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.

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).

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.

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Securities Exchanges Market Size 2025-2029

The securities exchanges market size is forecast to increase by USD 56.67 billion at a CAGR of 12.5% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for investment opportunities. This trend is fueled by a global economic recovery and a rising interest in various asset classes, particularly in emerging markets. Another key driver is the increasing focus on sustainable and environmental, social, and governance (ESG) investing. This shift reflects a growing awareness of the importance of long-term value creation and the role of exchanges in facilitating socially responsible investments. This trend is driven by the expanding securities business units, including stocks, bonds, mutual funds, and other securities, which cater to the needs of investment firms and individual investors. However, the market is not without challenges. Increasing market volatility poses a significant risk for exchanges and their clients.
Furthermore, the rapid digitization of trading and the emergence of alternative trading platforms are disrupting traditional exchange business models. To navigate these challenges, exchanges must adapt by investing in technology, expanding their product offerings, and building strong regulatory frameworks. Data analytics and big data are also crucial tools for e-brokerage firms to gain insights and make informed decisions. By doing so, they can capitalize on the market's growth potential and maintain their competitive edge. Geopolitical tensions, economic instability, and regulatory changes can all contribute to market fluctuations and uncertainty.

What will be the Size of the Securities Exchanges Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic market, financial instrument classification plays a crucial role in facilitating efficient trade matching through advanced execution quality metrics and order book liquidity. Quantitative trading models leverage options clearing corporation data to optimize portfolio holdings, while trade matching engines utilize high-speed data storage solutions and portfolio optimization algorithms to minimize latency and enhance market depth indicators. Data center infrastructure and network bandwidth capacity are essential components for supporting complex algorithmic trading strategies, including latency reduction and price volatility forecasting. Market impact measurement and risk assessment methodologies are integral to managing market impact and mitigating fraud, ensuring regulatory compliance through transaction reporting standards and regulatory compliance software.

Exchange traded funds (ETFs) have gained popularity, necessitating robust quote dissemination systems and trade surveillance analytics. Server virtualization and cybersecurity threat mitigation strategies further strengthen the market's resilience, enabling seamless integration of data-driven quantitative models and sophisticated fraud detection algorithms. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data.

How is this Securities Exchanges Industry segmented?

The securities exchanges industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  Market platforms
  Capital access platforms
  Others


Trade Finance Instruments

  Equities
  Derivatives
  Bonds
  Exchange-traded funds
  Others


Type

  Large-cap exchanges
  Mid-cap exchanges
  Small-cap exchanges


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Switzerland
    UK


  APAC

    China
    Hong Kong
    India
    Japan


  Rest of World (ROW)

By Service Insights

The Market platforms segment is estimated to witness significant growth during the forecast period. The market is characterized by advanced technologies and systems that enable efficient price discovery, manage settlement risk, and ensure regulatory compliance. Market platforms, which include trading platforms, order-matching systems, and market data dissemination, hold the largest share of the market. These platforms facilitate the buying and selling of securities, providing market liquidity and transparency. Real-time market surveillance and high-frequency trading infrastructure are crucial components, ensuring fair and orderly markets and enabling efficient trade execution. Financial modeling techniques and algorithmic trading platforms optimize trading strategies, while electronic communication networks and central counterparty clearing minimize r

Compilation of research data related to the report "Analysis of the international Stock Market situation"

Dataset Information

The dataset contains tweets for top 25 most watched stock tickers on Yahoo FInance from 30-09-2021 to 30-09-2022, additionally was added stock market price and volume data for corresponding dates and stocks.

Dataset description

• Date - date and time of tweet
• Tweet - full text of the tweet
• Stock Name - full stock ticker name for which the tweet was scraped
• Company Name - full company name for corresponding tweet and stock ticker

Inspiration

Dataset was inspired by following datasets: Stock Market Tweet | Sentiment Analysis lexicon by Zeus and Stock-Market Sentiment Dataset

This dataset can be used for: - experimenting with sentiment analysis - predicting stock prices - exploring the connection between public sentiment and stock price movement

Hope you enjoy this dataset!

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

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

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Vendor Performance

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.

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.

Discover the booming stock analysis software market! Our in-depth analysis reveals a $2.5B market in 2025 projected to reach $6.3B by 2033, driven by algorithmic trading, retail investor growth, and AI. Explore key trends, leading companies (TradeStation, MetaStock, eSignal), and regional insights.

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.

Global Stock Market Financial Dataset (from TradingView)

This collection provides a comprehensive snapshot of over 11,800 publicly traded companies worldwide. It combines multiple financial statements and performance indicators extracted from TradingView to support data analysis, stock screening, and financial modeling.

