REST API access to macroeconomic indicators for over 100 countries. 30+ indicators including GDP, unemployment rates, inflation, consumption, international trade. Data available from December 1960. 100,000 requests/day.

Permutable AI’s Global Macro Sentiment API provides aggregated sentiment data for global macroeconomic topics, including inflation, GDP, monetary policy, fiscal policy, geopolitics, and natural disasters. With support for Python, R, and Java client libraries, plus webhook integration, the API allows developers and analysts to retrieve structured insights from news sources within custom date ranges. Parameters include start and end dates (30-day lookback), filtering by sources, and strict real-time extraction options. Data outputs include sentiment scores, topic classifications, and aggregated publication timestamps—ideal for market insights, trading strategies, and research applications. Full API reference documentation is available at copilot-api.permutable.ai/redoc .

Context

The dataset was created to predict market recession as inspired by assignment notebook in an online course, Python and Machine Learning for Asset Management by Edhec Business School, on Coursera. However, I aimed at doing this exercise for Indian economy but due to lack of monthly data for most indicators, I used FRED database similarly used in the course.

Content

The time period chosen is 1996-2020 according to most data available.

Acknowledgements

1. Python and Machine Learning for Asset Management by EDHEC Business School, www.coursera.org for recession period data formed according to NBER. 2.FRED, fred.stloiusfed.org for macro-economic indicator data
2. Business cycle dating, www.nber.org
3. Yahoo Finance for stock market data
4. Macrotrends.net for PE ratio
5. quandl.com for snp dividend yield

Inspiration

This dataset is inspired by the assignment notebook in the online course mentioned to predict market recession for portfolio management.

The Economic Indicator Service (EIS) aims to deliver economic content to financial institutions on both buy and sell-side and service providers. This new service currently covers 34,351 recurring macro-economic indicators from 135 countries ( as of December 16, 2019 ) such as GDP data, unemployment releases, PMI numbers etc.

Economic Indicator Service gathers the major economic events from a variety of regions and countries around the globe and provides an Economic Events Data feed and Economic Calendar service to our clients. This service includes all previous historic data on economic indicators that are currently available on the database.

Depending on availability, information regarding economic indicators, including the details of the issuing agency as well as historical data series can be made accessible for the client. Key information about EIS: • Cloud-based service for Live Calendar – delivered via HTML/JavaScript application formats, which can then be embedded onto any website using iFrames • Alternatives methods available – such as API and JSON feed for the economic calendar that can be integrated into the company’s system • Live data – updated 24/5, immediately after the data has been released • Historical data – includes a feed of all previous economic indicators available We are currently adding additional indicators/countries from Africa as well as expanding our coverage of Indicators in G20. The calendar includes the following. • Recurring & Non-recurring indicators covering 136 countries across 21 regions. • Indicators showing high, medium, and low impact data. • Indicators showing actual, previous, and forecast data. • Indicators can be filtered across 16 subtypes. • News generation for selected high-impact data. • Indicator description and historical data up to the latest eight historical points with a chart.

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

Permutable AI’s G7 macroeconomic sentiment datasets deliver structured quantitative analytics by transforming unstructured global news into actionable insights. Powered by proprietary large language models, our platform captures sentiment around monetary policy from the Federal Reserve, ECB, Bank of England, and Bank of Japan, refreshing every five minutes. Additional feeds quantify reactions to key economic indicators such as employment, GDP, and inflation, while geopolitical intelligence tracks trade tensions, elections, and G7 summit communications. Real-time natural disaster tracking scores supply chain risks, with ten years of historical datasets available for backtesting systematic trading strategies. Accessible through the Co-Pilot API, the platform offers millisecond-latency sentiment scores, event classifications, and risk metrics for institutional-grade market analysis.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

This Dataset comes from the R Package wbstats. The World Bank[https://www.worldbank.org/] is a tremendous source of global socio-economic data; spanning several decades and dozens of topics, it has the potential to shed light on numerous global issues. To help provide access to this rich source of information, The World Bank themselves, provide a well structured RESTful API. While this API is very useful for integration into web services and other high-level applications, it becomes quickly overwhelming for researchers who have neither the time nor the expertise to develop software to interface with the API. This leaves the researcher to rely on manual bulk downloads of spreadsheets of the data they are interested in. This too is can quickly become overwhelming, as the work is manual, time consuming, and not easily reproducible. The goal of the wbstats R-package is to provide a bridge between these alternatives and allow researchers to focus on their research questions and not the question of accessing the data. The wbstats R-package allows researchers to quickly search and download the data of their particular interest in a programmatic and reproducible fashion; this facilitates a seamless integration into their workflow and allows analysis to be quickly rerun on different areas of interest and with realtime access to the latest available data.

World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. Copied from https://databank.worldbank.org/source/world-development-indicators.

Highlighted features of the wbstats R-package: * Uses version 2 of the World Bank API that provides access to more indicators and metadata than the previous API version * Access to all annual, quarterly, and monthly data available in the API * Support for searching and downloading data in multiple languages * Returns data in either wide (default) or long format * Support for Most Recent Value queries * Support for grep style searching for data descriptions and names * Ability to download data not only by country, but by aggregates as well, such as High Income or South Asia

More information can be found at https://www.rdocumentation.org/packages/wbstats/versions/1.0.4

Note for Version 1. Version 1 published January 2023. Its primary focus is on the featured indicator of climate change. Other versions planned will cover other featured indicators such as economy, education, energy, environment, debt, gender, health, infrastructure, poverty, science and technology.

