Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this December 2 of 2025.

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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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.

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

This statistic shows the stock price development of selected companies in the technology industry from January 6, 2020 to February 3, 2025. During this period, stock prices of most of the tech companies have increased. Out of all companies shown here, stock values of **** saw the most substantial increase between January and October 2020. In February 3, 2025, ***** stock prices increased more than others with over an increase of *** index points.

Prices for US Tech Composite Index including live quotes, historical charts and news. US Tech Composite Index was last updated by Trading Economics this December 2 of 2025.

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

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) and more!

There are 14 CSV files in the data/ folder named with the stock symbol for each of the 14 companies.

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

Stock Price Time Series for Daou Tech. Daou Technology Inc., together with its subsidiaries, provides IT and finance services. The company offers marketing communication services, such as texting, business text message, mobile coupon, corporate mobile coupon, internet fax, and bulk mail; online commerce solutions, including escrow, 050 virtual number, and integrated management service; biz infra services comprising business platform, cloud, IDC, and domain services; and financial IT professional services. It also engages in the advertising services, real estate development, and building management. The company was founded in 1986 and is headquartered in Seongnam-si, South Korea

What is inside dataset?

This dataset contains information about the stock of five high-tech companies: AMD, Intel, Tesla, Google, HP. Each file have sevral columns, such as open, close, high, low price and volume. For these data user can built his model and get other skills.

Who is dataset for?

For people, who want to learn create time-series model with ARIMA, RNN and other. In my opinion, it`s best choise for begginer data scientist.

Acknowledgments

Thanks for yahoo finance for free acces to prices.

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

This statistic presents a ranking of the market capitalization of selected U.S. tech and internet companies in 2006, and from 2014 to 2020. Apple's market cap soared from ****** billion U.S. dollars in 2014 to **** trillion dollars in 2020. Apple's market cap pushed the company ahead of last year's leader Microsoft. Public offerings of tech and internet companies A public offering is the offering of securities of a company or a similar corporation to the public. Generally, the securities are to be listed on a stock exchange. The initial public offering (IPO) of a company occurs when a company offers its shares for the first time for public ownership and trading. Hardware companies such as Apple or IBM have been traded publicly for a while but younger, online-based companies such as Google or most notably Facebook and most recently, Snap Inc. have been generating a lot of buzz surrounding their IPOs and subsequent stock prices. Facebook’s initial public offering was intensely hyped over months with projections of a 100 billion US dollar valuation but it dwindled down to a range of ** to ** billion US dollars prior to the listing. Other tech stock performances have been more stable – both online retailer Amazon and search and digital advertising giant Google’s - now Alphabet's - shares have been on a more upwards trend. The most impressive development however came from Apple which totally changed its stock performance after the 2008 introduction of the iPhone. Since then, the company has been catapulted to the top of the smartphone market, multiplying its market capitalization as well as regularly being ranked as one of the most valuable brands worldwide.

Stock Price Time Series for Shanghai Dragonnet Tech. Shanghai DragonNet Technology Co.,Ltd. engages in the provision of information technology (IT) services, infrastructure localization, and smart industry application solutions in China and internationally. The company offers IT support and maintenance services, such as system environment check, equipment health check, technical support response, fault resolution, parts replacement, and other support services; IT outsourcing services comprising on-site, and remote and system hosting operation and maintenance services; and IT professional consulting and technical implementation services, including system consulting, design, evaluation, development, tuning, integration, upgrade, and relocation services, as well as equipment configuration, and other implementation services for data center IT infrastructure, industry users, and IT business application systems. It is also involved in the provision of IT software services, such as software and software modules development, as well as installation, debugging, testing, training, software developer outsourcing, etc.; and engages in the agency sale of third-party software, and hardware equipment and spare parts. In addition, the company offers PBData, a database all-in-one machine with cloud platform which helps enterprises in minimizing TCO and simplifying IT operation and maintenance; PriData, a hyper-converged all-in-one machine that provides real private cloud capabilities based on virtualization and IT resource delivery services; and PhegData, a distributed storage platform, for realizing product delivery based on domestically produced server chips and operating systems. Shanghai DragonNet Technology Co.,Ltd. was founded in 2001 and is headquartered in Shanghai, China.

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

Stock Price Time Series for Netac Tech. Netac Technology Co., Ltd. engages in the research and development, production, and sale of storage products in the People's Republic of China and internationally. It offers solid state drives, external storage, USB flash drives, memory cards, DRAM memory sticks, embedded storage, mobile storage, portable SSD, hard drive disk, DDR, and other memory storage products, as well as provides OEM solutions for storage products. The company also offers wearable devices, such as bluetooth headsets, smart audio glasses, and other products; computer peripherals products, including dual-protocol HDD cases, audio disk boxes, hubs, and other products; ear buds; and other consumer electronic products. Its products are used in mobile phones, tablet computers, electronic computers, driving records, video recorders, cameras, drones, automotive electronics, smart homes, wearable devices, and other fields. Netac Technology Co., Ltd. was founded in 1999 and is headquartered in Shenzhen, the People's Republic of China.

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

Stock Price Time Series for Suntront Tech. Suntront Technology Co., Ltd. develops, manufactures, and sells smart meters in China. The company offers smart water, gas, heat, and energy meters; and remote devices and other products. It also provides prepayment, automatic meter reading, and technique related solutions, as well as smart management software with various functions, such as file creation, account opening, daily business dealing, sale and multi inquiry, report summary, read/write card, input/collect data, bill printing, and blacklist checking. It serves electricity, gas, and water utility and distribution industries. Suntront Technology Co., Ltd. was founded in 2000 and is headquartered in Zhengzhou, China.

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

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.