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.

📊 Intel Stock Dataset (1980-2024)

🌟 This dataset contains daily stock trading data for Intel Corporation (ticker: INTC) from 1980 to 2024, sourced from Yahoo Finance. It provides a comprehensive view of Intel's stock performance over four decades, including key metrics like opening/closing prices, trading volume, dividends, and stock splits.

📄 Dataset Overview

• 🗓️ Time Period: 1980 to 2024
• 📈 Total Records: 11,289 rows
• 📂 File Size: ~989.25 KB

This dataset is ideal for financial analysis, stock trend forecasting, machine learning models, and portfolio optimization studies.

📋 Columns and Descriptions

🌟 Key Features

• Clean and Complete: No missing values across all columns.
• Rich Historical Data: Captures Intel's stock trends and major events over the years.
• Ready for Analysis: Ideal for time-series analysis, regression models, and financial forecasting.

🚀 Applications

1. 📈 Trend Analysis: Identify long-term trends and patterns in Intel's stock performance.
2. 🤖 Machine Learning: Train predictive models for stock price forecasting.
3. 💼 Portfolio Insights: Analyze Intel's stock as part of an investment portfolio.
4. 🧮 Statistical Research: Study correlations between market events and stock performance.

Feel free to dive into the dataset and unlock its potential! Let me know if you need help with analysis or visualization. 😄

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.

Use our Stock prices 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.

The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

Graph and download economic data for Index of Common Stock Prices, New York Stock Exchange for United States (M11007USM322NNBR) from Jan 1902 to May 1923 about New York, stock market, indexes, and USA.

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.

The US_Stock_Data.csv dataset offers a comprehensive view of the US stock market and related financial instruments, spanning from January 2, 2020, to February 2, 2024. This dataset includes 39 columns, covering a broad spectrum of financial data points such as prices and volumes of major stocks, indices, commodities, and cryptocurrencies. The data is presented in a structured CSV file format, making it easily accessible and usable for various financial analyses, market research, and predictive modeling. This dataset is ideal for anyone looking to gain insights into the trends and movements within the US financial markets during this period, including the impact of major global events.

Key Features and Data Structure

The dataset captures daily financial data across multiple assets, providing a well-rounded perspective of market dynamics. Key features include:

• Commodities: Prices and trading volumes for natural gas, crude oil, copper, platinum, silver, and gold.
• Cryptocurrencies: Prices and volumes for Bitcoin and Ethereum, including detailed 5-minute interval data for Bitcoin.
• Stock Market Indices: Data for major indices such as the S&P 500 and Nasdaq 100.
• Individual Stocks: Prices and volumes for major companies including Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta.

The dataset’s structure is designed for straightforward integration into various analytical tools and platforms. Each column is dedicated to a specific asset's daily price or volume, enabling users to perform a wide range of analyses, from simple trend observations to complex predictive models. The inclusion of intraday data for Bitcoin provides a detailed view of market movements.

Applications and Usability

This dataset is highly versatile and can be utilized for various financial research purposes:

• Market Analysis: Track the performance of key assets, compare volatility, and study correlations between different financial instruments.
• Risk Assessment: Analyze the impact of commodity price movements on related stock prices and evaluate market risks.
• Educational Use: Serve as a resource for teaching market trends, asset correlation, and the effects of global events on financial markets.

The dataset’s daily updates ensure that users have access to the most current data, which is crucial for real-time analysis and decision-making. Whether for academic research, market analysis, or financial modeling, the US_Stock_Data.csv dataset provides a valuable foundation for exploring the complexities of financial markets over the specified period.

Acknowledgements:

This dataset would not be possible without the contributions of Dhaval Patel, who initially curated the US stock market data spanning from 2020 to 2024. Full credit goes to Dhaval Patel for creating and maintaining the dataset. You can find the original dataset here: US Stock Market 2020 to 2024.

The Rolling Stock Market Report is Segmented by Type (Locomotives, Metros and Light Rail Vehicles, Passenger Coaches, and More), Propulsion Type (Diesel, Electric, and More), Application (Passenger Rail and Freight Rail), End-User (National Rail Operators and More), Technology (Conventional and More) and Geography. The Market Forecasts are Provided in Terms of Value (USD) and Volume (Units).

France's main stock market index, the FR40, rose to 8121 points on December 2, 2025, gaining 0.29% from the previous session. Over the past month, the index has climbed 0.13% and is up 11.93% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from France. France Stock Market Index (FR40) - values, historical data, forecasts and news - updated on December of 2025.

This dataset, 'Amazon Stock Data and Key Affiliated Companies,' provides comprehensive daily stock data for Amazon (AMZN) and several companies that have significantly contributed to Amazon's business growth and success. The dataset includes historical data for key players such as Intel (INTC), FedEx (FDX), United Parcel Service (UPS), Salesforce (CRM), NVIDIA (NVDA), Visa (V), and Mastercard (MA).

