The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

The Indian Stock Market Dataset provides a comprehensive collection of stock market data sourced from secondary sources, primarily Google, offering insights into investment opportunities and trends within the Indian financial landscape. This dataset encompasses a wide array of information, with a primary focus on Return on Investment (ROI) metrics and the respective industry sectors in which investments are made.

With a reliability rating of 80%, this dataset offers valuable insights for investors, analysts, researchers, and enthusiasts seeking to understand and navigate the complexities of the Indian stock market. The dataset serves as a foundational resource for analyzing market performance, identifying lucrative investment opportunities, and making informed decisions in a dynamic financial environment.

Key features of the dataset include:

ROI Analysis: The dataset provides detailed ROI metrics, allowing stakeholders to assess the profitability of various investment avenues over specific timeframes. By analyzing ROI trends, investors can gauge the performance of individual stocks, portfolios, or entire industry sectors, facilitating strategic investment planning and risk management.

Industry Classification: Each investment entry in the dataset is categorized according to its respective industry sector. This classification enables users to explore investment opportunities within specific sectors such as technology, healthcare, finance, energy, consumer goods, and more. Understanding industry dynamics and market trends is essential for optimizing investment portfolios and diversifying risk exposure.

Historical Data: The dataset includes historical stock market data, offering insights into past performance trends and market behavior. By examining historical data, users can identify patterns, correlations, and anomalies that may impact future investment decisions. Historical analysis empowers investors to make informed predictions and adapt strategies in response to evolving market conditions.

Data Accuracy: While the dataset boasts an accuracy rate of 80%, users should exercise diligence and consider additional sources for validation and verification. While the majority of data points are reliable, occasional discrepancies or inaccuracies may exist, highlighting the importance of due diligence and comprehensive analysis in the investment process.

Accessibility: The Indian Stock Market Dataset is easily accessible and user-friendly, catering to a diverse audience ranging from seasoned investors to novices exploring the world of finance. The dataset can be utilized for various purposes, including academic research, financial modeling, algorithmic trading, and investment portfolio management.

In summary, the Indian Stock Market Dataset offers a valuable resource for analyzing ROI and industry trends within the Indian financial landscape. With a focus on accuracy, accessibility, and comprehensive data coverage, this dataset empowers stakeholders to make informed investment decisions, optimize portfolio performance, and navigate the complexities of the dynamic stock market environment. Whether you're a seasoned investor or a novice enthusiast, this dataset provides valuable insights for unlocking the potential of the Indian stock market.

Disclaimer: Educational Purposes Only

The financial and International Securities Identification Number (ISIN) data listed on this platform is provided solely for educational purposes. The information is intended to serve as general guidance and does not constitute financial advice, an endorsement, or a recommendation for the purchase or sale of any securities.

While we strive to ensure the accuracy and timeliness of the information presented, we make no representations or warranties, express or implied, regarding the completeness, accuracy, reliability, suitability, or availability of the provided data. Users are encouraged to independently verify any information obtained from this platform before making any investment decisions.

This platform and its operators are not responsible for any errors, omissions, or inaccuracies in the provided data, nor for any actions taken in reliance on such information. Users are strongly advised to conduct thorough research and seek the advice of qualified financial professionals before making any investment decisions.

The use of International Securities Identification Numbers (ISINs) and other financial data is subject to various regulations and licensing agreements. Users are responsible for complying with all applicable laws and respecting any terms and conditions associated with the use of such data.

By accessing and using this platform, users acknowledge and agree that they are doing so at their own risk and discretion. This educational content is not a substitute for professional financial advice, and users should consult with qualified professionals for specific guidance tailored to their individual circumstances.

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.

Context

In this dataset you will find several characteristics on global companies listed on the stock exchange. These characteristics are analyzed by millions of investors before they invest their money.

Analyze the stock market performance of thousands of companies ! This is the objective of this dataset !

