We extract historical pricing, options chains, SEC filings data, insider transactions, and ESG scores from Yahoo Finance. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Equities & Quotes objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AAPL", "current_price": 173.5, "volume": 54291034, "market_cap": 2730000000000, "pe_ratio": 28.4, "eps": 6.11, "dividend_yield": 0.55
| # | ticker | exchange | current_price | previous_close | open_price | bid |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financial Statements objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "MSFT", "statement_type": "income_statement", "fiscal_date": "2025-06-30", "total_revenue": 211915000000, "gross_profit": 146052000000, "net_income": 72361000000, "ebitda": 102384000000
| # | ticker | statement_type | fiscal_date | total_revenue | cost_of_revenue | gross_profit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Options Chain objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "TSLA", "expiration_date": "2026-01-16", "strike_price": 250.0, "contract_symbol": "TSLA260116C00250000", "option_type": "call", "last_price": 34.5, "implied_volatility": 0.542, "open_interest": 14205
| # | ticker | expiration_date | strike_price | contract_symbol | option_type | last_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Profile objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "NVDA", "company_name": "NVIDIA Corporation", "sector": "Technology", "industry": "Semiconductors", "full_time_employees": 29600, "headquarters_country": "United States", "website": "https://www.nvidia.com"
| # | ticker | company_name | sector | industry | full_time_employees | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Analyst & ESG objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AMZN", "analyst_rating": "Strong Buy", "target_price_mean": 210.5, "number_of_analysts": 48, "esg_score": 24.3, "environment_score": 6.8, "social_score": 12.1
| # | ticker | analyst_rating | target_price_low | target_price_mean | target_price_high | number_of_analysts |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Yahoo Finance pipeline bypasses rate limits and parses complex React state objects to deliver clean, normalised financial models directly to your data warehouse.
Extract daily, weekly, or monthly open, high, low, close, and volume data across decades of trading history.
Parse income statements, balance sheets, and cash flow statements with standardisation across annual and quarterly reporting periods.
Capture calls, puts, strike prices, implied volatility, and open interest across all available expiration dates.
Track consensus ratings, target prices, earnings estimates, and historical upgrades or downgrades from major brokerages.
Extract environmental, social, and governance risk scores, including controversy levels and peer group comparisons.
Monitor executive buying and selling activity, share counts, and transaction values reported in SEC filings.
Track top mutual fund and institutional holders, including position sizes and recent percentage changes.
Scrape headline text, publication dates, and source URLs for ticker-specific news streams.
Extract data from international exchanges including LSE, TSX, ASX, and NSE using Yahoo Finance ticker suffixes.
Brief in. Clean data out.
Provide ticker lists, target exchanges, and required data models. We define the extraction schema.
We configure Scrapy crawlers, API state hydration, proxy rotation, and normalisation logic for financial units.
Schema validation, null-rate checks, currency standardisation, and unit testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial data pipelines cannot tolerate dropped records or schema drift. Here is how we maintain data integrity.
Yahoo Finance renders heavily via React. Instead of brittle DOM parsing, our crawlers intercept and hydrate the underlying JSON state objects, ensuring extraction is faster and immune to minor UI changes.
Frequent requests to Yahoo Finance endpoints trigger 429 Too Many Requests errors. We distribute load across ISP-grade residential proxies, managing request volumes per IP to maintain high-throughput pipelines.
Yahoo Finance displays values in millions (M) or billions (B) depending on the context. Our pipeline normalises all financial figures into raw integers and standardises currency codes before delivery.
European proxy exits face aggressive cookie consent walls that block API responses. We manage session cookies and consent tokens programmatically to ensure uninterrupted data flow.
Missing earnings data ruins backtesting models. We alert on null-rate spikes and schema drift, pausing delivery and notifying engineers if Yahoo Finance alters its data structure.
Quantitative funds use decades of historical price and volume data to backtest trading strategies and train predictive models.
Asset managers sync daily closing prices, dividend yields, and analyst ratings to internal dashboards for portfolio rebalancing.
Universities and researchers extract bulk financial statements and ESG scores for macroeconomic studies and market trend analysis.
Corporate strategy teams track peer group valuations, revenue growth, and operating margins across specific industry sectors.
NLP teams scrape headline text and article metadata to correlate news sentiment with intraday price movements.
Compliance officers monitor environmental and social controversy scores across supply chain partners and investment targets.
"Yahoo Finance is the internet default market data feed, but standardising its disparate HTML tables and React stores into queryable financial models requires dedicated infrastructure."
Financial data pipelines cannot tolerate dropped records or schema drift. Scraping Yahoo Finance requires managing React state hydration, bypassing rate limits on hidden API endpoints, and normalising currency and unit formats across global exchanges. DataFlirt absorbs that complexity so your quants can focus on alpha generation.
Everything supported by our finance.yahoo.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Instead of parsing brittle HTML tables, our scrapers intercept Yahoo Finance internal API calls and Redux state objects, resulting in cleaner data and lower failure rates.
We distribute requests across ISP-grade residential proxies to avoid 429 rate limits, ensuring high-volume ticker lists complete within designated market windows.
Pipelines automatically convert string representations of financial units into database-ready numeric types, handling currency standardisation and missing data imputation.
Data delivered to where your team already works — no new tooling required.
About finance.yahoo.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available financial data is generally permissible. DataFlirt targets only public, non-authenticated market data, financial statements, and news. We do not extract Yahoo Finance Plus gated content or circumvent authentication walls. Clients should review Yahoo Terms of Service and consult legal counsel for specific use cases.
We use residential ISP proxies and intelligent request timing. By distributing requests across thousands of IPs and hydrating internal React state objects rather than loading full pages, we extract data efficiently without triggering 429 Too Many Requests errors.
We support all global exchanges listed on Yahoo Finance. You simply provide the ticker with the appropriate suffix (e.g., RELIANCE.NS for National Stock Exchange of India, or TSCO.L for London Stock Exchange).
We run pipelines at daily, hourly, or 15-minute intervals depending on your requirements. Note that Yahoo Finance itself applies a 15-minute delay to certain international exchanges. We extract the data as it appears on the platform.
Yes. Yahoo Finance often displays values like '1.2B' or '450M'. Our pipeline converts these into absolute integers (1200000000) and ensures all fields match strict numeric types before delivery.
Yes. We can extract daily, weekly, or monthly historical price and volume data going back to the earliest date available for a given ticker on Yahoo Finance.
Our smallest packages start at a defined list of 500 tickers with daily delivery of closing prices and financial statements. For larger universes or intraday extraction, we price based on volume and frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical extraction or a continuous daily feed across 10,000 global equities : we scope, build, and operate the pipeline. Tell us what you need.
