Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-07-13 to 2025-07-11 about stock market, average, industry, and USA.

Update

Unfortunately, the API this dataset used to pull the stock data isn't free anymore. Instead of having this auto-updating, I dropped the last version of the data files in here, so at least the historic data is still usable.

Content

This dataset provides free end of day data for all stocks currently in the Dow Jones Industrial Average. For each of the 30 components of the index, there is one CSV file named by the stock's symbol (e.g. AAPL for Apple). Each file provides historically adjusted market-wide data (daily, max. 5 years back). See here for description of the columns: https://iextrading.com/developer/docs/#chart

Since this dataset uses remote URLs as files, it is automatically updated daily by the Kaggle platform and automatically represents the latest data.

Acknowledgements

List of stocks and symbols as per https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average

Thanks to https://iextrading.com for providing this data for free!

Terms of Use

Data provided for free by IEX. View IEX’s Terms of Use.

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 6226 points on July 14, 2025, losing 0.54% from the previous session. Over the past month, the index has climbed 3.20% and is up 10.56% 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.

The main stock market index of United States, the US500, fell to 6238 points on July 4, 2025, losing 0.65% from the previous session. Over the past month, the index has climbed 5.04% and is up 12.06% 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.

About this dataset

Context

The dataset tabulates the data for the Dow City, IA population pyramid, which represents the Dow City population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Dow City, IA, is 22.4.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Dow City, IA, is 39.7.
• Total dependency ratio for Dow City, IA is 62.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Dow City, IA is 2.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Dow City population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Dow City for the selected age group is shown in the following column.
• Population (Female): The female population in the Dow City for the selected age group is shown in the following column.
• Total Population: The total population of the Dow City for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dow City Population by Age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Dow City: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 38(22.49%) households where the householder is under 25 years old, 16(9.47%) households with a householder aged between 25 and 44 years, 56(33.14%) households with a householder aged between 45 and 64 years, and 59(34.91%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of Dow City, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dow City median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Dow City: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 32(15.02%) households where the householder is under 25 years old, 41(19.25%) households with a householder aged between 25 and 44 years, 93(43.66%) households with a householder aged between 45 and 64 years, and 47(22.07%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the city of Dow City, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Dow City median household income by age. You can refer the same here

DOW-USD Bot Positions Dataset

This dataset stores positions for DOW/USD trading strategies. It includes position types, timestamps, and other relevant data generated from a trading bot using machine learning techniques. It is updated hourly using this link dataset The dataset on huggingface is not up to date but is given as an example Use this link dataset to update (hourly)

  Dataset Description

The dataset consists of the following columns:

timestamp: The time when… See the full description on the dataset page: https://huggingface.co/datasets/earnliners/dow-usd-positions.

This dataset provides information about the number of properties, residents, and average property values for Old Dow Road cross streets in Carolina Beach, NC.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Dow Jones Industrial Average

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The 3D Hydrography Program (3DHP) data is an integrated, National, 3D-enabled hydrologic dataset derived from the USGS 3D Elevation Program (3DEP) data. For areas where Elevation-derived Hydrography (EDH) has not yet been collected, 3DHP data is supplemented by hydrologic vector data from the National Hydrography Dataset (NHD). As further EDH data is collected, it will replace the NHD data in those areas. 3DHP data ingested from EDH sources includes ‘value added’ catchments and flowline network derivative attributes. All the data is open and non-proprietary. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. This dataset is not intended to be used for site-specific regulatory determinations. 3DHP datasets include a three-dimensional (3D) hydrography network generated from, and integrated with, elevation data from the 3DEP to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. This product is new in federal fiscal year 2025 (FY25), and consists only of vector data in a series of feature classes. The product represents the 3DHP dataset and the schema in which it is contained as of September 30, 2024 Future Annual Staged Product releases will reflect the schema at the time the product is generated and include more EDH-sourced data holdings.

Russia's main stock market index, the MOEX, fell to 2642 points on July 11, 2025, losing 3.31% from the previous session. Over the past month, the index has declined 3.94% and is down 11.21% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Russia. Russia Stock Market Index MOEX CFD - values, historical data, forecasts and news - updated on July of 2025.

https://upload.wikimedia.org/wikipedia/commons/thumb/0/09/The_Coca-Cola_Company_%282020%29.svg/330px-The_Coca-Cola_Company_%282020%29.svg.png" alt=""> https://upload.wikimedia.org/wikipedia/commons/f/f6/15-09-26-RalfR-WLC-0098.jpg" alt="">

https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/The_Coca-Cola_Company_logo.svg/330px-The_Coca-Cola_Company_logo.svg.png" alt="">

The Coca-Cola Company is an North American multinational beverage corporation incorporated under Delaware's General Corporation Law[a] and headquartered in Atlanta, Georgia. The Coca-Cola Company has interests in the manufacturing, retailing, and marketing of non-alcoholic beverage concentrates and syrups, and alcoholic beverages. The company produces Coca-Cola, the sugary drink for which it is best known for, invented in 1886 by pharmacist John Stith Pemberton. At the time, the product was made with coca leaves, which added an amount of cocaine to the drink, and with kola nuts, which added caffeine, so that the coca and the kola together provided a stimulative effect. This stimulative effect is the reason the drink was sold to the public as a healthy "tonic", and the coca and the kola are also the source of the name of the product and of the company.In 1889, the formula and brand were sold for $2,300 (roughly $68,000 in 2021) to Asa Griggs Candler, who incorporated The Coca-Cola Company in Atlanta in 1892.

Since 1919, Coca-Cola has been a publicly traded company. Its stock is listed on the New York Stock Exchange under the ticker symbol "KO". One share of stock purchased in 1919 for $40, with all dividends reinvested, would have been worth $9.8 million in 2012, a 10.7% annual increase adjusted for inflation. A predecessor bank of SunTrust received $100,000 for underwriting Coca-Cola's 1919 public offering; the bank sold that stock for over $2 billion in 2012. In 1987, Coca-Cola once again became one of the 30 stocks which makes up the Dow Jones Industrial Average, which is commonly referenced as a proxy for stock market performance; it had previously been a Dow stock from 1932 to 1935. Coca-Cola has paid a dividend since 1920 and, as of 2019, had increased it each year for 57 years straight.

China's main stock market index, the SHANGHAI, rose to 3520 points on July 14, 2025, gaining 0.27% from the previous session. Over the past month, the index has climbed 3.86% and is up 18.35% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

This table contains 14 series, with data starting from 1953 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

Browse Dow Jones Real Estate Futures (RX) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones Telecom: Ready for the Next Surge?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Points of Erosion as found in the river foreshore assessment. DISCLAIMER: While the dataset has been prepared by the Department of Water and Environmental Regulation, it contains information from State and federally funded foreshore assessment projects conducted at different times by Natural Resource Management groups with support from DWER regional offices. It should be noted that for any given location, the data provides a ‘snapshot’ of the attributes recorded at one specific time. Any information or representation expressed or implied in this database is made in good faith and on the basis that the Department of Water and Environmental Regulation and its employees are not liable for any damage or loss whatsoever which may occur as a result of action taken or not taken, as the case may be in respect of any information or representation referred to herein. Professional advice should be obtained to verify the information contained in this database before applying to particular circumstances. The Department of Water and Environmental Regulation accepts no responsibility for collecting or updating this data but some known errors are being addressed. This dataset was formerly known as Foreshore Conditions - Erosion (DOW-045)

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Oil Equipment & Services Sector Poised for Moderate Growth, Analyst Forecasts for Dow Jones U.S. Select Oil Equipment & Services index.

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data