We extract economic time series, indicator metadata, release schedules, and regional data from FRED. 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 Time Series Metadata objects from fred.stlouisfed.org. All fields typed and schema-versioned.
"series_id": "GDP", "title": "Gross Domestic Product", "observation_start": "1947-01-01", "observation_end": "2023-10-01", "frequency": "Quarterly", "units": "Billions of Dollars", "seasonal_adjustment": "Seasonally Adjusted Annual Rate", "popularity": 98
| # | series_id | title | observation_start | observation_end | frequency | units |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Observations objects from fred.stlouisfed.org. All fields typed and schema-versioned.
"series_id": "GDP", "date": "2023-10-01", "value": 27938.831, "realtime_start": "2024-02-28", "realtime_end": "9999-12-31", "revision_flag": true
| # | series_id | date | value | realtime_start | realtime_end | status_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories objects from fred.stlouisfed.org. All fields typed and schema-versioned.
"category_id": 106, "name": "Gross Domestic Product", "parent_id": 18, "children_count": 0, "series_count": 142, "scraped_at": "2023-11-12T08:14:00Z"
| # | category_id | name | parent_id | notes | children_count | series_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Releases objects from fred.stlouisfed.org. All fields typed and schema-versioned.
"release_id": 53, "name": "Gross Domestic Product", "press_release": true, "link": "http://www.bea.gov/newsreleases/national/gdp/gdpnewsrelease.htm", "source_institution": "Bureau of Economic Analysis", "upcoming_dates": "['2024-03-28', '2024-04-25']"
| # | release_id | name | press_release | link | realtime_start | realtime_end |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sources objects from fred.stlouisfed.org. All fields typed and schema-versioned.
"source_id": 1, "name": "Board of Governors of the Federal Reserve System (US)", "link": "http://www.federalreserve.gov/", "series_count": 8432, "realtime_start": "2000-01-01", "scraped_at": "2023-11-12T08:15:33Z"
| # | source_id | name | link | realtime_start | realtime_end | series_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our FRED scraper handles every layer of the platform: time series observations, indicator metadata, release schedules, and regional economic data. Built for scale and precision.
Extract millions of daily, weekly, monthly, and quarterly observations across 800,000+ economic indicators.
Capture point-in-time historical revisions. Track how economic data changes across different release dates.
Extract units, seasonal adjustments, frequencies, and descriptive notes for every single series.
Monitor upcoming data releases and press release links to synchronise your trading or analytical models.
Extract state, county, and MSA level data for granular geographic economic analysis.
Poll specific high-priority series at minute-level intervals during critical economic data releases.
Reconstruct the entire FRED category tree to categorise and filter time series in your own warehouse.
Only process new observations or metadata revisions. Minimise warehouse compute and storage bloat.
Bypass rate limits by distributing extraction across our infrastructure, delivering complete historical datasets in hours.
Brief in. Clean data out.
Provide series IDs, category IDs, or search terms. We design the extraction schema together.
We configure Scrapy crawlers, manage rate limits, and map the time series JSON structures.
Schema validation, null-rate checks, and continuity verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting massive time series datasets requires dedicated infrastructure. Here is how we ensure data continuity.
FRED implements strict rate limits on both their web interface and public API. We distribute requests across a large pool of US-based IP addresses, ensuring high-throughput extraction without triggering 429 Too Many Requests errors.
Syncing the entire FRED database involves millions of observations. Our pipelines process data in parallel chunks, writing directly to columnar formats like Parquet to optimise downstream query performance.
We maintain a hash index of last-seen values per series. Subsequent runs only push new observations or metadata revisions, reducing compute cost and storage bloat in your warehouse.
Time series data is useless if there are missing dates. We run automated continuity checks to detect gaps in observation sequences and trigger automated backfill routines immediately.
Some metadata is only exposed within the dynamic chart configurations on the web interface. We parse the underlying JSON state to extract precise unit definitions and seasonal adjustment methodologies.
Hedge funds ingest interest rates, inflation metrics, and employment data to feed macroeconomic trading models.
Economic research firms track historical trends and vintage data revisions to publish market outlook reports.
PropTech companies monitor regional housing starts, mortgage rates, and local employment statistics to forecast property values.
Retailers and supply chain analysts correlate CPI and PPI indicators with internal cost metrics to adjust pricing strategies.
Logistics companies use industrial production and inventory metrics to predict shipping volumes and capacity requirements.
Think tanks and government contractors analyse demographic and economic series to evaluate the impact of fiscal policies.
"FRED is the definitive source for US macroeconomic data, but syncing 800,000 time series into a private warehouse requires dedicated infrastructure."
Most teams underestimate the compute required to track hundreds of thousands of economic indicators daily. Reliable FRED extraction requires distributed rate-limiting, incremental diffing for new observations, and anomaly monitoring. DataFlirt absorbs that complexity so your quants can focus on modelling, not infrastructure.
Everything supported by our fred.stlouisfed.org 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 high-throughput extraction of static time series data, while Playwright manages complex interactions and dynamic chart state parsing when required.
We maintain large IP pools to distribute request load, ensuring we can sync hundreds of thousands of time series without hitting rate limits or causing service degradation.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About fred.stlouisfed.org scraping, legality, and pipeline operations.
Ask us directly →The FRED API has strict rate limits and request quotas that make syncing the entire 800,000+ series database impractically slow for large-scale quantitative modelling. Scraping the web endpoints allows for higher throughput and access to metadata sometimes excluded from standard API responses.
FRED is a public service provided by the Federal Reserve Bank of St. Louis, and the data is public domain. We extract only publicly available, non-authenticated economic data. Clients should review the St. Louis Fed's terms of service regarding data usage and attribution.
Our pipelines include automated continuity checks. If a series is missing expected dates based on its stated frequency, the system alerts our operations team and triggers a verification routine to ensure data integrity.
Yes. We can extract point-in-time historical revisions, allowing you to see exactly what data was available on a specific past date before subsequent revisions were published.
We can schedule pipelines to run daily, hourly, or even trigger specific extractions immediately following known economic data release times.
Our smallest packages start at a defined list of indicators with weekly delivery. For full database syncs, we price based on compute volume and delivery frequency.
Yes. We provide a sample run of up to 100 time series 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 specific set of interest rate indicators or a full sync of 800,000 time series, we build and operate the pipeline. Tell us what you need.
