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.

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 dataset comprises historical stock price and trading volume data from S&P 500 component stocks over a period of about 10 years (from 01/02/2009 to 12/24/2018), used to evaluate the proposed Mid-LSTM stock prediction model.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

The Apple Inc. Historical Stock Data Dataset provides daily records of stock prices and trading volumes for Apple Inc. over the past year. The dataset includes key features such as the opening price, highest price, lowest price, closing price, adjusted closing price, and trading volume. It is a valuable resource for analyzing stock price trends, performing time-series analysis, and building predictive models for stock market behavior.

Key Features

• Daily Stock Data: Historical records of Apple Inc. stock prices and trading volumes.
• Key Price Metrics: Open, High, Low, Close, and Adjusted Close prices.
• Trading Volume: Daily trading volume of Apple Inc. stock.
• Time-Series Data: Sequential daily records suitable for time-series analysis and modeling.
• Numerical Structure: Numerical data for regression and predictive modeling.

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 Market Data: 9,000+ Tickers (1962 - Present)

Dataset Overview

This dataset offers comprehensive historical stock market data covering over 9,000 tickers from 1962 to the present day. It includes essential daily trading information, making it suitable for various financial analyses, trend studies, and algorithmic trading model development.

Columns

• Date: The date of the recorded trading data.
• Ticker: The stock symbol of the company.
• Open: Opening price of the stock on the trading day.
• High: Highest price reached during the trading day.
• Low: Lowest price reached during the trading day.
• Close: Closing price of the stock on the trading day.
• Volume: The total number of shares traded during the day.
• Dividends: Cash dividends issued on the date, if applicable.
• Stock Splits: Stock split factor for the date, if any split occurred.

Usage

This dataset is ideal for: - Time-Series Analysis: Track stock price trends over time, examining daily, monthly, and yearly patterns across sectors. - Algorithmic Trading: Develop and backtest trading strategies using historical price movements and volume data. - Machine Learning Applications: Train models for stock price prediction, volatility forecasting, or portfolio optimization. - Quantitative Research: Perform event studies, analyze the impact of dividends and stock splits, and assess long-term investment strategies. - Comparative Analysis: Evaluate performance across industries or against broader market trends by analyzing multiple tickers in one dataset.

This dataset serves as a robust resource for academic research, quantitative finance studies, and financial technology development.

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.

What are the most successful trading algorithms? (NTAP Stock Forecast)

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.

Can we predict stock market using machine learning? (META Stock Forecast)

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

About Dataset:

This dataset includes the daily historical stock prices for Google (GOOGL) spanning from 2020 to 2025. It features essential financial metrics such as opening and closing prices, daily highs and lows, adjusted close prices, and trading volumes. The information offers valuable insights into the stock's performance over a five-year timeframe.

Column Descriptions:

• Price: Date of the stock data (needs cleaning as the first two rows are headers).
• Adj Close: Adjusted closing price, accounting for events like dividends and splits.
• Close: Closing price of the stock at the end of the trading day.
• High: Highest price of the stock during the trading day.
• Low: Lowest price of the stock during the trading day.
• Open: Opening price of the stock at the start of the trading day.
• Volume: Number of shares traded during the day.

What Can You Achieve and Apply on This Data:

• Time Series Analysis: Examine trends and patterns over time.
• Stock Price Prediction: Use machine learning models to forecast future prices.
• Volatility Analysis: Measure the stock's price fluctuations.
• Technical Analysis: Calculate indicators like moving averages, RSI, and MACD.
• Correlation Analysis: Investigate the relationship between volume and price changes.
• Investment Strategy Backtesting: Test trading strategies like moving average crossovers.

Note: 1. This data is scraped from Yahoo Finance by me using python code. 2. Some of the About Data is generated from AI, but verified from me.

Indonesia Capital Market: Stock Trading: Average Daily Trading Volume: Growth data was reported at 13.350 % in Feb 2025. This records an increase from the previous number of -10.150 % for Jan 2025. Indonesia Capital Market: Stock Trading: Average Daily Trading Volume: Growth data is updated monthly, averaging 4.532 % from Dec 2017 (Median) to Feb 2025, with 87 observations. The data reached an all-time high of 216.159 % in Jan 2021 and a record low of -57.303 % in Feb 2020. Indonesia Capital Market: Stock Trading: Average Daily Trading Volume: Growth data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Monetary – Table ID.KAI020: Financial System Statistics: Capital Market Sector.

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.

This dataset provides a comprehensive, pre-processed collection of U.S. stock market data, specifically curated for quantitative analysis, financial modeling, and machine learning applications focused on volatility and asset pricing. It is optimized to include essential price and volume change metrics, along with market fundamentals, to facilitate efficient research.

The data is collected into previous 1000 & 3500 market open days since 10/12/2025. Note for a stock to be in each dataset it must have at least 1000 & 3500 days of history. The source data is located at https://stooq.com/db/h/ and an extract script can be found in my accompanying notebook.

📊 Key Data Fields & Structure

The time-series data files (log_change.pkl) are optimized for quantitative modeling, where raw prices are replaced by daily change metrics to capture volatility and momentum efficiently.

