Yahoo Finance Data Extractor

Extract Yahoo Finance stock data — prices, volume, adjusted close — for any ticker. Get historical OHLCV records and real-time market metrics for stocks, crypto, ETFs, and indices in structured JSON.

Use Cases

• Stock price tracking — Monitor daily price movements across multiple tickers
• Portfolio analysis — Build datasets for asset allocation and performance review
• Market research — Gather historical price data for sector and market analysis
• Backtesting — Feed historical OHLCV data into trading strategy simulations
• Quantitative finance — Supply clean financial data to quantitative models and algorithms

How It Works

1. Provide a list of ticker symbols (e.g., AAPL, TSLA, BTC-USD)
2. The Actor calls Yahoo Finance internal API endpoints to fetch price history
3. Daily OHLCV data (open, high, low, close, volume) is returned per ticker
4. Real-time metrics (current price, day range, 52-week range, percent change) are included
5. Results are pushed to the default dataset as structured JSON items

Input Parameters

Field	Type	Required	Default	Description
tickers	array	Yes	—	List of ticker symbols (e.g., ["AAPL", "TSLA", "BTC-USD"])
start_date	string	No	2024-01-01	Start date for historical data (YYYY-MM-DD format)
maxItems	integer	No	50	Maximum number of tickers to process per run
proxyConfiguration	object	No	—	Apify Proxy settings to avoid rate limits

Example Input

{
  "tickers": ["AAPL", "MSFT", "NVDA", "ETH-USD"],
  "start_date": "2024-01-01",
  "maxItems": 100,
  "proxyConfiguration": {
    "useApifyProxy": true
  }
}

Output

Each item in the dataset contains:

Field	Type	Description
ticker	string	The ticker symbol (e.g., AAPL)
date	string	Trading date (YYYY-MM-DD)
open	number	Opening price
high	number	Highest price of the day
low	number	Lowest price of the day
close	number	Closing price
adjusted_close	number	Adjusted closing price (accounts for splits/dividends)
volume	number	Trading volume

Example Output

[
  {
    "ticker": "AAPL",
    "date": "2024-01-02",
    "open": 185.16,
    "high": 185.45,
    "low": 183.25,
    "close": 184.25,
    "adjusted_close": 184.25,
    "volume": 47674200
  },
  {
    "ticker": "AAPL",
    "date": "2024-01-03",
    "open": 183.50,
    "high": 184.38,
    "low": 182.67,
    "close": 183.15,
    "adjusted_close": 183.15,
    "volume": 52337200
  }
]

Integrations & Automation

• Schedule runs — Use Apify Scheduler to run daily or weekly price extraction
• Webhooks — Trigger downstream workflows when data is ready
• Google Sheets — Export dataset items directly to spreadsheets
• Apify CLI — Run from command line: apify call ahmed_jasarevic/yahoo-finance
• Python/JS SDK — Integrate via apify-client packages

Related Actors

• Yahoo Finance Scraper — Alternative Yahoo Finance scraper
• Stock Scraper — General stock data extraction
• Financial Data Extractor — Multi-source financial data

FAQ

What ticker formats are supported? Stocks (AAPL), crypto (BTC-USD), ETFs (SPY), and indices (^GSPC). Use Yahoo Finance symbol format.

How far back can I get historical data? Depends on the ticker. Most stocks have data going back 10+ years. Crypto availability varies by exchange.

Do I need proxy configuration? Recommended for large batches. Without proxy, Yahoo may rate-limit your requests. Apify Proxy is pre-configured.

What is adjusted_close? The closing price adjusted for stock splits and dividend payments — the correct price for return calculations.

Is this real-time data? Historical data is end-of-day. Real-time metrics (current price, day range) reflect the latest available data at run time.

SEO Keywords

yahoo finance api, stock price data, historical stock data, stock market scraper, finance data api, stock ticker data, market data extraction, financial data api, stock price history, yahoo finance scraper, stock market data, ticker symbol data, stock chart data, finance api alternative, stock data export, market research data, stock analysis tool, investment data, portfolio tracking data, stock price monitoring

For AI Agents & LLM Apps

This Actor is optimized for AI agent pipelines:

• Structured output — Each dataset item has consistent fields: ticker, date, open, high, low, close, adjusted_close, volume
• Batch processing — Process multiple tickers in a single call with configurable maxItems
• API integration — Use the Apify API to trigger runs and fetch results programmatically
• Schema-validated — Output conforms to the dataset schema for reliable downstream parsing

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("ahmed_jasarevic/yahoo-finance").call(
    run_input={
        "tickers": ["AAPL", "MSFT"],
        "start_date": "2024-01-01",
        "maxItems": 50
    }
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['ticker']} {item['date']}: {item['close']}")

Legal & Compliance Disclaimer

This Actor retrieves publicly available financial data from Yahoo Finance. Users are responsible for compliance with Yahoo Finance Terms of Service and applicable regulations. This tool is not affiliated with or endorsed by Yahoo. Financial data is provided for informational purposes only and should not be considered investment advice. The developer assumes no liability for financial decisions made using data obtained through this Actor.