The main stock market index of United States, the US500, rose to 6764 points on April 7, 2026, gaining 2.30% from the previous session. Over the past month, the index has declined 0.47%, though it remains 35.75% 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 April of 2026.

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 10609 points on April 8, 2026, gaining 2.51% from the previous session. Over the past month, the index has climbed 3.51% and is up 38.15% 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 April of 2026.

The main stock market index of United States, the US500, rose to 6785 points on April 8, 2026, gaining 2.53% from the previous session. Over the past month, the index has declined 0.17%, though it remains 24.33% 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 April of 2026.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2016-04-11 to 2026-04-08 about stock market, average, industry, and USA.

The Dow Jones Industrial Average (DJIA) is a stock market index used to analyze trends in the stock market. While many economists prefer to use other, market-weighted indices (the DJIA is price-weighted) as they are perceived to be more representative of the overall market, the Dow Jones remains one of the most commonly-used indices today, and its longevity allows for historical events and long-term trends to be analyzed over extended periods of time. Average changes in yearly closing prices, for example, shows how markets developed year on year. Figures were more sporadic in early years, but the impact of major events can be observed throughout. For example, the occasions where a decrease of more than 25 percent was observed each coincided with a major recession; these include the Post-WWI Recession in 1920, the Great Depression in 1929, the Recession of 1937-38, the 1973-75 Recession, and the Great Recession in 2008.

Graph and download economic data for NASDAQ Composite (NASDAQCOM) from 1971-02-05 to 2026-04-07 about composite, NASDAQ, stock market, indexes, and USA.

INFY OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Infosys (INFY), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_infy_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns: * timestamp: The human-readable timestamp of the candle, precisely parsed to the second. * open: The opening price of the stock during that interval. * high: The highest price reached during that interval. * low: The lowest price reached during that interval. * close: The closing price of the stock during that interval. * volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for INFY. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to INFY. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for INFY. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Infosys. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

📊 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...

In 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.

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 April 2 of 2026.

View monthly updates and historical trends for S&P 500 1 Year Return (DISCONTINUED). from United States. Source: Standard and Poor's. Track economic data …

Japan's main stock market index, the JP225, rose to 56924 points on April 10, 2026, gaining 1.84% from the previous session. Over the past month, the index has climbed 3.45% and is up 69.49% 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.

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.

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ICICIBANK OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for ICICI Bank (ICICIBANK), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_icicibank_ohlcv_data.csv), which has been consolidated from multiple original JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• timestamp: The human-readable timestamp of the candle, precisely parsed to the second.
• open: The opening price of the stock during that interval.
• high: The highest price reached during that interval.
• low: The lowest price reached during that interval.
• close: The closing price of the stock during that interval.
• volume: The trading volume (number of shares traded) during that interval.

The dataset covers a substantial historical period, and the total number of records will be the sum of records from each interval file.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for ICICIBANK. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to ICICIBANK. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for ICICIBANK. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of ICICI Bank. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

Sweden's main stock market index, the Stockholm 30, rose to 2968 points on April 7, 2026, gaining 0.08% from the previous session. Over the past month, the index has declined 0.83%, though it remains 32.66% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on April of 2026.

Argentina's main stock market index, the Merval, rose to 3009362 points on April 8, 2026, gaining 1.24% from the previous session. Over the past month, the index has climbed 14.30% and is up 37.82% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Argentina. Argentina Stock Market (MERVAL) - values, historical data, forecasts and news - updated on April of 2026.

Canada's main stock market index, the TSX, rose to 33624 points on April 8, 2026, gaining 1.16% from the previous session. Over the past month, the index has climbed 1.31% and is up 41.71% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on April of 2026.

ASIANPAINT OHLCV Data: Multi-Interval Historical Stock Prices (NSE)

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Asian Paints (ASIANPAINT), a prominent Indian stock listed on the National Stock Exchange (NSE). The data has been consolidated from various time intervals (1-minute, 5-minute, 15-minute, and 1-day), offering a granular yet unified view for diverse analytical needs, from high-frequency trading simulations to long-term trend analysis.

Data Source

The raw data was collected programmatically using the Groww API. The specific API endpoint used for fetching charting data is: https://groww.in/v1/api/charting_service/v4/chart/exchange/NSE/segment/CASH. While efforts have been made during data fetching and consolidation to ensure accuracy, please be aware that financial data can sometimes be subject to minor corrections or revisions by data providers.

Dataset Contents

This dataset is provided as a single, unified CSV file (e.g., unified_asianpaint_ohlcv_data.csv), which has been consolidated from multiple JSON files representing different time intervals (1m, 5m, 15m, 1d).

The unified CSV file contains the following columns:

• Symbol: The stock ticker symbol (ASIANPAINT).
• Interval: The original time interval of the candle (1m, 5m, 15m, 1d).
• DateTime: The human-readable timestamp of the candle (derived from Unix Timestamp, e.g., YYYY-MM-DD HH:MM:SS).
• Open: The opening price of the stock during that interval.
• High: The highest price reached during that interval.
• Low: The lowest price reached during that interval.
• Close: The closing price of the stock during that interval.
• Volume: The trading volume (number of shares traded) during that interval.
• Timestamp: The raw Unix timestamp.

Overall Date Range and Record Count: The exact historical date range and total number of records for the complete dataset depend on the full content and consolidation process of the original JSON files. Your conversion script will provide the precise earliest and latest dates, as well as the total number of records in this unified CSV file. Based on the original file snippets, the data appears to span from at least mid-2022 into mid-2025.

Potential Use Cases

This dataset can be highly valuable for various applications in quantitative finance and data science, including: * Algorithmic Trading Strategy Development: Backtesting and optimizing trading strategies across different timeframes for ASIANPAINT. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to ASIANPAINT. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for ASIANPAINT. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Asian Paints. * Educational Purposes: A clean, multi-interval dataset ideal for learning and practicing data analysis with financial time series.

Disclaimer

This dataset is provided for informational and educational purposes only. It should not be considered financial advice, investment recommendations, or a solicitation to buy or sell any securities. Trading and investing in financial markets involve significant risk, and past performance is not indicative of future results. Always conduct your own thorough research and consult with a qualified financial advisor before making any investment decisions. The creators of this dataset are not liable for any losses incurred from its use.

Graph and download economic data for Nikkei Stock Average, Nikkei 225 (NIKKEI225) from 1949-05-16 to 2026-04-09 about stocks, stock market, Japan, and indexes.