We extract fundamental data, ETF holdings, IPO schedules, and market quotes from stockanalysis.com. 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 Income Statement objects from stockanalysis.com. All fields typed and schema-versioned.
"ticker": "AAPL", "fiscal_year": "2023", "revenue": 383285000000, "gross_profit": 169148000000, "operating_income": 114301000000, "net_income": 96995000000, "eps": 6.13, "ebitda": 125820000000
| # | ticker | fiscal_year | revenue | gross_profit | operating_income | net_income |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Balance Sheet objects from stockanalysis.com. All fields typed and schema-versioned.
"ticker": "AAPL", "fiscal_year": "2023", "total_assets": 352583000000, "total_liabilities": 290437000000, "total_equity": 62146000000, "cash_and_equivalents": 29965000000, "total_debt": 111088000000
| # | ticker | fiscal_year | total_assets | total_liabilities | total_equity | cash_and_equivalents |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for ETF Holdings objects from stockanalysis.com. All fields typed and schema-versioned.
"etf_ticker": "SPY", "holding_ticker": "MSFT", "holding_name": "Microsoft Corporation", "weight_pct": 7.25, "shares_held": 84512045, "market_value": 34821000000, "sector": "Technology"
| # | etf_ticker | fund_name | holding_ticker | holding_name | weight_pct | shares_held |
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Complete list of extractable fields for IPO Calendar objects from stockanalysis.com. All fields typed and schema-versioned.
"company_name": "Reddit, Inc.", "symbol": "RDDT", "exchange": "NYSE", "ipo_date": "2024-03-21", "offer_amount": 748000000, "status": "Priced"
| # | company_name | symbol | exchange | ipo_date | price_range_low | price_range_high |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Market Quotes objects from stockanalysis.com. All fields typed and schema-versioned.
"ticker": "NVDA", "company_name": "NVIDIA Corporation", "current_price": 875.28, "change_pct": 2.45, "volume": 45120300, "market_cap": 2180000000000, "pe_ratio": 74.2
| # | ticker | company_name | current_price | change_abs | change_pct | volume |
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Our Stockanalysis scraper handles every layer of the platform: financial statements, dynamic ETF holdings, IPO schedules, and real-time market quotes with JavaScript rendering and session management built in.
Income statements, balance sheets, and cash flow data spanning multiple fiscal years. Extracted as clean, typed numerical arrays.
Capture complete constituent lists, weight percentages, share counts, and market values for thousands of funds.
Monitor upcoming, priced, and withdrawn IPOs. Extract expected price ranges, share counts, and total offer amounts.
Historical dividend payouts, ex-dividend dates, yields, and stock split ratios for accurate backtesting.
Extract entire screener result sets based on custom criteria across thousands of equities.
Consensus ratings, price targets, and earnings estimates from Wall Street analysts covering specific tickers.
Pre-calculated metrics including PE, PB, ROE, debt-to-equity, and profit margins updated dynamically.
Earnings dates, press releases, and SEC filing notifications linked to specific company profiles.
Run one-off historical exports or configure continuous pipelines at daily or weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide ticker lists, fund symbols, or screener criteria. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and rate-limit handling for stockanalysis.com.
Schema validation, null-rate checks, numerical outlier detection, and sample outputs before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial data platforms invest heavily in scraping detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Financial sites employ strict rate limiting and Cloudflare protection. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management - trained on real user behaviour patterns.
Stockanalysis.com relies on dynamic charting and lazy-loaded tables. We run full Playwright browser sessions with JavaScript execution and hydration - capturing data that headless HTTP clients miss entirely.
Table structures for financial statements change based on reporting standards. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and text-pattern matching - so a layout change does not break your data pipeline overnight.
For large ticker universes, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, numerical formatting errors, and coverage drops - and respond before you notice.
Quant funds ingest historical financial statements and ratios to backtest fundamental trading strategies.
Asset managers track ETF holdings and weightings to monitor sector exposure and rebalance portfolios.
Universities compile decades of corporate financial data to study market trends and economic cycles.
Risk teams correlate balance sheet health metrics with market volatility to assess counterparty risk.
Fintech platforms power retail dashboards with real-time quotes, dividend histories, and analyst ratings.
Corporate strategy teams monitor peer financial performance, margins, and growth rates across specific sectors.
"Stockanalysis.com aggregates decades of financial filings and market data, but institutional usage requires automated, structured extraction pipelines."
Most teams underestimate the investment required: reliable financial scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on alpha generation, not infrastructure.
Everything supported by our stockanalysis.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About stockanalysis.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available financial information is generally permissible. DataFlirt targets only public, non-authenticated financial statements, ETF holdings, and market quotes. We do not circumvent authentication walls for premium features. Clients should review the target platform ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 429/503 rate spikes in real time and trigger pool rotation automatically.
We can extract all publicly visible historical data on the platform, which typically covers 10 to 15 years of annual and quarterly income statements, balance sheets, and cash flow statements.
Yes. We paginate through complete ETF holding lists, extracting ticker, company name, weight percentage, shares held, and market value for every constituent.
Market quotes can be extracted at hourly or daily cadences. For millisecond-level intraday tick data, we recommend direct exchange feeds rather than web scraping.
We extract the raw reported fields exactly as they appear on the platform. Any standardisation or normalisation of accounting terms is handled downstream in your data warehouse.
Absolutely. We provide a sample run of up to 100 tickers as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical export or a continuous fundamental data feed across 10,000 tickers - we scope, build, and operate the pipeline. Tell us what you need.
