This dataset provides realistic stock market data generated using Geometric Brownian Motion for price movements and Markov Chains for trend prediction. It is designed for time-series forecasting, financial modeling, and algorithmic trading simulations.

Key Features

• 1000 days of synthetic stock market data (from January 1, 2022, onwards).
• Multiple companies from diverse industries (Technology, Finance, Healthcare, Energy, Consumer Goods, Automotive, Aerospace, etc.).
• Stock price details: Open, High, Low, Close prices.
• Trading volume and market capitalization.
• Financial metrics: P/E Ratio, Dividend Yield, Volatility.
• Sentiment Score: A measure of market sentiment (-1 to 1 scale).
• Trend Labeling: Bullish, Bearish, or Stable, based on Markov Chain modeling.

Usage Scenarios

🔹 Time-Series Forecasting: Train models like LSTMs, Transformers, or ARIMA for stock price prediction.
🔹 Algorithmic Trading: Develop trading strategies based on trends and sentiment.
🔹 Feature Engineering: Explore correlations between financial metrics and stock movements.
🔹 Quantitative Finance Research: Analyze market trends using simulated yet realistic data.

PS: If you find this dataset helpful, please consider upvoting :)

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

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

Dataset Description:

Title: Sales Order Dataset

Description: This dataset contains sales order information from an e-commerce platform for a specific period. The dataset includes the following columns:

Order Number: A unique identifier for each order. Order Date: The date when the order was placed. SKU ID: Stock Keeping Unit (SKU) identifier for the product. Warehouse ID: Identifier for the warehouse from which the product was shipped. Customer Type: Type of customer (e.g., individual, business). Order Quantity: The quantity of the product ordered. Unit Sale Price: The price per unit of the product. Revenue: The total revenue generated by the order. Purpose: This dataset is suitable for exploring sales patterns, analyzing customer behavior, and predicting future sales trends. It can be used by data analysts, data scientists, and business analysts to gain insights into sales performance, identify potential areas for improvement, and make data-driven business decisions.

Potential Use Cases:

Analyzing sales trends over time. Identifying best-selling products and customer segments. Predicting future sales based on historical data. Evaluating the effectiveness of marketing campaigns and promotions. Optimizing inventory management and supply chain operations. Data Source: The dataset was collected from an e-commerce platform and has been anonymized to protect sensitive information. It represents a subset of sales order data for analysis and research purposes.

Acknowledgements: We acknowledge the contribution of the e-commerce platform for providing the sales order data used in this dataset.

License: This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, share, and adapt the data, provided you give appropriate credit to the original source.

Stock Analysis Software Market Outlook

The global stock analysis software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The growth of this market is driven by the increasing adoption of advanced analytics tools by individual investors and financial institutions to make informed investment decisions. The rising demand for automated trading systems and the integration of artificial intelligence (AI) and machine learning (ML) in stock analysis software are significant growth factors contributing to the market expansion.

One of the primary growth factors for the stock analysis software market is the increasing complexity and volume of financial data. With the exponential growth of data from various sources such as social media, news articles, and financial statements, investors and financial analysts require sophisticated tools to process and interpret this information accurately. Stock analysis software equipped with AI and ML algorithms can analyze vast datasets in real-time, providing valuable insights and predictive analytics that enhance investment strategies. Moreover, the growing trend of algorithmic trading, which relies heavily on high-speed data processing and automated decision-making, is further propelling the market growth.

Another crucial growth driver is the rising awareness and adoption of stock analysis software among individual investors. As more individuals seek to actively manage their investment portfolios, there is a growing demand for user-friendly and cost-effective stock analysis tools that offer comprehensive market analysis, technical indicators, and personalized investment recommendations. The proliferation of mobile applications and the increasing accessibility of cloud-based stock analysis solutions have made it easier for retail investors to access advanced analytical tools, thereby contributing to market expansion.

The integration of innovative technologies such as natural language processing (NLP) and sentiment analysis into stock analysis software is also a significant growth factor. These technologies enable the software to interpret and analyze unstructured data from news articles, social media, and other textual sources to gauge market sentiment and predict stock price movements. This capability is particularly valuable in today's fast-paced financial markets, where sentiment and news events can have a substantial impact on stock prices. The continuous advancements in AI and NLP technologies are expected to drive further innovations and improvements in stock analysis software, thereby boosting market growth.

In the evolving landscape of financial technology, Investor Relations Tools have become indispensable for companies seeking to maintain transparent and effective communication with their stakeholders. These tools facilitate seamless interaction between companies and their investors, providing real-time updates, financial reports, and strategic insights. By leveraging these tools, companies can enhance their investor engagement strategies, build trust, and foster long-term relationships with their shareholders. The integration of advanced analytics and AI-driven insights into Investor Relations Tools further empowers companies to tailor their communication strategies, ensuring that they meet the diverse needs of their investor base. As the demand for transparency and accountability in financial markets continues to grow, the adoption of sophisticated Investor Relations Tools is expected to rise, playing a crucial role in the broader ecosystem of stock analysis software.

