Description

This dataset offers a comprehensive historical record of stock prices for the world's most famous brands, with daily updates. The data spans from January 1, 2000, to the present day , providing an extensive timeline of stock market information for various global brands.

Key Features

• Date: The date of the stock price data.
• Open: The opening price of the stock on that date.
• High: The highest price the stock reached during the trading day.
• Low: The lowest price the stock reached during the trading day.
• Close: The closing price of the stock on that date.
• Volume: The trading volume, i.e., the number of shares traded on that date.
• Dividends: Dividends paid on that date (if any).
• Stock Splits: Information about stock splits (if any).
• Brand_Name: The name of the brand or company.
• Ticker: Ticker symbol for the stock.
• Industry_Tag: The industry category or sector to which the brand belongs.
• Country: The country where the brand is headquartered or primarily operates.

Potential Use Cases

• Stock Market Analysis: Analyze historical stock prices to identify trends and patterns in the stock market.
• Brand Performance: Evaluate the performance of various brands in the stock market over time.
• Investment Strategies: Develop investment strategies based on historical stock data for specific brands.
• Sector Analysis: Explore how different industries or sectors are performing in the stock market.
• Country Comparison: Compare the stock performance of brands across different countries.
• Market Sentiment Analysis: Analyze stock price movements in relation to news or events affecting specific brands or industries.

If you find this dataset useful, please consider giving it a vote! 🙂❤️

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.

Use our Stock prices 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.

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.

This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

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.

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

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.

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.

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.

United Kingdom's main stock market index, the GB100, rose to 10056 points on March 30, 2026, gaining 0.88% from the previous session. Over the past month, the index has declined 6.72%, though it remains 17.16% higher than a year ago, 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 March of 2026.

This dataset provides realistic stock market data generated using Geometric Brownian Motion for price movements and Markov Chains for trend prediction. It is designed for time-series forecasting, financial modeling, and algorithmic trading simulations.

Key Features

• 1000 days of synthetic stock market data (from January 1, 2022, onwards).
• Multiple companies from diverse industries (Technology, Finance, Healthcare, Energy, Consumer Goods, Automotive, Aerospace, etc.).
• Stock price details: Open, High, Low, Close prices.
• Trading volume and market capitalization.
• Financial metrics: P/E Ratio, Dividend Yield, Volatility.
• Sentiment Score: A measure of market sentiment (-1 to 1 scale).
• Trend Labeling: Bullish, Bearish, or Stable, based on Markov Chain modeling.

Column Name	Description
Date	Trading date
Company	Stock name (e.g., Apple, Tesla, JPMorgan, etc.)
Sector	Industry classification
Open	Opening price of the stock
High	Highest price of the stock for the day
Low	Lowest price of the stock for the day
Close	Closing price of the stock
Volume	Number of shares traded
Market_Cap	Market capitalization (in USD)
PE_Ratio	Price-to-Earnings ratio
Dividend_Yield	Percentage of dividends relative to stock price
Volatility	Measure of stock price fluctuation
Sentiment_Score	Market sentiment (-1 to 1 scale)
Trend	Stock market trend (Bullish, Bearish, or Stable)

Usage Scenarios

🔹 Time-Series Forecasting: Train models like LSTMs, Transformers, or ARIMA for stock price prediction.
🔹 Algorithmic Trading: Develop trading strategies based on trends and sentiment.
🔹 Feature Engineering: Explore correlations between financial metrics and stock movements.
🔹 Quantitative Finance Research: Analyze market trends using simulated yet realistic data.

PS: If you find this dataset helpful, please consider upvoting :)

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 Financial Market: Share Prices for United States (SPASTT01USM661N) from Jan 1957 to Jan 2026 about stock market and USA.

Graph and download economic data for Financial Market: Share Prices for United Kingdom (SPASTT01GBM661N) from Dec 1957 to Feb 2026 about stock market and United Kingdom.

Graph and download economic data for Index of Stock Prices (General) for Germany (M1123BDEM334NNBR) from Jan 1924 to Dec 1935 about stock market, Germany, and indexes.

Stock Market Data 2025

This synthetic dataset contains comprehensive daily trading information for 81 major S&P 500 companies spanning in 2025. The data represents synthetically generated but highly realistic stock market conditions with accurate price ranges, sector distributions, and financial metrics that mirror real-world market behavior.

