# Stock Market Price History Scraper (`scrapemint/stock-price-history-scraper`) Actor Daily open, high, low, close and volume for any stock or ETF ticker over a date range. Returns clean JSON from a keyless public API, no browser and no proxy. - **URL**: https://apify.com/scrapemint/stock-price-history-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 Price History Scraper Daily **open, high, low, close and volume (OHLCV)** for any stock or ETF ticker over a date range, returned as clean JSON. No login, no API key, no proxy. It reads a keyless public API, so runs are fast and cheap. This is the price data behind every chart and backtest. ### What you get One row per ticker per trading day: ```json { "symbol": "AAPL", "date": "2026-06-26", "open": 275.0, "high": 285.95, "low": 274.21, "close": 283.78, "volume": 261775500 } ```` Prices and volume are parsed to numbers (no `$` or commas), and dates are normalized to `YYYY-MM-DD`, so the output drops straight into a dataframe or database. ### Input | Field | Description | | --- | --- | | `tickers` | Symbols to fetch, e.g. `["AAPL","MSFT","SPY"]` (required) | | `fromDate` | Start date `YYYY-MM-DD` (default: 30 days ago) | | `toDate` | End date `YYYY-MM-DD` (default: today) | | `assetClass` | `auto` (default), `stocks`, `etf` or `index` | | `maxRowsPerTicker` / `maxRowsTotal` | Caps on output size | ### Example ```json { "tickers": ["AAPL", "MSFT", "SPY"], "fromDate": "2026-01-01", "toDate": "2026-06-30" } ``` ### Who it's for Quants and backtesters who need clean OHLCV, charting tools and dashboards, fintech apps, and researchers pulling historical prices without a paid data plan. Pairs naturally with the **Stock Earnings Calendar Scraper** — the calendar tells you when a company reports, this gives you the price action around it. ### Pricing Pay per daily price row. The first 20 rows of every run are free so you can validate the output before you pay. # Actor input Schema ## `tickers` (type: `array`): Stock or ETF symbols to fetch, e.g. \["AAPL", "MSFT", "SPY"]. ## `fromDate` (type: `string`): Start date YYYY-MM-DD. Defaults to 30 days ago. ## `toDate` (type: `string`): End date YYYY-MM-DD. Defaults to today. ## `assetClass` (type: `string`): Which Nasdaq asset class to query. Auto tries stocks, then ETF, then index. ## `maxRowsPerTicker` (type: `integer`): Cap daily rows returned per ticker (newest first). ## `maxRowsTotal` (type: `integer`): Stop after this many rows across all tickers. ## Actor input object example ```json { "tickers": [ "AAPL", "MSFT", "SPY" ], "fromDate": "2026-01-01", "toDate": "2026-06-30", "assetClass": "auto", "maxRowsPerTicker": 5000, "maxRowsTotal": 50000 } ``` # 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", "MSFT" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapemint/stock-price-history-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": [ "AAPL", "MSFT", ] } # Run the Actor and wait for it to finish run = client.actor("scrapemint/stock-price-history-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": [ "AAPL", "MSFT" ] }' | apify call scrapemint/stock-price-history-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapemint/stock-price-history-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Stock Market Price History Scraper", "description": "Daily open, high, low, close and volume for any stock or ETF ticker over a date range. Returns clean JSON from a keyless public API, no browser and no proxy.", "version": "0.1", "x-build-id": "uLFanO5OPv3WJp7xE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapemint~stock-price-history-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapemint-stock-price-history-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-price-history-scraper/runs": { "post": { "operationId": "runs-sync-scrapemint-stock-price-history-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-price-history-scraper/run-sync": { "post": { "operationId": "run-sync-scrapemint-stock-price-history-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", "required": [ "tickers" ], "properties": { "tickers": { "title": "Tickers", "type": "array", "description": "Stock or ETF symbols to fetch, e.g. [\"AAPL\", \"MSFT\", \"SPY\"].", "items": { "type": "string" } }, "fromDate": { "title": "From date", "type": "string", "description": "Start date YYYY-MM-DD. Defaults to 30 days ago." }, "toDate": { "title": "To date", "type": "string", "description": "End date YYYY-MM-DD. Defaults to today." }, "assetClass": { "title": "Asset class", "enum": [ "auto", "stocks", "etf", "index" ], "type": "string", "description": "Which Nasdaq asset class to query. Auto tries stocks, then ETF, then index.", "default": "auto" }, "maxRowsPerTicker": { "title": "Max rows per ticker", "minimum": 1, "type": "integer", "description": "Cap daily rows returned per ticker (newest first).", "default": 5000 }, "maxRowsTotal": { "title": "Max rows total", "minimum": 1, "type": "integer", "description": "Stop after this many rows across all tickers.", "default": 50000 } } }, "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 } } } } } } } } } } ```