# GuruFocus Fundamentals Scraper (`marek_honzal/gurufocus-fundamentals-scraper`) Actor Extract 100+ fundamental data points for any stock ticker from GuruFocus as structured JSON — valuation multiples (P/E, PEG, EV/EBITDA, GF Value), profitability & moat, financial health (Piotroski, Altman Z), growth, dividends, risk & technicals. Export JSON/CSV/Excel, run via API, or schedule. - **URL**: https://apify.com/marek\_honzal/gurufocus-fundamentals-scraper.md - **Developed by:** [Marek Honzal](https://apify.com/marek_honzal) (community) - **Categories:** Automation - **Stats:** 3 total users, 0 monthly users, 50.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $8.00 / 1,000 ticker results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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 ## GuruFocus Fundamentals Scraper **Get 100+ fundamental data points for any stock ticker in structured JSON — valuation multiples, profitability scores, balance sheet health, growth estimates, dividends, risk metrics, and more.** No spreadsheet wrangling. No manual copy-paste from financial websites. Just feed in your tickers, get back clean, structured data ready for analysis. ![Output table showing fundamental data for multiple stock tickers](https://cdn.marekhonzal.com/apify-gurufocus-fundamentals-scraper.webp) ### What does GuruFocus Fundamentals Scraper do? GuruFocus Fundamentals Scraper takes a list of **stock ticker symbols** (e.g. `AAPL`, `MSFT`, `NVDA`) and returns one complete JSON record per ticker with ~100 fundamental data points scraped from [GuruFocus](https://www.gurufocus.com) summary pages. Each ticker requires several page visits to collect all data. The scraper handles this automatically — visiting multiple pages per ticker, extracting the relevant metrics, and merging everything into a single structured output item. Use it to power **stock screeners**, **quantitative analysis**, **portfolio monitoring**, **AI-powered research agents**, or any workflow where you need structured fundamental data without manual effort. ### What data do you get? Each record covers 10 analytical domains: | Category | What's included | Fields | | -------------------- | --------------------------------------------------------------------------------------------------------------------------------------------- | ------ | | **Company** | Name, exchange, sector, industry, ISIN, index membership, IPO date, employees, description | 10 | | **Price & Market** | Current price, daily change, 52-week range, market cap, enterprise value, volume, shares outstanding | 13 | | **Valuation** | P/E (TTM, forward, NRI, Shiller), PEG, P/S, P/B, P/FCF, EV/EBITDA, EV/Revenue, FCF yield, GF Value, WACC, Graham Number, DCF models, and more | 30+ | | **Profitability** | Score (0-10), revenue, EPS, margins (net, EBITDA, FCF, OCF), ROE, ROA, ROIC, moat score, tariff resilience | 17 | | **Financial Health** | Score (0-10), debt ratios, interest coverage, Piotroski F-Score, Altman Z-Score, Beneish M-Score, liquidity ratios, working capital cycle | 15 | | **Growth** | Score (0-10), 3-year historical growth (revenue, EBITDA, EPS, FCF, book value), analyst estimates 3-5y | 8 | | **Dividend** | Yield, payout ratio, 3-year growth, forward yield, yield-on-cost, buyback ratio, shareholder yield | 7 | | **Risk** | Beta, Sharpe ratio (3y), Sortino ratio (3y), volatility | 4 | | **Technicals** | RSI (14), ATR (14), SMA (20), 12-1 month momentum | 4 | | **GF Score** | GuruFocus composite quality score (0-100) combining all five dimensions | 1 | ### How to scrape GuruFocus fundamental data 1. Click **Try for free** on the [GuruFocus Fundamentals Scraper](https://apify.com/marek_honzal/gurufocus-fundamentals-scraper) page. 2. Enter your stock ticker symbols (e.g. `AAPL`, `MSFT`, `NVDA`). 3. Click **Start** and wait for the run to finish. 