Content

Among thse charateristics you will find :

• The symbol : The stock symbol is a unique series of letters assigned to a security for trading purposes.
• The shortname : The name of the company
• The sector : The sector of the company (Technology, Financial services, consumer cyclical...)
• The country : The location of the head office.
• The market capitalisation : Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. It is calculated by multiplying the total number of a company's outstanding shares by the current market price of one share.
• The current ratio : The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default.
• The beta : Beta is a measure of a stock's volatility in relation to the overall market. A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
• The dividend rate : Represents the ratio of a company's annual dividend compared to its share price. (%)

All this data is public data, obtained from the annual financial reports of these companies. They have been retrieved from the Yahoo Finance API and have been checked beforehand.

Inspiration

This dataset has been designed so that it is possible to build a recommendation engine. For example, from an existing position in a portfolio, recommend an alternative with similar characteristics (sector, market capitalization, current ratio,...) but more in line with an investor's expectations (may be with less risk or with more dividends etc...)

If you have question about this dataset you can contact me

Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

Reddit: /r/stocks

Analyzing User Engagement to Identify Market Trends

By Reddit [source]

About this dataset

This dataset provides a valuable opportunity for researchers to explore the fascinating world of stock exchange markets through the eyes of those participating in discussions on Reddit. We have compiled posts from the subredditstocks subreddit to provide researchers with an invaluable source of information on how stock market trends may be impacted by user sentiment. With detailed data columns such as post titles, scores, id's, URLs, comments counts and created times for each post we are offering a unique vantage point into understanding how stocks market discussions may inform our better understanding of these dynamics. By delving further into user sentiment and engagement with stock topics, investigators can put together meaningful pieces in assembling full-fledged investments picture that is based off sound evidence gained from real people’s experiences and opinion. Discovering new insights has never been made easier – let’s venture out on this journey together!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨! ### Research Ideas
• Using the score and comments data, researchers can determine which stocks are being discussed and tracked the most, indicating potential areas of interest in the stock market.
• Analyzing the body text of posts to identify common topics of conversation related to various stocks assists in providing a better understanding of users' feelings towards different stock investments.
• Through analyzing fluctuations in user engagement over time, researchers can observe which stocks have experienced an increase or decrease in user interest and reaction to new developments within different markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: stocks.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------| | title | The title of the post. (String) | | score | The score of the post, based on the Reddit voting system. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments on the post. (Integer) | | created | The date and time the post was created. (Timestamp) | | body | The body text of the post. (String) | | timestamp | The date and time the post was last updated. (Timestamp) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

Dhaka Stock Exchange (DSE) Historical Stock Prices (2000-2025)

Dataset Overview:

This dataset provides a comprehensive historical record of stock prices from the Dhaka Stock Exchange (DSE), the primary stock exchange of Bangladesh. Spanning from January 1, 2000, to February 26, 2025, it offers a detailed look into the daily trading activity of 464 unique stocks.

Key Features:

• Date: The trading date (YYYY-MM-DD format).
• Script (Stock Name): The name or ticker symbol of the listed company.
• Open: The opening price of the stock on the given trading day.
• High: The highest price reached by the stock during the trading day.
• Low: The lowest price reached by the stock during the trading day.
• Close: The closing price of the stock on the given trading day.
• Volume: The total number of shares traded for the stock on the given trading day.

Data Characteristics:

• Time Span: January 1, 2000, to February 26, 2025.
• Number of Unique Stocks: 464
• Frequency: Daily
• Accuracy: Clean and accurate data, suitable for reliable analysis.

Potential Uses:

• Financial Analysis: Analyze stock trends, volatility, and performance over time.
• Machine Learning: Develop predictive models for stock price forecasting.
• Economic Research: Study the impact of economic events on the Bangladeshi stock market.
• Investment Strategies: Backtest trading strategies and identify potential investment opportunities.
• Educational Purposes: Learn about stock market dynamics and data analysis in finance.

Acknowledgements:

This dataset was meticulously compiled and cleaned to provide a valuable resource for researchers, analysts, and investors interested in the Dhaka Stock Exchange.

Note:

While efforts have been made to ensure the accuracy of the data, users are advised to conduct their own due diligence and validation before making any investment decisions based on this dataset.

