We extract global market news, corporate tearsheets, economic indicators, and Lex column analysis from ft.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 News Articles objects from ft.com. All fields typed and schema-versioned.
"article_id": "0b1a2c3d-4e5f-6g7h-8i9j", "headline": "Global markets rally on inflation data", "author": "Katie Martin", "published_date": "2026-05-12T08:30:00Z", "topic_tags": "['Equities', 'Inflation', 'Global Economy']", "paywall_status": "hard"
| # | article_id | headline | subheadline | author | published_date | updated_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Market Data objects from ft.com. All fields typed and schema-versioned.
"ticker": "AAPL", "exchange": "NSQ", "current_price": 185.42, "price_change_pct": 1.24, "volume": 45210000, "market_cap": "2.8T", "pe_ratio": 28.4
| # | ticker | exchange | company_name | current_price | currency | price_change_abs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Tearsheets objects from ft.com. All fields typed and schema-versioned.
"company_id": "847291", "name": "Unilever PLC", "sector": "Consumer Defensive", "revenue_ttm": "60.1B", "net_income": "7.6B", "hq_location": "London, UK"
| # | company_id | name | sector | industry | description | hq_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lex Column objects from ft.com. All fields typed and schema-versioned.
"lex_id": "lex-998877", "title": "Tech valuations: back to reality", "published_date": "2026-05-11T14:00:00Z", "companies_mentioned": "['Microsoft', 'Alphabet']", "tickers_mentioned": "['MSFT', 'GOOGL']", "sentiment_score": -0.45
| # | lex_id | title | teaser | published_date | companies_mentioned | tickers_mentioned |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Economic Indicators objects from ft.com. All fields typed and schema-versioned.
"country": "United Kingdom", "indicator_name": "CPI YoY", "current_value": 2.1, "previous_value": 2.3, "unit": "Percentage", "release_date": "2026-05-10T07:00:00Z"
| # | country | indicator_name | current_value | previous_value | unit | frequency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our FT scraper processes high-velocity news cycles, complex market data tables, and corporate tearsheets. We handle session management, dynamic charts, and anti-bot circumvention.
Headlines, metadata, summaries, and topic tags extracted across all geographic and sector-specific news feeds.
Opinion and analysis targeting specific tickers, captured with author metadata and publication timestamps.
Equities, commodities, and FX prices captured from FT's market data portal with full historical snapshots.
Fundamentals, key executives, and corporate descriptions parsed from nested HTML financial tables.
Track specific journalists or macro themes across the entire ft.com domain.
Central bank rates, inflation data, and GDP prints structured into queryable time-series data.
Extracting sustainability reporting data and corporate governance news mentions.
Parsing deal announcements, valuations, and advisor metadata from the deals section.
Run intraday updates for breaking news or daily historical dumps for quantitative modelling.
Brief in. Clean data out.
Provide topics, tickers, authors, or market indices. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session handling for ft.com.
Schema validation, null-rate monitoring, ticker mapping, and sample records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Financial Times employs strict paywalls, complex dynamic data visualisations, and aggressive bot mitigation. Here is how we maintain pipeline stability.
We identify paywall states dynamically and extract all public metadata, tags, and summaries without violating access controls.
Market data and interactive charts require full DOM hydration. We run headless Playwright sessions to capture data that standard HTTP requests miss.
Datacenter IPs are blocked instantly. We route requests through residential ISP proxies to maintain high success rates and avoid rate limits.
Corporate tearsheets use complex, frequently changing table structures. Our selectors normalise these into flat, predictable JSON schemas.
We maintain hash indexes of article states to detect updates and corrections in real time, pushing only the diffs to your warehouse.
Quantitative funds run sentiment analysis on breaking news and Lex columns to inform high-frequency trading models.
Corporate strategy teams monitor sector-specific news, executive moves, and M&A activity.
Economists track global economic indicators and central bank commentary to adjust macro models.
Asset managers aggregate sustainability reports and corporate governance news for portfolio screening.
Risk teams monitor negative news flow and market data for corporate debt issuers.
Analysts feed quantitative models with corporate fundamental data extracted from FT tearsheets.
"Financial Times dictates the narrative for global markets. Without structured extraction, quantitative teams miss the critical sentiment signals embedded in the Lex column and breaking news."
Extracting data from ft.com requires navigating strict access controls, dynamic market widgets, and aggressive rate limiting. DataFlirt manages the infrastructure layer: proxy rotation, session handling, and schema maintenance: so your quantitative analysts can focus on signal generation rather than DOM parsing.
Everything supported by our ft.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 orchestration and deduplication. Playwright handles JavaScript rendering and interaction flows for market data.
We maintain pools of residential ISP proxies across UK and US regions. Rotation happens per request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About ft.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available metadata, headlines, and market data is generally permissible. We do not bypass paywalls to extract gated full-text content without client-provided credentials. Clients must review FT terms of service and consult legal counsel for their specific use case.
No. For unauthenticated pipelines, we only extract publicly visible metadata, summaries, tags, and market data. Full-text extraction requires the client to supply valid FT enterprise credentials for an isolated, authenticated pipeline.
For targeted sections or specific tickers, we can configure sub-minute polling intervals with webhook delivery, ensuring your trading models receive signals instantly.
Yes. We extract Lex column metadata, publication timestamps, author details, and the specific companies or tickers mentioned, which is highly valuable for sentiment analysis.
Yes. We parse the FT market data portal to extract equity pricing, corporate fundamentals, key executives, and historical performance metrics.
We deliver structured JSON, CSV, XLS, and Parquet files directly to AWS S3, Google BigQuery, or Snowflake. We also support Webhooks and API endpoints.
Yes. If your organisation has an enterprise FT subscription that permits automated access, we can configure an authenticated pipeline using your credentials in an isolated environment.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off historical news dump or a continuous market data feed, we scope, build, and operate the pipeline. Tell us what you need.
