Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this October 27 of 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.

Tech Titans' Next Frontier?

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

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

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

Stock Price Time Series for Beijing Zhidemai Technology. Beijing Zhidemai Technology Co., Ltd. engages in the Internet information promotion activities in China and internationally. The company provides consumer content services; and support services, including training, content creation and production, supply chain resources, legal affairs, public relations, etc. It offers its services through smzdm.com website. Beijing Zhidemai Technology Co., Ltd. was founded in 2011 and is headquartered in Beijing, China.

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.

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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

Note

Fixed full version - https://www.kaggle.com/jacksoncrow/stock-market-dataset

Prices for US Tech Composite Index including live quotes, historical charts and news. US Tech Composite Index was last updated by Trading Economics this October 27 of 2025.

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.

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 Guangdong Tecsun Science & Technology Co Ltd. Guangdong Tecsun Science & Technology Co.,Ltd. provides information system construction and related operation services. The company provides human resources, resident service card, social security card issuance and application, digital public employment, intelligent knowledge operation, and social security finance services, as well as AI convenience service station. It also offers livelihood data product service system using the Internet, big data, AI, and other technologies; C-end intelligent customer services; rural e-commerce operation; and information technology services, such as AIoT applications, resident service operations, etc. In addition, the company engages in the computer, communications, and other electronic equipment manufacturing. It provides services to social security and people's livelihood, human resources, employment, finance, medical care, big data, etc. The company was formerly known as Guangdong Desheng Technology Co., Ltd. Guangdong Tecsun Science & Technology Co.,Ltd. was founded in 1999 and is headquartered in Guangzhou, 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

Allianz 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.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

The Netflix Stock Price Dataset provides historical trading data including date, opening, high, low, closing, adjusted closing prices, and trading volume. It is ideal for financial analysis, forecasting, and machine learning applications.

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 dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

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