The stock data spans over various years, capturing important trading metrics like open, close, high, low, and volume. Amazon, a global leader in e-commerce, cloud computing, and AI, has thrived with the support of these affiliated companies. From Intel's processors powering Amazon Web Services (AWS) to Salesforce's CRM solutions used by Amazon, and the logistics support provided by FedEx and UPS, each company plays a critical role.

This dataset can be used for financial analysis, stock market prediction models, correlation studies between Amazon and its key partners, or any other research involving the financial performance of these major corporations. Whether you're interested in understanding Amazon's stock trends or the interdependency of companies in its ecosystem, this dataset provides valuable insights.

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.

In general, the stock prices of the same industry have a similar trend, but those of different industries do not. When investing in stocks of different industries, one should select the optimal model from lots of trading models for each industry because any model may not be suitable for capturing the stock trends of all industries. However, the study has not been carried out at present. In this paper, firstly we select 424 S&P 500 index component stocks (SPICS) and 185 CSI 300 index component stocks (CSICS) as the research objects from 2010 to 2017, divide them into 9 industries such as finance and energy respectively. Secondly, we apply 12 widely used machine learning algorithms to generate stock trading signals in different industries and execute the back-testing based on the trading signals. Thirdly, we use a non-parametric statistical test to evaluate whether there are significant differences among the trading performance evaluation indicators (PEI) of different models in the same industry. Finally, we propose a series of rules to select the optimal models for stock investment of every industry. The analytical results on SPICS and CSICS show that we can find the optimal trading models for each industry based on the statistical tests and the rules. Most importantly, the PEI of the best algorithms can be significantly better than that of the benchmark index and “Buy and Hold” strategy. Therefore, the algorithms can be used for making profits from industry stock trading.

Israel's main stock market index, the TA-125, rose to 3538 points on December 2, 2025, gaining 1.75% from the previous session. Over the past month, the index has climbed 4.40% and is up 50.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on December of 2025.

📊 NIFTY500 Stocks Data

Get comprehensive historical stock data for the NIFTY 500 index! 📈 This dataset includes stock prices at various time intervals for each NIFTY 500 company, organized into Excel files.

📁 File Naming Convention

• Files named by NSE code, e.g., RELIANCE.xlsx

📜 Data Description

Each sheet in an Excel file contains: - 📅 Date: Date and time - 🏦 Open: Opening price - 📈 High: Highest price - 📉 Low: Lowest price - 🔒 Close: Closing price - 🔄 Volume: Shares traded

🎯 Usage

Ideal for: - Financial analysts 📊 - Data scientists 🤖 - Market researchers 🔍 Analyze stock trends, develop trading strategies, or conduct research with varied timeframes for both long-term and short-term analysis.

📝 Licensing

• MIT License: Free to use for any purpose, even commercially 🌐

📥 How to Access

1. Download from Kaggle ⬇️
2. Unzip the file 🗂️
3. Open Excel files to explore 📂

Happy analyzing! 📊🚀

The United States Capital Market Exchange Market is Segmented by Type of Market (Primary Market and Secondary Market), by Capital Market (Stocks and Bonds), and by Stock Type (Common & Preferred Stock, and Other), by Bond Type (Government Bonds, Corporate Bonds, and Other), and by Geography (Northeast, Midwest, and Other). The Market Forecasts are Provided in Terms of Value (USD).

Historical dataset of the Spain Stock Market Index (IBEX 35), covering values from 1991-09-01 to 2025-11-26, with the latest releases and long-term trends. Available for free download in CSV format.

