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

The main stock market index of United States, the US500, rose to 6375 points on March 30, 2026, gaining 0.09% from the previous session. Over the past month, the index has declined 7.37%, though it remains 13.59% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on March of 2026.

📌 About This Dataset

This dataset contains daily historical stock market data for three major US companies:

• 🍎 Apple (AAPL) • 🪟 Microsoft (MSFT) • 🚗 Tesla (TSLA)

It includes essential trading features such as:

✔ Open ✔ High ✔ Low ✔ Close ✔ Adjusted Close ✔ Volume

The data is structured in a multi-ticker format, making it perfect for: • 📊 Financial analysis • 🤖 Machine learning • 🔮 Price prediction • 📈 Portfolio research • 🧠 Time series forecasting

🧪 Data Description Column Group Description Price Adjusted Close & Close prices Open Opening price of the stock High Highest price of the day Low Lowest price of the day Volume Total number of shares traded Ticker Stock symbol (AAPL, MSFT, TSLA) Date Trading date (YYYY-MM-DD)

Each company has its own values under the same feature category for easy comparison.

stock-market finance time-series tesla apple microsoft machine-learning deep-learning lstm forecasting trading investment kaggle-dataset ai python

The main stock market index of United States, the US500, rose to 6899 points on February 25, 2026, gaining 0.13% from the previous session. Over the past month, the index has declined 0.73%, though it remains 15.84% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on February of 2026.

The main stock market index of United States, the US500, fell to 6369 points on March 27, 2026, losing 1.67% from the previous session. Over the past month, the index has declined 7.45%, though it remains 14.12% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on March of 2026.

Dataset

This dataset was created by Joseph Armstrong

Released under Database: Open Database, Contents: © Original Authors

Contents

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.

The US Stock Market Historical Dataset contains past trading data of major companies listed on prominent American stock exchanges such as the New York Stock Exchange (NYSE) and NASDAQ. This dataset typically includes daily records of stock prices such as Open, High, Low, Close (OHLC) values, trading Volume, and sometimes Adjusted Close prices.

It provides long-term historical data that helps analysts study market trends, price movements, volatility, and investment performance over time. The dataset may cover large-cap companies, including firms listed in the S&P 500, as well as technology-focused stocks from the NASDAQ Composite.

This dataset is widely used for:

• 📈 Financial analysis and forecasting
• 🤖 Machine learning model training
• 📊 Time series analysis
• 💼 Investment strategy development
• 📉 Risk management and portfolio optimization

Researchers, students, and financial professionals use this dataset to understand historical market behavior, compare company performance, and predict future trends based on past patterns. It is a valuable resource for anyone working in finance, data science, or economic research.

Graph and download economic data for Index of Preferred Stock Prices, New York Stock Exchange for United States (M11008USM322NNBR) from Jan 1902 to May 1923 about stock market, New York, indexes, and USA.

PURPOSE (possible uses)

Non-professional investors often try to find an interesting stock among those in an index (such as the Standard and Poor's 500, Nasdaq, etc.). They need only one company, the best, and they don't want to fail (perform poorly). So, the metric to optimize is accuracy, described as:

Accuracy = True Positives / (True Positives + False Positives)

And the predictive model can be a binary classifier.

The data covers the price and volume of shares of 31 NASDAQ companies in the year 2022.

Context

Every data set I found to predict a stock price (investing) aims to find the price for the next day, and only for that stock. But in practical terms, people like to find the best stocks to buy from an index and wait a few days hoping to get an increase in the price of this investment.

Content

Rows are grouped by companies and their age (newest to oldest) on a common date. The first column is the company. The following are the age, market, date (separated by year, month, day, hour, minute), share volume, various traditional prices of that share (close, open, high...), some price and volume statistics and target. The target is mainly defined as 1 when the closing price increases by at least 5% in 5 days (open market days). The target is 0 in any other case.

Complex features and target were made by executing: https://www.kaggle.com/code/luisandresgarcia/202307

Thanks

Many thanks to everyone who participates in scientific papers and Kaggle notebooks related to financial investment.

4974 Stock Symbols End of day data. Includes close open high low volume and date. Data was collected from Google finance public data. +—————+——————+ | Table | Size in MB | +—————+——————+ | surf_eod | 1109.00 | +—————+——————+ 1 row in set (0.00 sec) mysql> SELECT COUNT(DISTINCT( ticker )) FROM surf_eod; +—————————————-+ | COUNT(DISTINCT( ticker )) | +—————————————-+ | 4974 | +—————————————-+ 1 row in set (6.31 sec) mysql> describe surf_eod; +————+——————-+—&mdash

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

Stock market index in the USA, February, 2026 The most recent value is 217.92 points as of February 2026, an increase compared to the previous value of 212.01 points. Historically, the average for the USA from January 1960 to February 2026 is 47.1 points. The minimum of 2.98 points was recorded in June 1962, while the maximum of 217.92 points was reached in February 2026. | TheGlobalEconomy.com

