Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this March 28 of 2026.

Investors believed the stock price of two large U.S. tech companies in particular would grow by between 2020 and 2025. According to a survey conducted in ************, Tesla especially was believed to witness a stock growth. Nearly half of all respondents selected Tesla, close to double the number of respondents who selected the next-most popular option, Amazon. The source used a large definition of "tech", as the survey included companies that are active in different categories.

Dataset Description

Overview:

This dataset contains the historical stock prices and related financial information for five major technology companies: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Google (GOOGL), and Tesla (TSLA). The dataset spans a five-year period from January 1, 2019, to January 1, 2024. It includes key stock metrics such as Open, High, Low, Close, Adjusted Close, and Volume for each trading day.

Data Collection:

The data was sourced using the yfinance library in Python, which provides convenient access to historical market data from Yahoo Finance.

Contents:

The dataset contains the following columns:

Date: The trading date. Open: The opening price of the stock on that date. High: The highest price of the stock on that date. Low: The lowest price of the stock on that date. Close: The closing price of the stock on that date. Adj Close: The adjusted closing price, accounting for dividends and splits. Volume: The number of shares traded on that date. Ticker: The stock ticker symbol representing each company.

Prices for US Tech Composite Index including live quotes, historical charts and news. US Tech Composite Index was last updated by Trading Economics this March 28 of 2026.

Context

This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META), Adobe (ADBE), Cisco Systems (CSCO), IBM, Intel Corporation (INTC), Netflix (NFLX), Tesla (TSLA), NVIDIA (NVDA), and more!

Note: All stock_symbols have 3271 prices, except META (2688) and TSLA (3148) because they were not publicly traded for part of the period examined.

Content

Geography: Worldwide

Time period: Jan 2010- Jan 2023

Unit of analysis: Big Tech Giants Stock Price Data

Variables

Acknowledgements

Datasource: Yahoo Finance Credit: Evan Gower

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Complete list of technology stocks stocks with real-time data, financial metrics, and screening criteria

This dataset provides a comprehensive view of daily stock prices and key financial metrics for some of the most prominent technology companies: Apple (AAPL), Microsoft (MSFT), Amazon (AMZN), Google (GOOGL), Tesla (TSLA), Meta (META), NVIDIA (NVDA), IBM (IBM), Oracle (ORCL), and Intel (INTC). Covering the period from January 1, 2020, to the present(08/08/2024), this dataset is ideal for financial analysis, stock market modeling, and trend analysis.

The data includes daily stock prices (Open, High, Low, Close, Adjusted Close, Volume) as well as additional financial metrics such as the Price-to-Earnings (P/E) ratio, Market Capitalization, Price/Sales Ratio, Price/Book Ratio, Dividend Yield, and more. These metrics provide a deeper insight into each company's financial health and market performance.

The dataset was collected using the yfinance library in Python, which pulls historical data from Yahoo Finance. This dataset is particularly useful for those interested in stock price prediction, portfolio analysis, and financial data visualization.

NASDAQ exchange statistics including market capitalization, trading volume, listed companies, index performance, and historical data 1971-2026.

As of January 13, 2026, Nvidia was the leading tech company by market capitalization globally at 4.48 trillion U.S. dollars. Nvidia became the first company to ever achieve the five-trillion milestone, having hit this figure for the first time in October 2025. Alphabet, the parent company of Google, ranked second at 4.05 trillion U.S. dollars, followed by Apple, Microsoft, and Amazon. Nvidia's immense growth With a focus that began with origins in gaming, Nvidia's business strategy has been transformed by demand from data centers that sit at the heart of the AI boom. The company's chips have been favored to support the training and running of a range of large language models, most notably in the development of OpenAI's ChatGPT. Apple is also among the leaders Since its foundation in a Californian garage in 1976, Apple has expanded massively, becoming one of the most valuable companies in the world. The company started its origins in the PC industry with the Macintosh but soon entered other segments of the consumer electronics market. Today, the iPhone is the most popular Apple product, although Mac, iPad, wearables, and services also contribute to its high revenues. Aiming at innovation, Apple invests every year in research and development, spanning a wide array of technologies from AI through to extended reality.

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.

Tech Stocks Surge Driven by AI Enthusiasm

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

Can you build a model to predict future stock prices based on historical hire, departure, and headcount data?

This dataset includes the following for 50 US-based tech stocks - 15+ years of employees hired - 15+ years of employee departures - 10+ years of monthly headcount data - 15+ years of employee departures - 10+ years of monthly headcount data - 5+ years of stock prices - 5+ years of market caps

Data dictionary describing the fields in the dataset

Graph and download economic data for NASDAQ-100 Technology Sector (NASDAQNDXT) from 2006-02-23 to 2026-03-18 about NASDAQ, sector, indexes, and USA.

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.

Tech Tide Rising: Can Technology Stocks Ride the 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

Graph and download economic data for NASDAQ-100 (NASDAQ100) from 1986-01-02 to 2026-03-27 about stock market, NASDAQ, indexes, and USA.

Complete list of biotech stocks stocks with real-time data, financial metrics, and screening criteria

Dataset extracted from the post NASDAQ 100 | 14th Oct 2025 | FAANG – TECH Stocks | Technicals, Price Targets and Geopolitical Effects on Smart Investello.

Marvell Technology stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

AI-powered TECH stock price predictions with 7-day, 14-day, and 28-day forecasts, confidence intervals, and zone analysis. Updated daily.

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.

Tech Stock Volatility Sparks Investor Caution

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

A list of the top 50 Renaissance Technologies holdings showing which stocks are owned by Jim Simons's hedge fund.