12 datasets found
  1. T

    Nvidia | NVDA - Sales Revenues

    • tradingeconomics.com
    csv, excel, json, xml
    Updated Apr 15, 2025
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    TRADING ECONOMICS (2025). Nvidia | NVDA - Sales Revenues [Dataset]. https://tradingeconomics.com/nvda:us:sales
    Explore at:
    excel, xml, csv, jsonAvailable download formats
    Dataset updated
    Apr 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 1, 2000 - Jul 13, 2025
    Area covered
    United States
    Description

    Nvidia reported $44.1B in Sales Revenues for its fiscal quarter ending in April of 2025. Data for Nvidia | NVDA - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

  2. šŸ’±15Y Stock Data: NVDA, AAPL, MSFT, GOOGL & AMZNšŸ’¹

    • kaggle.com
    Updated Apr 20, 2025
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    maria nadeem (2025). šŸ’±15Y Stock Data: NVDA, AAPL, MSFT, GOOGL & AMZNšŸ’¹ [Dataset]. https://www.kaggle.com/datasets/marianadeem755/stock-market-data/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 20, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    maria nadeem
    License

    Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
    License information was derived automatically

    Description
    • This is the Historical Stock Market Data of five major Big Tech companies: NVIDIA (NVDA), Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) over a 15 years from January 1, 2010 to January 1, 2025.
    • It includes daily stock data with opening and closing prices, highs, lows and trading volume.
    • This dataset serves as a valuable resource for analyzing long term growth trends, volatility and market behavior of leading tech giants.
    • By analyzing this dataset, we can gain a deeper understanding of NVDA, AAPL, MSFT, GOOGL, and AMZN's historical stock behavior over 15 years and make predictions about their future performance.

    Columns Description:

    1. Date: The trading date of the stock data entry.
    2. Close_AAPL: Apple’s stock price at market close at the end of the trading days.
    3. Close_AMZN: Amazon’s stock price at market close at the end of the trading days.
    4. Close_GOOGL: Google’s stock price at market close at the end of the trading days.
    5. Close_MSFT: Microsoft’s stock price at the end of the trading days.
    6. Close_NVDA: NVIDIA’s stock price at the end of the trading days.
    7. High_AAPL: The highest price of Apple’s stock reached during the trading days.
    8. High_AMZN: The highest price of Amazon’s stock reached during the trading days.
    9. High_GOOGL: The highest price of Google’s stock reached during the trading days.
    10. High_MSFT: The highest price of Microsoft’s stock reached during the trading days.
    11. High_NVDA: The highest price of NVIDIA’s stock reached during the trading days.
    12. Low_AAPL: The lowest price of Apple’s stock reached during the trading days.
    13. Low_AMZN: The lowest price of Amazon’s stock reached during the trading days.
    14. Low_GOOGL: The lowest price of Google’s stock reached during the trading days.
    15. Low_MSFT: The lowest price of Microsoft’s stock reached during the trading days.
    16. Low_NVDA: The lowest price NVIDIA’s stock reached during the trading days.
    17. Open_AAPL: Apple’s opening stock price at the beginning of the trading days.
    18. Open_AMZN: Amazon’s opening stock price at the beginning of the trading days.
    19. Open_GOOGL: Google’s opening stock price at the beginning of the trading days.
    20. Open_MSFT: Microsoft’s opening stock price at the beginning of the trading days.
    21. Open_NVDA: NVIDIA’s opening stock price at the beginning of the trading days.
    22. Volume_AAPL: The number of shares traded of Apple’s stock during the trading days.
    23. Volume_AMZN: The number of shares traded of Amazon’s stock during the trading days.
    24. Volume_GOOGL: The number of shares traded of Google’s stock during the trading days.
    25. Volume_MSFT: The number of shares traded of Microsoft’s stock during the trading days.
    26. Volume_NVDA: The number of shares traded of NVIDIA’s stock during the trading days.

    Usefulness of Data:

    1. Trend Analysis: This dataset can be used for the analysis of long term stock price trends for major 5 tech companies. By analyzing this dataset and taking deep insights about the data and stock patterns over 15 years, investors can identify potential opportunities.
    2. Volatility and Risk Assessment: The data helps to assess the volatility of 5 big tech companies' stocks by comparing highs and lows and provides the management strategies to the investors.
    3. Predictive Modeling: With stock prices, this dataset can be used for developing predictive models such as forecasting future stock prices using techniques such as ARIMA, SARIMAX, or Deep Learning Models.
    4. Comparative Analysis: By analyzing this Dataset, researchers and analysts can compare the performance of NVIDIA, Apple, Microsoft, Google, and Amazon over 15 years, which helps to identify trends in the stock market and relative growth between these companies.
    5. Market Behavior Understanding: By analyzing how each stock reacts to major market events (e.g., earnings reports & macroeconomic changes, etc.), we can understand the companies' growth & patterns.

