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.

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.

NSE Stock Historical Price Data (Market Cap > 500 Cr)

Dataset Description

This dataset contains the historical closing price data for all stocks listed on the National Stock Exchange (NSE) of India with a market capitalization exceeding 500 crore INR. The dataset is ideal for analysts, researchers, and enthusiasts who wish to perform detailed analysis, develop trading algorithms, or study market trends of substantial companies within the Indian stock market.

Features

1. Stock Ticker: Unique symbol representing each stock.
2. Date: The specific trading date.
3. Closing Price: The price at which the stock closed on a given day.

Source

The data is sourced from official NSE records and includes all companies meeting the market capitalization criteria as of the latest update.

Applications

• Trend Analysis: Understand how stock prices of major companies have fluctuated over time.
• Algorithmic Trading: Develop and backtest trading algorithms using real historical data.
• Market Research: Study the performance of large-cap stocks to gain insights into market dynamics.
• Educational Use: Serve as a practical dataset for educational purposes in finance, economics, and data science courses.

Usage

The dataset can be used for various purposes including but not limited to: - Financial modeling and forecasting - Risk management and portfolio optimization - Academic research and projects - Machine learning and AI-driven stock prediction models

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

This dataset is about stocks per day. It has 1 row and is filtered where the stock is KW and the date is the 26th of March 2025. It features 4 columns: stock, opening price, and closing price.

1) Data Introduction • The Twitter Stock Prices Dataset contains stock price data for Twitter from November 2013 to October 2022. This dataset is a time series dataset that provides daily stock trading information. • The key attributes include the stock's opening price (Open), highest price (High), lowest price (Low), closing price (Close), adjusted closing price (Adj Close), and volume (Volume).

2) Data Utilization (1) Characteristics of the Twitter Stock Prices Data • This dataset is a time series, offering daily stock price fluctuations and allows tracking of price changes over time. • It includes 7 main attributes related to stock trading, allowing for analysis of price movements (open, high, low, close) and volume, to better understand Twitter’s stock price dynamics. • This data helps analyze market trends, price volatility patterns, and price fluctuation analysis, providing insights into the dynamics of the stock market.

(2) Applications of the Twitter Stock Prices Data • Predictive Modeling: This dataset can be used to develop stock price prediction models, including predicting price increases/decreases or forecasting future stock prices using machine learning models. • Business Insights: Investment experts can use this dataset to evaluate Twitter’s stock performance, and it provides useful information for optimizing investment strategies in response to market changes. This dataset can be used for trend forecasting and investor analysis. • Trend Analysis: By analyzing stock upward/downward trends, this dataset can help evaluate the company's market performance and develop trend-based investment strategies.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 163 companies listed on the Kuwait Stock Exchange (XKUW) in Kuwait. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

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Top 5 used data fields in the End-of-Day Pricing Dataset for Kuwait:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

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Top 5 financial instruments with End-of-Day Pricing Data in Kuwait:

Kuwait Stock Exchange (KSE) - Price Index: The main index that tracks the performance of all companies listed on the Kuwait Stock Exchange (KSE), providing insights into the Kuwaiti equity market.

Kuwaiti Dinar (KWD): The official currency of Kuwait. It is widely used for transactions and serves as the backbone of the country's financial system.

National Bank of Kuwait (NBK): The largest and one of the oldest banks in Kuwait, offering a wide range of banking and financial services.

Kuwait Finance House (KFH): A leading Islamic bank in Kuwait, providing Sharia-compliant banking services and products to individuals and businesses.

Zain Group (ZAIN): A telecommunications company based in Kuwait, with operations in multiple countries across the Middle East and North Africa, providing mobile and data services.

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If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Kuwait, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

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Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Kuwait ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Kuwait?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Kuwait exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

‍Techsalerator provides the End-of-Day Pricing Data through multiple delivery methods, such as FTP, SFTP, S3 bucket, or email, ensuring easy access and integration...

This dataset is about stocks per day. It has 13 rows and is filtered where the stock is CGAAY. It features 4 columns: stock, opening price, and closing price.

This dataset is about stocks per day. It has 1 row and is filtered where the stock is 9285.T and the date is the 15th of April 2025. It features 4 columns: stock, opening price, and closing price.

This dataset is about stocks per day. It has 840 rows and is filtered where the stock is CCOLA.IS. It features 4 columns: stock, highest price, and closing price.

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.

This dataset is about stocks per day. It has 837 rows and is filtered where the stock is GNE. It features 4 columns: stock, highest price, and closing price.

Description

This project involves collecting and analyzing financial data for Electronic Arts (EA) using the Alpha Vantage API. The data includes historical stock prices, dividend payments, and stock splits. The project aims to provide a detailed view of EA’s financial performance and corporate actions over time.

Detail

The project consists of three main datasets:

1) Stock Price Data: Daily records of EA’s stock prices, including opening, high, low, and closing prices, as well as trading volume.

2) Dividend Data: Historical records of dividend payments by EA, detailing declaration dates, record dates, payment dates, and dividend amounts.

3) Stock Split Data: Records of stock split events, showing the date of each split and the split ratio.

The data is sourced from the Alpha Vantage API, which provides comprehensive financial market data. The datasets are cleaned and formatted to ensure consistency and accuracy. They are then saved in CSV files for easy access and analysis.

Usage

The collected data can be used for various financial analyses and insights:

Stock Price Analysis: Evaluate EA’s stock price trends, volatility, and trading volumes over time.

Dividend Analysis: Analyze dividend payment trends, yield, and changes in dividend policy.

