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.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December 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.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

Indian Stock Market Dataset

This dataset contains various types of data related to the Bombay Stock Exchange (BSE), the oldest and largest stock exchange in India. Includes information about:

• Active companies listed on BSE, their industry, security ID, and status
• Stock quotes for each company, including open, high, low, close, volume, and other metrics
• Stock quote buy and sell data for each company
• Period trend data for each company for 1 month, 3 months, 6 months, and 12 months
• Historical and latest quarter financial statements for each company, including year-on-year results, quarter results, balance sheet, and cash flow
• Statement analysis for each company based on various indicators and ratios
• Peers comparisons for each company based on market capitalization, price-to-earnings ratio, dividend yield, and other metrics
• Corporate news for each company from various sources

The data was collected using Python libraries such as bsedata and bselib, which allow extracting real-time data from BSE website. The data was then cleaned, formatted, and organized into different CSV files for easy access and analysis.

The dataset can be used for various types of projects that require getting live quotes or historical data for a given stock or index, or building large data sets for data analysis and machine learning. Some possible applications are:

• Exploratory data analysis and visualization of the Indian stock market trends and patterns
• Fundamental analysis and valuation of individual companies based on their financial performance and ratios
• Technical analysis and trading strategies based on price movements and indicators
• Portfolio optimization and risk management based on diversification and correlation
• Sentiment analysis and natural language processing of corporate news and their impact on stock prices

The dataset is updated regularly with new data as it becomes available on BSE website. The dataset is also open-sourced and reproducible using Kaggle Notebooks, a cloud computational environment that enables interactive and collaborative analysis.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Feb 2018 note: I have just updated the dataset to include data up to Feb 2018. I have also accounted for changes in the stocks on the S&P 500 index (RIP whole foods etc. etc.).

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder).

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv contains the same data, presented in a merged .csv file. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Acknowledgements

Due to volatility in google finance, for the newest version I have switched over to acquiring the data from The Investor's Exchange api, the simple script I use to do this is found here. Special thanks to Kaggle, Github, pandas_datareader and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

About Dataset Context Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Feb 2018 note: I have just updated the dataset to include data up to Feb 2018. I have also accounted for changes in the stocks on the S&P 500 index (RIP whole foods etc. etc.).

Content The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the allstocks5yr.csv and corresponding folder).

The folder individualstocks5yr contains files of data for individual stocks, labelled by their stock ticker name. The allstocks5yr.csv contains the same data, presented in a merged .csv file. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Acknowledgements Due to volatility in google finance, for the newest version I have switched over to acquiring the data from The Investor's Exchange api, the simple script I use to do this is found here. Special thanks to Kaggle, Github, pandas_datareader and The Market.

Inspiration This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Context

Alphabet Inc. is a listed US holding company of the former Google LLC, which continues to exist as a subsidiary. The headquarters is Mountain View in Silicon Valley. The company is led by Sundar Pichai as CEO.

With sales of $137 billion, a profit of $30.7 billion and a market value of $ 863.2 billion, Alphabet Inc. ranks 17th among the world's largest companies according to Forbes Global 2000 (as of 4th November 2019). The company had a market cap of $ 766.4 billion in early 2018. In 2019, Alphabet had annual sales of $161.9 billion and an annual profit of $34.3 billion.

Market capitalization of Alphabet (Google) (GOOG)

Market cap: $2.442 Trillion USD

As of August 2025 Alphabet (Google) has a market cap of $2.442 Trillion USD. This makes Alphabet (Google) the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content

Geography: USA

Time period: August 2004- August 2025

Unit of analysis: Google Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F84937d0d9ac664fa6c705c0da59564e0%2FScreenshot%202024-12-18%20153807.png?generation=1734532695847825&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2Fa927d7f9ef11a23685bbb86a25b44d8d%2FScreenshot%202024-12-18%20153822.png?generation=1734532715073647&alt=media" alt="">