United Kingdom Total Economy (TE): Uses: Allocations of Primary Income Account (API data was reported at 673,594.000 GBP mn in Mar 2018. This records a decrease from the previous number of 673,744.000 GBP mn for Dec 2017. United Kingdom Total Economy (TE): Uses: Allocations of Primary Income Account (API data is updated quarterly, averaging 420,984.000 GBP mn from Mar 1987 (Median) to Mar 2018, with 125 observations. The data reached an all-time high of 673,744.000 GBP mn in Dec 2017 and a record low of 168,250.000 GBP mn in Mar 1987. United Kingdom Total Economy (TE): Uses: Allocations of Primary Income Account (API data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.AB023: ESA10: Resources and Uses: Total Economy: Primary Income.

The provided Python code is developed to extract data from the Federal Reserve Economic Data (FRED) regarding Bachelor's or Higher degree education in the United States, specifically at the state and county levels. The code generates data based on the current date and is available up until the year 2021.

This code is useful for research purposes, particularly for conducting comparative analyses involving educational and economic indicators. There are two distinct CSV files associated with this code. One file contains information on the percentage of Bachelor's or Higher degree holders among residents of all USA states, while the other file provides data on states, counties, and municipalities throughout the entire USA.

The extraction process involves applying different criteria, including content filtering (such as title, frequency, seasonal adjustment, and unit) and collaborative filtering based on item similarity. For the first CSV file, the algorithm extracts data for each state in the USA and assigns corresponding state names to the respective FRED codes using a loop. Similarly, for the second CSV file, data is extracted based on a given query, encompassing USA states, counties, and municipalities.

The Latin Macro Watch (LMW) is a comprehensive macroeconomic and financial dataset developed by the Inter-American Development Bank (IDB). It provides economic indicators and historical data on growth, employment, fiscal accounts, external balances, financial markets, and forecasts for Latin America and the Caribbean.

The dataset includes monthly, quarterly, and annual data beginning in 1990, covering the 26 borrowing member countries of the IDB. Users can explore variables such as GDP, consumption, trade, inflation, and debt to analyze macroeconomic trends and regional development patterns.

The Latin Macro Watch Dataset is currently undergoing a revision and enhancement process to improve data quality, coverage, and accessibility. Updated data will continue to be made available through the IDB Open Data Portal, enabling policymakers, researchers, and economists to make data-driven decisions.

Table of INEBase Economic indicators by Autonomous Communities and Cities. Autonomous Cities and Communities. Surveys on Water Supply and Sewerage

United Kingdom PF: Uses: API: PI: Rent data was reported at 0.000 GBP mn in Jun 2018. This stayed constant from the previous number of 0.000 GBP mn for Mar 2018. United Kingdom PF: Uses: API: PI: Rent data is updated quarterly, averaging 0.000 GBP mn from Mar 1987 (Median) to Jun 2018, with 126 observations. United Kingdom PF: Uses: API: PI: Rent data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.AB031: ESA10: Resources and Uses: Public Non Financial Corporations: Primary Income.

This chart shows how many individuals can carry a conversation in English only, in French only, in both English and French, or in neither English nor French.

The ERS content and data APIs (including our pre-made widgets for embedding charts) are currently out of service while we redesign our site. Check back here for updates--we'll keep you informed as to the progress. Contact us at webadmin@ers.usda.gov with questions.

The Data APIs provide programmatic access to select data sets.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: https://www.ers.usda.gov/developer/ For complete information, please visit https://data.gov.

Table of INEBase Economic aspects. Annual. National. Urban Indicators

Macro Foods Sa Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Llc Macro Team Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Comprehensive global lodging intelligence covering more than seven million hotel and short-term rental properties worldwide.

The Complete Lodging Dataset provides a full-market view of the global accommodation landscape by integrating data from hotel reservation systems, Online Travel Agencies (OTAs), and directly connected property management systems. It includes verified property identifiers, occupancy rates, ADR, RevPAR, pricing trends, and physical attributes across both traditional hotel inventory and short-term rental supply. Sourced from real booking and reservation data and refined through proprietary normalization processes, this dataset ensures consistency and accuracy across all lodging types. Updated on a frequent cadence, it enables robust benchmarking, forecasting, and investment analysis across countries, cities, and submarkets.

Key Highlights: Extensive Global Coverage: More than 7 million verified hotel and short-term rental properties across 200+ countries.

Unified Market View: Combines professional rental data, OTA listings, and hotel system performance for complete supply visibility.

Comprehensive Metrics: Includes occupancy, ADR, RevPAR, booking patterns, and property-level attributes.

Standardized Data Structure: Harmonized schema for cross-market and cross-segment analysis.

Flexible Delivery: Available via secure API or downloadable datasets with customizable geography and temporal depth.

Use It To: Analyze total lodging supply and demand across regions and property types.

Benchmark market performance between hotels and short-term rentals.

Support tourism, development, and investment strategies with unified lodging insights.

Integrate verified, cross-channel performance data into valuation, forecasting, and economic models.

Release Date: 2020-12-17.Release Schedule:.The data in this file come from the 2017 Economic Census. For more information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll...Data Items and Other Identifying Records:.Number of establishments.Sales, value of shipments, or revenue ($1,000).Sales on own account ($1,000).Purchases ($1,000).Total inventories, beginning of year ($1,000).Total inventories, end of year ($1,000).Cost of goods sold ($1,000).Gross margin ($1,000).Gross margin as percent of sales on own account (%)..Geography Coverage:.The data are shown for employer establishments at the U.S. level only. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown at the 2- through 7-digit and selected 8-digit 2017 NAICS code levels. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector42/EC1742MARGIN.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.