We extract financial news, market indices, company profiles, executive intelligence, and ESG metrics from Bloomberg. 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 Market Data objects from bloomberg.com. All fields typed and schema-versioned.
"ticker": "AAPL:US", "current_price": 178.72, "price_change_pct": 1.24, "volume": 54291000, "market_cap": 2810000000000, "pe_ratio": 28.4, "dividend_yield": 0.54, "currency": "USD"
| # | ticker | exchange | current_price | price_change_abs | price_change_pct | volume |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financial News objects from bloomberg.com. All fields typed and schema-versioned.
"article_id": "R9X2L4T1P9V", "headline": "Tech Stocks Rally on Rate Cut Hopes", "author": "Jane Doe", "publish_date": "2023-10-14T14:30:00Z", "related_tickers": "['AAPL:US', 'MSFT:US', 'GOOGL:US']", "tags": "['Markets', 'Technology', 'Federal Reserve']", "paywall_status": "metered"
| # | article_id | headline | subheadline | author | publish_date | update_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Profiles objects from bloomberg.com. All fields typed and schema-versioned.
"company_name": "Apple Inc", "ticker": "AAPL:US", "sector": "Technology", "industry": "Consumer Electronics", "employee_count": 164000, "founded_year": 1976, "hq_address": "One Apple Park Way, Cupertino, CA 95014", "website": "https://www.apple.com"
| # | company_name | ticker | exchange | sector | industry | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Executive Intelligence objects from bloomberg.com. All fields typed and schema-versioned.
"exec_name": "Tim Cook", "title": "Chief Executive Officer", "company": "Apple Inc", "age": 63, "compensation_total": 99420000, "tenure": "12 years", "board_memberships": "['Nike Inc', 'National Football Foundation']", "profile_url": "https://www.bloomberg.com/profile/person/12345"
| # | exec_name | title | company | ticker | age | biography |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for ESG Metrics objects from bloomberg.com. All fields typed and schema-versioned.
"ticker": "AAPL:US", "esg_disclosure_score": 68.4, "environmental_score": 72.1, "social_score": 64.5, "governance_score": 68.6, "board_diversity_pct": 41.6, "peer_percentile": 88, "rating_date": "2023-09-01"
| # | ticker | company_name | esg_disclosure_score | environmental_score | social_score | governance_score |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Bloomberg scraper navigates dynamic charting libraries, stringent bot protection, and metered paywalls to extract structured market data, news, and executive intelligence.
Extract full article text, headlines, authors, and publish timestamps across Bloomberg News, Politics, and Technology sections.
Capture end-of-day or intraday pricing, volume, market cap, and fundamental ratios for equities, commodities, and FX pairs.
Scrape detailed corporate profiles, sector classifications, employee counts, and HQ locations for global entities.
Track leadership changes, board memberships, compensation packages, and career histories across public companies.
Extract Bloomberg's proprietary ESG disclosure metrics, environmental impact data, and corporate governance stats.
Map supplier and customer dependencies disclosed in company filings and aggregated on Bloomberg entity pages.
Parse full text from quarterly earnings calls, investor days, and executive interviews tagged by ticker.
Extract data across US, European, and Asian markets with native currency normalisation and timezone standardisation.
Maintain a hash index of last-seen values per field. Subsequent runs only push diffs for executive changes or profile updates.
Brief in. Clean data out.
Provide ticker lists, news categories, or executive names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for bloomberg.com.
Schema validation, null-rate checks, and sample article extraction before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial publishers deploy aggressive scraping countermeasures. Here is how we maintain data flow and why teams choose managed infrastructure.
Bloomberg uses enterprise bot protection that analyses TLS fingerprints, JS execution environments, and request velocity. We deploy ISP-grade residential proxies, Playwright-driven browser sessions, and automated CAPTCHA solvers to maintain access.
Market data and historical charts are rendered via complex JavaScript libraries and WebSocket connections. We execute full browser sessions to trigger data hydration, capturing the underlying JSON payloads before they hit the canvas.
Bloomberg enforces strict article limits per IP/session. We cycle residential IPs, clear local storage, and rotate user agents to extract public article text without triggering hard paywall blocks.
Aggressive polling leads to subnet bans. Our Airflow orchestrator distributes requests across thousands of IPs with randomised delays, keeping request volume well below Bloomberg's anomaly detection thresholds.
Financial news layouts change frequently. We use structured data extraction (LD+JSON) where available, backed by multi-layered XPath and CSS selectors to ensure pipeline continuity.
Quant funds ingest real-time news headlines and press releases to train NLP models for sentiment-driven trading signals.
Corporate strategy teams monitor competitor news, executive appointments, and supply chain disclosures to map industry dynamics.
Asset managers extract environmental and governance scores to ensure portfolio compliance with sustainability mandates.
Fixed income analysts track corporate restructurings, earnings transcripts, and leadership changes to assess default probability.
Universities build historical corpora of financial journalism to study market reactions to macroeconomic events over decades.
Procurement teams map supplier dependencies and monitor vendor-related news for operational disruption signals.
"Financial markets run on information asymmetry. Extracting Bloomberg's public data at scale removes the friction between raw news and actionable alpha."
Most teams underestimate the investment required: reliable Bloomberg scraping requires Datadome bypass, full JavaScript rendering, residential IP rotation, and daily selector maintenance. DataFlirt absorbs that complexity so your quants and engineers can focus on signal extraction, not infrastructure maintenance.
Everything supported by our bloomberg.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 global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda for burst workloads and Kubernetes for sustained extraction. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About bloomberg.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Bloomberg is generally permissible under applicable law, reinforced by rulings like hiQ v. LinkedIn. DataFlirt targets only public, non-authenticated news, market data, and company profiles. We do not circumvent authentication walls or extract proprietary Terminal data. Clients should review Bloomberg's ToS and consult legal counsel.
We utilise residential IP rotation, session clearing, and user-agent spoofing to access metered articles. We do not bypass hard paywalls that require active subscriber credentials.
Yes. We can paginate through author archives, category feeds, and site search results to build historical corpora of financial news spanning multiple years.
We can configure pipelines to run at end-of-day, hourly, or higher frequencies depending on target volume. Note that public web quotes are typically delayed by 15 minutes compared to direct exchange feeds.
Yes. We capture Bloomberg's publicly surfaced ESG disclosure scores, environmental metrics, and mapped supply chain relationships present on company profile pages.
Our packages typically start at a defined list of 500 to 5,000 tickers or specific news categories with daily delivery. Contact us with your specific volume requirements for a scoped quote.
Absolutely. We provide a sample run of up to 100 tickers or 50 news articles as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical news corpus or a continuous feed of executive intelligence across 10,000 tickers, we scope, build, and operate the pipeline. Tell us what you need.
