We extract stock quotes, historical price series, fundamentals, analyst estimates, earnings data, insider filings, and financial news 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 Stock Quotes objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AAPL", "exchange": "NASDAQ", "company_name": "Apple Inc.", "price": 213.42, "change_pct": 1.24, "volume": 58291044, "market_cap": 3280000000000, "pe_ratio": 34.21, "52w_high": 237.23, "52w_low": 164.08, "dividend_yield": 0.51, "quote_timestamp": "2026-05-12T20:00:00Z"
| # | ticker | exchange | company_name | currency | price | open |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Historical OHLCV objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AAPL", "date": "2026-05-12", "open": 210.88, "high": 214.91, "low": 209.42, "close": 213.42, "adj_close": 213.42, "volume": 58291044, "split_coefficient": 1.0, "interval": "1d"
| # | ticker | date | open | high | low | close |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Financials objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AAPL", "period": "Q1 2026", "period_type": "quarterly", "total_revenue": 124300000000, "net_income": 36330000000, "eps_diluted": 2.40, "free_cash_flow": 29820000000, "total_debt": 101200000000, "report_date": "2026-01-30"
| # | ticker | period | period_type | currency | total_revenue | gross_profit |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Analyst Estimates objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AAPL", "analyst_count": 42, "recommendation_key": "buy", "recommendation_mean": 2.1, "target_price_mean": 238.50, "target_price_high": 300.00, "target_price_low": 185.00, "eps_estimate_next_year": 8.14, "earnings_growth_estimate": 11.2
| # | ticker | analyst_count | recommendation_mean | recommendation_key | target_price_mean | target_price_high |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Earnings & Calendar objects from finance.yahoo.com. All fields typed and schema-versioned.
"ticker": "AAPL", "earnings_date": "2026-05-01", "earnings_time": "AMC", "eps_estimate": 1.57, "eps_actual": 1.65, "eps_surprise_pct": 5.10, "revenue_actual": 95360000000, "revenue_surprise_pct": 2.40, "fiscal_quarter": "Q2 FY2026"
| # | ticker | earnings_date | earnings_time | eps_estimate | eps_actual | eps_surprise |
|---|---|---|---|---|---|---|
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Yahoo Finance is the world's most-visited financial data platform. Our scraper covers every object type analysts and quant teams need: real-time quotes, historical OHLCV, full financial statements, analyst consensus, insider filings, options chains, and news — all in a single schema-consistent pipeline.
Price, change, volume, market cap, P/E, EPS, 52-week range, moving averages, short interest, and float — captured per ticker at your chosen cadence from pre-market open to post-market close.
Adjusted and unadjusted daily, weekly, monthly, and intraday (1m, 5m, 15m, 60m) OHLCV going back decades — with split coefficients and dividend amounts embedded per row.
Annual and quarterly financial statements: revenue, gross profit, EBITDA, net income, EPS, total assets, total debt, free cash flow — normalised across all reporting currencies.
Consensus recommendation, mean/high/low price targets, analyst count, EPS and revenue estimates for current and next fiscal year, and earnings and revenue growth forecasts.
Upcoming earnings dates, EPS and revenue estimates vs actuals, surprise percentage, guidance ranges, and AMC/BMO timing flags — across any ticker universe.
Insider transactions (buy/sell/option exercise), Form 4 filing details, institutional 13F holdings by fund, ownership percentage, and period-over-period changes.
Full options chain extraction: strike, expiry, bid/ask, last price, implied volatility, open interest, volume, delta, gamma, and theta — for any ticker and expiry date.
News articles and press releases indexed per ticker: headline, source, publication timestamp, article URL, and sentiment score (positive/negative/neutral) via NLP post-processing.
Yahoo Finance ESG risk score, environmental, social, and governance sub-scores, controversy level, and peer comparison percentile — per ticker where available.
Brief in. Clean data out.
Provide ticker lists, indices, screener criteria, or sector filters. We design the extraction schema — including which financial statement periods, estimate horizons, and market data fields you need.
We configure Scrapy / Playwright crawlers with US residential proxies, financial data parsers, and OHLCV normalisation logic — calibrated for Yahoo Finance's rate limits and page structure.
Schema validation, price sanity checks, financial statement cross-balancing, OHLCV continuity testing, and split-adjustment verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on your cadence — market-hours-aware scheduling available.
Yahoo Finance has aggressive rate limiting, JavaScript-rendered financials, and frequent schema changes. Here's how we maintain reliable extraction at scale.
Yahoo Finance aggressively throttles bulk requests. Our pipeline manages request pacing at the ticker level — distributing load across US residential proxies, staggering concurrency during market hours, and implementing exponential backoff on rate-limit responses. Large ticker universes (5,000–50,000 symbols) are batched and distributed across multi-hour windows without gaps.
Stock splits and dividend events require backward adjustment of the entire historical series. Our pipeline detects split and dividend events from Yahoo Finance's event feed and applies backward-adjusted close prices to all historical records — ensuring your time series is consistent across the full history from day one.
Yahoo Finance renders income statements, balance sheets, and cash flow statements dynamically via React. We run full Playwright sessions to capture the rendered financial tables, then apply a normalisation layer that maps Yahoo's variable label names to a stable, cross-ticker schema — regardless of how Yahoo labels line items for different companies or sectors.
