We extract FOMC statements, Beige Book reports, board member speeches, press releases, and regulatory actions. 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 FOMC Materials objects from federalreserve.gov. All fields typed and schema-versioned.
"document_id": "fomc_20240131", "type": "Statement", "date": "2024-01-31", "title": "Federal Reserve issues FOMC statement", "content_text": "Recent indicators suggest that economic activity has been expanding at a solid pace...", "url": "https://www.federalreserve.gov/newsevents/pressreleases/monetary20240131a.htm"
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Complete list of extractable fields for Speeches & Testimonies objects from federalreserve.gov. All fields typed and schema-versioned.
"speaker": "Jerome H. Powell", "role": "Chair", "date": "2024-03-06", "location": "Washington, D.C.", "title": "Semiannual Monetary Policy Report to the Congress", "content_text": "Chairman McHenry, Ranking Member Waters, and other members of the Committee...", "page_url": "https://www.federalreserve.gov/newsevents/testimony/powell20240306a.htm"
| # | speaker | role | date | location | event | title |
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Complete list of extractable fields for Beige Book Reports objects from federalreserve.gov. All fields typed and schema-versioned.
"release_date": "2024-03-06", "district": "National", "summary": "Economic activity increased slightly, on balance, since early January...", "employment_summary": "Employment rose at a slight to modest pace...", "prices_summary": "Price pressures persisted, but several Districts noted some moderation...", "report_url": "https://www.federalreserve.gov/monetarypolicy/beigebook202403.htm"
| # | release_date | district | district_name | summary | employment_summary | prices_summary |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Enforcement Actions objects from federalreserve.gov. All fields typed and schema-versioned.
"action_date": "2024-02-15", "institution_name": "Example Bank", "action_type": "Cease and Desist", "docket_number": "24-001-B-SM", "penalty_amount": 1500000, "description": "Action against Example Bank for unsafe practices regarding risk management."
| # | action_date | institution_name | institution_type | action_type | docket_number | penalty_amount |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for FEDS Working Papers objects from federalreserve.gov. All fields typed and schema-versioned.
"paper_id": "2024-012", "title": "Inflation Dynamics in a Post-Pandemic Economy", "authors": "['Jane Doe', 'John Smith']", "publication_date": "2024-02-01", "abstract": "We analyze the recent inflation dynamics using a structural VAR model...", "series": "Finance and Economics Discussion Series"
| # | paper_id | title | authors | publication_date | abstract | keywords |
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We convert the Federal Reserve's unstructured archives — PDFs, HTML tables, and text transcripts — into queryable datasets for macroeconomic modelling and algorithmic trading.
Extract full text, voting records, dissents, and participant lists from FOMC meetings, statements, and minutes.
Capture speaker, role, location, event context, and full transcript text for every Board of Governors speech.
Parse Beige Book releases into structured fields for National summary and individual Federal Reserve District reports.
Extract docket numbers, institution names, penalty amounts, and action types from regulatory enforcement PDFs.
Convert legacy HTML tables and fixed-width text files (like H.15 interest rates) into clean tabular records.
Process FEDS working papers and regulatory rulemakings using OCR and text-extraction libraries to yield full-text fields.
Monitor the news and events feed continuously, capturing press release titles, dates, and body text.
Extract public schedules for the Chair and Governors, including meeting topics, attendees, and locations.
Traverse decades of paginated archives to build complete historical datasets for backtesting models.
Configure webhooks to receive instant JSON payloads the moment a new FOMC statement or press release is published.
Brief in. Clean data out.
Specify the document types, date ranges, and fields required. We map the extraction schema for speeches, PDFs, or tabular data.
We configure crawlers to traverse archives, parse legacy HTML, and extract text from PDFs using OCR and layout analysis.
We validate text completeness, check for missing footnotes, and ensure tabular data aligns correctly before production.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or delivered via webhook for real-time releases.
Federal websites present unique extraction challenges: inconsistent legacy HTML, heavy reliance on PDFs, and strict rate limits. We handle the normalisation.
Many enforcement actions and working papers are published exclusively as PDFs. We use pdfplumber and Tesseract OCR to extract text, preserve paragraph structures, and capture footnotes, turning dead documents into NLP-ready strings.
The Federal Reserve website hosts archives dating back to the 1990s. We maintain multiple fallback selectors to handle structural shifts in HTML over time, ensuring a 1998 speech parses into the same schema as a 2024 speech.
Government servers employ strict rate limiting and IP blocking for aggressive scrapers. We manage concurrency limits, use residential proxies when necessary, and queue requests to ensure complete extraction without triggering firewalls.
Speeches and minutes often contain irregular formatting, inline citations, and varying speaker attributions. Our pipelines apply regex and string normalisation to standardise names, dates, and locations.
For FOMC statements and critical press releases, we poll endpoints at high frequency. Changes are detected instantly and pushed via webhook, critical for algorithmic trading strategies.
Economists aggregate historical Beige Book data and statistical releases to model inflation trends and economic growth.
Quantitative hedge funds run NLP sentiment analysis on FOMC statements and speeches to trigger automated fixed-income trades.
Risk teams monitor enforcement actions and regulatory rulemakings to update internal compliance policies and assess counterparty risk.
Universities compile decades of speeches and FEDS working papers into massive text corpora for linguistic and economic analysis.
Bond traders track H.15 interest rate releases and policy signals to adjust yield curve expectations.
Banks incorporate district-level Beige Book summaries and enforcement trends into regional credit risk assessments.
"The Federal Reserve dictates global liquidity, but their data is trapped in raw text, PDFs, and legacy HTML tables. We turn policy into queryable arrays."
Parsing federalreserve.gov requires more than basic HTTP requests. Extracting clean text from decades of PDF enforcement actions, normalising unstructured speeches, and structuring tabular statistical releases demands specialised parsing logic. DataFlirt handles the extraction, OCR, and normalisation so your quants can focus on signal generation.
Everything supported by our federalreserve.gov scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We utilise pdfplumber and Tesseract OCR within Docker containers to process thousands of PDFs, extracting text, tables, and metadata reliably.
Custom Python parsers apply regex and NLP heuristics to clean transcripts, standardise dates, and map speaker names consistently.
Airflow schedules daily historical sweeps, while AWS Lambda functions handle high-frequency polling for real-time press release detection.
Data delivered to where your team already works — no new tooling required.
About federalreserve.gov scraping, legality, and pipeline operations.
Ask us directly →Yes. Data published on federalreserve.gov is public domain government information. We strictly target publicly accessible pages, press releases, and documents, adhering to standard rate limits to avoid disrupting government servers.
We download the PDFs and process them using Python-based extraction libraries (like pdfplumber). For scanned documents, we fall back to Tesseract OCR to ensure we capture the text. The output is delivered as a structured string field alongside the document metadata.
Yes. We can traverse the archives back to the earliest available digital records (often the mid-1990s), extracting statements, minutes, and transcripts to build a complete historical time-series.
For critical URLs like the FOMC statements page, we configure high-frequency polling pipelines. When a new statement is published, it is parsed and pushed via webhook within seconds.
Yes. We parse the national summary and use structural markers in the text to segment the report into the 12 individual Federal Reserve districts, delivering them as separate fields or nested arrays.
We support JSON, CSV, and Parquet formats, delivered directly to AWS S3, Google Cloud Storage, BigQuery, Snowflake, or via API and webhooks.
20-minute scoping call. Pilot dataset within the week. Production within two. Stop copying and pasting from PDFs. We build managed pipelines that deliver structured Federal Reserve data directly to your warehouse.