Files Overview

1.tradingview_all_stocks.csv Contains general stock information and market statistics.

Columns: ticker, name, close, change, change_abs, volume, market_cap_basic, price_earnings_ttm, sector, industry Size: 11,806 rows × 10 columns Description: Lists all active stocks with latest prices, PE ratios, and sector/industry classifications.

2.tradingview_performance.csv Tracks short- and long-term stock performance.

Columns (sample): ticker, name, close, Perf.W, Perf.1M, Perf.3M, Perf.6M, Perf.YTD, Perf.1Y, Perf.5Y, etc. Size: 11,814 rows × 17 columns Description: Shows relative percentage performance across multiple timeframes.

3.balance_sheet.csv Summarizes financial position and liquidity metrics.

Columns: total_assets_fq, cash_n_short_term_invest_fq, total_liabilities_fq, total_debt_fq, net_debt_fq, total_equity_fq, current_ratio_fq Size: 11,821 rows × 12 columns Description: Includes key balance sheet values, enabling leverage and liquidity analysis.

4.cashflow.csv Focuses on company cash generation and sustainability.

Columns: free_cash_flow_ttm Size: 11,821 rows × 4 columns Description: Provides trailing twelve-month free cash flow figures for profitability evaluation.

5.dividends.csv Details dividend-related statistics.

Columns: dividends_yield, dividend_payout_ratio_ttm Size: 11,823 rows × 5 columns Description: Useful for income-focused investors; includes dividend yields and payout ratios.

6.income_statement.csv Presents company earnings metrics.

Columns: total_revenue_ttm, gross_profit_ttm, net_income_ttm, ebitda_ttm Size: 11,821 rows × 7 columns Description: Captures profitability over the last 12 months for revenue and margin analysis.

7.profitability.csv Shows margin-based performance indicators.

Columns: gross_margin_ttm, operating_margin_ttm, net_margin_ttm, ebitda_margin_ttm Size: 11,823 rows × 7 columns Description: Enables efficiency and operational performance comparisons across companies.

Use Cases 1. Stock market and financial analysis 2. Portfolio optimization and factor modeling 3. Machine learning for price prediction 4. Company benchmarking and screening 5. Academic or educational use in finance courses

Data Source & Notes 1. All data was aggregated from TradingView using public financial data endpoints. 2. Missing values may occur for companies that do not report certain metrics. 3. All monetary figures are based on the latest available TTM (Trailing Twelve Months) or FQ (Fiscal Quarter) data at the time of extraction.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

FTSE 100: Where to Next?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Spain's main stock market index, the ES35, rose to 16493 points on December 2, 2025, gaining 0.63% from the previous session. Over the past month, the index has climbed 2.84% and is up 38.90% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Spain. Spain Stock Market Index (ES35) - values, historical data, forecasts and news - updated on December of 2025.

Discover the booming Securities Brokerage & Stock Exchange Services market! Our in-depth analysis reveals a $5 trillion market in 2025, projected to reach $9 trillion by 2033, driven by online trading, fintech, and global investment. Explore regional breakdowns, key players, and future trends.

Stock market forecasting remains a complex and challenging task to forecast, traditional technical analysis methods like RSI, EMA, and Candlestick Patterns often fail to analyze the stock market time series pattern with many recent studies have now explored forecasting using machine learning or neural networks, other studies have improved the increase in accuracy or decrease in regression loss by applying technical indicator and sentiment analysis. This paper aims to analyze the performance of the combined reinforcement learning and machine learning models in predicting the stock market’s next day trend by incorporating both technical and sentiment-based features. Technical indicators were derived from historical price data focused on multi-timeframe trend and swing trend in the market, then sentiment features were extracted using FinBERT from Benzinga Pro as a reliable financial news source. The reinforcement learning model used is the Proximal Policy Optimization model, while a variety of machine learning models, such as XGBoost, Gradient Boosting, Random Forest, Decision Tree, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression were trained to assess its predictive performance. Results indicate that the ensemble model outperformed the other tested machine learning models with an accuracy score of 69.97%. These reports highlight the effectiveness of the ensemble model combining sentiment and technical features to enhance stock market predictions accuracy. However, limitations such as news data availability and the small training time, remain a key challenge that could potentially increase the performance. Future research could experiment with alternative models, more training time, advance technical patterns, and more news datasets.

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).

Discover the booming market for stock trading analysis software & tools. Learn about the 15% CAGR, key drivers like algorithmic trading, and leading companies like TradingView and MetaStock. Explore regional market share and future trends in this insightful market analysis.