Prices for US 100 Tech Index including live quotes, historical charts and news. US 100 Tech Index was last updated by Trading Economics this July 15 of 2025.

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.

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 Sector Outlook: Bullish Trend Expected for Dow Jones U.S. Technology Index.

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.

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

The latest closing stock price for Fuel Tech as of June 20, 2025 is 2.14. An investor who bought $1,000 worth of Fuel Tech stock at the IPO in 1993 would have $-724 today, roughly -1 times their original investment - a -3.94% compound annual growth rate over 32 years. The all-time high Fuel Tech stock closing price was 37.93 on June 21, 2007. The Fuel Tech 52-week high stock price is 2.19, which is 2.3% above the current share price. The Fuel Tech 52-week low stock price is 0.87, which is 59.3% below the current share price. The average Fuel Tech stock price for the last 52 weeks is 1.08. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

The latest closing stock price for ESS Tech as of June 06, 2025 is 1.22. An investor who bought $1,000 worth of ESS Tech stock at the IPO in 2020 would have $-992 today, roughly -1 times their original investment - a -61.72% compound annual growth rate over 5 years. The all-time high ESS Tech stock closing price was 357.00 on October 12, 2021. The ESS Tech 52-week high stock price is 14.09, which is 1054.9% above the current share price. The ESS Tech 52-week low stock price is 0.76, which is 37.7% below the current share price. The average ESS Tech stock price for the last 52 weeks is 6.19. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

The main stock market index of United States, the US500, rose to 6271 points on July 14, 2025, gaining 0.19% from the previous session. Over the past month, the index has climbed 3.94% and is up 11.36% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

The latest closing stock price for U-BX Technology as of June 27, 2025 is 2.81. An investor who bought $1,000 worth of U-BX Technology stock at the IPO in 2024 would have $-957 today, roughly -1 times their original investment - a -95.72% compound annual growth rate over 1 years. The all-time high U-BX Technology stock closing price was 548.80 on August 21, 2024. The U-BX Technology 52-week high stock price is 567.04, which is 20079.4% above the current share price. The U-BX Technology 52-week low stock price is 2.36, which is 16% below the current share price. The average U-BX Technology stock price for the last 52 weeks is 32.71. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

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Acknowledgements

Google Finance.

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As of May 23, 2025, Microsoft was the leading tech company by market capitalization globally at 3.38 trillion U.S. dollars. Nvidia ranked second at 3.24 trillion U.S. dollars. Tech company stocks were impacted through 2025 as a result of various global tariff threats by the United States government. Apple 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. 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 in the training and running of a range of large language models, most notably in the development of OpenAI's ChatGPT.

The latest closing stock price for YSX Tech as of July 07, 2025 is 5.37. An investor who bought $1,000 worth of YSX Tech stock at the IPO in 2024 would have $246 today, roughly 0 times their original investment - a 24.59% compound annual growth rate over 1 years. The all-time high YSX Tech stock closing price was 7.91 on June 03, 2025. The YSX Tech 52-week high stock price is 9.96, which is 85.5% above the current share price. The YSX Tech 52-week low stock price is 2.06, which is 61.6% below the current share price. The average YSX Tech stock price for the last 52 weeks is 4.12. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

The latest closing stock price for Sensient Technologies as of July 09, 2025 is 108.75. An investor who bought $1,000 worth of Sensient Technologies stock at the IPO in 1984 would have $136,955 today, roughly 137 times their original investment - a 12.77% compound annual growth rate over 41 years. The all-time high Sensient Technologies stock closing price was 108.75 on July 09, 2025. The Sensient Technologies 52-week high stock price is 109.09, which is 0.3% above the current share price. The Sensient Technologies 52-week low stock price is 66.14, which is 39.2% below the current share price. The average Sensient Technologies stock price for the last 52 weeks is 79.61. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

Investors weigh the risks and rewards of stocks at 52-week lows, analyzing companies like Microchip Technology, Floor & Decor, and Standex for potential bargains or value traps.

This dataset is about stocks. It has 1 row and is filtered where the company is Tech Long. It features 8 columns including stock name, company, exchange, and exchange symbol.

A dataset of mentions, growth rate, and total volume of the keyphrase 'Tech Stocks' over time.

• 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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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.

The latest closing stock price for OneConnect Financial Technology as of June 27, 2025 is 7.20. An investor who bought $1,000 worth of OneConnect Financial Technology stock at the IPO in 2019 would have $-928 today, roughly -1 times their original investment - a -35.50% compound annual growth rate over 6 years. The all-time high OneConnect Financial Technology stock closing price was 269.90 on July 10, 2020. The OneConnect Financial Technology 52-week high stock price is 7.38, which is 2.5% above the current share price. The OneConnect Financial Technology 52-week low stock price is 0.87, which is 87.9% below the current share price. The average OneConnect Financial Technology stock price for the last 52 weeks is 3.75. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

AI-powered price forecasts for TECH stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

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.

OpenDoor's Rocky Road: A Tech Stock's Fate in the Housing Market? (OPEN)

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