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.

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

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

HelpSteer: Helpfulness SteerLM Dataset

HelpSteer is an open-source Helpfulness Dataset (CC-BY-4.0) that supports aligning models to become more helpful, factually correct and coherent, while being adjustable in terms of the complexity and verbosity of its responses. Leveraging this dataset and SteerLM, we train a Llama 2 70B to reach 7.54 on MT Bench, the highest among models trained on open-source datasets based on MT Bench Leaderboard as of 15 Nov 2023. This model is available on… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/HelpSteer.

• 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.

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https://upload.wikimedia.org/wikipedia/en/thumb/a/a4/NVIDIA_logo.svg/731px-NVIDIA_logo.svg.png" alt="NVidia">

• Nvidia Corporation is an American multinational technology company incorporated in Delaware and based in Santa Clara, California.

• It designs graphics processing units (GPUs) for the gaming and professional markets, as well as system on a chip units (SoCs) for the mobile computing and automotive market.

• Its primary GPU line, labeled "GeForce", is in direct competition with the GPUs of the "Radeon" brand by Advanced Micro Devices (AMD). Nvidia expanded its presence in the gaming industry with its handheld game consoles Shield Portable, Shield Tablet, and Shield Android TV and its cloud gaming service GeForce Now.

• Its professional line of GPUs are used in workstations for applications in such fields as architecture, engineering and construction, media and entertainment, automotive, scientific research, and manufacturing design.

• In addition to GPU manufacturing, Nvidia provides an application programming interface (API) called CUDA that allows the creation of massively parallel programs which utilize GPUs.They are deployed in supercomputing sites around the world. More recently, it has moved into the mobile computing market, where it produces Tegra mobile processors for smartphones and tablets as well as vehicle navigation and entertainment systems.It recently acquired ARM

# Let us analyze the performance of this solid star!

Describe Anything: Detailed Localized Image and Video Captioning

NVIDIA, UC Berkeley, UCSF Long Lian, Yifan Ding, Yunhao Ge, Sifei Liu, Hanzi Mao, Boyi Li, Marco Pavone, Ming-Yu Liu, Trevor Darrell, Adam Yala, Yin Cui [Paper] | [Code] | [Project Page] | [Video] | [HuggingFace Demo] | [Model/Benchmark/Datasets] | [Citation]

  Dataset Card for Describe Anything Datasets

Datasets used in the training of describe anything models (DAM). The datasets are in tar files. These… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/describe-anything-dataset.

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

Japan's main stock market index, the JP225, fell to 39519 points on July 14, 2025, losing 0.13% from the previous session. Over the past month, the index has climbed 3.15%, though it remains 4.25% lower 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 July of 2025.

Company Description

NVIDIA Corp. engages in the design and manufacture of computer graphics processors, chipsets, and related multimedia software. It operates through the following segments: Graphics Processing Unit (GPU), Tegra Processor, and All Other. The GPU segment comprises of product brands, which aims specialized markets including GeForce for gamers; Quadro for designers; Tesla and DGX for AI data scientists and big data researchers; and GRID for cloud-based visual computing users. The Tegra Processor segment integrates an entire computer onto a single chip, and incorporates GPUs and multi-core CPUs to drive supercomputing for autonomous robots, drones, and cars, as well as for consoles and mobile gaming and entertainment devices. The All Other segment refers to the stock-based compensation expense, corporate infrastructure and support costs, acquisition-related costs, legal settlement costs, and other non-recurring charges. The company was founded by Jen Hsun Huang, Chris A. Malachowsky, and Curtis R. Priem in January 1993 and is headquartered in Santa Clara, CA.

Contact Information

NVIDIA Corp. 2788 San Tomas Expressway Santa Clara California 95051 P:(408) 486-2000 www.nvidia.com

Shareholders

Mutual fund holders 39.32% Other institutional 30.89% Individual stakeholders 3.75%

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

This dataset provides comprehensive daily stock and leveraged ETF data for NVDA (NVIDIA Corporation) and NVDL (Direxion Daily Semiconductor Bull 3X Shares) from 2020 to 2024. It includes key market indicators such as open, close, high, low, and volume data for both assets, allowing users to analyze trends, volatility, and market performance over time.

The dataset is suitable for financial analysis, algorithmic trading models, and machine learning applications focused on stock and ETF performance. Whether you're exploring price movements, developing prediction models, or studying market patterns, this dataset offers valuable insights into NVDA and NVDL.

Key features:

• Date range: 2020–2024
• Assets covered: NVDA (NVIDIA Corporation), NVDL (Leveraged ETF)
• Data points: Open, close, high, low, volume
• Ideal for: Financial analysis, time series forecasting, market trend exploration

Feel free to download and explore this dataset for your financial analysis and research needs. Let me know if you'd like further customization!

This dataset provides approximately 7,107 question and answer pairs related to NVIDIA documentation [5]. It has been generated from various NVIDIA websites, including development kits and guides [6]. The primary purpose of this dataset is to facilitate the fine-tuning of Large Language Models (LLMs), enabling them to acquire and demonstrate knowledge specific to NVIDIA technologies [6].

Columns

• question: This column contains the query or question.
• answer: This column provides the corresponding response or answer to the question.

Distribution

The dataset comprises around 7,107 individual question and answer records [5]. While the exact file format for distribution is not specified, data files of this nature are typically provided in CSV format [1]. Specific details regarding file size are not available, but the record count suggests a substantial collection of text data.

Usage

This dataset is ideally suited for training and fine-tuning Language Models (LLMs). Its primary application is to enhance LLMs with specialised knowledge concerning NVIDIA products, documentation, and related technical information [6]. It can be used to build intelligent agents, chatbots, or search systems capable of answering NVIDIA-specific queries.

