We extract fund profiles, ETF metrics, equity data, Morningstar Ratings, and historical NAV. 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 Mutual Funds objects from morningstar.com. All fields typed and schema-versioned.
"ticker": "VFIAX", "fund_name": "Vanguard 500 Index Fund Admiral Shares", "morningstar_rating": 4, "category": "Large Blend", "nav": 435.67, "expense_ratio": 0.04, "total_assets": 890000000000.0, "sustainability_rating": 3
| # | ticker | fund_name | morningstar_rating | category | nav | expense_ratio |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for ETFs objects from morningstar.com. All fields typed and schema-versioned.
"ticker": "SPY", "etf_name": "SPDR S&P 500 ETF Trust", "morningstar_rating": 4, "asset_class": "US Equity", "nav": 498.32, "market_price": 498.35, "expense_ratio": 0.09, "total_assets": 450000000000.0
| # | ticker | etf_name | morningstar_rating | asset_class | nav | market_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Equities objects from morningstar.com. All fields typed and schema-versioned.
"ticker": "AAPL", "company_name": "Apple Inc.", "sector": "Technology", "industry": "Consumer Electronics", "market_cap": 2850000000000.0, "pe_ratio": 28.4, "dividend_yield": 0.53, "beta": 1.28
| # | ticker | company_name | sector | industry | market_cap | pe_ratio |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Complete list of extractable fields for Portfolio Holdings objects from morningstar.com. All fields typed and schema-versioned.
"parent_ticker": "VFIAX", "holding_name": "Microsoft Corp", "holding_ticker": "MSFT", "weight_pct": 7.12, "shares_owned": 145000000, "sector": "Technology", "country": "United States", "market_value": 58000000000.0
| # | parent_ticker | holding_name | holding_ticker | weight_pct | shares_owned | sector |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Historical Performance objects from morningstar.com. All fields typed and schema-versioned.
"ticker": "VFIAX", "date": "2026-05-12", "nav": 435.67, "daily_return": 0.45, "ytd_return": 12.3, "one_year_return": 24.5, "three_year_return": 10.2, "five_year_return": 14.8
| # | ticker | date | nav | daily_return | ytd_return | one_year_return |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Morningstar scraper handles complex financial data structures: dynamic charts, XHR payload interception, pagination over thousands of holdings, and session management.
Extract NAV, expense ratios, yields, total assets, minimum investments, and Morningstar Star Ratings across global funds.
Capture market cap, P/E ratios, beta, dividend yields, and sector classifications for publicly traded companies.
Track quantitative ratings, analyst rating summaries, and category ranks updated daily.
Paginate through top 25 or full portfolio holdings. Extract weights, shares owned, and position changes.
Intercept XHR requests to extract raw historical NAV and return time-series data bypassing canvas rendering.
Capture Morningstar Sustainability Ratings, carbon metrics, and ESG risk scores for compliance reporting.
Support for US, European, and Asian market tickers with currency normalisation.
Extract historical dividend payouts, ex-dividend dates, and capital gain distributions.
Run daily end-of-day pipelines to capture closing NAVs or monthly bulk exports for portfolio rebalancing.
Brief in. Clean data out.
Provide ticker lists, ISINs, or fund categories. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, XHR interception, and proxy rotation for morningstar.com.
Schema validation, null-rate checks, and numeric outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial sites employ aggressive rate limiting and complex data rendering. Here is how we extract clean data.
Morningstar renders historical performance and asset allocation via client-side canvas elements. We intercept the underlying GraphQL and REST XHR payloads to extract the raw JSON arrays directly, ensuring high precision and zero OCR errors.
High-frequency requests to ticker pages trigger IP bans. We distribute requests across a pool of US residential proxies, normalising request headers and simulating human delay patterns to maintain 99.98% uptime.
Financial data often mixes strings and floats ('$1.2B', '45 bps'). Our pipeline includes a strict typing layer that converts all metrics into machine-readable floats and integers before delivery.
Extracting full portfolio holdings requires managing stateful pagination tokens. We handle session cookies and token rotation to extract thousands of holding rows per fund without dropping records.
A missing NAV breaks downstream quant models. We alert on null-rate spikes and standard deviation outliers in price data, pausing delivery if source data is corrupted.
Quant funds ingest historical NAV and expense ratios to backtest algorithmic trading strategies.
Advisors aggregate Morningstar Ratings and ESG scores to construct compliant client portfolios.
Asset managers track peer fund performance, fee structures, and asset flows to position new products.
Compliance teams monitor Morningstar Sustainability Ratings to ensure portfolios meet green mandates.
Analysts track sector weightings across thousands of ETFs to measure macro capital shifts.
Fintech applications consume daily NAV and yield data to power retail investment dashboards.
"Financial models require precision. Scraping Morningstar means translating complex XHR payloads into strict, typed relational schemas."
Extracting financial data is not about parsing HTML. It requires intercepting backend API calls, managing stateful pagination for massive holding lists, and enforcing strict data types. DataFlirt handles the infrastructure so your quants can focus on alpha.
Everything supported by our morningstar.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 and deduplication. Playwright intercepts XHR payloads and manages JavaScript execution for complex financial tables.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required for stateful pagination.
Pipelines run on AWS Lambda and ECS. 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 morningstar.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Morningstar is generally permissible under applicable law. DataFlirt targets only public, non-authenticated fund, equity, and rating data. We do not extract paywalled Morningstar Premium content or circumvent authentication walls. Clients should review Morningstar terms of service and consult legal counsel for specific use cases.
Instead of attempting to parse canvas elements or SVG paths, our Playwright integration intercepts the underlying XHR/GraphQL requests that Morningstar's frontend uses to request the data. We extract the raw JSON arrays directly from the network layer.
Yes. While the default view often shows only the top 25 holdings, we can paginate through the complete holdings list for funds where public disclosure is available, extracting weights, shares, and sector data for every position.
We typically schedule pipelines to run shortly after market close to capture updated daily NAVs. Pipeline completion time depends on the size of your ticker list, but most daily runs complete within a 2-4 hour window.
Yes. We can extract data for funds and equities listed on global exchanges. You can provide Morningstar specific identifiers, tickers, or ISINs, and we handle the mapping and extraction.
No. We do not bypass login walls or extract paywalled content such as full Morningstar Analyst Reports or premium quantitative models.
Our extraction schema explicitly defines types for all fields. Strings like '1.5B' are converted to floats (1500000000.0), percentages are normalised, and dates are cast to ISO 8601 format before delivery.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily NAV feed for 10,000 tickers or a historical holding extraction - we scope, build, and operate the pipeline. Tell us what you need.
