View the total value of the assets of all Federal Reserve Banks as reported in the weekly balance sheet.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

🏛️ Federal Reserve Document Scraper

1. Dataset description

This repository contains code for downloading and organizing Federal Reserve documents from the official Federal Reserve Board website.

These files were used as part of my NLP project. While collecting data, my data collection code is inspired by centralbank_analysis by yukit-k. However, that implementation had some limitations:

❌ Incomplete handling of newer HTML structures on the Fed website

❌ No support for Greenbook/Tealbook files

❌ File naming and folder structure not ideal for downstream processing

❌ No handling of failed downloads or noisy formatting

So I made som key Improvements:

✅ Supports both Greenbook and Minutes. You can choose which type to download

✅ Automatic directory organization. Files are saved using a consistent format as:

FOMC_[document type]_YYYY-MM-DD

✅ Duplicate check & resume support: Prevents redundant downloads and handles broken links gracefully

✅ Modular and extensible codebase Easy to extend for other Fed documents (e.g., SEP, transcripts)

2. Data detail

This repository contains modules for downloading and processing various official publications of the Federal Open Market Committee (FOMC). These documents, produced and released by the Federal Reserve, provide detailed insight into U.S. monetary policy formation, communication, and economic analysis over time.

Below is a reference guide to the major FOMC document types represented in this repository.

📅 FomcAgenda.py – Meeting Agendas

Agendas are created by the FOMC Secretariat in coordination with the Chair and outline the topics of discussion for each meeting, including standard items (e.g., open market operations, economic outlook) and special topics. Participants receive the agenda about one week in advance.

📄 FomcStatement.py – Policy Statements

FOMC statements are brief summaries of monetary policy decisions released immediately after each meeting. These statements have become a key communication tool since 1994 and are now issued after every scheduled meeting, even if policy remains unchanged.

📝 FomcMinutes.py – Meeting Minutes

Minutes provide a concise, narrative summary of policy discussions and rationales. Since 2004, they are released three weeks after each meeting. The minutes include details on voting outcomes and dissenting views, and are eventually included in the Fed’s Annual Report.

🧾 FomcPresConfScript.py – Press Conference Transcripts

Beginning in 2011, the Fed Chair has held press conferences following certain FOMC meetings. These transcripts document the Chair’s remarks and responses to journalists, offering additional context and forward guidance. Released shortly after the meeting.

🗣️ FomcMeetingScript.py – Meeting Transcripts

Verbatim transcripts of FOMC meetings, produced from audio recordings and lightly edited for readability. They are released with a 5-year delay. For meetings prior to 1994, transcripts were reconstructed from raw records and may contain transcription uncertainties.

📚 FomcGreenbook.py – Greenbook (1964–2010)

The Greenbook, officially titled Current Economic and Financial Conditions, was prepared by Board staff and delivered to FOMC members six days before each meeting. It provided forecasts, data analyses, and economic outlooks.

Part 1: Summary and forecast

Part 2: Detailed breakdowns

Supplement: Late-breaking updates

📘 FomcBlueBook.py – Bluebook (1965–2010)

The Bluebook, titled Monetary Policy Alternatives, outlined potential policy options and risks. It was distributed shortly after the Greenbook and informed FOMC decisions. The document evolved from earlier versions like Money Market and Reserve Relationships.

🧠 FomcTealbook.py – Tealbook (2010–Present)

The Tealbook replaced both the Greenbook and Bluebook in June 2010. It is split into two parts:

Tealbook A: Current Situation and Outlook — Forecasts and financial developments

Tealbook B: Strategies and Alternatives — Policy options and simulations

Both are released with a 5-year lag.

📙 FomcBeigeBook.py – Beige Book

The Beige Book, published eight times a year, summarizes anecdotal economic conditions across the 12 Federal Reserve Districts. Based on business surveys, interviews, and internal reports, it is released ~two weeks before each meeting.

🧾 FomcTestimony.py – Testimony before Congress

This includes the Chair’s Semiannual Monetary Policy Report to Congress and other testimonies. These communications explain the Fed’s outlook and policies directly to lawmakers and the public.

📚 References

Federal Reserve – FOMC Archive

Wikipedia – Federal Open Market Committee

Graph and download economic data for Liabilities and Capital: Other Factors Draining Reserve Balances: Reserve Balances with Federal Reserve Banks: Wednesday Level (WRBWFRBL) from 2002-12-18 to 2026-02-04 about balance, reserves, banks, depository institutions, and USA.

This dataset represents a snapshot of the FRED catalog, captured on 2025-03-24.

What is FRED? As per the FRED website,

Short for Federal Reserve Economic Data, FRED is an online database consisting of hundreds of thousands of economic data time series from scores of national, international, public, and private sources. FRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: It combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. In essence, FRED helps users tell their data stories. The purpose of this article is to guide the potential (or current) FRED user through the various aspects and tools of the database.

The FRED database is an abolute gold mine of economic data time series. Thousands of such series are published on the FRED website, organized by category and avialable for viewing and downloading. In fact, a number of these economic datasets have been uploaded to kaggle. With in the current notebook, however, we are not interested in the individual time series; rather, we are focused on catalog itself.

