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.

Context

The subject matter of this dataset contains the stock prices of the 10 popular companies ( Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber and Zoom)

Content

Within the dataset one will encounter the following: The date - "Date" The opening price of the stock - "Open" The high price of that day - "High" The low price of that day - "Low" The closed price of that day - "Close" The amount of stocks traded during that day - "Volume" The stock's closing price that has been amended to include any distributions/corporate actions that occurs before next days open - "Adj[usted] Close" Time period - 2015 to 2021 (day level)

Tasks - Exploratory Data Analysis - Tell a visualization story - Compare stock price growth between companies - Stock price prediction - Time series analysis

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.

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

Check Point Software Technologies stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Feb 2018 note: I have just updated the dataset to include data up to Feb 2018. I have also accounted for changes in the stocks on the S&P 500 index (RIP whole foods etc. etc.).

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder).

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv contains the same data, presented in a merged .csv file. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Acknowledgements

Due to volatility in google finance, for the newest version I have switched over to acquiring the data from The Investor's Exchange api, the simple script I use to do this is found here. Special thanks to Kaggle, Github, pandas_datareader and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

1) Data Introduction • The S&P 500 stock data is a tabular stock market dataset of daily stock price information (market, high price, low price, closing price, trading volume, etc.) for the last five years (the latest data is until February 2018) of all companies in the S&P 500 index.

2) Data Utilization (1) S&P 500 stock data has characteristics that: • Each row contains key stock metrics such as date, open, high, low, close, volume, and stock ticker name. • Data is provided as individual stock files and all stock integrated files, so it can be used for various analysis purposes. (2) S&P 500 stock data can be used to: • Stock Price Forecasting and Investment Strategy Development: Using historical stock price data, a variety of investment strategies and forecasting models can be developed, including time series forecasting, volatility analysis, and moving averages. • Market Trends and Corporate Comparison Analysis: It can be used to visualize stock price fluctuations across stocks, compare performance between stocks, analyze market trends, optimize portfolios, and more.

Greece's main stock market index, the Athens General, rose to 2108 points on December 2, 2025, gaining 0.41% from the previous session. Over the past month, the index has climbed 4.15% and is up 47.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Greece. Greece Stock Market (ASE) - 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.

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.

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.

About Dataset Context Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Feb 2018 note: I have just updated the dataset to include data up to Feb 2018. I have also accounted for changes in the stocks on the S&P 500 index (RIP whole foods etc. etc.).

Content The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the allstocks5yr.csv and corresponding folder).

The folder individualstocks5yr contains files of data for individual stocks, labelled by their stock ticker name. The allstocks5yr.csv contains the same data, presented in a merged .csv file. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Acknowledgements Due to volatility in google finance, for the newest version I have switched over to acquiring the data from The Investor's Exchange api, the simple script I use to do this is found here. Special thanks to Kaggle, Github, pandas_datareader and The Market.

Inspiration This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Stock Price Time Series for System Support Inc. System Support Holdings Inc. provides various information technology (IT) services in Japan and internationally. It offers Tate Yakusha, a construction work information management system; Shugyo Yakusha, an attendance/work management system; SHIFTEE, a cloud-based shift management system; Voicetant Writer and Voicetant Recorder, which enables voice operation/input; SmartDWH, a cloud-based DWH solution; PinMap that offers customer information mapping service; MOS, a mobile order reception system; PC Keneki Kenchikun, a security check system for safely connecting external devices; ADDPLAT, a data analysis platform; and Smart Rabbit, a food inventory management system for restaurants. The company also provides system consulting/direction, system integration, IT task support, and outsourcing services, as well as ERP, Oracle, cloud, ServiceNow, RPA, infrastructure, virtualization, and workflow solutions. The company was formerly known as System Support Inc. and changed its name to System Support Holdings Inc. in January 2025. System Support Holdings Inc. was incorporated in 1980 and is headquartered in Kanazawa, Japan.

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.

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.

Context

Stock market data can be interesting to analyze, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I view a dataset with historical stock prices for all companies on the S&P 500 index.

Content

All the files have the following columns:

Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - highest price reached in the day

Low - lowest price reached in the day

Close - close price

Volume - number of shares traded

Acknowledgements

Thanks to Kaggle, Github, yahoo finance.

Purpose of creating the dataset

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. And people can build quantitative models such as: build portfoio, predict volatility, arbitrage, trading strategies.

Sweden's main stock market index, the Stockholm 30, fell to 2782 points on December 2, 2025, losing 0.11% from the previous session. Over the past month, the index has climbed 0.95% and is up 8.08% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sweden. Sweden Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

Stock Price Time Series for Peoples Fin. Peoples Financial Services Corp. provides commercial and retail banking services. The company accepts money market, negotiable order of withdrawal, savings, individual retirement, demand deposit, and certificates of deposit accounts. It also provides residential real estate, automobile, manufactured housing, personal, and home equity loans; and commercial real estate, working capital, construction, small business administration, and agricultural loans, as well as loans for equipment and other commercial needs, and mineral rights. In addition, the company offers fiduciary and investment services; investment management, IRA trustee, estate administration, living and life insurance trust, trustee under will, guardianship, custodial/IRA custodial, and corporate trust services, as well as pension and profit-sharing plans. Further, it provides wealth management products and services comprising investment portfolio management, estate and business succession planning, annuities, insurance, retirement plan, and tax planning, as well as education funding strategies; and securities, brokerage, and investor services. Additionally, the company offers small business checking and merchant money market accounts, online and telephone banking, business credit cards, merchant line of credit, and financial checkup services; and remote deposit capture, automatic clearing house transaction, cash management, automated teller machine, point of sale transaction, safe deposit box, night depository, direct deposit, and official check services. It serves business, non-profit, governmental, municipal agency, and professional customers, as well as retail customers. The company was founded in 1905 and is headquartered in Moosic, Pennsylvania.

Stock Price Time Series for Farmers National Banc Corp. Farmers National Banc Corp. operates as a bank holding company for The Farmers National Bank of Canfield that engages in the banking, trust, retirement consulting, insurance, and financial management businesses. It offers commercial and retail banking services, including checking, savings, and time deposit accounts; commercial, mortgage and installment, and home equity loans; home equity lines of credit; and night depository, safe deposit box, money order, bank check, automated teller machine, Internet banking, travel card, E bond transaction, brokerage, and other services. The company also provides personal and corporate trust services in the areas of estate settlement, trust administration, employee benefit plans, and retirement services; property and casualty insurance products and services; and various insurance products through licensed representatives, as well as invests in municipal securities. Farmers National Banc Corp. was founded in 1887 and is headquartered in Canfield, Ohio.

Stock Price Time Series for Airtac International Group. Airtac International Group manufactures and sells pneumatic control components in Taiwan and internationally. The company offers preparation units and control components comprising solenoid, pneumatic control, ordinary, and other valves. It also provides actuators, such as standard, round line, compact, side table, guided, rodless magnetic, and rotary table cylinders; air grippers; GA series, GP series, pressure switch, vacuum components, and other series; and cylinder accessories. In addition, the company offers fitting and tubbing accessories, including shock absorbers, flame resistant tubes, and other fittings related products, as well as linear guide products. Its products are used in automotive, machinery manufacturing, metallurgy, electronics, rail transit, environmental protection, lighting, textiles, ceramics, medical equipment, food, packaging, and automated industrial sectors. Airtac International Group was founded in 1988 and is headquartered in Taipei City, Taiwan.