The global capital exchange ecosystem market, valued at $1.06 trillion in 2025, is projected to experience robust growth, driven by increasing global trade, the rise of fintech innovations, and a growing preference for digital trading platforms. The market's Compound Annual Growth Rate (CAGR) of 5.80% from 2025 to 2033 signifies a consistently expanding market opportunity. Key segments, including the primary and secondary markets, contribute significantly to this growth, with the primary market fueled by Initial Public Offerings (IPOs) and other new listings, while the secondary market thrives on the continuous trading of existing securities. The diverse range of stock and bond types (common, preferred, growth, value, defensive stocks; government, corporate, municipal, mortgage bonds) caters to a broad spectrum of investor profiles and risk appetites. Technological advancements, including high-frequency trading algorithms and improved data analytics, are further enhancing market efficiency and liquidity. However, regulatory hurdles, geopolitical uncertainties, and cybersecurity threats remain as potential restraints on market growth. The strong presence of established exchanges like the New York Stock Exchange (NYSE), NASDAQ, and the London Stock Exchange, alongside emerging players in Asia and other regions, contributes to the market's competitive landscape. Regional growth will likely be influenced by economic development, regulatory frameworks, and investor confidence, with North America and Asia Pacific anticipated to maintain leading positions. The future of the capital exchange ecosystem hinges on adaptation and innovation. The increasing integration of blockchain technology and decentralized finance (DeFi) is expected to reshape trading infrastructure and potentially challenge traditional exchange models. Increased regulatory scrutiny globally will likely necessitate further transparency and improved risk management practices by exchanges. Furthermore, the growing prominence of Environmental, Social, and Governance (ESG) investing will influence investment strategies and, consequently, trading activity across various asset classes. The market's future success will depend on its ability to effectively manage risks, embrace technological innovation, and meet the evolving needs of a diverse and increasingly sophisticated investor base. Continued growth is anticipated, driven by both established and emerging markets. Recent developments include: In December 2023, Defiance ETFs, introduced the Defiance Israel Bond ETF (NYSE Arca: CHAI) to facilitate investors' access to the Israeli bond market. CHAI commenced trading on the New York Stock Exchange. The ETF, CHAI, mirrors the MCM (Migdal Capital Markets) BlueStar Israel Bond Index, enabling investors to tap into both Israel government and corporate bonds. This index specifically monitors the performance of bonds, denominated in USD and shekels, issued by either the Israeli government or Israeli corporations., In January 2024, the National Stock Exchange (NSE) saw a 22% rise in its investor base, increasing from 70 million to 85.4 million during the calendar year 2023. This growth highlights the increasing participation of retail investors in the stock market.. Key drivers for this market are: Automating all processes, Regulatory Landscape. Potential restraints include: Automating all processes, Regulatory Landscape. Notable trends are: Increasing Stock Exchanges Index affecting Capital Market Exchange Ecosystem.

MetaData

Microsift Stock Price Data (2010-2024)

Dataset Description

This dataset contains historical stock price data for Microsoft from 2010 to 2024. This data is extracted by using Python's yfinance library and it provides detailed insights into Microsoft stock performance over the years. It includes daily values for the stock's opening and closing prices, adjusted close price, high and low prices, and trading volume. This dataset is ideal for time series analysis, stock trend analysis, and financial machine learning projects such as price prediction models and volatility analysis.

The dataset is extracted from Yahoo Finance

Column Descriptions

Date: The trading date for each entry, in the format.

Adj_Close: Adjusted closing price of Microsoft stock for each trading day, reflecting stock splits, dividends, and other adjustments.

Close: The raw closing price of Microsoft stock at the end of each trading day.

High: The highest price reached by Microsoft stock during the trading day.

Low: The lowest price reached by Microsoft stock during the trading day.

Open: The price of Microsoft stock at the start of the trading day.

Volume: The total number of shares traded during the trading day.

📊 Adobe Stock Dataset (1986-2024)

This dataset contains historical stock data for Adobe Inc. (ticker symbol: ADBE) obtained from Yahoo Finance. The dataset spans from 1986 to 2024, offering a rich insight into Adobe’s stock performance over nearly four decades. The data provides essential information about Adobe's market behavior, which can be used for various analyses, such as trend analysis, forecasting, and financial modeling.

🧑‍💻 How the Data is Made

The data is sourced from Yahoo Finance, where stock prices are recorded for every trading day. It includes key market information like opening, closing, highest, and lowest prices of the stock on any given day, along with the trading volume and adjusted close prices.

• Opening Price: The first price of the stock traded during market hours.
• Closing Price: The last price at which the stock was traded during market hours.
• Adjusted Close: The closing price adjusted for dividends and stock splits to reflect a true value over time.
• High/Low Prices: The highest and lowest prices at which the stock traded throughout the day.
• Volume: The number of shares traded during the day.

The data is processed daily, and over the years, it has been aggregated to offer a long-term view of Adobe's stock performance.

📋 Column Descriptions

📅 Time Span of Data

• Start Date: 1986 (The year Adobe was first publicly listed on the stock market)
• End Date: 2024 (Latest available data)

💡 Key Insights from the Dataset

• Market Trends: Track the upward and downward trends in Adobe's stock value, identifying key periods of growth or decline.
• Volatility: Analyze the fluctuations in stock prices using the high and low values to understand Adobe’s stock market volatility.
• Volume Activity: Understand market sentiment and investor interest by examining trading volumes.
• Stock Performance: Assess Adobe’s performance over time using adjusted closing prices, which account for stock splits and dividends.

This dataset offers a detailed, long-term view of Adobe's stock and is a valuable resource for anyone interested in financial analysis, stock price prediction, or market behavior study.

📈 Whether you are a data scientist, financial analyst, or simply someone interested in stock market trends, this dataset will provide the necessary foundation for conducting deep and insightful analyses.