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.

The main stock market index of United States, the US500, rose to 6074 points on June 24, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 2.57% and is up 11.05% 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.

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.

Crude Oil rose to 64.67 USD/Bbl on June 9, 2025, up 0.13% from the previous day. Over the past month, Crude Oil's price has risen 4.39%, but it is still 16.82% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Crude Oil - values, historical data, forecasts and news - updated on June of 2025.

The latest closing stock price for Alphabet as of May 27, 2025 is 172.90. An investor who bought $1,000 worth of Alphabet stock at the IPO in 2004 would have $68,190 today, roughly 68 times their original investment - a 22.35% compound annual growth rate over 21 years. The all-time high Alphabet stock closing price was 206.14 on February 04, 2025. The Alphabet 52-week high stock price is 207.05, which is 19.8% above the current share price. The Alphabet 52-week low stock price is 140.53, which is 18.7% below the current share price. The average Alphabet stock price for the last 52 weeks is 172.27. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

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New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

--- Original source retains full ownership of the source dataset ---

Graph and download economic data for Consumer Price Index for All Urban Consumers: Used Cars and Trucks in Size Class B/C (CUURX000SETA02) from Dec 1997 to May 2025 about used, trucks, vehicles, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

The global natural gas price index stood at 184.84 index points in April 2025. Natural gas prices decreased by nearly 30 index points that month as heating fuel demand fell. The global price index takes into account indices from Europe, Japan, and the United States – some of the largest natural gas trading markets. The U.S. is the leading natural gas exporter in the world. Means of trading natural gas Liquefied natural gas (LNG) is the most common form of trading natural gas. Although piped gas is often the preferred choice for transportation between neighboring producing and consuming countries, seaborne trade as LNG has grown in market volume. This is in part thanks to high consumption in pipeline-inaccessible areas such Japan, Korea, and China, as well as the recent increase in LNG trade by European countries. Major natural gas price benchmarks The natural gas prices often used as global benchmarks are Europe’s Dutch TTF traded on the Intercontinental Exchange, Indonesian LNG in Japan, and the U.S. Henry Hub traded on the New York Mercantile Exchange. 2022 was an especially volatile year for natural gas prices, as supply was severely constrained following sanctions on Russian imports. Other reasons for recent spikes in gas prices are related to issues at refineries, changes in demand, and problems along seaborne supply routes.

Silver rose to 35.97 USD/t.oz on June 6, 2025, up 0.86% from the previous day. Over the past month, Silver's price has risen 10.82%, and is up 23.35% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on June of 2025.

man price history tracking allows crypto investors to easily monitor the performance of their investment. You can conveniently track the opening value, high, and close for man over time, as well as the trade volume. Additionally, you can instantly view the daily change as a percentage, making it effortless to identify days with significant fluctuations. According to our man price history data, its value soared to an unprecedented peak in 2025-05-12, surpassing -- USD. On the other hand, the lowest point in man's price trajectory, commonly referred to as the "man all-time low", occurred on 2025-05-12. If one had purchased man during that time, they would currently enjoy a remarkable profit of 0%. By design, man has no limit to its total supply. Its circulating supply is currently -- coins. All the prices listed on this page are obtained from Bitget, a reliable source. It is crucial to rely on a single source to check your investments, as values may vary among different sellers. Our historical man price dataset includes data at intervals of 1 minute, 1 day, 1 week, and 1 month (open/high/low/close/volume). These datasets have undergone rigorous testing to ensure consistency, completeness, and accuracy. They are specifically designed for trade simulation and backtesting purposes, readily available for free download, and updated in real-time.

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.

United States - Consumer Price Index for All Urban Consumers: Recreation in West - Size Class A was 138.11200 Index Dec 1997=100 in July of 2024, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Recreation in West - Size Class A reached a record high of 138.11200 in July of 2024 and a record low of 99.40000 in July of 1999. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Recreation in West - Size Class A - last updated from the United States Federal Reserve on June of 2025.

The WTI Crude Oil Futures Price Chart provides a visual representation of the historical prices of West Texas Intermediate (WTI) crude oil futures. Traders, investors, and analysts can use this chart to track price movements, study historical price patterns, and compare current prices with historical data. Additionally, the chart may include features such as trading volume and open interest to provide further insights into the market. However, it is important to consider other factors such as geopolitical e

CoinAPI's comprehensive set of crypto market indices gives traders and institutions the reliable price benchmarks they need. Our system tracks VWAP and PRIMKT indices data across more than 350 exchanges, updating every 100ms to ensure you always have the latest market information.