    https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F17226110%2Fb9d7d8fe0c03086606ebbd7e2e2db04d%2FSock%20Market%20Image.png?generation=1745136427757536&alt=media" alt="">

  3. NVIDIA: Still a Wise Investment? (NVDA) (Forecast)

    • kappasignal.com
    Updated Apr 27, 2024
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    KappaSignal (2024). NVIDIA: Still a Wise Investment? (NVDA) (Forecast) [Dataset]. https://www.kappasignal.com/2024/04/nvidia-still-wise-investment-nvda.html
    Explore at:
    Dataset updated
    Apr 27, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    NVIDIA: Still a Wise Investment? (NVDA)

    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

  4. The Nvidia Stock Price: A Game Theory Analysis (Forecast)

    • kappasignal.com
    Updated Jun 2, 2023
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    KappaSignal (2023). The Nvidia Stock Price: A Game Theory Analysis (Forecast) [Dataset]. https://www.kappasignal.com/2023/06/the-nvidia-stock-price-game-theory.html
    Explore at:
    Dataset updated
    Jun 2, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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 Nvidia Stock Price: A Game Theory Analysis

    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

  5. Top Tech Companies Stock Price

    • kaggle.com
    Updated Nov 24, 2020
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    Tomas Mantero (2020). Top Tech Companies Stock Price [Dataset]. https://www.kaggle.com/datasets/tomasmantero/top-tech-companies-stock-price
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 24, 2020
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Tomas Mantero
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    In this dataset you can find the Top 100 companies in the technology sector. You can also find 5 of the most important and used indices in the financial market as well as a list of all the companies in the S&P 500 index and in the technology sector.

    The Global Industry Classification Standard also known as GICS is the primary financial industry standard for defining sector classifications. The Global Industry Classification Standard was developed by index providers MSCI and Standard and Poor’s. Its hierarchy begins with 11 sectors which can be further delineated to 24 industry groups, 69 industries, and 158 sub-industries.

    You can read the definition of each sector here.

    The 11 broad GICS sectors commonly used for sector breakdown reporting include the following: Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, Information Technology, Telecommunication Services, Utilities and Real Estate.

    In this case we will focuse in the Technology Sector. You can see all the sectors and industry groups here.

    To determine which companies, correspond to the technology sector, we use Yahoo Finance, where we rank the companies according to their ā€œMarket Capā€. After having the list of the Top 100 best valued companies in the sector, we proceeded to download the historical data of each of the companies using the NASDAQ website.

    Regarding to the indices, we searched various sources to find out which were the most used and determined that the 5 most frequently used indices are: Dow Jones Industrial Average (DJI), S&P 500 (SPX), NASDAQ Composite (IXIC), Wilshire 5000 Total Market Inde (W5000) and to specifically view the technology sector SPDR Select Sector Fund - Technology (XLK). Historical data for these indices was also obtained from the NASDQ website.

    Content

    In total there are 107 files in csv format. They are composed as follows:

    • 100 files contain the historical data of tech companies.
    • 5 files contain the historical data of the most used indices.
    • 1 file contain the list of all the companies in the S&P 500 index.
    • 1 file contain the list of all the companies in the technology sector.

    Column Description

    Every company and index file has the same structure with the same columns:

    Date: It is the date on which the prices were recorded. High: Is the highest price at which a stock traded during the course of the trading day. Low: Is the lowest price at which a stock traded during the course of the trading day. Open: Is the price at which a stock started trading when the opening bell rang. Close: Is the last price at which a stock trades during a regular trading session. Volume: Is the number of shares that changed hands during a given day. Adj Close: The adjusted closing price factors in corporate actions, such as stock splits, dividends, and rights offerings.

    The two other files have different columns names:

    List of S&P 500 companies

    Symbol: Ticker symbol of the company. Name: Name of the company. Sector: The sector to which the company belongs.

    Technology Sector Companies List

    Symbol: Ticker symbol of the company. Name: Name of the company. Price: Current price at which a stock can be purchased or sold. (11/24/20) Change: Net change is the difference between closing prices from one day to the next. % Change: Is the difference between closing prices from one day to the next in percentage. Volume: Is the number of shares that changed hands during a given day. Avg Vol: Is the daily average of the cumulative trading volume during the last three months. Market Cap (Billions): Is the total value of a company’s shares outstanding at a given moment in time. It is calculated by multiplying the number of shares outstanding by the price of a single share. PE Ratio: Is the ratio of a company's share (stock) price to the company's earnings per share. The ratio is used for valuing companies and to find out whether they are overvalued or undervalued.

