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.

This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

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Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

Sure! Here's a copy-paste friendly version of the dataset details you can use directly in your Kaggle dataset description:

📊 Dataset Title: Top 500+ US Stocks - Price, Volume, Market Cap & More (2025)

🧾 Overview

This dataset provides detailed information on over 500 publicly traded US companies, including their current stock price, volume, market capitalization, P/E ratio, and performance indicators such as daily change and 52-week change. It is ideal for financial analysis, algorithmic trading models, or studying market behavior.

📁 Dataset Information

• File Name: stocks.csv
• Total Rows: 529
• Total Columns: 10
• File Size: ~94 KB
• Encoding: UTF-8
• Format: CSV

📌 Column Descriptions

📈 Usage Ideas

• Build a stock screener
• Analyze price performance vs. market cap
• Train machine learning models for price prediction
• Explore relationships between volume, P/E ratio, and growth
• Filter stocks based on volatility or long-term growth

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.

Graph and download economic data for Average Prices of 40 Common Stocks for United States (M11006USM315NNBR) from Jan 1890 to Dec 1915 about stock market and USA.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

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.

T-Mobile Us stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

Prices for United States Stock Market Index (US5000) including live quotes, historical charts and news. United States Stock Market Index (US5000) was last updated by Trading Economics this December 2 of 2025.

United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data was reported at 35.662 % in Apr 2025. This records an increase from the previous number of 33.832 % for Mar 2025. United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data is updated monthly, averaging 39.618 % from Jun 2013 (Median) to Apr 2025, with 143 observations. The data reached an all-time high of 51.840 % in Apr 2020 and a record low of 33.767 % in Jun 2022. United States SCE: Stock Price: Probability That US Stock Prices will be Higher 1 Year from Now data remains active status in CEIC and is reported by Federal Reserve Bank of New York. The data is categorized under Global Database’s United States – Table US.H085: Survey of Consumer Expectations: Financial.

Graph and download economic data for Dow-Jones Industrial Stock Price Index for United States (M1109AUSM293NNBR) from Jan 1897 to Sep 1916 about stock market, industry, price index, indexes, price, and USA.

Prices for United States Stock Market Index (US30) including live quotes, historical charts and news. United States Stock Market Index (US30) was last updated by Trading Economics this December 2 of 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Dow Jones U.S. Completion Total Stock Market Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Huge US Stocks prices + 1292 columns extra data from Indicators. This Dataset provides historical Open, High, Low, Close, and Volume (OHLCV) prices of stocks traded in the United States financial markets AND calculated 1292 columns of indicators. You can use all this hyge data for stock price predictions.

Columns with Momentum Indicator values ADX - Average Directional Movement Index ADXR - Average Directional Movement Index Rating APO - Absolute Price Oscillator AROON - Aroon AROONOSC - Aroon Oscillator BOP - Balance Of Power CCI - Commodity Channel Index CMO - Chande Momentum Oscillator DX - Directional Movement Index MACD - Moving Average Convergence/Divergence MACDEXT - MACD with controllable MA type MACDFIX - Moving Average Convergence/Divergence Fix 12/26 MFI - Money Flow Index MINUS_DI - Minus Directional Indicator MINUS_DM - Minus Directional Movement MOM - Momentum PLUS_DI - Plus Directional Indicator PLUS_DM - Plus Directional Movement PPO - Percentage Price Oscillator ROC - Rate of change : ((price/prevPrice)-1)*100 ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice ROCR - Rate of change ratio: (price/prevPrice) ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100 RSI - Relative Strength Index STOCH - Stochastic STOCHF - Stochastic Fast STOCHRSI - Stochastic Relative Strength Index TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA ULTOSC - Ultimate Oscillator WILLR - Williams' %R

Columns with Volatility Indicator values ATR - Average True Range NATR - Normalized Average True Range TRANGE - True Range

Columns with Volume Indicator values AD - Chaikin A/D Line ADOSC - Chaikin A/D Oscillator OBV - On Balance Volume

Columns with Overlap Studies values BBANDS - Bollinger Bands DEMA - Double Exponential Moving Average EMA - Exponential Moving Average HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline KAMA - Kaufman Adaptive Moving Average MA - Moving average MAMA - MESA Adaptive Moving Average MAVP - Moving average with variable period MIDPOINT - MidPoint over period MIDPRICE - Midpoint Price over period SAR - Parabolic SAR SAREXT - Parabolic SAR - Extended SMA - Simple Moving Average T3 - Triple Exponential Moving Average (T3) TEMA - Triple Exponential Moving Average TRIMA - Triangular Moving Average WMA - Weighted Moving Average

Columns with Cycle Indicator values HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase HT_PHASOR - Hilbert Transform - Phasor Components HT_SINE - Hilbert Transform - SineWave HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode

If you want to download actual data - on today for example, then you can use python code from my github. tickers = ['CE.US', 'WELL.US', 'GRMN.US', 'IEX.US', 'CAG.US', 'BEN.US', 'ATO.US', 'WY.US', 'TSCO.US', 'COR.US', 'MOS.US', 'SWKS.US', 'ORCL.US', 'URI.US', 'INCY.US', 'MPC.US', 'HD.US', 'PPG.US', 'NUE.US', 'DDOG.US', 'HSIC.US', 'CAT.US', 'HSY.US', 'MKTX.US', 'CCEP.US', 'GWW.US', 'LEN.US', 'IFF.US', 'GL.US', 'MDB.US', 'SNPS.US', 'KR.US', 'DVN.US', 'SYY.US', 'USB.US', 'DRI.US', 'PARA.US', 'FMC.US', 'UBER.US', 'WRK.US', 'DLR.US', 'SO.US', 'AMGN.US', 'MA.US', 'STT.US', 'BWA.US', 'KVUE.US', 'GFS.US', 'BBY.US', 'BK.US', 'MRVL.US', 'VFC.US', 'EIX.US', 'ADSK.US', 'ZBH.US', 'MU.US', 'HUBB.US', 'PEAK.US', 'CVX.US', 'CPB.US', 'GILD.US', 'BXP.US', 'DD.US', 'MCD.US', 'KDP.US', 'GE.US', 'PKG.US', 'HST.US', 'WTW.US', 'XOM.US', 'ED.US', 'SPG.US', 'PFG.US', 'LVS.US', 'FAST.US', 'ROST.US', 'TTD.US', 'CNC.US', 'PGR.US', 'CMI.US', 'TEAM.US', 'MELI.US', 'BKR.US', 'EBAY.US', 'CPRT.US', 'MSFT.US', 'HOLX.US', 'ABBV.US', 'AMZN.US', 'FE.US', 'WYNN.US', 'KMI.US', 'APA.US', 'CRWD.US', 'DPZ.US', 'EQT.US', 'NOC.US', 'TAP.US', 'ETR.US', 'T.US', 'OMC.US', 'MTCH.US', 'TRMB.US', 'EXPE.US', 'DTE.US', 'PNR.US', 'LH.US', 'ALL.US', 'CTRA.US', 'VMC.US', 'XRAY.US', 'NWS.US', 'GOOGL.US', 'WEC.US', 'BIIB.US', 'LLY.US', 'BMY.US', 'STE.US', 'NI.US', 'MKC.US', 'AMT.US', 'CFG.US', 'LW.US', 'HIG.US', 'ETSY.US', 'AON.US', 'ULTA.US', 'DVA.US', 'LKQ.US', 'MPWR.US', 'TEL.US', 'FICO.US', 'CVS.US', 'CMA.US', 'NVDA.US', 'TDG.US', 'AWK.US', 'PSA.US', 'FOXA.US', 'ON.US', 'ODFL.US', 'NVR.US', 'ROP.US', 'TFX.US', 'HLT.US', 'EXPD.US', 'FOX.US', 'D.US', 'AMAT.US', 'AZO.US', 'DLTR.US', 'TT.US', 'SBUX.US', 'JNJ.US', 'HAS.US', 'DASH.US', 'NRG.US', 'JNPR.US', 'BIO.US', 'AMD.US', 'NFLX.US', 'VLTO.US', 'BRO.US', 'REGN.US', 'WRB.US', 'LRCX.US', 'SYK.US', 'MCO.US', 'CSGP.US', 'TROW.US', 'ETN.US', 'RTX.US', 'CRM.US', 'SIRI.US', 'UPS.US', 'HES.US', 'RSG.US', 'PEP.US', 'MET.US', 'HON.US', 'IQV.US', 'JPM.US', 'DG.US', 'CBRE.US', 'NDSN.US', 'DOW.US', 'SBAC.US', 'TSN.US', 'IT.US', 'WM.US', 'TPR.US', 'IBM.US', 'CHTR.US', 'HAL.US', 'ROL.US', 'FDS.US', 'SHW.US', 'EW.US', 'RJF.US', 'APH.US', 'AIZ.US', 'ZBRA.US', 'SRE.US', 'CTAS.US', 'PXD.US', 'MTD.US', 'NOW.US', 'MAS.US', 'FFIV.US', 'ELV.US', 'SYF.US', 'CSCO.US', 'APTV...

We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

Stock prices of selected regional banks in the United States plummeted in March 2023, as investor confidence dropped due to economic uncertainties. Silicon Valley Bank failed on 10th of March 2023. The stock prices of the displayed banks fell particularly sharply on and after that day, which was the second largest bank failure since 2001. The observed period was particularly devastating for First Republic, whose stock price was more than ** percent lower on the 20th of March than on the first day of the month.

Companies in this industry conduct research on the financial conditions of publicly traded companies and analyze how they may affect equity prices and prospects for growth.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.