United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The main stock market index of United States, the US500, fell to 6236 points on July 4, 2025, losing 0.69% from the previous session. Over the past month, the index has climbed 4.99% and is up 12.01% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The description for this record is not currently available.

Egypt EG: Stocks Traded: Total Value data was reported at 14.429 USD bn in 2017. This records an increase from the previous number of 10.080 USD bn for 2016. Egypt EG: Stocks Traded: Total Value data is updated yearly, averaging 21.767 USD bn from Dec 2006 (Median) to 2017, with 12 observations. The data reached an all-time high of 95.827 USD bn in 2008 and a record low of 10.080 USD bn in 2016. Egypt EG: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

The RAM Legacy Stock Assessment Database is a compilation of stock assessment results for commercially exploited marine populations from around the world. The RAM Legacy Stock Assessment Database is grateful to the many stock assessment scientists whose work this database is based upon and the many collaborators who recorded the assessment model results for inclusion in the RAM Legacy Stock Assessment Database. Since 2011 the RAM Legacy Data base has been hosted and managed at the University of Washington with financial assistance from a consortium of Seattle-based seafood companies and organizations, and from the Walton Family Foundation. Initial development of the database from 2006-2010 was supported by the Census of Marine Life, Canadian Foundation for Innovation, NCEAS, NSERC, the Smith Conservation Research Fellowship, New Jersey Sea Grant, and the National Science Foundation.

United States US: Stocks Traded: Turnover Ratio of Domestic Shares data was reported at 116.078 % in 2017. This records an increase from the previous number of 94.719 % for 2016. United States US: Stocks Traded: Turnover Ratio of Domestic Shares data is updated yearly, averaging 114.857 % from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 407.630 % in 2008 and a record low of 51.444 % in 1991. United States US: Stocks Traded: Turnover Ratio of Domestic Shares data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. Turnover ratio is the value of domestic shares traded divided by their market capitalization. The value is annualized by multiplying the monthly average by 12.; ; World Federation of Exchanges database.; Weighted average; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Comprehensive database of European stock exchanges and trading venues

Dataset

This dataset was created by Sergio Barreto97

Contents

Comprehensive dataset of 23,820 Stock brokers in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-06 to 2025-07-03 about stock market, average, industry, and USA.

Comprehensive dataset of 2,361 Stock brokers in Texas, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Carbon stock data that covers: five carbon pools: above- and belowground biomass, woody debris and litter, understorey and soil. As a Meta Database, it also provides general information related to the site, such as geographic location, land cover or forest type, and climate and soil. The data is harvested from CIFOR's Forest carbon database system (ForestCDB), is part of the effort to support initiatives such as greenhouse gas inventories, the development of forest reference emission level, monitoring, reporting and verification. ForestCDB invites its visitors, especially those who maintain forest inventory data, manage permanent sample plots or conduct research on carbon stocks, to participate in this initiative.

New York Stock Exchange: Index: Dow Jones US Beverages Index data was reported at 959.180 NA in Apr 2025. This records a decrease from the previous number of 981.520 NA for Mar 2025. New York Stock Exchange: Index: Dow Jones US Beverages Index data is updated monthly, averaging 980.645 NA from Mar 2024 (Median) to Apr 2025, with 14 observations. The data reached an all-time high of 1,046.450 NA in Sep 2024 and a record low of 913.890 NA in Jan 2025. New York Stock Exchange: Index: Dow Jones US Beverages Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: Dow Jones: Monthly.

Comprehensive dataset of 465 Stock brokers in Oregon, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This data archive describes region definitions for the RAM Legacy Stock Assessment Database. Within the RAM Legacy database, stock assessments are associated with named areas. We approximate coordinates and bounding boxes for each of these areas, using country EEZs and fishing area shapefiles when appropriate. In addition, we develop a simple language to encode the GIS shapes of the areas, along with an interpreter to translate these codes into polygons. The syntax supports using political entities, shapefile regions, circles and rectangles, clipped versions of these, and combinations of these.

The archive contains the following contents:

• syntax.pdf: This document describes the geocoding syntax, and lists all of the geocoding descriptions for the assessment regions.

• results: This folder contains a shapefile of assessment regions (ram.shp) and a summary file of each region's centroid and size.

• sources: This folder contains shapefiles for FAO regions and New Zealand fishing regions, used by the syntax system, and latlon.csv which contains the region descriptions for each assessment region.

• code: load_areas.R contains functions that interpret the geocoding syntax and genshape.R generates the ram.shp shapefile.

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

FAO Agricultural Capital Stock Database. Activity coverage: Agriculture, forestry, fishery (ISIC Rev.3: A+B). Main indicators: Agricultural Gross Fixed Capital Formation (GFCFAFF), Agricultural Net and Gross Capital Stock (NCSAFF & GCSAFF), Agricultural Consumption of Fixed Capital (CFCAFF), Agricultural Investment ratio (AIR), and the Agriculture Orientation Index in physical investment flows (INV_AOI). As part of the FAO Agricultural Capital Stock database, ESS-FAO publishes country-by-country data on annual physical investment flows in agriculture, forestry and fishery as measured by the System of National Accounts (SNA) concept of Gross Fixed Capital Formation (GFCF).The FAO Capital Stock Database is an analytical database. For most countries, published series start in 1990. Whenever available, the database integrates national accounts data harvested from UNSD National Accounts Official Country Data (UNSD OCD) or OECD STAN and OECD Annual National Accounts (OECD ANA). To make data comparable across countries and over time, national data series have been rescaled to pair the ISIC Rev. 3 levels (linking of series is done by applying ratios computed on overlapping years of data) and re-referenced using the UNSD National Accounts Estimates of Main Aggregates database. If the full set of national accounts data on the above-mentioned set of agriculture capital related variables is not available for a specific country from these sources, estimation procedures are employed to construct complete time series. For a description of the procedures implemented to obtain complete time series on GFCFAFF, net and gross CSAFF, and CFCAFF, see the “Data Compilation†section underneath. Country data on Gross Fixed Capital Formation (GFCF) in agriculture, forestry and fishery, either as complete time series or just data for a few individual years, are available for just over 100 countries, originating mainly from the UNSD NA OCD, and OECD STAN and OECD ANA. Country data on agricultural Net Capital Stock (NCSAFF), Gross Capital Stock (GCSAFF) and Consumption of Fixed Capital (CFCAFF) are available only for a limited number of countries - to a large extent from OECD countries and included in the OECD STAN database. For some 20 other countries data are also availed from the UNSD National Accounts Official Country Data. Data on Gross Capital Stock (GCS) is available only for a few OECD countries. Based on the dataset on agriculture GFCF, FAO calculates NCSAFF, GCSAFF and CFCAFF series for all countries for which country data are not available from the above mentioned sources. To that end, a variation of the perpetual inventory method is used (for further details, see “Data Compilation†section below). Series are also presented in Constant prices. The total economy GFCF deflators from UNSD National Accounts Estimates have been used for non-OECD countries. As for OECD countries, GFCFAFF specific deflator series in ISIC Rev.3 A+B are used when available. For other cases, the GFCF-total economy deflator for GFCF has been used. The same deflators as for GFCFAFF have been used for GCSAFF, NCSAFF and CFCAFF.