The provided dataset is a CSV file titled "US Stock Market Dataset" containing financial data for various stocks and indices from January 1, 2020, to January 26, 2024. The dataset includes the following columns: Date Natural Gas Price Natural Gas Vol. Crude Oil Price Crude Oil Vol. Copper Price Copper Vol. Bitcoin Price Bitcoin Vol. Platinum Price Platinum Vol. Ethereum Price Ethereum Vol. S&P 500 Price Nasdaq 100 Price Nasdaq 100 Vol. Apple Price Apple Vol. Tesla Price Tesla Vol. Microsoft Price Microsoft Vol. Silver Price Silver Vol. Google Price Google Vol. Nvidia Price Nvidia Vol. Berkshire Price Berkshire Vol. Netflix Price Netflix Vol. Amazon Price Amazon Vol. Meta Price Meta Vol. Gold Price Gold Vol. Bitcoin Price (5 Minute) Bitcoin Vol. (5 Minute)

The main stock market index of United States, the US500, rose to 6420 points on March 30, 2026, gaining 0.81% from the previous session. Over the past month, the index has declined 6.70%, though it remains 14.41% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on March of 2026.

All data acquired on December 11th 2023

1) Ticker: Stock symbol identifying the company.

2) Company: Name of the company.

3) Sector: Industry category to which the company belongs.

4) Industry: Specific sector or business category of the company.

5) Country: Country where the company is based.

6) Market Cap: Total market value of a company's outstanding shares.

7) Price: Current stock price.

8) Change (%): Percentage change in stock price.

9) Volume: Number of shares traded.

10) Price to Earnings Ratio: Ratio of stock price to earnings per share.

11) Price to Earnings: Price-to-earnings ratio based on past earnings.

12) Forward Price to Earnings: Expected price-to-earnings ratio.

13) Price/Earnings to Growth: Ratio of P/E to earnings growth.

14) Price to Sales: Ratio of stock price to annual sales.

15) Price to Book: Ratio of stock price to book value.

16) Price to Cash: Ratio of stock price to cash per share.

17) Price to Free Cash Flow: Ratio of stock price to free cash flow.

18) Earnings Per Share This Year (%): Percentage change in earnings per share for the current year.

19) Earnings Per Share Next Year (%): Percentage change in earnings per share for the next year.

20) Earnings Per Share Past 5 Years (%): Percentage change in earnings per share over the past 5 years.

21) Earnings Per Share Next 5 Years (%): Estimated percentage change in earnings per share over the next 5 years.

22) Sales Past 5 Years (%): Percentage change in sales over the past 5 years.

23) Dividend (%): Dividend yield as a percentage of the stock price.

24) Return on Assets (%): Percentage return on total assets.

25) Return on Equity (%): Percentage return on shareholder equity.

26) Return on Investment (%): Percentage return on total investment.

27) Current Ratio: Ratio of current assets to current liabilities.

28) Quick Ratio: Ratio of liquid assets to current liabilities.

29) Long-Term Debt to Equity: Ratio of long-term debt to shareholder equity.

30) Debt to Equity: Ratio of total debt to shareholder equity.

31) Gross Margin (%): Percentage difference between revenue and cost of goods sold.

32) Operating Margin (%): Percentage of operating income to revenue.

33) Profit Margin: Percentage of net income to revenue.

34) Earnings: Net income of the company.

35) Outstanding Shares: Total number of shares issued by the company.

36) Float: Tradable shares available to the public.

37) Insider Ownership (%): Percentage of company owned by insiders.

38) Insider Transactions: Recent insider buying or selling activity.

39) Institutional Ownership (%): Percentage of company owned by institutional investors.

40) Float Short (%): Percentage of tradable shares sold short by investors.

41) Short Ratio: Number of days it would take to cover short positions.

42) Average Volume: Average number of shares traded daily.

43) Performance (Week) (%): Weekly stock performance percentage.

44) Performance (Month) (%): Monthly stock performance percentage.

45) Performance (Quarter) (%): Quarterly stock performance percentage.

46) Performance (Half Year) (%): Semi-annual stock performance percentage.

47) Performance (Year) (%): Annual stock performance percentage.

48) Performance (Year to Date) (%): Year-to-date stock performance percentage.

49) Volatility (Week) (%): Weekly stock price volatility percentage.

50) Volatility (Month) (%): Monthly stock price volatility percentage.

51) Analyst Recommendation: Analyst consensus recommendation on the stock.

52) Relative Volume: Volume compared to the average volume.

53) Beta: Measure of stock price volatility relative to the market.

54) Average True Range: Average price range of a stock.

55) Simple Moving Average (20) (%): Percentage difference from the 20-day simple moving average.

56) Simple Moving Average (50) (%): Percentage difference from the 50-day simple moving average.