This description highlights the key aspects of your dataset, its potential uses, and its reliability. Feel free to adjust it further based on any specific details or insights you want to emphasize!

Stock Data of Top USA Companies: Apple, Tesla, Amazon

👨‍💻 Author: Azhar Saleem

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Dataset Description

This dataset provides daily stock data for some of the top companies in the USA stock market, including major players like Apple, Microsoft, Amazon, Tesla, and others. The data is collected from Yahoo Finance, covering each company’s historical data from its starting date until today. This comprehensive dataset enables in-depth analysis of key financial indicators and stock trends for each company, making it valuable for multiple applications.

Column Descriptions

The dataset contains the following columns, consistent across all companies:

• Date: The date of the stock data entry.
• Open: The stock's opening price for the day.
• High: The highest price reached during the trading day.
• Low: The lowest price during the trading day.
• Close: The stock’s closing price for the day.
• Volume: The total number of shares traded on that day.
• Dividends: Any dividends paid out on that day.
• Stock Splits: Records stock split events, if any, on that day.

Potential Use Cases

1. Machine Learning & Deep Learning:

   • Stock Price Prediction: Use historical prices to train models for forecasting future stock prices.
   • Sentiment Analysis and Price Correlation: Combine with external sentiment data to predict price movements based on market sentiment.
   • Anomaly Detection: Detect unusual price patterns or volume spikes using classification algorithms.

2. Data Science:

   • Trend Analysis: Identify long-term trends for each company or compare trends between companies.
   • Volatility Analysis: Calculate volatility to assess risk and return patterns over time.
   • Correlation Analysis: Compare stock performance across companies to study market relationships.

3. Data Analysis:

   • Historical Performance: Review historical data to understand growth trends, market impact of stock splits, and dividends.
   • Seasonal Patterns: Analyze data for seasonal trends or recurring patterns across years.
   • Investment Strategy Backtesting: Test various investment strategies based on historical data to assess potential profitability.

4. Financial Research:

   • Economic Impact Studies: Investigate how major events affected stock prices across top companies.
   • Sector-Specific Analysis: Identify performance differences across sectors, such as tech, healthcare, and retail.

This dataset is a powerful tool for analysts, researchers, and financial enthusiasts, offering versatility across multiple domains from stock analysis to algorithmic trading models.

A collection of financial datasets that are regularly updated

Day Level Data | Type | Link | | --- | --- | | Stocks | https://www.kaggle.com/datasets/liewyousheng/historical-stock-dataset | | ETF | https://www.kaggle.com/datasets/liewyousheng/historical-etf |

The dataset follows a standard stock market format collected from investment.com. It has data of 10 years per day (2895 samples). The data have five columns as follow, - Date - Open Price - Highest Value - Lowest Value - Closing Price of stock on that day.

Many academics and analysts have found it challenging to master the art of predicting stock values. Investors are actually quite interested in the field of stock price forecasting research. Many investors are interested in knowing the stock market's future scenario in order to make a smart and successful investment. By giving helpful information like the stock market's future direction, good and successful stock market prediction systems assist traders, investors, and analysts.

Amazon (AMZN) Stock Price Time-Series Dataset: May 2012 - November 2012

Dataset Overview

This dataset provides a detailed, intraday view of Amazon's stock (AMZN) price movements from May 21, 2012, to November 14, 2012. Meticulously compiled, it offers a granular perspective on market dynamics, enabling robust quantitative analysis and modeling.

Content

The dataset encompasses the following key financial metrics for each trading day:

• Date: The specific date of the trading session.
• Open: The initial price at the commencement of trading.
• High: The maximum price attained during the trading day.
• Low: The minimum price recorded during the trading day.
• Close: The final trading price at the market's close.
• Adj Close: The closing price adjusted for corporate actions like dividends and stock splits, providing a true return on investment.
• Volume: The number of shares exchanged throughout the trading day, indicating market activity and liquidity.

