We extract country indicators, market indices, commodity prices, and economic calendars from Trading Economics. 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 Macro Indicators objects from tradingeconomics.com. All fields typed and schema-versioned.
"country": "United States", "indicator": "Inflation Rate", "latest_value": 3.1, "previous_value": 3.2, "unit": "percent", "frequency": "Monthly", "date": "2024-02-29", "next_release": "2024-03-12"
| # | country | indicator | latest_value | previous_value | highest_value | lowest_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Economic Calendar objects from tradingeconomics.com. All fields typed and schema-versioned.
"event_id": "EV-99214", "country": "Euro Area", "event_name": "ECB Interest Rate Decision", "actual": 4.5, "consensus": 4.5, "previous": 4.5, "time": "13:15:00", "date": "2024-03-07"
| # | event_id | country | event_name | actual | consensus | previous |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Market Data objects from tradingeconomics.com. All fields typed and schema-versioned.
"symbol": "SPX", "name": "S&P 500", "asset_class": "Index", "price": 5123.69, "change_abs": 14.2, "change_pct": 0.28, "timestamp": "2024-03-08T16:00:00Z"
| # | symbol | name | asset_class | price | change_abs | change_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Forecasts objects from tradingeconomics.com. All fields typed and schema-versioned.
"country": "Japan", "indicator": "GDP Growth Rate", "current": -0.1, "q1_forecast": 0.2, "q2_forecast": 0.3, "q3_forecast": 0.4, "year_end": 0.8, "update_date": "2024-03-01"
| # | country | indicator | current | q1_forecast | q2_forecast | q3_forecast |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bond Yields objects from tradingeconomics.com. All fields typed and schema-versioned.
"country": "Germany", "maturity": "10-Year", "yield_pct": 2.34, "daily_change": -0.02, "monthly_change": 0.15, "yearly_change": -0.41, "previous_close": 2.36, "update_time": "2024-03-08T17:30:00Z"
| # | country | maturity | yield_pct | daily_change | monthly_change | yearly_change |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Trading Economics scraper handles every layer of the platform: global indicators, economic calendars, bond markets, and commodity prices : with JavaScript rendering and anti-bot circumvention built in.
Extract GDP, inflation, unemployment, and interest rates across 196 countries. Normalised and mapped to standard country codes.
Capture event schedules, consensus estimates, actual releases, and historical revisions with microsecond timestamps.
Track major global indices, sector performance, and historical closing prices across all supported exchanges.
Monitor energy, metals, agricultural, and industrial commodity prices with spot and futures contract data.
Extract yield curves, spread data, and historical bond performance across multiple maturities globally.
Capture fiat currency pairs, historical exchange rates, and central bank fixing rates.
Extract analyst expectations, quarterly projections, and long-term trend models for key economic indicators.
Intercept and extract the raw JSON payloads powering the interactive charts to build deep historical time-series.
Run continuous pipelines at minute, hourly, or daily cadences to capture data immediately upon release.
Brief in. Clean data out.
Provide country lists, indicator types, or calendar filters. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for tradingeconomics.com.
Schema validation, null-rate checks, and data normalisation against known baseline metrics.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Financial data platforms invest heavily in rate limiting and data obfuscation. Here is how we stay resilient.
Trading Economics monitors request velocity aggressively. Our crawlers use distributed proxy networks and randomise request intervals to mimic human navigation patterns, preventing IP blocks and rate limits.
Instead of attempting to scrape SVG or Canvas elements, our Playwright instances intercept the underlying JSON network requests that populate the charts, yielding clean, high-fidelity historical data arrays.
Economic calendar events cause massive traffic spikes at release time. We distribute requests across multiple nodes to ensure we capture the 'actual' figures within seconds of publication without hitting rate limits.
Macro data is often presented in varying formats (Millions, Billions, %, absolute). We normalise all extracted values to standard numeric types and map countries to ISO 3166-1 alpha-2 codes before delivery.
Macro indicators rarely jump by 500% overnight. Our pipeline includes statistical checks to flag extraction errors or platform display bugs before they corrupt your data warehouse.
Hedge funds feed real-time macroeconomic releases into algorithmic trading models to exploit short-term market inefficiencies.
Economists and analysts track historical data series across emerging markets to identify long-term growth trends and structural shifts.
Corporate treasury teams monitor inflation rates, bond yields, and currency fluctuations to adjust hedging strategies.
Procurement teams correlate commodity price trends and industrial production indicators to optimise inventory purchasing.
Multinational corporations use central bank interest rate decisions and consensus forecasts to manage foreign exchange exposure.
Universities aggregate decades of macroeconomic indicators to train econometric models and publish peer-reviewed papers.
"Trading Economics aggregates the world's macroeconomic indicators into one interface, but historical depth requires automated pipeline extraction to become truly queryable."
Most teams underestimate the complexity of scraping financial data at scale. Reliable extraction requires handling high-frequency updates, intercepting complex chart data payloads, and managing strict rate limits. DataFlirt absorbs that complexity so your quants can focus on Alpha generation, not infrastructure.
Everything supported by our tradingeconomics.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 network interception.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request to distribute load and prevent rate limiting.
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 tradingeconomics.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available macroeconomic data is generally permissible. DataFlirt extracts only public, non-authenticated indicators and calendar events. We do not bypass login walls to access premium historical data or proprietary forecasts. Clients must review the source platform Terms of Service.
We distribute requests across large pools of residential proxies and implement strict concurrency limits per IP. We also use caching and conditional requests to minimise unnecessary load on the target servers.
Yes. Instead of parsing the visual elements, our Playwright instances intercept the background XHR requests that fetch the chart data, allowing us to extract the raw historical values directly.
For critical releases, we configure high-frequency polling pipelines that can extract and push data via Webhook within seconds of the value appearing on the public page.
Trading Economics limits the historical depth available to public, unauthenticated users. We can extract whatever is visible on the public page. Deeper historical data requires a premium account and cannot be scraped via public pipelines.
Yes. We clean strings, normalise dates to ISO 8601 format, map countries to ISO codes, and convert abbreviated numbers (e.g., 1.2B) into standard numeric floats.
Our minimum engagement covers daily extraction of a defined set of countries and indicators. Contact us with your specific coverage requirements for a precise quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need daily indicator updates across 50 countries or sub-minute calendar event polling, we build and operate the pipeline. Tell us what you need.