57) Simple Moving Average (200) (%): Percentage difference from the 200-day simple moving average.

58) Yearly High (%): Percentage difference from the yearly high stock price.

59) Yearly Low (%): Percentage difference from the yearly low stock price.

60) Relative Strength Index: Momentum indicator measuring the speed and change of price movements.

61) Change from Open (%): Percentage change from the opening stock price.

62) Gap (%): Percentage difference between the previous close and the current open price.

63) Volume: Total number of shares traded.

📊 Overview

Welcome to the Fortune 90+ US Stock Market Dataset - a comprehensive collection of historical market data covering over 90 major American corporations. This dataset is designed for data scientists, financial analysts, machine learning enthusiasts, and anyone interested in understanding stock market dynamics and building predictive models.

Whether you're exploring market trends, developing trading algorithms, or conducting financial research, this dataset provides the foundation you need to unlock valuable insights from the US stock market.

🎯 What's Inside?

This dataset contains rich historical trading data for 90+ publicly traded US companies spanning multiple sectors and industries. The data includes:

Stock Price Information: Open, High, Low, Close prices for comprehensive price action analysis Trading Volume Data: Daily trading volumes to understand market liquidity and investor interest Market Capitalization: Company valuation metrics over time Temporal Coverage: Multi-year historical data enabling long-term trend analysis Diverse Sectors: Companies from Technology, Finance, Healthcare, Energy, Consumer Goods, and more

Each ticker symbol represents a major American corporation, providing a well-rounded view of the US equity market landscape.

💡 Use Cases & Applications

This dataset is perfect for:

📈 Financial Analysis & Research

• Analyze historical price movements and market trends
• Study sector-specific performance and correlations
• Investigate market volatility and risk metrics
• Conduct fundamental and technical analysis

🤖 Machine Learning & Predictive Modeling

• Build stock price prediction models using various algorithms
• Develop time series forecasting systems (LSTM, ARIMA, Prophet)
• Create classification models for trend direction prediction
• Train ensemble models (Random Forest, XGBoost, Gradient Boosting)

📊 Data Visualization & Insights

• Create interactive dashboards and visual analytics
• Generate candlestick charts and technical indicators
• Visualize market correlations and sector performance
• Build portfolio performance tracking tools

💼 Educational Projects

• Learn data analysis with real-world financial data
• Practice data preprocessing and feature engineering
• Understand stock market mechanics and trading patterns
• Develop algorithmic trading strategies

🔍 Comparative Analysis

• Compare performance across different sectors
• Identify market leaders and underperformers
• Analyze inter-company relationships and dependencies
• Study market reactions to economic events

🛠️ Technical Details

• Format: CSV (Comma-Separated Values)
• Structure: Clean, ready-to-use tabular data
• Quality: Pre-processed and validated for accuracy
• Accessibility: Easy to load with Python (pandas), R, or any data analysis tool

Typical Data Schema:

• Date/Timestamp
• Open Price
• High Price
• Low Price
• Close Price
• Stock Splits Price
• Trading Volume Additional market metrics (if applicable)

🚀 Getting Started

Quick Start with Python:

`import pandas as pd

Load the dataset

df = pd.read_csv('fortune_90_stock_data.csv')

Basic exploration

print(df.head()) print(df.info()) print(df.describe())`

Suggested Libraries:

• Data Analysis: pandas, numpy
• Visualization: matplotlib, seaborn, plotly
• Machine Learning: scikit-learn, TensorFlow, PyTorch
• Financial Analysis: TA-Lib, yfinance, pandas-ta

📚 Who Should Use This Dataset?

✅ Data Scientists looking for real-world financial data for ML projects ✅ Financial Analysts conducting market research and investment analysis ✅ Students & Researchers learning about stock markets and quantitative finance ✅ Developers building fintech applications and trading platforms ✅ Traders exploring algorithmic trading strategies ✅ Portfolio Managers analyzing diversification and risk management

🎓 Learning Opportunities

This dataset enables you to:

• Master time series analysis techniques
• Understand market behavior and economic cycles
• Apply statistical methods to financial data
• Build and evaluate predictive models
• Develop data-driven investment strategies
• Create professional-grade financial visualizations

🌟 Why This Dataset Stands Out

✨ Comprehensive Coverage: 90+ major US corporations across diverse sectors ✨ High Quality: Clean, validated, and ready for immediate analysis ✨ Versatile: Suitable for beginners and advanced practitioners alike ✨ Real-World Data: Actual market data reflecting true market dynamics ✨ Multi-Purpose: Supports academic research, ML projects, and professional analysis

🔑 Keywords for Discovery

Primary Keywords:

stock market data, US stocks, historical stock prices, stock market dataset, NYSE data, NASDAQ data, equity market data, trading data, financial data, stock price pre...

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.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2016-03-28 to 2026-03-27 about stock market, average, industry, and USA.