This is a dataset of Dow Jones Industrials Average 1-minute bars for Q1 2020, reproduced with permission from FirstRateData Free Intraday Data.

The 1-minute bars are open, high, low, close values for each interval, no volume is available.

Context The Dow Jones Industrial Average is one of the most followed stock market indexes by investors, financial professionals and the media. Content It measures the daily price movements of 30 large American companies on the Nasdaq and the New York Stock Exchange. The Dow Jones Industrial Average is widely viewed as a proxy for general market conditions and even the economy of the United States. This Dow Jones Industrial Average dataset is downloaded from https://www.investing.com/indices/us-30-historical-data, including 2767 closing records from January 4th 2009 to December 31st 2019.

Dataset Name: Yfinance Dow Jones Industrial Average (2025-04)

Description: This dataset contains historical stock price data for the Dow Jones Industrial Average, sourced from Yahoo Finance.

Columns of dji.csv: - Date: The date of the stock market record. - Open: The opening price of the Dow Jones Industrial Average on that day. - High: The highest price the Dow Jones Industrial Average reached during the day. - Low: The lowest price the Dow Jones Industrial Average reached during the day. - Close (Adj Close): The adjusted closing price, which reflects corporate actions such as dividends and stock splits. - Volume: The trading volume of the Dow Jones Industrial Average. - Ticker: The name of the stock.

Columns of dow_jones_data.csv: - Date: The date of the stock market record. - Open: The opening price of the stock ticker on that day. - High: The highest price the stock ticker reached during the day. - Low: The lowest price the stock ticker reached during the day. - Close (Adj Close): The adjusted closing price, which reflects corporate actions such as dividends and stock splits. - Volume: The trading volume of the stock. - Ticker: The name of the stock.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2016-04-29 to 2026-04-28 about stock market, average, industry, and USA.

The Dow Jones Industrial Average began as 12 companies on May 5, 1896 and currently contains 30 component companies. Over the years, companies have been added and replaced. This is a complete listing of current and former component companies.

This data was compiled to create this quiz: https://hugequiz.com/quizzes/historical-components-of-the-dow-jones-djia/

The main stock market index of United States, the US500, rose to 7152 points on April 29, 2026, gaining 0.18% from the previous session. Over the past month, the index has climbed 12.74% and is up 28.42% 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 April of 2026.

Stock Data of Top USA Companies: Apple, Tesla, Amazon

👨‍💻 Author: Azhar Saleem

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Dataset Description

This dataset provides daily stock data for some of the top companies in the USA stock market, including major players like Apple, Microsoft, Amazon, Tesla, and others. The data is collected from Yahoo Finance, covering each company’s historical data from its starting date until today. This comprehensive dataset enables in-depth analysis of key financial indicators and stock trends for each company, making it valuable for multiple applications.

Column Descriptions

The dataset contains the following columns, consistent across all companies:

• Date: The date of the stock data entry.
• Open: The stock's opening price for the day.
• High: The highest price reached during the trading day.
• Low: The lowest price during the trading day.
• Close: The stock’s closing price for the day.
• Volume: The total number of shares traded on that day.
• Dividends: Any dividends paid out on that day.
• Stock Splits: Records stock split events, if any, on that day.

Potential Use Cases

1. Machine Learning & Deep Learning:

   • Stock Price Prediction: Use historical prices to train models for forecasting future stock prices.
   • Sentiment Analysis and Price Correlation: Combine with external sentiment data to predict price movements based on market sentiment.
   • Anomaly Detection: Detect unusual price patterns or volume spikes using classification algorithms.

2. Data Science:

   • Trend Analysis: Identify long-term trends for each company or compare trends between companies.
   • Volatility Analysis: Calculate volatility to assess risk and return patterns over time.
   • Correlation Analysis: Compare stock performance across companies to study market relationships.

3. Data Analysis:

   • Historical Performance: Review historical data to understand growth trends, market impact of stock splits, and dividends.
   • Seasonal Patterns: Analyze data for seasonal trends or recurring patterns across years.
   • Investment Strategy Backtesting: Test various investment strategies based on historical data to assess potential profitability.

4. Financial Research:

   • Economic Impact Studies: Investigate how major events affected stock prices across top companies.
   • Sector-Specific Analysis: Identify performance differences across sectors, such as tech, healthcare, and retail.

This dataset is a powerful tool for analysts, researchers, and financial enthusiasts, offering versatility across multiple domains from stock analysis to algorithmic trading models.

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

Dataset

This dataset was created by Jonathan Withrow

Released under Database: Open Database, Contents: Database Contents

Contents

List of Licensed Companies to Deal at Amman Stock Market and any other related data such as Address, company capital, Legal Capacity and Granted License

Dataset

This dataset was created by dilshan aththanyaka

Released under Database: Open Database, Contents: Database Contents

Contents

Dataset

This dataset was created by Avik samantha

Contents

The dataset consists of companies listed in the S&P500, stock market index that measures the stock performance of 500 large companies listed on stock exchanges in the United State.

The S&P 500 stock market index, maintained by S&P Dow Jones Indices, comprises 505 common stocks issued by 500 large-cap companies and traded on American stock exchanges (including the 30 companies that compose the Dow Jones Industrial Average)

The S&P500 or SPX is the most commonly followed equity index, it covers about 80 percent of the American equity market by capitalization.