Intended Use Cases

This dataset is tailored for sophisticated financial analysis, model development, and academic research. Potential applications include:

• Algorithmic Trading Strategy Development: Design and back-test trading algorithms using historical price movements and volume data.
• Volatility Modeling: Analyze and forecast stock price volatility using time-series analysis techniques (e.g., GARCH models).
• Financial Forecasting: Implement machine learning models to predict future stock prices based on historical patterns.
• Event Study Analysis: Examine the impact of specific events or news announcements on Amazon's stock price.
• Risk Management: Evaluate potential risks associated with investing in Amazon stock during this period.
• Academic Research: Conduct studies on market efficiency, price discovery, and the impact of market microstructure on stock behavior.

Data Considerations

• Time Zone: Data is timestamped with Eastern Time (ET).
• Data Cleaning: The dataset has been verified for accuracy, but users are encouraged to perform their own data quality checks.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

-E_mail

-Linkdin

-Kaggle

-X

-Github

CONTEXT

"This dataset contains historical stock market data for Tata Consultancy Services (TCS), an Indian multinational information technology services and consulting company." The dataset includes daily stock prices, trading volume, and other financial metrics for TCS from April 29, 2013, to April 28, 2023. The information was gathered from publicly available sources such as Yahoo Finance and NSE India.

CONTENT

Tata Consultancy Services (TCS) is a global provider of IT services and consulting. TCS's stock price is closely tracked by investors, traders, and financial experts all over the world, considering it is a prominent player in the global technology business. This dataset includes 2,769 rows and 9 columns, including Date, Open Price, High Price, Low Price, Close Price, Adj. Close, Volume, Dividends, and Stock Splits.

ACKNOWLEDGEMENT

The data was scraped from finance.yahoo.com

This dataset is designed to support research and model development in financial market forecasting. It consists of daily stock market data for multiple companies, enriched with macroeconomic indicators and simulated market stress events to reflect real-world volatility.

Key features include:

Stock price details (Open, High, Low, Close) and Trading Volume

Macroeconomic indicators such as GDP growth rate, inflation rate, interest rate, and unemployment rate

A Market Stress Level (normalized between 0 and 1) indicating systemic volatility

A binary Event Flag to simulate major financial shocks or critical economic events

Data spans across multiple tickers (e.g., AAPL, GOOGL, TSLA) for 500+ trading days

This dataset contains historical stock price data for major banks from the year 2014 to 2024. The dataset includes daily stock prices, trading volume, and other relevant financial metrics for prominent banks. The stock prices are provided in IDR (Indonesian Rupiah) currency.

PT Bank Central Asia Tbk (BBCA.JK), more commonly recognized as Bank Central Asia (BCA). As one of Indonesia's largest privately-owned banks, BCA was founded in 1955 and provides a diverse array of banking services encompassing consumer banking, corporate banking, investment banking, and asset management. With a widespread presence throughout Indonesia, including numerous branches and ATMs, BCA is esteemed for its robust financial achievements, inventive banking offerings, and dedication to customer satisfaction.

Dataset Variables:

1. Date: The date of the stock price data.
2. Open Price: The opening price of the bank's stock on the given date.
3. Close Price: The closing price of the bank's stock on the given date.
4. High Price: The highest price reached by the bank's stock during the trading day.
5. Low Price: The lowest price reached by the bank's stock during the trading day.
6. Adjusted Low Price: The closing price on a given trading day, adjusted to reflect any corporate actions, such as stock splits, dividends, rights offerings, or other adjustments that may affect the stock price.
7. Volume: The number of shares traded on the given date.

Data Sources: The dataset is compiled from reliable financial sources, including stock exchanges, financial news websites, and reputable financial data providers. Data cleaning and preprocessing techniques have been applied to ensure accuracy and consistency. More info: https://finance.yahoo.com/quote/BBCA.JK/history/

Use Case: This dataset can be utilized for various purposes, including financial analysis, stock market forecasting, algorithmic trading strategies, and academic research. Researchers, analysts, and data scientists can explore the trends, patterns, and relationships within the data to derive valuable insights into the performance of the banking sector over the specified period. Additionally, this dataset can serve as a benchmark for evaluating the performance of machine learning models and quantitative trading strategies in the banking industry.