4. Preview your data in the **Output** tab, or download it as JSON, CSV, or Excel. That's it. No configuration needed beyond the ticker list. ### Input The only required input is a list of ticker symbols. You can optionally adjust concurrency and request limits. | Field | Type | Required | Description | | --------------------- | -------- | -------- | ------------------------------------------------------------------ | | `tickers` | string[] | Yes | Stock ticker symbols to scrape (e.g. `AAPL`, `MSFT`, `GOOGL`) | | `maxRequestsPerCrawl` | integer | No | Max pages the crawler can visit. Default: `500` | | `maxConcurrency` | integer | No | Max parallel requests. Lower if you get rate-limited. Default: `3` | Example input: ```json { "tickers": ["AAPL", "MSFT", "NVDA", "GOOGL", "AMZN"] } ```` ### Output Each ticker produces one JSON object. You can download the dataset in **JSON, CSV, Excel, XML, or HTML** format. The output is organized into multiple table views in the Apify Console: - **Overview** — Scores, identity, and key price metrics for quick screening - **Valuation Multiples** — All 30+ valuation ratios and intrinsic value estimates - **Quality & Health** — Profitability, financial health, growth, dividends, and risk combined Here is a condensed example for one ticker: ```json { "ticker": "MCD", "gfScore": 88, "company": { "name": "McDonald's Corp", "exchange": "NYSE", "sector": "Consumer Cyclical", "industry": "Restaurants", "isin": "US5801351017", "totalEmployees": 150000, "ipoDate": "1966-07-05", "indexMembership": ["Dow 30", "S&P 500", "Russell 1000"] }, "price": { "currency": "USD", "current": 307.01, "changePct": 0.71, "weekLow52": 283.47, "weekHigh52": 341.75, "marketCap": 218640000000, "enterpriseValue": 271980000000 }, "valuation": { "gfValue": 322.44, "gfValueLabel": "Fairly Valued", "peTtm": 25.69, "peForward": 23.2, "pegRatio": 3.07, "psRatio": 8.18, "evToEbitda": 19.12, "evToRevenue": 10.12, "fcfYieldPct": 3.29, "waccPct": 5.63, "grahamNumber": null, "peterLynchFairValue": null }, "profitability": { "score": 9, "revenueTtm": 26884000000, "epsTtm": 11.95, "netMarginPct": 31.85, "roicTtmPct": 16.97, "moatScore": 9 }, "financialHealth": { "score": 4, "piotroskiFScore": 8, "altmanZScore": 3.28, "beneishMScore": -2.59, "currentRatio": 0.95, "debtToEbitda": 3.81 }, "growth": { "score": 7, "revenueGrowth3yPct": 6.3, "epsGrowth3yPct": 6.5, "epsGrowthEst3_5yPct": 8.46 }, "dividend": { "dividendYieldPct": 2.39, "payoutRatio": 0.59, "shareholderYieldPct": 2.15 }, "risk": { "beta": 0.361, "sharpeRatio3y": 0.03, "volatilityPct": 16.66 }, "technicals": { "rsi14": 39.3, "sma20": 313.045, "momentum12_1mPct": 9.42 }, "meta": { "url": "https://www.gurufocus.com/stock/MCD/summary", "scrapedAt": "2026-04-09T09:22:48.637Z" } } ``` Fields return `null` when data is not available for a given ticker (e.g. Graham Number for companies with negative equity). ### How much does it cost to scrape GuruFocus? Each ticker result costs **$0.008** (~$8 per 1,000 tickers). Apify gives you **$5 free usage credits every month** on the [Apify Free plan](https://apify.com/pricing), which covers roughly **600 tickers per month at no cost**. | Tickers | Cost | |---------|------| | 10 | $0.08 | | 100 | $0.80 | | 500 | $4.00 | | 1,000 | $8.00 | For larger volumes or scheduled runs, the [$29/month Starter plan](https://apify.com/pricing) gives you enough credits for thousands of tickers per month. ### Tips for getting the best results - **Start small** — Test with 5-10 tickers first to verify you're getting the data you need. - **Use standard ticker symbols** — The scraper uses the same ticker format as GuruFocus (e.g. `AAPL`, `BRK.B`, `TSM`). Check the GuruFocus site if you're unsure about the correct symbol. - **Lower concurrency if needed** — If you notice failed requests, reduce `maxConcurrency` to `1` or `2`. The default of `3` works well for most runs. - **Schedule regular runs** — Use [Apify Schedules](https://docs.apify.com/platform/schedules) to automatically refresh your data daily or weekly. - **Combine with other tools** — Export to Google Sheets, feed into Make/Zapier workflows, or use the [Apify API](https://docs.apify.com/api/v2) to pull data directly into your applications. ### Integrations You can connect GuruFocus Fundamentals Scraper with other tools using the [Apify platform integrations](https://docs.apify.com/platform/integrations): - **Google Sheets** — Automatically export results to a spreadsheet - **Zapier / Make** — Trigger workflows when new data is available - **Slack / Email** — Get notified when a run finishes - **REST API** — Pull data programmatically into any application - **MCP Server** — Use with AI agents and LLM pipelines ### FAQ **What tickers are supported?** Any ticker available on GuruFocus, including US stocks (NYSE, NASDAQ), international stocks, and ADRs. Use the same ticker format as the GuruFocus website. **How often is the data updated on GuruFocus?