Time-Series (3D NumPy Array Structure)

The 3D array (trimmed_market_data_log_change_1000.pkl) is structured as (Days, Features, Tickers) and contains the following 5 features per day:

ticker

date

log_Ret (Close-to-Close): Logarithmic return, ln(Closet​/Closet−1​). Used for overall volatility and total return.

log_Vol: Log change in volume, ln(Volt​/Volt−1​). Used to measure trading activity change.

OC_Log_Change (Open-to-Close): Intraday logarithmic return, ln(Closet​/Opent​). Used to isolate intraday volatility from overnight gaps.

HL_Range_Pct: Daily High-Low range normalized by previous close, (Hight​−Lowt​)/Closet−1​. Used as a proxy for realized daily volatility (Parkinson-like measure).

Fundamentals (market_fundamentals.csv)

This file contains point in time cross-sectional data, including fields like:

Ticker

Company Name (e.g., Agilent Technologies, Inc.)

marketCap

sector

industry

Read using pd.read_pickle('')

💡 Potential Use Cases

Volatility Forecasting: Use the historical time-series features (Log_Ret, HL_Range_Pct) to train models (e.g., GARCH, machine learning) to predict future volatility.

Alpha Generation: Develop trading signals based on the cross-sectional fundamentals combined with recent momentum/volatility changes.

Anomaly Detection: Use the difference between overnight return (implied by CC minus OC) to detect potential mispricings or significant after-hours news impact.

Factor Modeling: Construct stock factors based on market capitalization, price levels, and the novel volatility features provided.

Graph and download economic data for Stock Market Turnover Ratio (Value Traded/Capitalization) for United States (DDEM01USA156NWDB) from 1975 to 2019 about ratio, stock market, and USA.

Poland Turnover: Main Market: WSE: Volume: Shares: Continuous Trading System data was reported at 797,649,795.000 Unit in Nov 2025. This records a decrease from the previous number of 908,833,648.000 Unit for Oct 2025. Poland Turnover: Main Market: WSE: Volume: Shares: Continuous Trading System data is updated monthly, averaging 2,142,322,391.000 Unit from Jan 2001 (Median) to Nov 2025, with 299 observations. The data reached an all-time high of 3,987,454,898.000 Unit in Jan 2012 and a record low of 6,344,356.000 Unit in Jan 2001. Poland Turnover: Main Market: WSE: Volume: Shares: Continuous Trading System data remains active status in CEIC and is reported by Warsaw Stock Exchange. The data is categorized under Global Database’s Poland – Table PL.Z: Warsaw Stock Exchange: Turnover and No of Transactions.

Canada's main stock market index, the TSX, fell to 30943 points on December 2, 2025, losing 0.51% from the previous session. Over the past month, the index has climbed 2.21% and is up 20.70% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on December 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.

S&P 500: A Bull or a Bear?

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

🥤 Coca-Cola (KO) Stock Price History (1919–2025)

This dataset provides daily historical stock price data for The Coca-Cola Company (ticker: KO) from January 2, 1962 to April 6, 2025. It captures Coca-Cola’s stock performance through decades of economic cycles, technological shifts, and global events — making it a rich resource for time-series analysis, investment research, and machine learning projects.

📂 Dataset Overview

🧮 Dataset Dimensions

• Total Rows: 15,922
• Total Columns: 7
• Missing Values: None ✅
• Date Range: 1962-01-02 to 2025-04-06

📊 Summary Statistics

• Highest Close Price: $73.18
• Lowest Close Price: $0.19
• Max Volume: 124M+ shares
• Average Close Price: ~$18.45
• Adjusted Prices: Range from $0.03 to $73.18

💡 Use Cases

• Time-series forecasting with LSTM, ARIMA, Prophet
• Volatility analysis and pattern detection
• Financial data visualization across decades
• Backtesting trading strategies on long-term data
• Comparing adjusted vs. raw stock prices

🧠 Project Ideas

• Predict future stock prices using ML models
• Visualize price trends during major economic events
• Analyze the effect of dividends and stock splits
• Build a financial dashboard using Plotly or Streamlit

📎 License

This dataset is for educational and research purposes only. For financial trading or commercial use, always consult a licensed data provider.

🙌 Acknowledgment

This dataset was compiled to support learning in data science, finance, and AI fields. Feel free to use it in your projects — and if you do, share your work! 📬 Contect info:

You can contect me for more data sets any type of data you want.

-E_mail

-Linkdin

-Kaggle

-X

-Github

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

About:

This dataset encompasses the historical data of major stock indices from around the world, sourced directly from Yahoo Finance. With data reaching back to the early 1920s (where available), it serves as an invaluable repository for academic researchers, financial analysts, and market enthusiasts. Users can delve into trends across decades, evaluate historical market behaviors, or even design and validate predictive financial models.

Photo by Tötös Ádám on Unsplash

Info on CSVs:

1. all_indices_data.csv:

   • Description: A consolidated dataset merging all the stock indices from Yahoo Finance.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.
     • ticker: The ticker symbol of the stock index.

2. individual_indices_data/[SYMBOL]_data.csv:

   • Description: Individual datasets for each stock index, where [SYMBOL] denotes the ticker symbol of the respective stock index. Each dataset is curated from Yahoo Finance's historical data archives.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.