The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December of 2025.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

Overview This dataset contains combined news headlines and stock market data, specifically the Dow Jones Industrial Average (DJIA). It is designed to facilitate the study of the relationship between news sentiment and stock market movements.

Dataset Description The dataset includes the following features:

Date: The date corresponding to the news headlines and DJIA data. Label: A binary label indicating whether the DJIA increased (1) or decreased (0) on that particular date. Top 1 to Top 25: The top 25 news headlines for each date. These columns contain the news headlines that were published on that date. Usage This dataset can be utilized for various analytical and modeling purposes, including but not limited to:

Sentiment Analysis: Develop models to analyze the sentiment of news headlines and correlate them with stock market movements. Stock Market Prediction: Build predictive models to forecast stock market trends based on news headlines. Text Preprocessing Techniques: Implement and evaluate text preprocessing methods such as tokenization, stemming, and lemmatization. Natural Language Processing: Apply NLP techniques to extract meaningful insights from news headlines. Potential Applications Enhancing trading strategies by incorporating news sentiment analysis. Building robust machine learning pipelines for financial forecasting. Studying the impact of news on stock market volatility. Experimenting with various NLP and machine learning techniques for financial data. File information The dataset is provided in CSV format and contains 1990 records and 27 columns.

The main stock market index of United States, the US500, rose to 6849 points on November 28, 2025, gaining 0.54% from the previous session. Over the past month, the index has declined 0.60%, though it remains 13.54% higher than a year ago, 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 November of 2025.

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.

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is google trends of stock (Dow, S&P500 index, Nasdaq index to update later) from pytrends (It is not official). Contains value of trend's result normalized as date of about 1 year (2020-06-14, 2021-06-06).

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want to compare stock's recent price, you should check this data set and refer to the Notebook.

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In Trend_sp500.json It is presented that trend of google to be normalized by index of S&P500

• In Trend_dow.json. It is presented that trend of google to be normalized by index of Dow

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

It is funning model comparing trend of google if it has correlation or not.

This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code,Please see another data sets like S&P500 price and financial statements, Dow price and financial statements

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

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

This dataset contains several daily features of NASDAQ Composite, Dow Jones Industrial Average, and NYSE Composite from 2010 to 2024. It covers features from various categories of technical indicators, futures contracts, price of commodities, important indices of markets around the world, price of major companies in the U.S. market, and treasury bill rates. Sources and thorough description of features have been mentioned in the paper of "CNNpred: CNN-based stock market prediction using a diverse set of variables" published at Expert Systems with Applications. This dataset has been used in "SAMBA: A Graph-Mamba Approach for Stock Price Prediction" published at ICASSP 2025. Link to Code: https://github.com/Ali-Meh619/SAMBA

I'm fascinated how banking and large hedge funds are utilizing the huge amount of data they possess to drive the stock market in either direction. Many companies do gather these data and sell them again, while they are already public information. I'm a beginner in this world and I want to make majority of the scattered data available to everyone in one place to ease process of analysis with minimal cost possible. his dataset contains the prices for stocks since establishment until November 1st, 2021. The stocks divided based on the market index (Dow Johns, Nasdaq 100, S&P 500, and Russell 3000).

Hope you find dataset is useful.

Income-Before-Tax Time Series for Dow Inc. Dow Inc., through its subsidiaries, provides various materials science solutions for packaging, infrastructure, mobility, and consumer applications in the United States, Canada, Europe, the Middle East, Africa, India, the Asia Pacific, and Latin America. The company operates through Packaging & Specialty Plastics, Industrial Intermediates & Infrastructure, and Performance Materials & Coatings segments. The Packaging & Specialty Plastics segment provides ethylene, propylene, polyethylene, and aromatics products; and other ethylene derivatives, such as polyolefin elastomers, ethylene vinyl acetate, and ethylene propylene diene monomer rubber. The Industrial Intermediates & Infrastructure segment offers polyurethanes, including propylene oxide, propylene glycol, and polyether polyols; aromatic isocyanates and fully formulated polyurethane systems; and chlor-alkali and vinyl comprising chlorine and caustic soda, ethylene dichloride, and vinyl chloride monomer; and construction chemicals consisting of cellulose ethers, redispersible latex powders, and acrylic emulsions, as well as coatings, adhesives, sealants, elastomers, and composites. The Performance Materials & Coatings segment provides architectural paints and coatings, and industrial coatings; and acrylics-based building blocks, silicon metals, siloxanes, and intermediates. The company also engages in the property and casualty insurance, as well as reinsurance business. Dow Inc. was founded in 1897 and is headquartered in Midland, Michigan.

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

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, 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 December of 2025.

Current 30 components of the Dow Jones Industrial Average, and their Open, High, Low, Close, Adjusted Close, Volume data since 1980, for stocks that were listed then. Updates daily using the Yahoo Finance API.

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 data set contains the value of the Dow Jones Industrial Average on daily close for all available dates (to the best of my knowledge) from 1885 to the most recently concluded calendar year. Extensions shouldn't be too difficult with existing packages.

Observations before October 7, 1896 are from the single Dow Jones Average. Observations from October 7, 1896 to July 30, 1914 are from the first DJIA. Observations before the 1914 closure of the first DJIA in July 1914 come from MeasuringWorth. Observations from its reopening in Dec. 12, 1914 to January 28, 1985 come from Pinnacle Systems. Observations from January 29, 1985 to the most recent observation come from a quantmod call.

References

Samuel H. Williamson, 'Daily Closing Value of the Dow Jones Average, 1885 to Present,' MeasuringWorth, 2019.

Jeffrey A. Ryan and Joshua M. Ulrich, 'quantmod: Quantitative Financial Modelling Framework,' 2018.

Acknowledgement

Original Data

Foto von Aditya Vyas auf Unsplash

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.

The United States Documented Unplugged Orphaned Oil and Gas Well (DOW) dataset contains 117,672 wells in 27 states. The definition of an orphaned oil or gas well varies across data sources; the dataset includes oil or gas wells where the state indicates that the well is an unplugged orphan, or the following criteria are met: 1) no production for an average of 12 months (6 to 24 months depending on the state), 2) the well is unplugged, 3) there is no responsible party to manage the well for future re-use or for plugging and abandonment, and 4) the location of the well is documented. The dataset includes location coordinates, American Petroleum Institute (API) number, or other identification number, well type, well status, and additional information for each unplugged orphaned well. All data were collected by direct requests to the respective state agency overseeing oil and gas wells or data downloads from their online databases. Location format conversion was performed on wells without coordinate locations using tools provided by the Bureau of Land Management and some state agencies. No other data manipulations were performed to the source data aside from reformatting or the addition of explanatory notes.

Statistics 8–10 indicate that trades occurring during dislocations involve approximately 5% more value per trade on average than those that occur while feeds are synchronized. The values reported above are sums of daily observations, except for statistics 8–10, and are conservative estimates of the true, unobserved quantities since positive (favoring the SIP) and negative (favoring the direct feeds) ROC can cancel in summary calculations.

Point locations of specified weeds 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, some known errors are being addressed. This dataset was formerly known as Foreshore Conditions - Weeds (DOW-053)