100+ datasets found
  1. Monthly development Dow Jones Industrial Average Index 2018-2025

    • statista.com
    • ai-chatbox.pro
    Updated Jun 26, 2025
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    Statista (2025). Monthly development Dow Jones Industrial Average Index 2018-2025 [Dataset]. https://www.statista.com/statistics/261690/monthly-performance-of-djia-index/
    Explore at:
    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2018 - Mar 2025
    Area covered
    United States
    Description

    The value of the DJIA index amounted to ********* at the end of March 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

  2. F

    S&P 500

    • fred.stlouisfed.org
    json
    Updated Jul 3, 2025
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    (2025). S&P 500 [Dataset]. https://fred.stlouisfed.org/series/SP500
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jul 3, 2025
    License

    https://fred.stlouisfed.org/legal/#copyright-pre-approvalhttps://fred.stlouisfed.org/legal/#copyright-pre-approval

    Description

    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.

  3. Weekly development Dow Jones Industrial Average Index 2020-2025

    • statista.com
    • ai-chatbox.pro
    Updated Mar 20, 2023
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    Statista (2023). Weekly development Dow Jones Industrial Average Index 2020-2025 [Dataset]. https://www.statista.com/statistics/1104278/weekly-performance-of-djia-index/
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    Dataset updated
    Mar 20, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2020 - Mar 2, 2025
    Area covered
    United States
    Description

    The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

  4. The Dow Jones U.S. Completion Total Stock Market Index (Forecast)

    • kappasignal.com
    Updated May 8, 2023
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    KappaSignal (2023). The Dow Jones U.S. Completion Total Stock Market Index (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/the-dow-jones-us-completion-total-stock.html
    Explore at:
    Dataset updated
    May 8, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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 U.S. Completion Total Stock Market 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

  5. Weekly development S&P 500 Index 2024

    • statista.com
    Updated Jun 26, 2025
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    Statista (2025). Weekly development S&P 500 Index 2024 [Dataset]. https://www.statista.com/statistics/1104270/weekly-sandp-500-index-performance/
    Explore at:
    Dataset updated
    Jun 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 1, 2020 - Dec 29, 2024
    Area covered
    United States
    Description

    Between March 4 and March 11, 2020, the S&P 500 index declined by ** percent, descending into a bear market. On March 12, 2020, the S&P 500 plunged *** percent, its steepest one-day fall since 1987. The index began to recover at the start of April and reached a peak in December 2021. As of December 29, 2024, the value of the S&P 500 stood at ******** points. Coronavirus sparks stock market chaos Stock markets plunged in the wake of the COVID-19 pandemic, with investors fearing its spread would destroy economic growth. Buoyed by figures that suggested cases were leveling off in China, investors were initially optimistic about the virus being contained. However, confidence in the market started to subside as the number of cases increased worldwide. Investors were deterred from buying stocks, and this was reflected in the markets – the values of the Dow Jones Industrial Average and the Nasdaq Composite also dived during the height of the crisis. What is a bear market? A bear market occurs when the value of a stock market suffers a prolonged decline of more than 20 percent over a period of at least 2 months. The COVID-19 pandemic caused severe concern and sent stock markets on a steep downward spiral. The S&P 500 achieved a record closing high of ***** on February 19, 2020. However, just over 3 weeks later, the market closed on *****, which represented a decline of around ** percent in only 16 sessions.

  6. Stock Market Dataset

    • kaggle.com
    zip
    Updated Apr 2, 2020
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    Oleh Onyshchak (2020). Stock Market Dataset [Dataset]. http://doi.org/10.34740/kaggle/dsv/1054465
    Explore at:
    zip(547714524 bytes)Available download formats
    Dataset updated
    Apr 2, 2020
    Authors
    Oleh Onyshchak
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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.

  7. T

    BSE SENSEX Stock Market Index Data

    • tradingeconomics.com
    • id.tradingeconomics.com
    • +13more
    csv, excel, json, xml
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    TRADING ECONOMICS, BSE SENSEX Stock Market Index Data [Dataset]. https://tradingeconomics.com/india/stock-market
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    excel, json, xml, csvAvailable download formats
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Apr 3, 1979 - Jul 4, 2025
    Area covered
    India
    Description

    India's main stock market index, the SENSEX, rose to 83433 points on July 4, 2025, gaining 0.23% from the previous session. Over the past month, the index has climbed 2.44% and is up 4.30% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

  8. S&P 500 stock data

    • kaggle.com
    zip
    Updated Aug 11, 2017
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    Cam Nugent (2017). S&P 500 stock data [Dataset]. https://www.kaggle.com/camnugent/sandp500
    Explore at:
    zip(31994392 bytes)Available download formats
    Dataset updated
    Aug 11, 2017
    Authors
    Cam Nugent
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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.