The Value Line Investment Survey is one of the oldest, continuously running investment advisory publications. Since 1955, the Survey has been published in multiple formats including print, loose-leaf, microfilm and microfiche. Data from 1997 to present is now available online. The Survey tracks 1700 stocks across 92 industry groups. It provides reported and projected measures of firm performance, proprietary rankings and analysis for each stock on a quarterly basis. This dataset, a subset of the Survey covering the years 1980-1989 has been digitized from the microfiche collection available at the Dewey Library (FICHE HG 4501.V26). It is only available to MIT students and faculty for academic research. Published weekly, each edition of the Survey has the following three parts: Summary & Index: includes an alphabetical listing of all industries with their relative ranking and the page number for detailed industry analysis. It also includes an alphabetical listing of all stocks in the publication with references to their location in Part 3, Ratings & Reports. Selection & Opinion: contains the latest economic and stock market commentary and advice along with one or more pages of research on interesting stocks or industries, and a variety of pertinent economic and stock market statistics. It also includes three model stock portfolios. Ratings & Reports: This is the core of the Value Line Investment Survey. Preceded by an industry report, each one-page stock report within that industry includes Timeliness, Safety and Technical rankings, 3-to 5-year analyst forecasts for stock prices, income and balance sheet items, up to 17 years of historical data, and Value Line analysts’ commentaries. The report also contains stock price charts, quarterly sales, earnings, and dividend information. Publication Schedule: Each edition of the Survey covers around 130 stocks in seven to eight industries on a preset sequential schedule so that all 1700 stocks are analyzed once every 13 weeks or each quarter. All editions are numbered 1-13 within each quarter. For example, in 1980, reports for Chrysler appear in edition 1 of each quarter on the following dates: January 4, 1980 – page 132 April 4, 1980 – page 133 July 4, 1980 – page 133 October 1, 1980 – page 133 Reports for Coca-Cola were published in edition 10 of each quarter on: March 7, 1980 – page 1514 June 6, 1980 – page 1518 Sept. 5, 1980 – page 1517 Dec. 5, 1980 – page 1548 Any significant news affecting a stock between quarters is covered in the supplementary reports that appear at the end of part 3, Ratings & Reports. File format: Digitized files within this dataset are in PDF format and are arranged by publication date within each compressed annual folder. How to Consult the Value Line Investment Survey: To find reports on a particular stock, consult the alphabetical listing of stocks in the Summary & Index part of the relevant weekly edition. Look for the page number just to the left of the company name and then use the table below to identify the edition where that page number appears. All editions within a given quarter are numbered 1-13 and follow equally sized page ranges for stock reports. The table provides page ranges for stock reports within editions 1-13 of 1980 Q1. It can be used to identify edition and page numbers for any quarter within a given year. Ratings & Reports Edition Pub. Date Pages 1 04-Jan-80 100-242 2 11-Jan-80 250-392 3 18-Jan-80 400-542 4 25-Jan-80 550-692 5 01-Feb-80 700-842 6 08-Feb-80 850-992 7 15-Feb-80 1000-1142 8 22-Feb-80 1150-1292 9 29-Feb-80 1300-1442 10 07-Mar-80 1450-1592 11 14-Mar-80 1600-1742 12 21-Mar-80 1750-1908 13 28-Mar-80 2000-2142 Another way to navigate to the Ratings & Reports part of an edition would be to look around page 50 within the PDF document. Note that the page numbers of the PDF will not match those within the publication.

Hong Kong's main stock market index, the HK50, rose to 26095 points on December 2, 2025, gaining 0.24% from the previous session. Over the past month, the index has declined 0.24%, though it remains 32.15% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Hong Kong. Hong Kong Stock Market Index (HK50) - values, historical data, forecasts and news - updated on December of 2025.

What is New York Stock Exchange?

https://user-images.githubusercontent.com/36210723/200162286-0eb91e64-33d7-4625-8318-15bcdd5b7d55.jpg" alt="Nsy">

How to use this dataset

• Explore market Growth by year

• Explore and Create Business Metrics

Acknowledgments

When we use this dataset in our research, we credit the authors.