The FRED API has been used for gaining access to the catalog. The catalog consists of two files

• categories.csv - the heirarchy of categories used for organizing the time series
• series.csv - the list of avialable time series themselves

A given category is identified by a category_id. And, in a similar fashion, a given series is identified by a series_id. In a given category, one may find both a group of series and a set of sub-categories. As such every series record contains a category_id to identify the immediate category under which it is found category record contains a parent_id to indicate where in the category heirarchy it resides

Content

For further information please see the Board of Governors web site at http://www.federalreserve.gov/releases/housedebt/default.htm http://www.federalreserve.gov/releases/housedebt/about.htm

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1980-01-01

• Observation End : 2019-07-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by cetteup on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The Survey of Household Economics and Decisionmaking (SHED) is conducted annually by the Federal Reserve Board. It measures the economic well-being of U.S. households and identifies potential risks to their finances. The survey includes modules on a range of topics of current relevance to financial well-being including credit access and behaviors, savings, retirement, economic fragility, and education and student loans. Data available on the Federal Reserve System website goes back to 2013.

Graph and download economic data for Federal Funds Target Range - Upper Limit (DFEDTARU) from 2008-12-16 to 2026-02-10 about federal, interest rate, interest, rate, and USA.

The Board would use the FR 3076 to seek input from users or potential users of the Board's public website, social media, outreach, and communication responsibilities. The survey would be conducted with a diverse audience of consumers, banks, media, government, educators, and others to gather information about their visit to the Board's public website. Responses to the survey would be used to help improve the usability and offerings on the Board's public website and other online public communications. The frequency of the survey and content of the questions would vary as needs arise for feedback on different resources and from different audiences. The Board anticipates the FR 3076 may be conducted up to 12 times per year, although the survey may not be conducted that frequently. In addition, the Board anticipates conducting up to four focus group sessions per year.

Content

For further information please see the Board of Governors web site at http://www.federalreserve.gov/releases/housedebt/default.htm http://www.federalreserve.gov/releases/housedebt/about.htm

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1980-01-01

• Observation End : 2019-07-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Federal Funds Rates Based on 7 Simple Rules is a part of the Simple Monetary Policy Rules indicator of the Federal Reserve Bank of Cleveland.

This dataset contains the text from Federal Reserve FOMC (Federal Open Market Committee) meeting minutes and statements, collected by scraping the Federal Reserve's website. The data spans a specific period of time, providing insights into the central bank's monetary policy decisions and discussions.

Content

The dataset consists of the following columns:

• Date: The date of the FOMC meeting or statement release in the format YYYYMMDD.
• Type: Indicator for the type of document. 0 represents a statement, while 1 represents meeting minutes.
• Text: The text content of each paragraph in the meeting minutes or statements.

Acknowledgements

The data is collected from the official Federal Reserve website (https://www.federalreserve.gov) using a custom Python scraper built with BeautifulSoup.

Inspiration

This dataset can be used for various purposes, such as:

1. Analyzing the sentiment and tone of FOMC meeting minutes and statements over time.
2. Identifying key phrases and words that indicate changes in monetary policy.
3. Developing natural language processing models to predict future policy decisions based on historical data.
4. Investigating the relationship between FOMC meeting minutes/statements and financial market reactions.

We present federal funds rates coming from a range of simple monetary policy rules based on multiple economic forecasts. Use our tool to create your own rule. Released quarterly.

Learn about the work of the president and CEO of the Federal Reserve Bank of Cleveland.

currencies central bank Federal reserve system. name, central bank, Country, continent, ISO code, symbol, Website, Issuer

Inflation Nowcasting Quarterly is a part of the Inflation Nowcasting indicator of the Federal Reserve Bank of Cleveland.

Ten-Year TIPS Yields versus Real Yields is a part of the Inflation Expectations indicator of the Federal Reserve Bank of Cleveland.

Content

Weighted average of the rates received on the interest-bearing assets included in M2. The interest-bearing assets include size of the other checkable deposits, thrift saving deposits, money market mutual fund holdings, and small time deposits that are weighted using their corresponding rates.

The construction of this series was discontinued as of July 12, 2019. The underlying data can be accessed through the following sources: size of the assets can be obtained from the H.6 release (https://www.federalreserve.gov/releases/h6/current/default.htm) published by the Board of Governors, rate on the money market mutual funds from iMoneyNet (https://financialintelligence.informa.com/products-and-services/data-analysis-and-tools/imoneynet), and the remaining rates from the Weekly National Rates and Rate Caps (https://www.fdic.gov/regulations/resources/rates/index.html) from the FDIC website. Listing of the sources is provided for informational purposes only: the Federal Reserve Bank of St. Louis is not associated with any listed private entities and cannot guarantee that the listed data sources will provide the data in the future.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1959-02-01

• Observation End : 2019-06-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Leon Skibitzki on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The Federal Reserve Bank of Cleveland (commonly known as the Cleveland Fed) is part of the Federal Reserve System, the central bank of the United States.

Text data from the documents surrounding Federal Open Market Committee meetings. Data is scraped from the Federal Reserve website using a web scraper I made using the Scrapy framework which can be found on GitHub at https://github.com/rw19842/Fed-Scraper.