The index constituents and the constituent weights are updated regularly using rules published by S&P Dow Jones Indices. Although called the S&P 500, the index contains 505 stocks

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Japan's main stock market index, the JP225, rose to 60267 points on April 27, 2026, gaining 0.92% from the previous session. Over the past month, the index has climbed 16.15% and is up 68.16% compared to the same time last year, 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 April of 2026.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Overview

This dataset provides historical daily stock price data for the 20 largest financial companies and major global stock indices. It is designed for financial analysis, market trend exploration, and correlation studies. The dataset includes stock price history for the past 1 year for financial companies and for stock indices.

Contents

The dataset consists of multiple CSV files:
1. Stock data files (stock_JPM.csv, stock_BAC.csv, etc.) – Each file contains daily price data for a specific financial company.
2. indices.csv – A merged dataset containing daily closing prices for major stock indices.

Dataset Structure

1. Stock Data Files (stock_JPM.csv, stock_BAC.csv, etc.)

Each stock file contains the following columns:
- Date – Trading day’s closing date (YYYY-MM-DD format).
- Open – Opening price of the stock.
- High – Highest price during the trading day.
- Low – Lowest price during the trading day.
- Close – Closing price of the stock.
- Volume – Number of shares traded.

Removed Columns:
- Dividends – Removed to focus on price movements.
- Stock Splits – Removed as it does not impact short-term trading analysis.

Each file represents a single company’s daily stock performance over the past 365 days.

2. Stock Indices File (indices.csv)

This file contains historical daily closing prices for major global stock indices over the past six months.

Columns:
- Date – Trading day’s closing date (YYYY-MM-DD format).
- GSPC – S&P 500 Index.
- IXIC – NASDAQ Composite Index.
- DJI – Dow Jones Industrial Average (DJIA).
- RUT – Russell 2000 Index.
- GDAXI – DAX Index (Germany).
- FTSE – FTSE 100 Index (United Kingdom).
- FCHI – CAC 40 Index (France).
- HSI – Hang Seng Index (Hong Kong).
- N225 – Nikkei 225 Index (Japan).
- BSESN – BSE Sensex Index (India).
- BVSP – Bovespa Index (Brazil).

Key Adjustments:
- The ticker for S&P 500 (^GSPC) has been renamed to GSPC to avoid special characters.
- The file does not include separate index files, only the merged dataset.
- Missing values are forward-filled to maintain continuity in time series analysis.

Data Sources & Processing

• Stock and index data were collected and processed using the Yahoo Finance API.
• The dataset was prepared with equity_analysis, a custom module developed for efficient equity data retrieval.
• The data updates cover:
  • Financial companies: Last 365 days of daily price data.
  • Stock indices: Last 180 days of daily closing prices.

Potential Use Cases

This dataset is valuable for:
✅ Stock Market Analysis – Studying price trends, volatility, and historical market behavior.
✅ Financial Modeling – Training machine learning models for market prediction.
✅ Portfolio Optimization – Analyzing the correlation between different financial assets.
✅ Comparing Global Markets – Investigating relationships between US, European, and Asian indices.
✅ Backtesting Trading Strategies – Using historical price data for algorithmic trading strategies.

Technical Details

• File format: CSV (comma-separated values)
• Date format: YYYY-MM-DD
• Data interval: Daily
• Timezone: UTC (without timezone adjustments)
• Size: Varies depending on the number of records

Final Notes

• The dataset is regularly updated with the latest stock and index data.
• It is intended for educational and research purposes only, not for real-time trading decisions.
• Future improvements may include additional financial indicators, sentiment analysis, or alternative data sources.

🚀 Use this dataset to explore market trends, build models, and gain financial insights!

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This dataset contains 184,138 daily stock market records for 120 leading US publicly traded companies, spanning 9 major economic sectors. Each record represents one trading day per company and includes essential OHLCV (Open, High, Low, Close, Adjusted Close, Volume) features used extensively in financial analysis, time-series forecasting, quantitative trading, and AI/ML research.

The dataset is clean, complete, and free of missing values, making it ideal for both educational and production-level projects.

🔍 Use cases include:

• 📊 Exploratory Data Analysis (EDA)
• 📈 Time Series Forecasting (ARIMA, LSTM, Prophet)
• 🤖 Machine Learning & Deep Learning models
• 💹 Portfolio analysis & sector comparison
• 🧠 AI-driven financial research and feature engineering

This dataset is especially valuable for multi-stock modeling, sector-wise trend analysis, and cross-company comparisons.

🧩 Column Descriptions (One Line Each)

• Date – Trading date of the stock record.
• Ticker – Stock market ticker symbol (e.g., AAPL, MSFT, TSLA).
• Company_Name – Full registered name of the company.
• Sector – Economic sector the company belongs to (9 total).
• Industry – Specific industry classification of the company.
• Open – Stock price at market opening.
• High – Highest stock price during the trading day.
• Low – Lowest stock price during the trading day.
• Close – Stock price at market close.
• Adj_Close – Closing price adjusted for splits and dividends.
• Volume – Total number of shares traded during the day.

📊 Dataset Summary

• Rows: 184,138
• Columns: 11
• Companies: 120
• Sectors: 9
• Missing Values: None
• Memory Size: ~15.5 MB