Stock Split Analysis: Understand the impact of stock splits on EA’s stock price and overall market behavior.

This data can be used by investors, financial analysts, and researchers to make informed decisions or conduct further financial research. It can also be integrated into financial models or visualizations to provide a clearer picture of EA’s financial health and corporate actions.

Summary

The project provides a detailed dataset of Electronic Arts’ financial data, including stock prices, dividends, and stock splits. By sourcing data from the Alpha Vantage API and carefully formatting it, the project offers valuable insights into EA’s historical financial performance. The data is organized into CSV files, making it accessible for analysis, research, and decision-making purposes.

This dataset is about stocks per day. It has 1 row and is filtered where the stock is OBA.F and the date is the 26th of March 2025. It features 3 columns: stock, and closing price.

This dataset is about stocks per day. It has 1 row and is filtered where the stock is LNN and the date is the 5th of May 2025. It features 3 columns: stock, and closing price.

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

This dataset is about stocks per day. It has 1 row and is filtered where the stock is 59JA.F and the date is the 5th of May 2025. It features 3 columns: stock, and closing price.

Walmart (WMT) Stock Price Data (1970 - 2025)

Dataset Overview:

This dataset contains historical stock price data for Walmart Inc. (WMT) from October 1, 1970, to January 31, 2025. The data includes key stock market indicators such as opening price, closing price, adjusted closing price, highest and lowest prices of the day, and trading volume. This dataset can be valuable for financial analysis, stock market trend prediction, and machine learning applications in quantitative finance.

Data Source

The data has been collected from publicly available financial sources and covers over 13,000 trading days, providing a comprehensive view of Walmart’s stock performance over several decades.

Columns Description

Date: The trading date (1970-10-01).

Open: The opening price of Walmart stock for the day.

High: The highest price reached during the trading session.

Low: The lowest price recorded during the trading session.

Close: The closing price at the end of the trading day.

Adj Close: The adjusted closing price, which accounts for stock splits and dividends.

Volume: The total number of shares traded on that particular day.

Potential Use Cases

This dataset can be used for a variety of financial and data science applications, including:

✔ Stock Market Analysis – Study historical trends and price movements.

✔ Time Series Forecasting – Develop predictive models using machine learning.

✔ Technical Analysis – Apply moving averages, RSI, and other trading indicators.

✔ Market Volatility Analysis – Assess market fluctuations over different periods.

✔ Algorithmic Trading – Backtest trading strategies based on historical data.

Data Integrity

No missing values.

Data spans over 50 years, ensuring long-term trend analysis.

Preprocessed and structured for easy use in Python, R, and other data science tools.

How to Use the Data?

You can load the dataset using Pandas in Python: ``` import pandas as pd

Load the dataset

df = pd.read_csv("WMT_1970-10-01_2025-01-31.csv")

Display the first few rows

df.head() ```

Acknowledgments

This dataset is provided for educational and research purposes. Please ensure proper attribution if used in projects or research.

More Dataset

This data set is scrape by Muhammad Atif Latif.

For more Datasets justCLICK HERE

This dataset is about stocks per day. It has 837 rows and is filtered where the stock is EFAD. It features 4 columns: stock, opening price, and closing price.

This dataset contains historical stock price data for Tesla, Inc. (TSLA) starting from its IPO date, June 29, 2010, to January 1, 2025. The dataset includes daily records of Tesla's stock performance on the NASDAQ stock exchange. It is ideal for time-series analysis, stock price prediction, and understanding the long-term performance of Tesla in the stock market.

The dataset consists of the following columns:

1. Date: The trading date.
2. Open: Opening stock price on the given date.
3. High: The highest stock price during the trading day.
4. Low: The lowest stock price during the trading day.
5. Close: The closing stock price for the day.
6. Adj Close: Adjusted closing price (corrected for dividends and stock splits).
7. Volume: The number of shares traded during the day.

Use Cases of Tesla Stock Historical Data

1. Time-Series Analysis

   • Analyze trends in Tesla's stock prices over time.
   • Identify seasonality, volatility, and long-term patterns in Tesla’s performance.

2. Stock Price Prediction

   • Develop predictive models to forecast future stock prices using techniques such as ARIMA, LSTMs, or regression.

3. Investment Strategy Evaluation

   • Backtest trading strategies by simulating trades based on historical price movements.
   • Analyze returns of investment strategies such as moving averages, RSI, or Bollinger Bands.

4. Market Sentiment Analysis

   • Correlate Tesla’s stock performance with news sentiment, earnings reports, and market events.

5. Portfolio Diversification

   • Evaluate Tesla’s performance compared to other stocks or indices to assess its role in a diversified portfolio.

6. Risk Management

   • Calculate volatility, beta, and other risk metrics to assess the risk associated with investing in Tesla stock.

7. Economic and Market Studies

   • Study how macroeconomic indicators (like inflation, interest rates) influence Tesla’s stock price.
   • Analyze Tesla’s performance during major economic events such as the COVID-19 pandemic or policy changes.

8. Stock Splits and Adjustments Analysis

   • Examine the impact of Tesla’s stock splits on price and trading volume.

9. Educational Purposes

   • Serve as a dataset for academic projects, coursework, or tutorials on financial data analysis.

10. Correlation with Sector Trends

    • Compare Tesla’s stock performance with other automotive or renewable energy companies.

11. Data Visualization and Dashboarding

    • Create dashboards using tools like Tableau, Power BI, or Python libraries to visualize Tesla’s stock performance metrics.

12. A/B Testing for Financial Applications

    • Use historical stock data for controlled experiments in finance-related applications to improve decision-making tools.