The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

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.

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.

This table contains 14 series, with data starting from 1953 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Stock market statistics (14 items: Toronto Stock Exchange; value of shares traded; United States common stocks; Dow-Jones industrials; high; United States common stocks; Dow-Jones industrials; low; Toronto Stock Exchange; volume of shares traded ...).

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.

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.

As of early 2025, companies in the information technology sector made up ** percent of the total market capitalization of all stock exchanges worldwide. Tech companies worldwide had a combined market capitalization of approximately ** trillion U.S. dollars. The second largest sector on stock markets worldwide was the financial services industry, with a market cap of ** trillion U.S. dollars, followed by the industrials sector with ** trillion U.S. dollars. On the other hand, real estate and utilities were the least represented sectors on stock markets worldwide.

Overview

This dataset contains historical daily prices for all tickers currently trading on NASDAQ. The up to date list is available from nasdaqtrader.com. The historic data is retrieved from Yahoo finance via yfinance python package.

It contains prices for up to 01 of April 2020. If you need more up to date data, just fork and re-run data collection script also available from Kaggle.

Data Structure

The date for every symbol is saved in CSV format with common fields:

• Date - specifies trading date
• Open - opening price
• High - maximum price during the day
• Low - minimum price during the day
• Close - close price adjusted for splits
• Adj Close - adjusted close price adjusted for both dividends and splits.
• Volume - the number of shares that changed hands during a given day

All that ticker data is then stored in either ETFs or stocks folder, depending on a type. Moreover, each filename is the corresponding ticker symbol. At last, symbols_valid_meta.csv contains some additional metadata for each ticker such as full name.

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.

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.

This table contains 25 series, with data for years 1956 - present (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Toronto Stock Exchange Statistics (25 items: Standard and Poor's/Toronto Stock Exchange Composite Index; high; Standard and Poor's/Toronto Stock Exchange Composite Index; close; Toronto Stock Exchange; oil and gas; closing quotations; Standard and Poor's/Toronto Stock Exchange Composite Index; low ...).

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

Photo by Maxim Hopman on Unsplash.

Introduction

According to Economatica, a company specializing in the Latin American stock market, the Brazilian stock exchange market, governed by Brasil, Bolsa, Balcão (B3), exchanged BRL ~25.9 billion per day in the first half of 2020, during the coronavirus epidemic. Furthermore, it is estimated that in this same period there was an 18% growth in the number of Brazilian investors, totaling ~2.6 million active investors. Therefore, the financial market moves a large amount of values and, consequently, produces a vast amount of information and data daily; These data represent the movements of shares, their respective prices, dollar exchange values, and so on. This dataset contains daily stock values and information about their companies.

Inspiration

• Data Analysis - Spark
• Price Prediction - Regression task
• Best Group of Stocks - Association Rules task

This dataset provides an environment (Data Warehouse-like) for analysis and visualization of financial business for users of decision support systems. Specifically, the data allow compare different assets (i.e. stocks) listed on B3, according to the sectors of the economy in which these assets operate. For example, with this Data Warehouse, the user will be able to answer questions similar to this one: What are the most profitable sectors for investment in a given period of time? In this way, the user can identify which are the sectors that are standing out, as well as which are the most profitable companies in the sector.

Dataset

https://i.imgur.com/28Mf0sN.png" alt="Data Warehouse">

This dataset is split into five files: - dimCoin.csv - Dimension table with information about the coins. - dimCompany.csv - Dimension table with information about the companies. - dimTime.csv - Dimension table with information about the datetime. - factCoins.csv - Fact table with coin value over time. - factStocks.csv - Fact table with stock prices over time.

Source

The data were available by B3. You can access in https://www.b3.com.br/en_us/market-data-and-indices/ .I just structure and model the data as Data Warehouse tables. You can access my code in https://github.com/leomaurodesenv/b3-stock-indexes

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.

United States US: Stocks Traded: Total Value data was reported at 39,785.881 USD bn in 2017. This records a decrease from the previous number of 42,071.330 USD bn for 2016. United States US: Stocks Traded: Total Value data is updated yearly, averaging 17,934.293 USD bn from Dec 1984 (Median) to 2017, with 34 observations. The data reached an all-time high of 47,245.496 USD bn in 2008 and a record low of 1,108.421 USD bn in 1984. United States US: Stocks Traded: Total Value data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Financial Sector. The value of shares traded is the total number of shares traded, both domestic and foreign, multiplied by their respective matching prices. Figures are single counted (only one side of the transaction is considered). Companies admitted to listing and admitted to trading are included in the data. Data are end of year values converted to U.S. dollars using corresponding year-end foreign exchange rates.; ; World Federation of Exchanges database.; Sum; Stock market data were previously sourced from Standard & Poor's until they discontinued their 'Global Stock Markets Factbook' and database in April 2013. Time series have been replaced in December 2015 with data from the World Federation of Exchanges and may differ from the previous S&P definitions and methodology.

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.

India's main stock market index, the SENSEX, fell to 85138 points on December 2, 2025, losing 0.59% from the previous session. Over the past month, the index has climbed 1.38% and is up 5.31% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

This table contains 10 series, with data for years 1946 - 2010 (not all combinations necessarily have data for all years), and was last released on 2010-06-21. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...) Components (5 items: Total volume; Mining; Mining and oils; Industrials ...) Transactions (2 items: Shares traded; Value of shares traded ...).

The dataset used in this paper is a collection of financial time series data, including daily open price, high price, low price, close price, and trading volume.