Financial data has a temporal logic. Our scheduler aligns pipeline runs to market events: pre-market (4:00 AM ET), market open, market close (4:00 PM ET), and after-hours. Earnings calendar integration triggers elevated-cadence runs around reporting dates — capturing pre/post earnings quote, estimate, and surprise data in the right sequence.
Every run emits structured logs to our observability stack. We alert on price outliers beyond expected daily move ranges, null-rate spikes in financial statement fields, ticker delisting events, and schema changes on Yahoo's side — and respond before your downstream models notice.
Quant teams use historical OHLCV, fundamentals, and analyst estimate time-series to build, test, and refine systematic trading strategies across equity universes.
Analysts and portfolio managers screen the investable universe using financial statement metrics, valuation ratios, analyst consensus, and earnings surprise history — sourced at scale.
Event-driven funds and research teams monitor earnings calendars, consensus estimates vs actuals, guidance changes, and surprise patterns across hundreds of tickers per reporting season.
Data vendors and hedge funds use Yahoo Finance fundamentals and estimates as a baseline layer to enrich with satellite imagery, credit card data, and other alternative signals.
Trading desks and FinTech products monitor Yahoo Finance news feeds per ticker for sentiment shifts, merger rumours, and macro event coverage — feeding NLP-driven alert systems.
FinTech companies building portfolio trackers, robo-advisors, and wealth management tools use Yahoo Finance as a data source for quotes, fundamentals, and news without building exchange connections.
"Yahoo Finance has more financial data objects per ticker than any other free source on the internet — quotes, financials, estimates, insider filings, options, news, and ESG in one place."
The challenge isn't access — it's reliability at scale. Yahoo Finance rate-limits aggressively, restructures its React frontend without warning, and changes financial label names across sectors. DataFlirt runs a production-grade pipeline that absorbs all of that: paced extraction across US residential proxies, Playwright-rendered financials, split-adjusted OHLCV, and market-hours-aware scheduling — delivered to your warehouse on the cadence your models need.
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.
Scrapy handles crawl orchestration, ticker batching, and retry logic. Playwright renders Yahoo Finance's React-based financial tables. A normalisation layer maps Yahoo's variable label names to a stable, typed schema — consistent across sectors, geographies, and reporting periods.
We maintain pools of US residential ISP proxies. Request pacing is managed at the ticker level with configurable concurrency caps — distributing extraction load across multi-hour windows to stay within Yahoo Finance's rate tolerance without triggering throttling.
Airflow DAGs are scheduled against NYSE/NASDAQ market hours: pre-market, open, close, and after-hours windows. Earnings calendar integration triggers elevated-cadence runs around reporting dates. All state stored in managed Postgres with ticker-level run history.
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 information from Yahoo Finance is generally permissible under applicable law — reinforced by the hiQ v. LinkedIn ruling and broader public data precedents in the US. DataFlirt extracts only public, non-authenticated market data, financial statements, and news content. We do not access Premium-gated content, personal account data, or real-time exchange feeds that require licensed redistribution agreements. We recommend clients review Yahoo Finance's ToS independently and consult legal counsel — particularly for commercial redistribution use cases.
Yes. Our pipeline detects split and dividend events from Yahoo Finance's event feed and applies backward split-adjustment to all historical close prices — producing a continuous adj_close series. The raw (unadjusted) OHLCV and the split coefficient are also delivered as separate fields so clients can apply their own adjustment logic if preferred.
We distribute extraction across US residential ISP proxies with ticker-level request pacing and configurable concurrency caps. Large ticker universes are batched across multi-hour windows. We implement exponential backoff on rate-limit responses and monitor block rates in real time — triggering pool rotation automatically when needed.
Yes. Our Airflow DAGs are built with market-hours awareness: runs can be scheduled to pre-market open, market close, after-hours, or any combination. We maintain an earnings calendar feed — so when a ticker in your universe reports, an elevated-cadence run fires automatically to capture pre/post earnings quote, estimate, and surprise data in the correct sequence.
Yes. Yahoo Finance labels financial line items inconsistently across sectors and geographies (e.g. 'Total Revenue' vs 'Net Revenue' vs 'Revenue'). Our normalisation layer maps Yahoo's variable labels to a stable, cross-ticker schema — so your downstream models and dashboards don't need to handle per-ticker label variation.
Yes. We extract full options chains per ticker across all available expiry dates — including strike, bid/ask, last price, implied volatility, open interest, volume, and available Greeks (delta, gamma, theta). Options data can be delivered on a daily end-of-day schedule or more frequently if intraday options analytics are required.
Yes. Yahoo Finance covers equities, ETFs, mutual funds, indices, currencies, and cryptocurrencies across exchanges globally. We support any ticker Yahoo Finance covers — including LSE, TSE, NSE, BSE, ASX, Euronext, and others — with currency-normalised output per record.
Yes. We provide a sample run of up to 200 tickers — including quotes, one year of daily OHLCV, the most recent quarterly financials, and analyst estimates — as part of pre-engagement scoping, so you can validate schema fit and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily fundamentals across 10,000 tickers, split-adjusted OHLCV going back 20 years, or an earnings calendar feed wired to your models — we scope, build, and operate the pipeline. Tell us what you need.