Dataset Overview

Temporal Coverage: 53 trading days (August 1 - August 31, 2025)
Market Universe: 81 S&P 500 constituent companies
Total Records: ~4,293 daily stock entries
Market Context: S&P 500 level at 6,310 with total market cap of $52.5T

Data Schema & Structure

Column Name	Data Type	Description	Example Range
Date	String (YYYY-MM-DD)	Trading date	2025-08-01 to 2025-08-31
Ticker	String	Stock ticker symbol	AAPL, MSFT, NVDA, etc.
Open Price	Float	Opening price for trading day (USD)	$19.00 - $3,800.00
Close Price	Float	Closing price for trading day (USD)	$19.00 - $3,850.00
High Price	Float	Intraday highest price (USD)	$19.50 - $3,900.00
Low Price	Float	Intraday lowest price (USD)	$18.50 - $3,750.00
Volume Traded	Integer	Number of shares traded	500K - 90M shares
Market Cap	Float	Market capitalization (USD)	$68B - $3.2T
PE Ratio	Float	Price-to-Earnings ratio	8.0 - 85.0
Dividend Yield	Float	Annual dividend yield (%)	0.0% - 7.1%
EPS	Float	Earnings Per Share (USD)	$1.50 - $70.00
52 Week High	Float	Highest price in past 52 weeks (USD)	$25.00 - $4,000.00
52 Week Low	Float	Lowest price in past 52 weeks (USD)	$15.00 - $3,200.00
Sector	String	Industry sector classification	10 GICS sectors

Market Composition & Sector Distribution

Sector Breakdown

Sector	Companies	Percentage	Avg Market Cap
Technology	18	22.2%	$850B
Healthcare	15	18.5%	$280B
Financials	14	17.3%	$290B
Consumer Discretionary	8	9.9%	$320B
Consumer Staples	8	9.9%	$310B
Communication Services	5	6.2%	$480B
Industrials	7	8.6%	$155B
Energy	2	2.5%	$385B
Utilities	3	3.7%	$110B
Real Estate	2	2.5%	$110B

Market Capitalization Tiers

• Mega Cap (>$1T): 6 companies (AAPL, MSFT, NVDA, AMZN, GOOGL, META)
• Large Cap ($200B-$1T): 28 companies
• Mid Cap ($50B-$200B): 47 companies

Key Market Characteristics

Price Volatility by Sector

• Technology: Higher volatility (±3.5% daily range)
• Energy: High volatility (±4.0% daily range)
• Utilities: Lower volatility (±1.5% daily range)
• Healthcare/Financials: Moderate volatility (±2.5% daily range)

Trading Volume Patterns

• Mega Cap: 25M - 90M shares daily
• Large Cap: 8M - 35M shares daily
• Mid Cap: 2M - 15M shares daily
• Small Cap: 500K - 5M shares daily

Financial Metrics Distribution

• Average P/E Ratio: 25.9 (market-wide)
• Average Dividend Yield: 1.25%
• Price Range: $19 (T) to $3,850 (BKNG)
• EPS Range: $1.50 to $70.00

Notable Market Features

High-Value Stocks

• BKNG (Booking Holdings): $3,650-$3,850 range
• AVGO (Broadcom): $1,650-$1,750 range
• REGN (Regeneron): $1,050-$1,150 range
• LLY (Eli Lilly): $920-$980 range

High-Dividend Yielders

• T (AT&T): 7.1% dividend yield
• VZ (Verizon): 6.2% dividend yield
• PFE (Pfizer): 5.8% dividend yield

Growth & Technology Leaders

• NOW (ServiceNow): P/E ratio of 85
• NVDA (NVIDIA): P/E ratio of 45
• TSLA (Tesla): P/E ratio of 55

Data Quality & Realism Features

✅ Authentic Price Ranges: Based on realistic 2025 market projections
✅ Sector-Appropriate Volatility: Different volatility patterns by industry
✅ Correlated Metrics: P/E ratios, dividend yields, and EPS align with market caps
✅ Realistic Trading Volumes: Volume scaled appropriately to market cap
✅ Temporal Consistency: Logical price progression over 53-day period
✅ Market Cap Accuracy: Daily fluctuations reflect actual price movements

Intended Use Cases

• Financial Analysis & Modeling: Portfolio optimization, risk assessment
• Machine Learning Applications: Predictive modeling, algorithmic trading
• Educational Purposes: Finance courses, data science training
• Algorithm Development: Backtesting trading strategies
• Market Research: Sector analysis, correlation studies
• Visualization Projects: Interactive dashboards, market trend analysis

This dataset provides a comprehensive foundation for quantitative finance research, offering both breadth across market sectors and depth in daily trading dynamics while maintaining statisti...

Prices for United States Stock Market Index (US500) including live quotes, historical charts and news. United States Stock Market Index (US500) was last updated by Trading Economics this March 18 of 2026.

The value of the DJIA index amounted to ********* at the end of February 2026, 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.

This statistic shows the stock prices of selected oil and gas commodities from January 2, 2020 to March 6, 2025. After the Russian invasion of Ukraine in February 2022, energy prices climbed significantly. The highest increase can be observed for natural gas, whose price peaked in August and September 2022. By the beginning of 2023, natural gas prices started to decline. Since the end of February, prices started to increase again due to the developments in the Middle East.