# FDA Approval Catalyst: Approvals Plus Insider Buying (`scrapemint/fda-approval-catalyst-pipeline`) Actor 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. - **URL**: https://apify.com/scrapemint/fda-approval-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 ## FDA Approval Catalyst: Approvals Plus Insider Buying Find where a company just won an FDA decision **and** its own insiders are buying the stock in the open market. An approval alone is public knowledge that takes time to reprice. An approval landing while insiders add to their own position is conviction. For each ticker you pass, this pipeline: 1. **Pulls FDA wins from openFDA.** It resolves the ticker to its company name via the SEC company map, then queries openFDA for that company's recent drug approvals (NDA/BLA), device PMA approvals, and 510(k) clearances. Each record is attributed to the company by a core-name match on the openFDA sponsor or applicant, so a stray name hit is dropped. 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 FDA event it counts insider buys and sells within a window around the decision date, scores conviction 0 to 100 from the event strength, recency, and insider dollar flow, then tiers the result. Both data sources are plain public HTTP and JSON (openFDA and SEC EDGAR). No browser, no residential proxy, no API keys. ### Why FDA events An FDA approval or clearance is a hard, dated, binary catalyst: the product can be sold the day after it clears, and the market reprices the company against that. A drug NDA/BLA approval is the highest bar, a device PMA next, a 510(k) clearance lower. Pairing the regulatory win with whether management is buying turns a calendar of public decisions into a ranked conviction list. This pipeline is the late-stage regulatory companion to a clinical-trials pipeline: trials tell you what might get approved, openFDA tells you what just did. ### Output One row per FDA event: ```json { "ticker": "MDT", "company": "Medtronic plc", "eventType": "device_pma", "eventLabel": "Device PMA approval (original)", "isMajor": true, "product": "OsteoCool RF Ablation System", "applicant": "Medtronic Sofamor Danek USA, Inc.", "idNumber": "P960000", "decisionDate": "2026-06-05", "daysAgo": 15, "fdaUrl": "https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P960000", "edgeScore": 71, "tier": "approval_confirmed", "insider": { "windowDays": 30, "buyCount": 2, "sellCount": 0, "buyValueUsd": 410000, "netValueUsd": 410000, "netBuying": true, "topBuys": [{ "insider": "...", "role": "Director", "valueUsd": 250000, "date": "2026-06-10" }] } } ```` ### Tiers and pricing Pay per event. The first `approval_confirmed` row per run is free so you can validate output. | Tier | Meaning | Price | | --- | --- | --- | | `watch` | A 510(k) clearance or supplement with no insider buying in the window | $0.05 | | `accumulation` | A major approval (drug NDA/BLA or device PMA), or any FDA event with insider buying in the window | $0.10 | | `approval_confirmed` | A recent major approval, 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 public HTTP, so total compute stays small. ### Input | Field | Default | Notes | | --- | --- | --- | | `tickers` | `[]` | One or more tickers to scan. Required. | | `eventTypes` | all three | Limit to drug approvals, device PMA, or 510(k) clearances. | | `maxAgeDays` | `120` | Ignore FDA decisions older than this. | | `insiderWindowDays` | `30` | Days on either side of the decision to count insider activity. | | `includeInsider` | `true` | Turn off to score FDA events alone. | | `minScore` | `0` | Drop and never charge events below this 0 to 100 score. | | `maxEventsPerCompany` | `25` | Cap FDA events pulled per company. | | `maxEventsTotal` | `200` | Cap total events scored per run. | | `userAgent` | generic | EDGAR asks for a descriptive User-Agent with an email. | ### Good use cases - Watch a basket of pharma and medtech names for approvals landing while insiders buy. - Confirm an approval headline against whether management is buying alongside. - Feed an event-driven or special-situations screen with one clean signal per FDA decision. # Actor input Schema ## `tickers` (type: `array`): Stock tickers to scan. Each is resolved to its company name via the SEC company map, then matched against openFDA sponsor/applicant records. ## `eventTypes` (type: `array`): Which openFDA event types to include. Drug approvals (NDA/BLA), device PMA approvals, and 510(k) clearances. ## `maxAgeDays` (type: `integer`): How far back to pull FDA decisions. Default 120 days catches the recent catalyst window. ## `insiderWindowDays` (type: `integer`): Days on each side of an FDA decision date to count insider open-market buys and sells. ## `includeInsider` (type: `boolean`): Pull SEC Form 4 open-market buys and sells and correlate them with each FDA event. Turn off to score FDA events alone. ## `minScore` (type: `integer`): Drop events scoring below this 0 to 100 convergence score. Zero returns every event in range. ## `maxEventsPerCompany` (type: `integer`): Cap FDA events pulled per company before scoring. ## `maxEventsTotal` (type: `integer`): Cap total FDA events scored across all tickers in one run. ## `userAgent` (type: `string`): SEC EDGAR requires a User-Agent containing a contact email. Example: 'yourname research@yourdomain.com'. Leave blank to use a default. ## `proxyConfiguration` (type: `object`): Apify proxy, passed through to the Form 4 child. openFDA and the EDGAR ticker map are called directly and do not need a proxy. ## Actor input object example ```json { "tickers": [ "PFE", "MRK", "MDT", "ABT", "ISRG" ], "eventTypes": [ "drug_approval", "device_pma", "device_510k" ], "maxAgeDays": 120, "insiderWindowDays": 30, "includeInsider": true, "minScore": 0, "maxEventsPerCompany": 25, "maxEventsTotal": 200, "userAgent": "", "proxyConfiguration": { "useApifyProxy": true } } ``` # 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": [ "PFE", "MRK", "MDT", "ABT", "ISRG" ], "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/fda-approval-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": [ "PFE", "MRK", "MDT", "ABT", "ISRG", ], "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scrapemint/fda-approval-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": [ "PFE", "MRK", "MDT", "ABT", "ISRG" ], "proxyConfiguration": { "useApifyProxy": true } }' | apify call scrapemint/fda-approval-catalyst-pipeline --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/fda-approval-catalyst-pipeline", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "FDA Approval Catalyst: Approvals Plus Insider Buying", "description": "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.", "version": "0.1", "x-build-id": "ZwcEDCcbmVeDDobJz" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~fda-approval-catalyst-pipeline/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-fda-approval-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapemint~fda-approval-catalyst-pipeline/runs": { "post": { "operationId": "runs-sync-scrapemint-fda-approval-catalyst-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapemint~fda-approval-catalyst-pipeline/run-sync": { "post": { "operationId": "run-sync-scrapemint-fda-approval-catalyst-pipeline", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "tickers" ], "properties": { "tickers": { "title": "Tickers", "type": "array", "description": "Stock tickers to scan. Each is resolved to its company name via the SEC company map, then matched against openFDA sponsor/applicant records.", "default": [], "items": { "type": "string" } }, "eventTypes": { "title": "FDA event types", "uniqueItems": true, "type": "array", "description": "Which openFDA event types to include. Drug approvals (NDA/BLA), device PMA approvals, and 510(k) clearances.", "items": { "type": "string", "enum": [ "drug_approval", "device_pma", "device_510k" ], "enumTitles": [ "Drug approvals (NDA/BLA)", "Device PMA approvals", "Device 510(k) clearances" ] }, "default": [ "drug_approval", "device_pma", "device_510k" ] }, "maxAgeDays": { "title": "FDA lookback (days)", "minimum": 1, "maximum": 1095, "type": "integer", "description": "How far back to pull FDA decisions. Default 120 days catches the recent catalyst window.", "default": 120 }, "insiderWindowDays": { "title": "Insider window (days)", "minimum": 1, "maximum": 180, "type": "integer", "description": "Days on each side of an FDA decision date to count insider open-market buys and sells.", "default": 30 }, "includeInsider": { "title": "Include insider transactions", "type": "boolean", "description": "Pull SEC Form 4 open-market buys and sells and correlate them with each FDA event. Turn off to score FDA events alone.", "default": true }, "minScore": { "title": "Minimum score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Drop events scoring below this 0 to 100 convergence score. Zero returns every event in range.", "default": 0 }, "maxEventsPerCompany": { "title": "Max events per company", "minimum": 1, "maximum": 200, "type": "integer", "description": "Cap FDA events pulled per company before scoring.", "default": 25 }, "maxEventsTotal": { "title": "Max events total", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Cap total FDA events scored across all tickers in one run.", "default": 200 }, "userAgent": { "title": "User-Agent (with email)", "type": "string", "description": "SEC EDGAR requires a User-Agent containing a contact email. Example: 'yourname research@yourdomain.com'. Leave blank to use a default.", "default": "" }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Apify proxy, passed through to the Form 4 child. openFDA and the EDGAR ticker map are called directly and do not need a proxy.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```