv4QOIaEFO4pH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~buyback-insider-conviction-pipeline/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-buyback-insider-conviction-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~buyback-insider-conviction-pipeline/runs": { "post": { "operationId": "runs-sync-scrapemint-buyback-insider-conviction-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~buyback-insider-conviction-pipeline/run-sync": { "post": { "operationId": "run-sync-scrapemint-buyback-insider-conviction-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 to scan for buyback 8-K announcements, e.g. [\"AAPL\", \"OLN\", \"EQT\"].", "default": [], "items": { "type": "string" } }, "buybackKeywords": { "title": "Buyback keywords", "type": "array", "description": "Words an 8-K must contain to count as a buyback announcement. EDGAR full-text matches these in the filing body.", "default": [ "repurchase", "buyback" ], "items": { "type": "string" } }, "maxAgeDays": { "title": "Max age (days)", "minimum": 1, "maximum": 730, "type": "integer", "description": "Ignore filings older than this many days. 120 = about a quarter.", "default": 120 }, "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 announcement.", "default": 21 }, "includeInsider": { "title": "Include insider buying", "type": "boolean", "description": "Attach insider open-market buys and sells around each announcement. Turn off for announcements 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 buyback announcement.", "default": 0 }, "maxFilingsPerCompany": { "title": "Max filings per company", "minimum": 1, "maximum": 100, "type": "integer", "description": "Cap on buyback 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 } } } } } } } } } } ```