The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, 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 December of 2025.

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 encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

Image attribute : Image by Freepik

Use our Stock Market dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

United Kingdom's main stock market index, the GB100, fell to 9690 points on December 2, 2025, losing 0.13% from the previous session. Over the past month, the index has declined 0.12%, though it remains 15.91% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United Kingdom. United Kingdom Stock Market Index (GB100) - values, historical data, forecasts and news - updated on December of 2025.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

This comprehensive dataset provides historical stock price data spanning various time periods, offering insights into the fluctuations and trends in the stock market over time. With records covering multiple decades, investors, analysts, and researchers can explore the dynamics of different stocks, industries, and market sectors.

The dataset includes essential information such as opening price, closing price, highest and lowest prices, trading volume, and adjusted closing prices. It encompasses a diverse range of stocks, including those from various exchanges and sectors, allowing for extensive analysis and comparison.

Researchers can utilize this dataset to conduct thorough analyses, develop financial models, backtest trading strategies, and gain a deeper understanding of market behavior. Investors can assess the performance of individual stocks or portfolios over extended periods, aiding in informed decision-making and risk management.

Whether you're a seasoned investor seeking historical insights or an analyst exploring market trends, this dataset serves as a valuable resource for studying the complexities of the stock market across different eras.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

The main stock market index of United States, the US500, rose to 6849 points on November 28, 2025, gaining 0.54% from the previous session. Over the past month, the index has declined 0.60%, though it remains 13.54% higher than a year ago, 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 November of 2025.

This dataset contains 2000 daily stock market records including price movements, trading volume, market trends, indices, economic scores, and market sentiment information. It covers multiple sectors with a general category column and includes a target column for the next-day closing price. Additional text columns capture market sentiment and news tags for each record. The dataset is designed to provide comprehensive insights into stock market behavior and trends.

Number of Records: 2000

Number of Columns: 18

Column Descriptions:

Category – General text representing the sector or type of stock (e.g., Tech, Finance, Health).

Date – The calendar date of the stock record.

Open – The opening price of the stock on that day.

High – The highest price of the stock during the day.

Low – The lowest price of the stock during the day.

Close – The closing price of the stock on that day.

Volume – The total number of shares traded during the day.

SMA_10 – The 10-day simple moving average of the closing price, showing short-term trend.

EMA_10 – The 10-day exponential moving average of the closing price, giving more weight to recent prices.

Volatility – The standard deviation of the closing price over a 10-day window, representing price fluctuation.

Wavelet_Trend – Trend component of the closing price over a 10-day period.

Wavelet_Noise – Difference between the actual closing price and the trend component, capturing minor fluctuations.

Wavelet_HighFreq – Daily price changes in closing price, showing high-frequency movement.

General_Index – A numeric indicator representing general market performance.

Economic_Score – A numeric score representing overall economic factors impacting the stock.

Market_Sentiment – Text describing the sentiment of the market for that day (Positive, Neutral, Negative).

News_Tag – Text describing the main type of news impacting the stock on that day (e.g., Earnings, Merger).

Close_Next – The closing price of the stock for the next day, serving as the target variable.

Stock market index in the USA, September, 2025 The most recent value is 199.97 points as of September 2025, an increase compared to the previous value of 194.61 points. Historically, the average for the USA from January 1960 to September 2025 is 46.08 points. The minimum of 2.98 points was recorded in June 1962, while the maximum of 199.97 points was reached in September 2025. | TheGlobalEconomy.com

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.

The main stock market index of United States, the US500, rose to 6825 points on December 2, 2025, gaining 0.18% from the previous session. Over the past month, the index has declined 0.39%, though it remains 12.82% higher than a year ago, 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 December of 2025.

France's main stock market index, the FR40, rose to 8121 points on December 2, 2025, gaining 0.29% from the previous session. Over the past month, the index has climbed 0.13% and is up 11.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on December of 2025.

About this dataset

India's National Stock Exchange (NSE) has a total market capitalization of more than US$3.4 trillion, making it the world's 10th-largest stock exchange as of August 2021, with a trading volume of ₹8,998,811 crore (US$1.2 trillion) and more 2000 total listings.

NSE's flagship index, the NIFTY 50, is a 50 stock index is used extensively by investors in India and around the world as a barometer of the Indian capital market.

This dataset contains data of all company stocks listed in the NSE, allowing anyone to analyze and make educated choices about their investments, while also contributing to their countries economy.

How to use this dataset

• Create a time series regression model to predict NIFTY-50 value and/or stock prices.
• Explore the most the returns, components and volatility of the stocks.
• Identify high and low performance stocks among the list.

Highlighted Notebooks

• Your kernel can be featured here!
• Related Dataset: S&P 500 Stocks - daily updated
• More datasets

Acknowledgements

License

CC0: Public Domain

Splash banner

Stonks by unknown memer.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

Japan's main stock market index, the JP225, rose to 49553 points on December 2, 2025, gaining 0.51% from the previous session. Over the past month, the index has declined 3.78%, though it remains 26.25% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on December of 2025.

Indonesia's main stock market index, the JCI, rose to 8617 points on December 2, 2025, gaining 0.80% from the previous session. Over the past month, the index has climbed 4.13% and is up 19.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on December of 2025.