We extract historical stock prices, balance sheets, PE ratios, and global economic indicators from Macrotrends. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Financial Statements objects from macrotrends.net. All fields typed and schema-versioned.
"ticker": "AAPL", "statement_type": "income_statement", "fiscal_year": 2023, "revenue": 383285000000, "gross_profit": 169148000000, "net_income": 96995000000, "currency": "USD"
| # | ticker | company_name | statement_type | fiscal_year | fiscal_quarter | revenue |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Stock Price History objects from macrotrends.net. All fields typed and schema-versioned.
"ticker": "MSFT", "date": "2023-10-24", "close": 330.11, "volume": 31153600, "market_cap": 2450000000000, "pe_ratio": 34.2, "currency": "USD"
| # | ticker | date | open | high | low | close |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Economic Indicators objects from macrotrends.net. All fields typed and schema-versioned.
"country": "India", "indicator_type": "GDP Growth Rate", "year": 2022, "value": 7.2, "annual_change_pct": -1.9, "unit_of_measure": "Percentage", "source_agency": "World Bank"
| # | country | indicator_type | year | value | annual_change_pct | historical_average |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Commodity Prices objects from macrotrends.net. All fields typed and schema-versioned.
"commodity_name": "Gold", "date": "2023-11-15", "closing_price": 1964.3, "ytd_change_pct": 8.4, "currency": "USD", "unit": "Ounce", "scraped_at": "2023-11-16T01:14:00Z"
| # | commodity_name | date | closing_price | open_price | day_high | day_low |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Demographic Data objects from macrotrends.net. All fields typed and schema-versioned.
"country": "Japan", "year": 2023, "total_population": 123294513, "population_growth_rate": -0.53, "life_expectancy": 84.6, "urban_population_pct": 92.0, "fertility_rate": 1.3
| # | country | year | total_population | population_growth_rate | birth_rate | death_rate |
|---|---|---|---|---|---|---|
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Macrotrends relies heavily on JavaScript chart libraries to display data. Our pipeline parses the underlying data objects directly, bypassing visual rendering to extract clean time-series arrays.
Extract decades of daily OHLCV data, market capitalisation, and split-adjusted pricing for thousands of equities.
Parse income statements, balance sheets, and cash flow statements across annual and quarterly reporting periods.
Capture GDP, inflation, unemployment, and interest rate histories across 190+ countries.
Extract historical PE ratios, price-to-book, ROE, and dividend yields mapped to specific trading days.
Track historical spot prices for gold, crude oil, natural gas, and major currency pairs.
We intercept and parse the raw JSON configurations embedded in Highcharts JavaScript objects to extract exact data points.
Extract population growth, life expectancy, and demographic shifts compiled from UN and World Bank sources.
Crawl index components and sector listings to maintain an updated universe of active stock tickers.
Configure daily or weekly pipelines to append new financial reports and closing prices to your historical datasets.
Brief in. Clean data out.
Provide ticker lists, countries, or economic indicators. We design the extraction schema together.
We configure Scrapy crawlers, JavaScript parsers, and proxy rotation to extract historical series.
Schema validation, null-rate checks, and time-series continuity verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or data warehouse on an agreed cadence.
Extracting data from charting platforms requires specific techniques. Here is how we build resilient pipelines.
Macrotrends displays tabular data within interactive Highcharts. Instead of scraping the visual DOM, our pipeline intercepts the embedded JavaScript objects and extracts the raw JSON arrays, ensuring precise numerical accuracy without UI interference.
Historical data sites implement strict rate limits to prevent bulk downloading. We distribute requests across a large pool of residential IPs, pacing our crawls to mimic normal human research behaviour and avoid IP bans.
Stock splits and ticker changes complicate historical data. We track these corporate actions and normalise the time-series data, ensuring your quantitative models receive clean, adjusted historical prices.
When Macrotrends updates its site structure or charting libraries, standard CSS selectors fail. Our parsers target the underlying data structures and use fallback regex patterns to maintain pipeline continuity.
Missing data points ruin backtests. Our observability stack alerts on missing dates, sudden value drops, and schema drift, ensuring the historical series remains contiguous.
Quants feed decades of historical price and fundamental data into backtesting engines to validate trading algorithms.
Analysts track long-term trends in revenue, margins, and PE ratios to evaluate company performance across market cycles.
Economists correlate GDP growth, inflation, and demographic shifts to model future economic scenarios.
Universities use clean historical datasets to study market behaviour, corporate finance, and global economics.
Logistics teams track historical commodity prices and industrial production metrics to forecast material costs.
Data scientists train predictive models on decades of fundamental and macroeconomic indicators.
"Macrotrends holds decades of financial history, but extracting structured time-series data from rendered charts requires purpose-built parsing infrastructure."
Most teams struggle to extract data embedded within Highcharts JavaScript objects or paginated financial tables. DataFlirt bypasses visual rendering entirely, targeting the underlying data structures to deliver clean, normalised time-series records directly to your warehouse.
Everything supported by our macrotrends.net 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 retry logic. Playwright handles JavaScript execution for complex chart rendering when static parsing is insufficient.
We maintain pools of residential ISP proxies. Rotation happens per-request to bypass rate limits and IP bans common on financial data portals.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for daily market close updates. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About macrotrends.net scraping, legality, and pipeline operations.
Ask us directly →Macrotrends uses Highcharts to render its visual graphs. Instead of scraping the visual elements, our parsers locate the embedded JavaScript configuration objects in the page source and extract the raw JSON arrays containing the exact dates and values.
Yes. The data extracted from Macrotrends is already adjusted for stock splits and dividends where applicable. We track ticker symbol changes to ensure historical continuity for companies that have rebranded or merged.
We extract the full history available on Macrotrends. For many US equities, this spans multiple decades. Macroeconomic indicators often extend back to 1960, depending on the source agency.
No. Macrotrends provides historical, end-of-day data. Our pipelines typically run after market close to append the latest daily data points to your historical series.
We focus on public, unauthenticated data. Features locked behind Macrotrends Premium, such as advanced screener exports, require user authentication and are not supported by our standard public pipelines.
For large-scale backtesting, we recommend Parquet delivered to S3 or BigQuery. Parquet is highly optimised for columnar time-series queries and reduces storage costs significantly compared to CSV.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete historical dump of all US equities or a daily feed of macroeconomic indicators, we build and operate the pipeline. Tell us what you need.
