The main stock market index of United States, the US500, rose to 6008 points on June 9, 2025, gaining 0.13% from the previous session. Over the past month, the index has climbed 2.80% and is up 12.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

The main stock market index in the United States (US500) increased 30 points or 0.51% since the beginning of 2025, 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 May of 2025.

Historical dataset of the Dow Jones Industrial Average (DJIA) stock market index for the last 100 years. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

Ten years of daily data for the Dow Jones Industrial Average (DJIA) market index. Each point of the dataset is represented by the daily closing price for the DJIA. Historical data can be downloaded via the red button on the upper right corner of the chart.

The main stock market index of United States, the US500, rose to 6000 points on June 6, 2025, gaining 1.03% from the previous session. Over the past month, the index has climbed 6.55% and is up 12.22% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June 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.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

United Kingdom's main stock market index, the GB100, rose to 8838 points on June 6, 2025, gaining 0.30% from the previous session. Over the past month, the index has climbed 3.25% and is up 7.19% compared to the same time last year, 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 June of 2025.

Japan's main stock market index, the JP225, rose to 38094 points on June 9, 2025, gaining 0.93% from the previous session. Over the past month, the index has climbed 1.19%, though it remains 2.42% lower 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 June of 2025.

Historical dataset for the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

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

The main stock market index of United States, the US500, fell to 5989 points on June 9, 2025, losing 0.18% from the previous session. Over the past month, the index has climbed 2.48% and is up 11.73% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

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.

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.

Historical dataset of the S&P 500 stock market index over the last 10 years. Values shown are daily closing prices. The most recent value is updated on an hourly basis during regular trading hours.

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!

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.

China's main stock market index, the SHANGHAI, rose to 3385 points on June 6, 2025, gaining 0.04% from the previous session. Over the past month, the index has climbed 1.28% and is up 10.95% 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 June of 2025.

Update

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

Content

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

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

Acknowledgements

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

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

Terms of Use

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

Stock Market Performance and Corporate Board Member Profiles

Analyzing the Influences of Leadership on Stock Market Performance

By Jon Loyens [source]

About this dataset

This powerful dataset brings together publically-available information from leading stock markets with extensive details about corporate board members. For each company, discover not only their board composition and background, but also current market dynamics, trends and rule changes affecting them. Whether you're a teacher looking to add more detail to a class presentation or an investor seeking a competitive edge in the market - this dataset provides comprehensive insights into the world of stocks and those that play an influential role on its direction. Unprecedented access awaits as you explore hypothetical investments and strategies or actual risks associated with established entities today

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How to use the dataset

Using this dataset, you can gain a better understanding of the relationship between corporate board members and stock market performance. You can analyze the data to determine the average performance of board members at different companies and compare it to the overall performance of other stocks. In addition, you can look into correlations between individual stocks, various industries, and different groups of companies with similar board membership profiles. This dataset provides an overview of all major stocks across multiple industries with detailed insights on each stock's current and past market performance as well as corporate boards

Research Ideas

• Analyzing the performance of individual board members in relation to their company’s stock market performance.
• Determining if certain board members are better at making decisions that benefit the company’s stock market position across all companies they have a stake in.
• Identifying correlations between trends in different companies' stocks and external factors such as the influence of particular board members or other events associated with that company's sectors or markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: boardmembers.csv | Column name | Description | |:--------------------|:-----------------------------------| | BoardMemberName | Name of the board member. (String) | | CompanyName | Name of the company. (String) | | Source | Source of the data. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Jon Loyens.