This dataset contains Apple's (AAPL) stock data for the last 10 years (from 2010 to date). I believe insights from this data can be used to build useful price forecasting algorithms to aid investment. I would like to thank Nasdaq for providing access to this rich dataset. I will make sure I update this dataset every few months.

** Microsoft (MSFT) Stock Price Data (1986-2025)**

Dataset Overview:

This dataset offers a comprehensive historical record of Microsoft Corporation (MSFT) stock prices, spanning from March 13, 1986, to January 31, 2025. With nearly 10,000 daily trading entries, this dataset is an essential resource for financial analysts, researchers, data scientists, and investors seeking insights into the long-term trends, volatility, and growth of one of the most influential technology companies in the world.

Data Features:

Date: The trading date.

Open Price: The stock’s price at the market’s opening.

High Price: The highest price recorded during the session.

Low Price: The lowest price recorded during the session.

Close Price: The final price at which the stock traded before market close.

Adjusted Close Price: The closing price adjusted for stock splits and dividends.

Trading Volume: The number of shares traded on the given day.

Key Highlights:

Historical Stock Performance: Covers Microsoft’s early public trading days, the Dot-com boom, the 2008 financial crisis, and recent AI-driven growth.

Stock Growth Trends: Track Microsoft’s rise from a startup to a trillion-dollar tech giant.

Market Volatility Analysis: Study trading volume fluctuations and identify high-activity market periods.

Machine Learning & Quantitative Finance Applications: Ideal for predictive modeling, algorithmic trading strategies, and financial risk assessments.

Potential Use Cases:

Long-term trend analysis of Microsoft’s stock.

Correlation studies with macroeconomic indicators and technology trends.

Development of predictive machine learning models.

Risk management and volatility forecasting.

Educational research in stock market dynamics and investment strategies.

Additional Notes:

The dataset is curated from publicly available sources and provides adjusted values for corporate actions like stock splits and dividends. Analysts should consider external financial and macroeconomic conditions while interpreting trends.

Unlock valuable insights into Microsoft’s financial history and market performance with this extensive dataset!

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This stock market dataset is designed for financial analysis and predictive modeling. It includes historical stock prices, technical indicators, macroeconomic factors, and sentiment scores to help in developing and testing machine learning models for stock trend prediction.

Dataset Features: Column Description Stock Random stock ticker (AAPL, GOOG, etc.) Date Random business date Open Open price High High price Low Low price Close Close price Volume Trading volume SMA_10 10-day Simple Moving Average RSI Relative Strength Index (10-90 range) MACD MACD indicator (-5 to 5) Bollinger_Upper Upper Bollinger Band Bollinger_Lower Lower Bollinger Band GDP_Growth Random GDP growth rate (2.5% to 3.5%) Inflation_Rate Inflation rate (1.5% to 3.0%) Interest_Rate Interest rate (0.5% to 5.0%) Sentiment_Score Random sentiment score (-1 to 1) Next_Close Next day's closing price (for regression) Target Binary classification (1: Price Increase, 0: Price Decrease)

Key Features: Stock Prices: Open, High, Low, Close, and Volume data. Technical Indicators: Simple Moving Average (SMA), Relative Strength Index (RSI), MACD, and Bollinger Bands. Macroeconomic Factors: Simulated GDP growth, inflation rate, and interest rates. Sentiment Scores: Randomized sentiment values between -1 and 1 to simulate market sentiment. Target Variables: Next-day close price (for regression) and price movement direction (for classification).

Stock Market Dataset Columns** The dataset generated using the yfinance library typically contains two types of data: - Historical Stock Prices - Company Metadata

A. Historical Stock Prices

This data provides a time series of a stock's market performance. Below are the main columns and their explanations:

Example:

B. Company Metadata

This data provides descriptive information about the company associated with the stock. Columns and their meanings include:

Example: