Extract equity prices, financial statements, analyst ratings, regulatory filings, earnings call transcripts, news sentiment, and alternative data signals from global exchanges and financial platforms. Institutional-grade market data infrastructure for quant researchers, investment platforms, and fintech applications.
Stock market data scraping is the automated collection of structured financial and market intelligence from financial platforms, exchange websites, regulatory databases, and investment information services. The universe of data that exists in public form is vast: real-time and historical price quotes, trading volume, fundamental financial metrics derived from filings, analyst price targets and rating changes, earnings call transcripts, news articles with sentiment signals, insider transaction disclosures, short interest data, options chain information, and ESG scores. Scraping this data systematically โ from multiple authoritative sources โ gives financial researchers, quant teams, and fintech developers the raw material for sophisticated investment models and data products.
The challenge with financial data is not that it doesn't exist in public form โ much of it does. The challenge is that it is scattered across dozens of sources with different formats, update frequencies, and access patterns. A company's financial statements are filed with SEBI or the SEC, its analyst ratings appear on financial portals, its news coverage is distributed across hundreds of publishers, and its historical price data lives on exchange platforms. Assembling a complete, point-in-time accurate picture of a company requires collecting from all of these sources and resolving them into a coherent record.
DataFlirt's financial data scraping infrastructure handles this complexity. We collect from exchange websites (NSE, BSE, NYSE, NASDAQ, LSE, and others), financial information portals, regulatory filing databases like EDGAR and SEBI's EDIFAR system, earnings transcript services, and financial news publishers. Each source requires different extraction techniques: some offer structured data through financial portals that render in JavaScript, others require PDF parsing for annual reports and filing documents, and others demand careful rate management to avoid being flagged by financial data gatekeepers.
A particularly important aspect of financial data collection is point-in-time correctness โ the principle that historical data should reflect what was known at each historical date, not what we know now with the benefit of hindsight. Restated financials, revised earnings estimates, and delayed filings can introduce look-ahead bias into backtesting systems if not handled carefully. DataFlirt's data pipelines are built with point-in-time accuracy as a core design principle, making the data suitable for rigorous quantitative research.
Comprehensive extraction built for reliability, accuracy, and scale.
Real-time and historical OHLCV data for equities, ETFs, indices, and mutual funds across 50+ global exchanges โ adjusted for splits and dividends.
Scrape income statements, balance sheets, and cash flow statements from company filings, financial portals, and regulatory databases with multi-year history.
Collect analyst upgrade and downgrade events, price target changes, earnings estimate revisions, and consensus ratings with source attribution.
Extract structured data from SEBI, EDGAR, and other regulatory databases โ annual reports, quarterly filings, insider transactions, and bulk deal data.
Aggregate financial news from hundreds of publishers with real-time entity tagging and sentiment scoring at the article and headline level.
Collect web traffic trends, job posting velocity, patent filings, satellite imagery signals, and other alternative datasets linked to equity tickers.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data โ reliably.
{ "status": "success", "source": "nse_india", "as_of": "2025-03-19T15:28:00+05:30", "ticker": "RELIANCE", "exchange": "NSE", "quote": { "open": 2847.50, "high": 2891.20, "low": 2831.00, "close": 2874.35, "volume":4182900, "52w_high":3024.90, "52w_low": 2220.30 }, "fundamentals": { "market_cap_cr": 1948220, "pe_ratio": 24.8, "eps": 115.90, "dividend_yield":0.41 }, "analyst_consensus": "BUY", "target_price": 3150.00 }
Built on proven open-source tools and cloud infrastructure โ no vendor lock-in.
All historical data records stamped with the date information was first available โ eliminating look-ahead bias in backtesting and research.
Annual reports, regulatory filings, and earnings transcripts parsed from PDF to structured data using layout-aware extraction and OCR.
Low-latency price and volume data collection during market hours with sub-minute refresh intervals for active monitoring and alerting.
Company identities resolved across sources using ISIN, CIN, ticker, and name matching to create unified company-level records.
NSE, BSE, NYSE, NASDAQ, LSE, TSE, HKEX, and 45+ additional exchanges covered with exchange-native timezone and currency handling.
TimescaleDB-powered time-series storage purpose-built for high-performance queries over price histories and fundamental time series.
From solo analysts to enterprise data teams โ here's how organizations use this data.
In quantitative investing, the quality of your data directly determines the quality of your signals. Survivorship bias, look-ahead bias, stale fundamentals, and mislinked entities turn research pipelines into generators of false confidence. DataFlirt builds financial data infrastructure with point-in-time correctness, multi-source validation, and entity resolution as foundational principles โ giving quant teams and fintech developers the rigorous data foundation that real investment decisions require.
Start free and scale as your data needs grow.
For small teams and projects getting started with data.
For growing teams with serious data requirements.
For large organizations with custom requirements.
Everything you need to know before getting started.
Join data teams worldwide using DataFlirt to power products, research, and operations with reliable, structured web data.