Coverage

The dataset's content is global in scope, drawing information from various NVIDIA documentation sources [6]. It represents a snapshot of knowledge from the NVIDIA websites from which it was generated. The dataset was listed on 16 June 2025 as Version 1.0 [6].

License

CC0

Who Can Use It

This dataset is intended for: * AI/Machine Learning Engineers: For training and fine-tuning custom LLMs. * Researchers: Studying question answering systems and knowledge induction in AI models. * Developers: Building applications that require in-depth knowledge about NVIDIA, such as support chatbots or intelligent search. * Data Scientists: Exploring and analysing large collections of technical documentation Q&A.

Dataset Name Suggestions

• NVIDIA Documentation Q&A Pairs
• NVIDIA LLM Training Data
• NVIDIA Knowledge Base Questions
• AI Model Training Data for NVIDIA Docs
• NVIDIA Tech Q&A Dataset

Attributes

Original Data Source: Nvidia Documentation Question and Answer pairs

Aegis AI Content Safety Dataset is an open-source content safety dataset (CC-BY-4.0), which adheres to Nvidia's content safety taxonomy, covering 13 critical risk categories (see Dataset Description).

Dataset Details Dataset Description The Aegis AI Content Safety Dataset is comprised of approximately 11,000 manually annotated interactions between humans and LLMs, split into 10,798 training samples and 1,199 test samples.

To curate the dataset, we use the Hugging Face version of human preference data about harmlessness from Anthropic HH-RLHF. We extract only the prompts, and elicit responses from Mistral-7B-v0.1. Mistral excels at instruction following and generates high quality responses for the content moderation categories. We use examples in the system prompt to ensure diversity by instructing Mistral to not generate similar responses. Our data comprises four different formats: user prompt only, system prompt with user prompt, single turn user prompt with Mistral response, and multi-turn user prompt with Mistral responses.

OpenMathInstruct-1

OpenMathInstruct-1 is a math instruction tuning dataset with 1.8M problem-solution pairs generated using permissively licensed Mixtral-8x7B model. The problems are from GSM8K and MATH training subsets and the solutions are synthetically generated by allowing Mixtral model to use a mix of text reasoning and code blocks executed by Python interpreter. The dataset is split into train and validation subsets that we used in the ablations experiments. These two subsets… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/OpenMathInstruct-1.

Comprehensive database of graphics card specifications including memory, clock speeds, power requirements, and performance metrics for NVIDIA, AMD, and Intel GPUs

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

GPU Database Market Outlook

The global GPU database market size was estimated to be approximately USD 500 million in 2023 and is projected to reach USD 1.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.3% during the forecast period. This robust growth is driven by the increasing demand for high-performance data analytics solutions across various industries. The proliferation of big data and the need for faster data processing capabilities have significantly contributed to the growth of the GPU database market. The integration of artificial intelligence (AI) and machine learning (ML) technologies with GPU databases further bolsters their adoption and utility, enabling companies to extract meaningful insights from vast datasets in real-time.

One of the key growth factors for the GPU database market is the escalating demand for real-time data analytics. As businesses strive to make data-driven decisions quickly, the requirement for databases that can process and analyze large volumes of data at high speed has become critical. GPU databases, with their parallel processing capabilities, are uniquely positioned to meet this demand. Unlike traditional CPU-based databases, GPUs can handle complex computations and large datasets more efficiently, providing quicker analytical insights and enhancing decision-making processes. This capability is particularly beneficial for industries such as finance and healthcare, where real-time analytics can drive significant operational efficiencies and competitive advantages.

Another prominent growth driver is the increasing adoption of AI and ML technologies across various industries. GPU databases provide the necessary computational power to support these advanced technologies, enabling organizations to implement sophisticated data models and algorithms. In areas such as fraud detection, predictive maintenance, and personalized marketing, the ability to process large datasets rapidly is crucial. GPU databases facilitate these processes, allowing businesses to innovate and improve their services and offerings. As AI and ML continue to evolve and become integral to business operations, the reliance on and demand for GPU databases are expected to rise accordingly.

The expansion of cloud computing services also plays a significant role in the growth of the GPU database market. Many organizations are transitioning from on-premises to cloud-based solutions, drawn by the scalability, flexibility, and cost-efficiency offered by the cloud. GPU databases in the cloud environment enable businesses to scale their data processing capabilities as needed without the requirement for substantial upfront infrastructure investments. This scalability is particularly attractive to small and medium enterprises (SMEs) that may lack the resources for extensive IT infrastructure. Consequently, the trend towards cloud adoption is anticipated to drive the demand for GPU databases further, creating new opportunities within the market.

Regionally, North America dominates the GPU database market, driven by technological advancements and the early adoption of innovative solutions across various industries. The presence of major market players and substantial investments in research and development further bolster the region's market position. However, the Asia Pacific region is expected to experience the fastest growth during the forecast period, attributed to the increasing industrialization, digital transformation initiatives, and rising demand for data analytics solutions in emerging economies such as China and India. The growing IT sector and the expanding use of AI and ML technologies in this region also contribute to the rising demand for GPU databases.

Component Analysis

In the component segment, the GPU database market is categorized into software, hardware, and services. Each of these components plays a crucial role in the functioning and deployment of GPU databases across various industries. The software component is critical as it encompasses the database management systems that enable the storage, retrieval, and analysis of data using GPU acceleration. This includes specialized software solutions designed to optimize the processing power of GPUs, allowing for faster data processing and analytics. As the demand for advanced analytics and AI-driven insights continues to grow, the software segment is expected to witness significant growth.

The hardware component of the GPU database market includes the physical GPUs and related infrastructure necessary to support the database operations. With the increasing need for high-perform

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 “Market Cap”. 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...

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