# Stock Earnings Calendar Scraper (`scrapemint/stock-earnings-calendar-scraper`) Actor Upcoming earnings dates, EPS estimates, dividends, IPOs and stock splits for a date range or your ticker watchlist. Returns clean JSON from keyless public APIs, no browser and no proxy. - **URL**: https://apify.com/scrapemint/stock-earnings-calendar-scraper.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 ## Stock Earnings Calendar Scraper Get the forward-looking stock market calendar as clean JSON: **upcoming earnings dates, EPS estimates, dividends, IPOs and stock splits** for a date range or your own ticker watchlist. No login, no API key, no proxy. The actor reads keyless public JSON, so runs are fast and cheap. ### What you get One row per event. Event types: - **earnings** — symbol, company, report date, report time (pre-market / after-hours), EPS estimate, number of analyst estimates, last-year EPS, fiscal quarter, market cap - **dividend** — ex-dividend date, record date, payment date, announcement date, dividend rate, indicated annual dividend - **ipo** — priced and upcoming IPOs with exchange, share price, shares offered, deal value, status - **split** — stock splits with ratio and execution date ### Input | Field | Description | | --- | --- | | `dateFrom` | Start date `YYYY-MM-DD` (default: today) | | `dateTo` | End date `YYYY-MM-DD` (default: +7 days, max range 31 days) | | `tickers` | Optional watchlist, e.g. `["AAPL","MSFT","NKE"]`. Empty = whole market | | `includeEarnings` / `includeDividends` / `includeIPOs` / `includeSplits` | Toggle event types (all on by default) | | `maxRows` | Stop after N rows (default 1000) | ### Example ```json { "dateFrom": "2026-06-30", "dateTo": "2026-07-07", "tickers": ["NKE", "AAPL"] } ```` ```json { "eventType": "earnings", "symbol": "NKE", "companyName": "Nike, Inc.", "date": "2026-06-30", "reportTime": "after hours", "epsForecast": "$0.11", "numAnalystEstimates": 10, "lastYearEps": "$0.14", "marketCap": "$60,346,146,595" } ``` ### Who it's for Active and options traders planning around report dates, swing traders working earnings season, fintech apps and trading newsletters that display the calendar, and quants feeding event dates into backtests. ### Pricing Pay per event row. The first 15 rows of every run are free so you can validate the output before you pay. # Actor input Schema ## `dateFrom` (type: `string`): Start of the calendar range, YYYY-MM-DD. Defaults to today. ## `dateTo` (type: `string`): End of the calendar range, YYYY-MM-DD. Defaults to 7 days after the start. Range is capped at 31 days. ## `tickers` (type: `array`): Only return events for these stock symbols. Leave empty to return the whole market calendar. ## `includeEarnings` (type: `boolean`): Companies reporting earnings, with EPS estimates and report time. ## `includeDividends` (type: `boolean`): Upcoming ex-dividend, record and payment dates. ## `includeIPOs` (type: `boolean`): Priced and upcoming initial public offerings. ## `includeSplits` (type: `boolean`): Upcoming stock splits with ratio and execution date. ## `maxRows` (type: `integer`): Stop after this many calendar rows. ## `dedupe` (type: `boolean`): Drop duplicate event rows (same type, symbol and date). ## Actor input object example ```json { "dateFrom": "2026-06-30", "dateTo": "2026-07-07", "tickers": [ "AAPL", "MSFT", "NKE" ], "includeEarnings": true, "includeDividends": true, "includeIPOs": true, "includeSplits": true, "maxRows": 1000, "dedupe": 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": [] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/stock-earnings-calendar-scraper").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": [] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/stock-earnings-calendar-scraper").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": [] }' | apify call scrapemint/stock-earnings-calendar-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/stock-earnings-calendar-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Stock Earnings Calendar Scraper", "description": "Upcoming earnings dates, EPS estimates, dividends, IPOs and stock splits for a date range or your ticker watchlist. Returns clean JSON from keyless public APIs, no browser and no proxy.", "version": "0.1", "x-build-id": "wUlUzfckqbSnoh917" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~stock-earnings-calendar-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-stock-earnings-calendar-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapemint~stock-earnings-calendar-scraper/runs": { "post": { "operationId": "runs-sync-scrapemint-stock-earnings-calendar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapemint~stock-earnings-calendar-scraper/run-sync": { "post": { "operationId": "run-sync-scrapemint-stock-earnings-calendar-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "dateFrom": { "title": "From date", "type": "string", "description": "Start of the calendar range, YYYY-MM-DD. Defaults to today." }, "dateTo": { "title": "To date", "type": "string", "description": "End of the calendar range, YYYY-MM-DD. Defaults to 7 days after the start. Range is capped at 31 days." }, "tickers": { "title": "Ticker watchlist (optional)", "type": "array", "description": "Only return events for these stock symbols. Leave empty to return the whole market calendar.", "items": { "type": "string" } }, "includeEarnings": { "title": "Include earnings", "type": "boolean", "description": "Companies reporting earnings, with EPS estimates and report time.", "default": true }, "includeDividends": { "title": "Include dividends", "type": "boolean", "description": "Upcoming ex-dividend, record and payment dates.", "default": true }, "includeIPOs": { "title": "Include IPOs", "type": "boolean", "description": "Priced and upcoming initial public offerings.", "default": true }, "includeSplits": { "title": "Include stock splits", "type": "boolean", "description": "Upcoming stock splits with ratio and execution date.", "default": true }, "maxRows": { "title": "Max rows", "minimum": 1, "type": "integer", "description": "Stop after this many calendar rows.", "default": 1000 }, "dedupe": { "title": "Deduplicate", "type": "boolean", "description": "Drop duplicate event rows (same type, symbol and date).", "default": 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 } } } } } } } } } } ```