    Acknowledgements

    SEC EDGAR | Company Filings NASDAQ | Historical Quotes Yahoo Finance | Technology Sector Wikipedia | List of S&P 500 companies S&P Dow Jones Indices | S&P 500 [S&P Dow Jones Indices | DJI](https://www.spglobal.com/spdji/en/i...

  6. Global import data of Nvidia

    • volza.com
    csv
    Updated Jun 30, 2025
    + more versions
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    Volza FZ LLC (2025). Global import data of Nvidia [Dataset]. https://www.volza.com/p/nvidia/import/import-in-malaysia/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jun 30, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Count of importers, Sum of import value, 2014-01-01/2021-09-30, Count of import shipments
    Description

    616 Global import shipment records of Nvidia with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

  7. Understanding the Volatility of NVIDIA Stock: A Comprehensive Analysis...

    • kappasignal.com
    Updated May 25, 2023
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    KappaSignal (2023). Understanding the Volatility of NVIDIA Stock: A Comprehensive Analysis (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/understanding-volatility-of-nvidia.html
    Explore at:
    Dataset updated
    May 25, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Understanding the Volatility of NVIDIA Stock: A Comprehensive Analysis

    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

  8. Nvidia Stock Hits All-Time High: What's Driving the Bull Run? (Forecast)

    • kappasignal.com
    Updated May 27, 2023
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    KappaSignal (2023). Nvidia Stock Hits All-Time High: What's Driving the Bull Run? (Forecast) [Dataset]. https://www.kappasignal.com/2023/05/nvidia-stock-hits-all-time-high-whats.html
    Explore at:
    Dataset updated
    May 27, 2023
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Nvidia Stock Hits All-Time High: What's Driving the Bull Run?

    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

  9. US Stock Market and Commodities Data (2020-2024)

    • kaggle.com
    Updated Sep 1, 2024
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    Muhammad Ehsan (2024). US Stock Market and Commodities Data (2020-2024) [Dataset]. https://www.kaggle.com/datasets/muhammadehsan02/us-stock-market-and-commodities-data-2020-2024/code
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Sep 1, 2024
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Muhammad Ehsan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    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.

  10. Nvidia (NVDA) Chip Giant's Next Chapter (Forecast)

    • kappasignal.com
    Updated Aug 27, 2024
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    KappaSignal (2024). Nvidia (NVDA) Chip Giant's Next Chapter (Forecast) [Dataset]. https://www.kappasignal.com/2024/08/nvidia-nvda-chip-giants-next-chapter.html
    Explore at:
    Dataset updated
    Aug 27, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    Nvidia (NVDA) Chip Giant's Next Chapter

    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

  11. (NVDA) NVIDIA: Riding the AI Wave (Forecast)

    • kappasignal.com
    Updated Sep 23, 2024
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    KappaSignal (2024). (NVDA) NVIDIA: Riding the AI Wave (Forecast) [Dataset]. https://www.kappasignal.com/2024/09/nvda-nvidia-riding-ai-wave.html
    Explore at:
    Dataset updated
    Sep 23, 2024
    Dataset authored and provided by
    KappaSignal
    License

    https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html

    Description

    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.

    (NVDA) NVIDIA: Riding the AI Wave

    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

  12. Global export data of Nvidia

    • volza.com
    csv
    Updated Sep 7, 2025
    + more versions
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    Volza FZ LLC (2025). Global export data of Nvidia [Dataset]. https://www.volza.com/exports-singapore/singapore-export-data-of-nvidia-to-india
    Explore at:
    csvAvailable download formats
    Dataset updated
    Sep 7, 2025
    Dataset provided by
    Volza
    Authors
    Volza FZ LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Count of exporters, Sum of export value, 2014-01-01/2021-09-30, Count of export shipments
    Description

    210 Global export shipment records of Nvidia with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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Share
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Click to copy link
Link copied
Close
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TRADING ECONOMICS (2025). Nvidia | NVDA - Sales Revenues [Dataset]. https://tradingeconomics.com/nvda:us:sales

Nvidia | NVDA - Sales Revenues

Explore at:
excel, xml, csv, jsonAvailable download formats
Dataset updated
Apr 15, 2025
Dataset authored and provided by
TRADING ECONOMICS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Jan 1, 2000 - Jul 13, 2025
Area covered
United States
Description

Nvidia reported $44.1B in Sales Revenues for its fiscal quarter ending in April of 2025. Data for Nvidia | NVDA - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last July in 2025.

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