** GuruFocus updates its data as new filings and market data become available. The scraper collects whatever is currently displayed on the summary pages at the time of the run. **Can I scrape thousands of tickers?** Yes. We've tested runs with 500+ tickers successfully. For very large runs, increase the `maxRequestsPerCrawl` limit accordingly (each ticker requires approximately 7 page visits). **What if a ticker returns null values?** Some fields are naturally `null` for certain companies. For example, Graham Number requires positive equity and earnings, so it will be `null` for companies like McDonald's that have negative equity. This is expected behavior — the data simply isn't available on GuruFocus for that metric. **Can I use this data commercially?** This scraper is intended for **personal and professional research purposes**. GuruFocus data is subject to their [Terms of Use](https://www.gurufocus.com/term-of-use.php). Users are responsible for complying with GuruFocus terms, including proper attribution when sharing data. Redistribution or resale of GuruFocus data requires a commercial data license from GuruFocus. **Is it legal to scrape GuruFocus?** Web scraping of publicly available data is generally legal. However, you should review and comply with GuruFocus's Terms of Use. This scraper is designed for personal research and analysis, which is permitted under their terms. If you're unsure about your specific use case, please seek professional legal advice. ### Support and feedback If you find a bug or have a feature request, please open an issue on the [Issues tab](https://apify.com/marek_honzal/gurufocus-fundamentals-scraper/issues). Data sourced from [GuruFocus](https://www.gurufocus.com). All GuruFocus data is subject to their [Terms of Use](https://www.gurufocus.com/term-of-use.php). # Actor input Schema ## `tickers` (type: `array`): List of stock ticker symbols to scrape (e.g. AAPL, MSFT, GOOGL). ## `maxRequestsPerCrawl` (type: `integer`): Maximum number of pages the crawler can visit. Useful for testing. ## `maxConcurrency` (type: `integer`): Maximum number of parallel requests. Lower this if you are getting blocked or rate-limited. ## Actor input object example ```json { "tickers": [ "AAPL", "MSFT" ], "maxRequestsPerCrawl": 500, "maxConcurrency": 3 } ``` # Actor output Schema ## `results` (type: `string`): All scraped stock records in the default dataset. # 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("marek_honzal/gurufocus-fundamentals-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("marek_honzal/gurufocus-fundamentals-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 marek_honzal/gurufocus-fundamentals-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=marek_honzal/gurufocus-fundamentals-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "GuruFocus Fundamentals Scraper", "description": "Extract 100+ fundamental data points for any stock ticker from GuruFocus as structured JSON — valuation multiples (P/E, PEG, EV/EBITDA, GF Value), profitability & moat, financial health (Piotroski, Altman Z), growth, dividends, risk & technicals. Export JSON/CSV/Excel, run via API, or schedule.", "version": "0.1", "x-build-id": "882s8gu4YNF5zRFvW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/marek_honzal~gurufocus-fundamentals-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-marek_honzal-gurufocus-fundamentals-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/marek_honzal~gurufocus-fundamentals-scraper/runs": { "post": { "operationId": "runs-sync-marek_honzal-gurufocus-fundamentals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/marek_honzal~gurufocus-fundamentals-scraper/run-sync": { "post": { "operationId": "run-sync-marek_honzal-gurufocus-fundamentals-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "List of stock ticker symbols to scrape (e.g. AAPL, MSFT, GOOGL).", "items": { "type": "string" } }, "maxRequestsPerCrawl": { "title": "Max Requests per Crawl", "minimum": 1, "type": "integer", "description": "Maximum number of pages the crawler can visit. Useful for testing.", "default": 500 }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "type": "integer", "description": "Maximum number of parallel requests. Lower this if you are getting blocked or rate-limited.", "default": 3 } } }, "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 } } } } } } } } } } ```