The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% 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 December of 2025.

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.

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.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, 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 December of 2025.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% 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 December of 2025.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-12-02 to 2025-12-01 about stock market, average, industry, and USA.

Other-Long-Term-Assets Time Series for Robinhood Markets Inc. Robinhood Markets, Inc. operates financial services platform in the United States. Its platform allows users to invest in stocks, exchange-traded funds (ETFs), American depository receipts, options, gold, and cryptocurrencies. The company offers fractional trading, recurring investments, fully-paid securities lending, access to investing on margin, cash sweep, instant withdrawals, retirement program, around-the-clock trading, joint investing accounts, event contracts, and future contract services. It also provides various learning and education solutions comprise Snacks, an accessible digest of business news stories for a new generation of investors.; Learn, which is an online collection of guides, feature tutorials, and financial dictionary; Newsfeeds that offer access to free, premium news from sites from various sites, such as Barron's, Reuters, and Dow Jones. In addition, the company offers In-App Education, a resource that covers investing fundamentals, including why people invest, a stock market overview, and tips on how to define investing goals, as well as allows customers to understand the basics of investing before their first trade; and Crypto Learn and Earn, an educational module available to various crypto customers through Robinhood Learn to teach customers the basics related to cryptocurrency. Further, it provides Robinhood credit cards, cash card and spending accounts, and wallets. The company also owns and operates a digital currency marketplace that allows companies and individuals from all around the world to buy and sell bitcoin, litecoin, ethereum, ripple, and bitcoin cash. Robinhood Markets, Inc. was incorporated in 2013 and is headquartered in Menlo Park, California.

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.

The main stock market index of United States, the US500, rose to 6825 points on December 2, 2025, gaining 0.18% from the previous session. Over the past month, the index has declined 0.39%, though it remains 12.82% 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 December of 2025.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-12-01 about composite, NASDAQ, stock market, indexes, and USA.

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 December 1 of 2025.

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

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Axis Bank (AXISBANK), 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_axisbank_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 (AXISBANK).
• 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 AXISBANK. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to AXISBANK. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for AXISBANK. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Axis 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.

Graph and download economic data for Nikkei Stock Average, Nikkei 225 (NIKKEI225) from 1949-05-16 to 2025-12-02 about stocks, stock market, Japan, and indexes.

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

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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.

India's main stock market index, the SENSEX, fell to 85138 points on December 2, 2025, losing 0.59% from the previous session. Over the past month, the index has climbed 1.38% and is up 5.31% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Your feedback, including upvotes, helps improve data resources for the community

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

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for HDFC Bank (HDFCBANK), 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_hdfcbank_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 HDFCBANK. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to HDFCBANK. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for HDFCBANK. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of HDFC 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.

Long-Term-Debt Time Series for Apartment Investment and Management Co. Aimco is a diversified real estate company primarily focused on value add and opportunistic investments, targeting the U.S. multifamily sector. Aimco's mission is to make real estate investments where outcomes are enhanced through our human capital so that substantial value is created for investors, teammates, and the communities in which we operate. Aimco is traded on the New York Stock Exchange as AIV.

Graph and download economic data for Stock Market Capitalization to GDP for United States (DDDM01USA156NWDB) from 1975 to 2020 about market cap, stock market, capital, GDP, and USA.

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

Overview

This dataset provides comprehensive historical Open, High, Low, Close, and Volume (OHLCV) data for Bajaj Finance (BAJFINANCE), 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_bajfinance_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 BAJFINANCE. * Technical Analysis: Generating charts, calculating technical indicators (e.g., Moving Averages, RSI, MACD, Bollinger Bands) specific to BAJFINANCE. * Machine Learning for Price Prediction: Training models to forecast future stock prices, trends, or volatility for BAJFINANCE. * Market Trend Analysis: Studying short-term and long-term market behavior, liquidity, and price action of Bajaj Finance. * 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.