From a regional perspective, North America is anticipated to hold the largest market share due to the high concentration of financial institutions, brokerage firms, and individual investors in the region. The presence of key market players and the early adoption of advanced technologies also contribute to the dominant position of North America in the global stock analysis software market. Additionally, the Asia Pacific region is expected to witness significant growth during the forecast period, driven by the increasing number of retail investors, rapid economic development, and the growing financial markets in countries such as China and India.

Component Analysis

This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

Complete list of stocks under 10 dollars stocks with real-time data, financial metrics, and screening criteria

Stock Analysis Software Market size was valued at USD 145.6 Million in 2024 and is projected to reach USD 450.68 Million by 2032, growing at a CAGR of 15.17% during the forecast period 2026-2032.Growing Retail Investor Participation: The Indian stock market has witnessed an unprecedented surge in retail investor participation. With the advent of user-friendly trading platforms, such as Zerodha, Groww, and Upstox, and the reduction of traditional barriers like high fees and the introduction of fractional shares, more individuals are now able to enter the market.Demand for Real-Time Data and Analytics: In today's fast-paced financial world, the need for real-time data and analytics is paramount. Investors, from seasoned professionals to burgeoning retail participants, require up-to-the-minute information on stock prices, breaking news, and crucial technical indicators to capitalize on fleeting opportunities.

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.

Discover the booming stock analysis software market! Explore its $2.5B valuation (2025), 12% CAGR, key drivers, trends, and regional insights. Learn about top players & segments like fundamental & technical analysis. Forecast to 2033 included.

Complete list of cheap stocks stocks with real-time data, financial metrics, and screening criteria

The Center for Research in Security Prices (CRSP) stock databases provide time-series and event data on individual stocks, augmented with market time-series. Daily and monthly time-series variables include returns, closing, low bid and high ask prices, and trading volume. Event data includes distributions, shares outstanding, names, etc.

Dataset is an external database available here for Cornell affiliates: https://johnson.library.cornell.edu/database/wharton-research-data-services-wrds/

Discover the booming stock software market! Our in-depth analysis reveals a $15 billion market in 2025 projected to reach $45 billion by 2033, driven by AI, mobile trading, and algorithmic strategies. Explore market trends, key players (Interactive Data, Ninja Trader, etc.), and regional insights. Invest wisely with our data-driven market overview.

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.

Complete list of undervalued stocks stocks with real-time data, financial metrics, and screening criteria

rahulholla1/stock-analysis dataset hosted on Hugging Face and contributed by the HF Datasets community

Complete list of software stocks stocks with real-time data, financial metrics, and screening criteria

Historical AI model predictions and analysis for Google stock across multiple timeframes and confidence levels

Description

This dataset offers a comprehensive historical record of stock prices for the world's most famous brands, with daily updates. The data spans from January 1, 2000, to the present day , providing an extensive timeline of stock market information for various global brands.

Key Features

• Date: The date of the stock price data.
• Open: The opening price of the stock on that date.
• High: The highest price the stock reached during the trading day.
• Low: The lowest price the stock reached during the trading day.
• Close: The closing price of the stock on that date.
• Volume: The trading volume, i.e., the number of shares traded on that date.
• Dividends: Dividends paid on that date (if any).
• Stock Splits: Information about stock splits (if any).
• Brand_Name: The name of the brand or company.
• Ticker: Ticker symbol for the stock.
• Industry_Tag: The industry category or sector to which the brand belongs.
• Country: The country where the brand is headquartered or primarily operates.

Potential Use Cases

• Stock Market Analysis: Analyze historical stock prices to identify trends and patterns in the stock market.
• Brand Performance: Evaluate the performance of various brands in the stock market over time.
• Investment Strategies: Develop investment strategies based on historical stock data for specific brands.
• Sector Analysis: Explore how different industries or sectors are performing in the stock market.
• Country Comparison: Compare the stock performance of brands across different countries.
• Market Sentiment Analysis: Analyze stock price movements in relation to news or events affecting specific brands or industries.

If you find this dataset useful, please consider giving it a vote! 🙂❤️

In 2025, ** percent of adults in the United States invested in the stock market. This figure has remained steady over the last few years and is still below the levels before the Great Recession, when it peaked in 2007 at ** percent. What is the stock market? The stock market can be defined as a group of stock exchanges where investors can buy shares in a publicly traded company. In more recent years, it is estimated an increasing number of Americans are using neobrokers, making stock trading more accessible to investors. Other investments A significant number of people think stocks and bonds are the safest investments, while others point to real estate, gold, bonds, or a savings account. Since witnessing the significant one-day losses in the stock market during the financial crisis, many investors were turning towards these alternatives in hopes for more stability, particularly for investments with longer maturities. This could explain the decrease in this statistic since 2007. Nevertheless, some speculators enjoy chasing the short-run fluctuations, and others see value in choosing particular stocks.