    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) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

    The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.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

    Acknowledgements

    I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github 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!

  9. Stock Market Data Europe ( End of Day Pricing dataset )

    • datarade.ai
    Updated Aug 24, 2023
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    Techsalerator (2023). Stock Market Data Europe ( End of Day Pricing dataset ) [Dataset]. https://datarade.ai/data-products/stock-market-data-europe-end-of-day-pricing-dataset-techsalerator
    Explore at:
    .json, .csv, .xls, .txtAvailable download formats
    Dataset updated
    Aug 24, 2023
    Dataset provided by
    Techsalerator LLC
    Authors
    Techsalerator
    Area covered
    Lithuania, Slovenia, Belgium, Croatia, Switzerland, Finland, Denmark, Italy, Andorra, Latvia, Europe
    Description

    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.

  10. Share of Americans investing money in the stock market 1999-2024

    • statista.com
    Updated Jun 25, 2025
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    Statista (2025). Share of Americans investing money in the stock market 1999-2024 [Dataset]. https://www.statista.com/statistics/270034/percentage-of-us-adults-to-have-money-invested-in-the-stock-market/
    Explore at:
    Dataset updated
    Jun 25, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    1999 - 2024
    Area covered
    United States
    Description

    In 2024, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years, and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges, where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the Financial Crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

  11. History of MAG7 stocks (20 years)

    • kaggle.com
    Updated Feb 13, 2025
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    IttiphoN (2025). History of MAG7 stocks (20 years) [Dataset]. https://www.kaggle.com/datasets/ittiphon/history-of-mag7-stocks-20-years
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 13, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    IttiphoN
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description

    1. Overview

    This dataset provides monthly stock price data for the MAG7 over the past 20 years (2004–2024). The data includes key financial metrics such as opening price, closing price, highest and lowest prices, trading volume, and percentage change. The dataset is valuable for financial analysis, stock trend forecasting, and portfolio optimization.

    2. What is MAG7 ?

    MAG7 refers to the seven largest and most influential technology companies in the U.S. stock market : - Microsoft (MSFT) - Apple (AAPL) - Google (Alphabet, GOOGL) - Amazon (AMZN) - Nvidia (NVDA) - Meta (META) - Tesla (TSLA)

    These companies are known for their market dominance, technological innovation, and significant impact on global stock indices such as the S&P 500 and Nasdaq-100.

    3. Dataset Details

    The dataset consists of historical monthly stock prices of MAG7, retrieved from Investing.com. It provides an overview of how these stocks have performed over two decades, reflecting market trends, economic cycles, and technological shifts.

    4. Columns Descriptions

    • Date The recorded month and year (DD-MM-YYYY)
    • Price The closing price of the stock at the end of the month
    • Open The price at which the stock opened at the beginning of the month
    • High The highest stock price recorded in the month
    • Low The lowest stock price recorded in the month
    • Vol. The total trading volume for the month
    • Change % The percentage change in stock price compared to the previous month # 5. Data Source & Format The dataset was obtained from Investing.com and downloaded in CSV format. The data has been processed to ensure consistency and accuracy, with date formats standardized for time-series analysis. # 6. Potential Use Cases This dataset can be used for :
    • 📈 Stock price trend analysis over 20 years
    • 📊 Building financial models for long-term investing
    • 🔎 Machine learning applications in stock market prediction
    • 📉 Evaluating market volatility and economic impact on MAG7 stocks

    7. Limitations & Considerations

    • ⚠️ The dataset is limited to monthly data, meaning short-term price fluctuations are not captured.
    • ⚠️ Trading volume (Vol.) may vary in availability due to differences in reporting.
    • ⚠️ External factors such as stock splits, dividends, and market crashes are not explicitly noted but may impact historical trends.
  12. k

    Sell High: Time to Cash in on 5 of Nasdaq's Best Stocks (Forecast)

    • kappasignal.com
    Updated Jun 8, 2023
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    KappaSignal (2023). Sell High: Time to Cash in on 5 of Nasdaq's Best Stocks (Forecast) [Dataset]. https://www.kappasignal.com/2023/06/sell-high-time-to-cash-in-on-5-of.html
    Explore at:
    Dataset updated
    Jun 8, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Sell High: Time to Cash in on 5 of Nasdaq's Best Stocks

    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

  13. Schneider National (SNDR) Stock: Buy, Hold, or Sell? (Forecast)

    • kappasignal.com
    Updated May 2, 2024
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    KappaSignal (2024). Schneider National (SNDR) Stock: Buy, Hold, or Sell? (Forecast) [Dataset]. https://www.kappasignal.com/2024/05/schneider-national-sndr-stock-buy-hold.html
    Explore at:
    Dataset updated
    May 2, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Schneider National (SNDR) Stock: Buy, Hold, or Sell?

    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

  14. Nifty 50 Stock Market Dataset (2018-2023)

    • kaggle.com
    Updated Aug 5, 2023
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    Aamir Kalimi (2023). Nifty 50 Stock Market Dataset (2018-2023) [Dataset]. https://www.kaggle.com/datasets/codekalimi/nifty-50-2018-2023
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 5, 2023
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Aamir Kalimi
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This dataset contains a comprehensive collection of historical data for the Nifty 50 stocks, a diversified stock market index in India. The data covers the period from January 2018 to August 2023, providing valuable insights into the performance of the Indian stock market over the years.

    Features: - Stock Symbol: The unique stock symbol of the company listed in the Nifty 50 index - Date: The date of the stock market data. - Open: The opening price of the stock on the given date. - High: The highest price reached by the stock during the trading session. - Low: The lowest price reached by the stock during the trading session. - Close: The closing price of the stock on the given date. - Volume: The trading volume of the stock on the given date.

  15. d

    TagX - Stock market data | End of Day Pricing Data | Shares, Equities &...

    • datarade.ai
    .json, .csv, .xls
    Updated Feb 27, 2024
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    TagX (2024). TagX - Stock market data | End of Day Pricing Data | Shares, Equities & bonds | Global Coverage | 10 years historical data [Dataset]. https://datarade.ai/data-products/stock-market-data-end-of-day-pricing-data-shares-equitie-tagx
    Explore at:
    .json, .csv, .xlsAvailable download formats
    Dataset updated
    Feb 27, 2024
    Dataset authored and provided by
    TagX
    Area covered
    Yemen, Germany, Guam, Equatorial Guinea, Pakistan, Guadeloupe, Japan, Niue, Mauritius, Kiribati
    Description

    TagX is your trusted partner for stock market and financial data solutions. We specialize in delivering real-time and end-of-day data feeds that power software, trading algorithms, and risk management systems globally. Whether you're a financial institution, hedge fund, or individual investor, our reliable datasets provide essential insights into market trends, historical pricing, and key financial metrics.

    TagX is committed to precision and reliability in stock market data. Our comprehensive datasets include critical information such as date, open/close/high/low prices, trading volume, EPS, P/E ratio, dividend yield, and more. Tailor your dataset to match your specific requirements, choosing from a wide range of parameters and coverage options across primary listings on NASDAQ, AMEX, NYSE, and ARCA exchanges.

    Key Features of TagX Stock Market Data:

    Custom Dataset Requests: Customize your data feed to focus on specific metrics and parameters crucial to your trading strategy.

    Extensive Coverage: Access data from reputable exchanges and market participants, ensuring accuracy and completeness in your analyses.

    Flexible Pricing Models: Choose pricing structures based on your selected parameters, offering cost-effective solutions tailored to your needs.

    Why Choose TagX? Partner with TagX for precise, dependable, and customizable stock market data solutions. Whether you require real-time updates or end-of-day valuations, our datasets are designed to support informed decision-making and enhance your competitive edge in the financial markets. Trust TagX to deliver the data integrity and accuracy essential for maximizing your trading potential.

  16. T

    China Shanghai Composite Stock Market Index Data

    • tradingeconomics.com
    • jp.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Aug 7, 2023
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    TRADING ECONOMICS (2023). China Shanghai Composite Stock Market Index Data [Dataset]. https://tradingeconomics.com/china/stock-market
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    xml, csv, excel, jsonAvailable download formats
    Dataset updated
    Aug 7, 2023
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 19, 1990 - Jul 4, 2025
    Area covered
    China
    Description

    China's main stock market index, the SHANGHAI, rose to 3472 points on July 4, 2025, gaining 0.32% from the previous session. Over the past month, the index has climbed 2.61% and is up 17.71% 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.

  17. NYSE and Nasdaq monthly market cap of listed companies comparison 2018-2025

    • statista.com
    Updated Jun 24, 2025
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    Statista (2025). NYSE and Nasdaq monthly market cap of listed companies comparison 2018-2025 [Dataset]. https://www.statista.com/statistics/1277195/nyse-nasdaq-comparison-market-capitalization-listed-companies/
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    Dataset updated
    Jun 24, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2018 - Jan 2025
    Area covered
    United States
    Description

    As of Janaury 2025, the New York Stock Exchange (NYSE) and the Nasdaq - the two largest stock exchange operators in the United States - held a combined market capitalization for domestic listed companies of over ** trillion U.S. dollars. Both markets were almost evenly sized at this point in time - at approximately ** and ** trillion U.S. dollars, respectively. However, the Nasdaq has grown much quicker than the NYSE since January 2018, when their respective domestic market caps were ** and ** trillion U.S. dollars. Much of this can be attributed to the success of information technology stocks during the global coronavirus (COVID-19) pandemic, as the Nasdaq is the traditional venue for companies operating in the tech sector.

  18. G

    Toronto Stock Exchange statistics

    • open.canada.ca
    • www150.statcan.gc.ca
    • +1more
    csv, html, xml
    Updated Nov 8, 2023
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    Statistics Canada (2023). Toronto Stock Exchange statistics [Dataset]. https://open.canada.ca/data/en/dataset/0e1e57aa-e664-41b5-a69f-d814d4407d62
    Explore at:
    csv, html, xmlAvailable download formats
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Statistics Canada
    License

    Open Government Licence - Canada 2.0https://open.canada.ca/en/open-government-licence-canada
    License information was derived automatically

    Description

    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 ...).

  19. k

    DJ US Healthcare: Poised for Recovery? (Forecast)

    • kappasignal.com
    Updated Apr 23, 2024
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    KappaSignal (2024). DJ US Healthcare: Poised for Recovery? (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/dj-us-healthcare-poised-for-recovery.html
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    Dataset updated
    Apr 23, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    DJ US Healthcare: Poised for Recovery?

    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

  20. XDGU-SDT Stock Market @Kraken

    • kaggle.com
    Updated Mar 8, 2022
    + more versions
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    olmatz (2022). XDGU-SDT Stock Market @Kraken [Dataset]. https://www.kaggle.com/datasets/olmatz/xdgusdt-stock-market-kraken
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 8, 2022
    Dataset provided by
    Kaggle
    Authors
    olmatz
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Real and up to date stock market exchange of cryptocurrencies can be quite expensive and are hard to get. However, historical financial data are the starting point to develop algorithm(s) to analyze market trend and why not beat the market by predicting market movement.

    Content

    Data provided in this dataset are historical data from the beginning of XDGU-SDT pair market on Kraken exchange up to the present (2021 December). This data comes frome real trades on one of the most popular cryptocurrencies exchange.

    Trading history

    Historical market data, also known as trading history, time and sales or tick data, provides a detailed record of every trade that happens on Kraken exchange, and includes the following information: - Timestamp - The exact date and time of each trade. - Price - The price at which each trade occurred. - Volume - The amount of volume that was traded.

    OHLCVT

    In addition, OHLCVT data are provided for the most common period interval: 1 min, 5 min, 15 min, 1 hour, 12 hours and 1 day. OHLCVT stands for Open, High, Low, Close, Volume and Trades and represents the following trading information for each time period: - Open - The first traded price - High - The highest traded price - Low - The lowest traded price - Close - The final traded price - Volume - The total volume traded by all trades - Trades - The number of individual trades

    Don't hesitate to tell me if you need other period interval 😉 ...

    Update

    This dataset will be updated every quarter to add new and up to date market trend. Let me know if you need an update more frequently.

    Inspiration

    Can you beat the market? Let see what you can do with these data!

Share
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Email
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Close
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Statista (2025). Monthly development Dow Jones Industrial Average Index 2018-2025 [Dataset]. https://www.statista.com/statistics/261690/monthly-performance-of-djia-index/
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Monthly development Dow Jones Industrial Average Index 2018-2025

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3 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Jun 26, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
Jan 2018 - Mar 2025
Area covered
United States
Description

The value of the DJIA index amounted to ********* at the end of March 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

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