The VWAP (Volume-Weighted Average Price) index shows you what's happening across the entire market by combining prices and trading volumes from multiple exchanges. By weighting each trade by its size, VWAP reveals the true market consensus price, filtering out noise from low-liquidity venues. This makes it perfect for making informed trading decisions or valuing your crypto holdings accurately.

Meanwhile, our PRIMKT (Principal Market Price) index focuses specifically on the exchanges with the highest trading volumes for each cryptocurrency pair. This approach meets important accounting standards like IFRS 13 and FASB ASC 820, making it especially valuable for companies that need to report crypto assets on their financial statements.

Both real-time and historical crypto index data are available, giving you the complete picture of market movements over time. Whether you're trading actively, conducting research, or preparing financial reports, our crypto market indices provide the accurate price discovery tools you need.

Why work with us?

Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Market indexes (VWAP, PRIMKT) - 13 Data Sources - +7k indexes tracked - +2k assets covered - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume

Technical Excellence: - 99,9% uptime guarantee - 100ms update frequency - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance

From Wall Street trading desks to Silicon Valley analytics firms, financial professionals worldwide rely on our indices when accuracy matters most. We've built our reputation on delivering clean, consistent market benchmarks that stand up to scrutiny. When organizations need to know the true price of digital assets - not just what's displayed on a single exchange - they turn to CoinAPI. Join the community of professionals who've made our crypto market indices their gold standard for price discovery.

Terra's native coin Luna saw its trading volume nearly reach an all-time high when Russia invaded Ukraine in March 2022. Following this news, the price of multiple cryptocurrencies declined whilst the LUNA token was one of the few that was not affected. The popularity comes from the Terra blockchain playing a major role in Decentralized Finance or DeFi. For example, the transaction speed of LUNA is allegedly much faster than that of Ethereum, allowing for faster financial services.

Bitcoin Historical Data (2018-2024) - 15M, 1H, 4H, and 1D Timeframes

Dataset Overview

This dataset contains historical price data for Bitcoin (BTC/USDT) from January 1, 2018, to the present. The data is sourced using the Binance API, providing granular candlestick data in four timeframes: - 15-minute (15M) - 1-hour (1H) - 4-hour (4H) - 1-day (1D)

This dataset includes the following fields for each timeframe: - Open time: The timestamp for when the interval began. - Open: The price of Bitcoin at the beginning of the interval. - High: The highest price during the interval. - Low: The lowest price during the interval. - Close: The price of Bitcoin at the end of the interval. - Volume: The trading volume during the interval. - Close time: The timestamp for when the interval closed. - Quote asset volume: The total quote asset volume traded during the interval. - Number of trades: The number of trades executed within the interval. - Taker buy base asset volume: The volume of the base asset bought by takers. - Taker buy quote asset volume: The volume of the quote asset spent by takers. - Ignore: A placeholder column from Binance API, not used in analysis.

Data Sources

Binance API: Used for retrieving 15-minute, 1-hour, 4-hour, and 1-day candlestick data from 2018 to the present.

File Contents

1. btc_15m_data_2018_to_present.csv: 15-minute interval data from 2018 to the present.
2. btc_1h_data_2018_to_present.csv: 1-hour interval data from 2018 to the present.
3. btc_4h_data_2018_to_present.csv: 4-hour interval data from 2018 to the present.
4. btc_1d_data_2018_to_present.csv: 1-day interval data from 2018 to the present.

Automated Daily Updates

This dataset is automatically updated every day using a custom Python program.
The source code for the update script is available on GitHub:
🔗 Bitcoin Dataset Kaggle Auto Updater

Licensing

This dataset is provided under the CC0 Public Domain Dedication. It is free to use for any purpose, with no restrictions on usage or redistribution.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Electricity in Size Class A (CUURA000SEHF01) from Dec 1986 to Apr 2025 about electricity, urban, consumer, CPI, inflation, price index, indexes, price, and USA.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Rent of Primary Residence in Size Class B/C (CUURX000SEHA) from Dec 1997 to May 2025 about primary, rent, urban, consumer, CPI, inflation, price index, indexes, price, and USA.