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

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F77ed318834f67e5ec3dea9fa961efe50%2Fpic1.png?generation=1718970886706508&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F68b2014347f4b9e388025f9f4c31248e%2Fpic2.png?generation=1718970898986658&alt=media" alt="">

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.

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.

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.

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

This dataset is just for personal, non-commercial purposes.

Overview

This dataset provides a comprehensive record of NVIDIA Corporation's (NVDA) daily stock prices over the last five years. NVIDIA, a prominent technology company known for its graphics processing units (GPUs), has experienced significant market activity, making its stock price data valuable for financial analysis, trading strategies, and market trend studies.

Data Fields

The dataset includes the following columns:

1. Date: The trading date (YYYY-MM-DD format).
2. Open: The price of the stock at market opening.
3. High: The highest price of the stock during the trading day.
4. Low: The lowest price of the stock during the trading day.
5. Close: The price of the stock at market close.
6. Adj Close: The adjusted closing price, accounting for dividends and stock splits.
7. Volume: The number of shares traded on that day.

Time Period

• Start Date: Five years prior from the current date (for example, if today's date is 2024-05-23, the dataset starts from 2019-05-23).
• End Date: The most recent trading day available in the dataset.

Data Source

The data is typically sourced from reliable financial database Yahoo Finance. It is crucial to ensure data accuracy and completeness for effective analysis.

Usage

This dataset can be used for: - Historical Analysis: Studying NVIDIA's stock performance over time. - Technical Analysis: Applying various technical indicators and chart patterns. - Machine Learning: Training models for stock price prediction. - Market Research: Understanding market trends and investor behavior. - Investment Strategies: Backtesting trading strategies to assess their performance.

Example Applications

• Trend Analysis: Identifying bullish or bearish trends in NVIDIA's stock price.
• Volatility Analysis: Measuring the stock's volatility and assessing risk.
• Correlation Studies: Examining the relationship between NVIDIA's stock price and other market variables.
• Event Impact Analysis: Analyzing the impact of company announcements, earnings reports, or macroeconomic events on the stock price.

Note

It is important to handle the data responsibly, considering market hours, holidays, and any corporate actions like stock splits or dividends that might affect the stock price. Adjustments for these factors are usually reflected in the "Adj Close" column to provide a more accurate historical comparison.

This dataset is ideal for analysts, investors, researchers, and students interested in financial markets, particularly in understanding the dynamics of a leading technology company's stock over a significant period.

Context

Alphabet Inc. is a listed US holding company of the former Google LLC, which continues to exist as a subsidiary. The headquarters is Mountain View in Silicon Valley. The company is led by Sundar Pichai as CEO.

With sales of $137 billion, a profit of $30.7 billion and a market value of $ 863.2 billion, Alphabet Inc. ranks 17th among the world's largest companies according to Forbes Global 2000 (as of 4th November 2019). The company had a market cap of $ 766.4 billion in early 2018. In 2019, Alphabet had annual sales of $161.9 billion and an annual profit of $34.3 billion.

Market capitalization of Alphabet (Google) (GOOG)

Market cap: $2.442 Trillion USD

As of August 2025 Alphabet (Google) has a market cap of $2.442 Trillion USD. This makes Alphabet (Google) the world's 4th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Content

Geography: USA

Time period: August 2004- August 2025

Unit of analysis: Google Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F84937d0d9ac664fa6c705c0da59564e0%2FScreenshot%202024-12-18%20153807.png?generation=1734532695847825&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2Fa927d7f9ef11a23685bbb86a25b44d8d%2FScreenshot%202024-12-18%20153822.png?generation=1734532715073647&alt=media" alt="">

Title: Stock Prices of 500 Biggest Companies by Market Cap (Last 5 Years)

Description: This dataset comprises historical stock market data extracted from Yahoo Finance, spanning a period of five years. It includes daily records of stock performance metrics for the top 500 companies based on market capitalization.

Attributes: 1. Date: The date corresponding to the recorded stock market data. 2. Open: The opening price of the stock on a given date. 3. High: The highest price of the stock reached during the trading day. 4. Low: The lowest price of the stock observed during the trading day. 5. Close: The closing price of the stock on a specific date. 6. Volume: The volume of shares traded on the given date. 7. Dividends: Any dividend payments made by the company on that date (if applicable). 8. Stock Splits: Information regarding any stock splits occurring on that date. 9. Company: Ticker symbol or identifier representing the respective company.

Usefulness: - Investors and analysts can leverage this dataset to conduct various analyses such as trend analysis, volatility assessment, and predictive modeling. - Researchers can explore correlations between stock prices of different companies, sector-wise performance, and market trends over the specified duration. - Machine learning enthusiasts can employ this dataset for developing predictive models for stock price forecasting or anomaly detection.

Note: Prior to using this dataset, it's recommended to perform data cleaning, handling missing values, and verifying the consistency of data across companies and time periods.

License: The dataset is sourced from Yahoo Finance and is provided for analytical purposes. Refer to Yahoo Finance's terms of use for further details on data usage and licensing.

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

Stock Price Time Series for Beijing Zhidemai Technology. Beijing Zhidemai Technology Co., Ltd. engages in the Internet marketing and data service-related businesses in China and internationally. It also involved in Internet information promotion activities. In addition, The company provides consumer content, and content creation services. Beijing Zhidemai Technology Co., Ltd. was founded in 2011 and is headquartered in Beijing, China.

Allianz Technology 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

Stock Price Time Series for Taiwan Union Technology. Taiwan Union Technology Corporation engages in the manufacture and sale of copper foil substrates, adhesive sheets, and multi-layer laminated boards in Taiwan and internationally. It also offers copper-clad laminates, prepregs, and printed circuit boards; and provides mass lamination services to the electronics industry. Its products are used in radio frequency, high-speed digital, high-density interconnect, automotive, and substrate applications. The company was formerly known as Taiwan Union Glass Industrial Co., Ltd. Taiwan Union Technology Corporation was founded in 1974 and is headquartered in Zhubei, Taiwan.

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 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.

This dataset was generated using yfinance (yahoo finance python api), and contain the historical information from 2016-02-17 to 2024-05-07.

The purpose of this dataset is for testing an Offline Deep Reinforcement Learning algorithm.

Columns description:

• episode_id (string): The column representing the episode identifier.
• seq (integer): The column representing the order of the data.
• Close (float): The close price in each day.
• change_ewm (floats): Represent Exponential Moving Average in 3, 7, 15 and 30 days.
• action (integer): The column representing the action variable made by an expert.
• reward (float): The column representing the reward variable, which represent del percentage of how much you increase your portfolio after 30 days of trading.

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

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

Stock Price Time Series for Genbyte Technology Inc. Genbyte Technology Inc. manufactures and sells controllers for use in household appliances, industrial inverters, and power supply and automotive products in China. It offers intelligent controllers, variable frequency drives, digital power supplies, intelligent IoT modules, energy storage systems, and inverters. The company was founded in 1999 and is based in Shenzhen, China.