The main stock market index of United States, the US500, fell to 6369 points on March 27, 2026, losing 1.67% from the previous session. Over the past month, the index has declined 7.45%, though it remains 14.12% 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 March of 2026.

This dataset contains historical stock market data obtained from Yahoo Finance using the yfinance Python library. The dataset spans a specific time period and includes seven key columns: Date, Open, High, Low, close, Adjusted Close, and Volume.

Use case:

This dataset is valuable for a wide range of analyses and applications in finance, economics, and data science. It can be used to: - Perform technical analysis to identify trading patterns and trends. - Develop and backtest trading strategies. - Conduct research on the relationship between trading volume and price movements. - Analyze the impact of macroeconomic events on individual stocks or the overall market. - Train machine learning models for stock price prediction, volatility forecasting, and risk management.

Disclaimer:

This dataset is provided for educational and research purposes only. The accuracy and completeness of the data are not guaranteed.

Thumbnail Photo by Tima Miroshnichenko: https://www.pexels.com/photo/stock-market-illustration-on-the-screen-7567223/

The main stock market index of United States, the US500, fell to 6941 points on February 26, 2026, losing 0.08% from the previous session. Over the past month, the index has declined 0.54%, though it remains 18.41% 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 February of 2026.

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of sales by type of processor on a monthly basis.

This dataset provides a comprehensive, pre-processed collection of U.S. stock market data, specifically curated for quantitative analysis, financial modeling, and machine learning applications focused on volatility and asset pricing. It is optimized to include essential price and volume change metrics, along with market fundamentals, to facilitate efficient research.

The data is collected into previous 1000 & 3500 market open days since 10/12/2025. Note for a stock to be in each dataset it must have at least 1000 & 3500 days of history. The source data is located at https://stooq.com/db/h/ and an extract script can be found in my accompanying notebook.

📊 Key Data Fields & Structure

The time-series data files (log_change.pkl) are optimized for quantitative modeling, where raw prices are replaced by daily change metrics to capture volatility and momentum efficiently.

Time-Series (3D NumPy Array Structure)

The 3D array (trimmed_market_data_log_change_1000.pkl) is structured as (Days, Features, Tickers) and contains the following 5 features per day:

ticker

date

log_Ret (Close-to-Close): Logarithmic return, ln(Closet​/Closet−1​). Used for overall volatility and total return.

log_Vol: Log change in volume, ln(Volt​/Volt−1​). Used to measure trading activity change.

OC_Log_Change (Open-to-Close): Intraday logarithmic return, ln(Closet​/Opent​). Used to isolate intraday volatility from overnight gaps.

HL_Range_Pct: Daily High-Low range normalized by previous close, (Hight​−Lowt​)/Closet−1​. Used as a proxy for realized daily volatility (Parkinson-like measure).

Fundamentals (market_fundamentals.csv)

This file contains point in time cross-sectional data, including fields like:

Ticker

Company Name (e.g., Agilent Technologies, Inc.)

marketCap

sector

industry

Read using pd.read_pickle('')

💡 Potential Use Cases

Volatility Forecasting: Use the historical time-series features (Log_Ret, HL_Range_Pct) to train models (e.g., GARCH, machine learning) to predict future volatility.

Alpha Generation: Develop trading signals based on the cross-sectional fundamentals combined with recent momentum/volatility changes.

Anomaly Detection: Use the difference between overnight return (implied by CC minus OC) to detect potential mispricings or significant after-hours news impact.

Factor Modeling: Construct stock factors based on market capitalization, price levels, and the novel volatility features provided.

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Government Agencies" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

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.

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Multiple and All Other Food Uses" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

Description

Historical daily stock market data for 4500+ Nasdaq listed companies. Dataset to be updated quarterly.

Content

Date: dates vary depending on stock, all in DD/MM/YYYY format Open: open price (in USD) High: high price (in USD) Low: low price (in USD) Close: close price (in USD) Adj close: adjusted close price (in USD) Volume: traded volume (in USD)

Acknowledgements:

Banner photo: https://www.pexels.com/photo/numbers-on-monitor-534216/

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Indonesia's main stock market index, the JCI, fell to 7097 points on March 27, 2026, losing 0.94% from the previous session. Over the past month, the index has declined 11.47%, though it remains 18.36% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Indonesia. Indonesia Stock Market (JCI) - values, historical data, forecasts and news - updated on March of 2026.

IvyDB Futures covers over 30 of the most liquid optionable US exchange-traded futures in six sectors. The earliest historical data begins in January 2005. The data includes both daily option pricing information (symbol, date, settlement price, volume, and open interest) and settlement prices for the underlying futures.

Greece's main stock market index, the Athens General, fell to 2024 points on March 27, 2026, losing 1.74% from the previous session. Over the past month, the index has declined 8.02%, though it remains 16.63% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Greece. Greece Stock Market (ASE) - values, historical data, forecasts and news - updated on March of 2026.

This dataset contains 20+ years of all items in the American art category sold on auction by Christie’s, Sotheby’s, Bonhams and Phillips from 2000 to date.

The dataset includes:

Stock Tickers: A comprehensive list of available stock tickers in Saudi Arabia, each identified with a .SR suffix. Historical Data: Time series data for each stock, including: Date: The trading date. Open: The opening price of the stock on that date. High: The highest price reached during the trading session. Low: The lowest price reached during the trading session. Close: The closing price of the stock on that date. Volume: The number of shares traded on that date. Ticker: The stock ticker symbol. The data spans from January 1, 2020, to January 1, 2024, providing a detailed view of stock performance over this period.

This collection consists of annual statewide aggregates for a variety of bank market structure variables in the United States. The data span 60 years from 1896 to 1955 and include information for all 50 states. These time-series data, collected in 1992-1994, pertain to historical bank market structure, soundness, and performance. Balance sheet data are presented for national and non-national banks, while income statement data are available only for national banks. Other variables include state population, state branching law, total loans, total deposits, number of national banks, and bank debits.

The booming Stock Market API market is projected for significant growth, driven by algorithmic trading, fintech innovation, and increasing demand for real-time financial data. Learn about market trends, key players, and future projections in this comprehensive analysis. Discover the best APIs for your needs.

Costa Rica's main stock market index, the BCT Corp, closed flat at 11902 points on March 26, 2026. Over the past month, the index has climbed 0.00% and is up 0.06% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Costa Rica. Costa Rica Stock Market - values, historical data, forecasts and news - updated on March of 2026.

IvyDB US contains a complete historical record of end-of-day data on all US exchange-traded equity and index options (including options on ETFs and ADRs) from January 1996 onward. The data includes both daily option pricing information (symbol, date, closing bid and ask quote, volume, and open interest) as well as high, low, and closing prices for the underlying equity or index. IvyDB US also provides all interest rate, dividend, and corporate action information for each security