Pricing

Pay per usage

Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys

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.

Pricing

Pay per usage

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Developer

Ken M

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3 days ago

Last modified

Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys API

Below, you can find a list of relevant HTTP API endpoints for calling the Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys Actor. For this, you’ll need an Apify account. Replace <YOUR_API_TOKEN> in the URLs with your Apify API token, which you can find under Integrations in Apify Console. For details, see the API reference.

Run Actor

POST
https://api.apify.com/v2/acts/scrapemint~buyback-insider-conviction-pipeline/runs?token=<YOUR_API_TOKEN>

Note: By adding the method=POST query parameter, this API endpoint can be called using a GET request and thus used in third-party webhooks. Please refer to our Run Actor API documentation.

Run Actor synchronously and get dataset items

POST
https://api.apify.com/v2/acts/scrapemint~buyback-insider-conviction-pipeline/run-sync-get-dataset-items?token=<YOUR_API_TOKEN>

Note: This endpoint supports both POST and GET request methods. However, only the POST method allows you to pass input data. For more information, please refer to our Run Actor synchronously and get dataset items API documentation.

Get Actor

GET
https://api.apify.com/v2/acts/scrapemint~buyback-insider-conviction-pipeline?token=<YOUR_API_TOKEN>

For more information, please refer to our Get Actor API documentation.

Actors can be used to scrape web pages, extract data, or automate browser tasks. Use the Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys API programmatically via the Apify API.

You can choose from:

Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys API in Python

Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys API in JavaScript

Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys API through CLI

Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys OpenAPI definition

You can start Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys with the Apify API by sending an HTTP POST request to the Run Actorendpoint. An Actor’s input and its content type can be passed as a payload of the POST request, and additional options can be specified using URL query parameters. The Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys is identified within the API by its ID, which is the creator’s username and the name of the Actor.

When the Buyback Insider Conviction: 8-K Repurchase Plus Insider Buys run finishes you can list the data from its default dataset(storage) via the API or you can preview the data directly on Apify Console.

Corporate Catalyst: Material 8-K Plus Insider Buying

scrapemint/corporate-catalyst-pipeline

For each ticker, correlates a material 8-K event with insider open-market buying in the days around it, scores conviction 0 to 100, and tiers events high_conviction, elevated, or watch. Insiders buying right as a material event files is the signal. Pure EDGAR data, no API keys.

Ken M

Activist Stake Catalyst: 13D/13G Plus Insider Buying

scrapemint/activist-stake-catalyst-pipeline

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.

Ken M

FDA Approval Catalyst: Approvals Plus Insider Buying

scrapemint/fda-approval-catalyst-pipeline

For each ticker, correlates recent FDA wins on openFDA (drug approvals, device PMA, 510(k) clearances) with insider open-market buying around the decision date, scores conviction 0 to 100, and tiers each as approval_confirmed, accumulation, or watch.

Ken M

SEC Insider Conviction Pipeline: Form 4 Buys + 8-K Catalysts

scrapemint/sec-insider-conviction-pipeline

Find companies where insiders are buying on the open market AND filing material 8-K catalysts. Chains SEC Form 4 with 8-K events, joins on issuer CIK, and scores each company 0-100 by conviction tier. Core tier is 100% SEC EDGAR, no antibot risk. Pay per scored company.

Ken M

Smart Money Buzz: Insider Buying Plus Reddit and HN Chatter

scrapemint/smart-money-buzz-pipeline

For each ticker, joins insider open-market buying (SEC Form 4) with social chatter on Reddit and Hacker News, scores 0 to 100, and tiers tickers converging, insider_only, buzz_only, or watch. Insiders buying while the crowd starts talking is the signal. Pure public data, no API keys.

Ken M

Clinical Catalyst Radar for Biotech

scrapemint/clinical-catalyst-pipeline

For each biotech company, correlates clinical pipeline progress (ClinicalTrials.gov) and research momentum (PubMed) with open-market insider buying (SEC Form 4), scores convergence 0 to 100, and tiers companies catalyst, clinical lead, insider lead, or watch. An advancing asset plus insiders buying.

Ken M

📈 Stock Buyback Announcement Tracker

nexgendata/stock-buyback-announcement-tracker

Track recent share-repurchase (buyback) announcements from SEC EDGAR filings, with authorized dollar amounts extracted. For equity investors, event-driven funds, and IR teams.

NexGenData

Insider Buying Signal Scraper

glowing_glove/insider-buying-signal-scraper

Collect public insider and holder-change filing signals and return compact market intelligence rows for stock research.

Ushba Khan

Federal Contract Momentum: Gov Awards + Insider Buying

scrapemint/federal-contract-momentum-pipeline

Joins accelerating federal contract awards (USAspending) with insider open-market buying (SEC Form 4), scores 0 to 100, and tiers companies converging, contract_only, insider_only, or watch. A contractor winning surging government dollars while insiders buy is the signal. Public data, no API keys.

Ken M