• License : CC0: Public Domain

• The data was collected through the money control API

.

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

Context

The script used to acquire all of the following data can be found in this GitHub repository. This repository also contains the modeling codes and will be updated continually, so welcome starring or watching!

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here provided a dataset with historical stock prices (last 12 years) for 29 of 30 DJIA companies (excluding 'V' because it does not have the whole 12 years data).

   ['MMM', 'AXP', 'AAPL', 'BA', 'CAT', 'CVX', 'CSCO', 'KO', 'DIS', 'XOM', 'GE',

   'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'MCD', 'MRK', 'MSFT', 'NKE', 'PFE',

   'PG', 'TRV', 'UTX', 'UNH', 'VZ', 'WMT', 'GOOGL', 'AMZN', 'AABA']

In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 13 years of stock data (in the all_stocks_2006-01-01_to_2018-01-01.csv and corresponding folder) and a smaller version of the dataset (all_stocks_2017-01-01_to_2018-01-01.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_2006-01-01_to_2018-01-01 contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_2006-01-01_to_2018-01-01.csv and all_stocks_2017-01-01_to_2018-01-01.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Acknowledgement

This Data description is adapted from the dataset named 'S&P 500 Stock data'. This data is scrapped from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and the Market.

Greece's main stock market index, the Athens General, rose to 2108 points on December 2, 2025, gaining 0.41% from the previous session. Over the past month, the index has climbed 4.15% and is up 47.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Greece. Greece Stock Market (ASE) - values, historical data, forecasts and news - updated on December 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.

This table contains 25 series, with data for years 1956 - present (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 ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

Stock Market Analysis of Nasdaq 100 Stock from it's Founding / Listing Years which is 2001 to 2022

Data Dictionary

About Nasdaq 100 Stock

The Nasdaq-100 is a stock market index made up of 102 equity securities issued by 101 of the largest non-financial companies listed on the Nasdaq stock exchange. It is a modified capitalization-weighted index. The stocks' weights in the index are based on their market capitalizations, with certain rules capping the influence of the largest components. It is limited to companies from a single exchange, and it does not have any financial companies. The financial companies are in a separate index, the NASDAQ Financial-100.

More - Find More Exciting🙀 Datasets Here - An Upvote👍 A Dayᕙ(`▿´)ᕗ , Keeps Aman Hurray Hurray..... ٩(˘◡˘)۶Hehe

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.

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.

📊 Nepali Stock Market Daily Price Data (2011–2025)

This dataset contains daily OHLCV (Open, High, Low, Close, Volume) data for 616 companies listed on the Nepali Stock Market, collected from ShareSansar.

📁 File Structure

• 616 CSV files, one per company
• ~454,928 total rows (including headers)
• Time range: 2011 to 2025 (daily granularity)
• Average rows per file: ~741

Files

• company_list.csv - 617 companies with metadata
• price_history/*_price_history.csv - 614 individual company files
• combined_price_history_lstm.csv - Combined dataset for ML

📊 Columns

🧠 Use Cases

• Time-series forecasting
• Stock price prediction (regression)
• Trend classification (up/down)
• Volatility analysis
• Sector-wise performance comparison

⚠️ Notes

• Data collected via web scraping from ShareSansar
• Some files may have missing dates or values
• Turnover and Qty columns are string-formatted with commas

📬 Contact

For questions or issues, leave a comment or message on Kaggle.

Content

This dataset provides the latest Nasdaq stock ticker information for all companies listed on Nasdaq in 2025. It includes key financial and industry-specific attributes, making it a valuable resource for investors, analysts, and researchers.

The dataset contains 6,975 unique stock tickers, representing companies across various sectors, market capitalizations, and industries. The data is available in multiple formats to suit different needs, whether for stock screening, financial modeling, or market research.

Columns:

Name – Full name of the company Symbol – Stock ticker symbol Sector – General sector classification Industry – Specific industry category Market Cap – Market capitalization (in USD) Country – Country where the company is headquartered Date – The date the data was last updated