Interactive historical chart showing the daily level of the CBOE VIX Volatility Index back to 1990. The VIX index measures the expectation of stock market volatility over the next 30 days implied by S&P 500 index options.

Graph and download economic data for CBOE S&P 500 3-Month Volatility Index (VXVCLS) from 2007-12-04 to 2025-07-10 about VIX, volatility, 3-month, stock market, and USA.

VIX Daily Price Data

Overview

Contains historical data of the VIX Volatility Index from 2000 - 2025. The data is obtained from the yfinance api created by yahoo finance and contains the daily price data for the VIX.

The dataset contains the daily Open, Close, High, and Low of the VIX.

Columns Open: Starting price level of VIX for the day Close: Final price level of VIX for the day High: Highest price level of VIX for the day Low: Lowest price level of VIX for the day

The VIX is an index that measures near term volatility expectations for the S&P 500 gathered from SPX options data. VIX was created and maintained by CBOE.

Uses

This data can be used to train models on predicting the market's volatility forecasts. The VIX can also be compared to the realized historical volatility over a period of time.

Download Historical CBOE S&P 500 3-Month Volatility Index Indicies Data. CQG daily, 1 minute, tick, and level 1 data from 1899.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-07-10 about VIX, volatility, stock market, and USA.

United States - CBOE S&P 500 3-Month Volatility was 19.02000 Index in July of 2025, according to the United States Federal Reserve. Historically, United States - CBOE S&P 500 3-Month Volatility reached a record high of 72.98000 in March of 2020 and a record low of 11.85000 in October of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - CBOE S&P 500 3-Month Volatility - last updated from the United States Federal Reserve on July of 2025.

The IvyDB Signed Volume dataset, available as an add-on product for IvyDB US, contains daily data on detailed option trading volume. Trades in the IvyDB US dataset are assigned as either buyer-initiated or seller-initiated based on the trade price and the bid-ask quote at the time of the trade. The total assigned daily volume is aggregated and updated nightly.

S&P 500 3-Month Volatility Index: 18 years of historical data from 2007 to 2025.

Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder) and a smaller version of the dataset (all_stocks_1yr.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv and all_stocks_1yr.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd Open - price of the stock at market open (this is NYSE data so all in USD) High - Highest price reached in the day Low Close - Lowest price reached in the day Volume - Number of shares traded Name - the stock's ticker name

Acknowledgements

I scraped this data from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

United States New York Stock Exchange: Index: S&P 500 Low Volatility Index data was reported at 11,714.230 NA in Apr 2025. This records a decrease from the previous number of 12,005.570 NA for Mar 2025. United States New York Stock Exchange: Index: S&P 500 Low Volatility Index data is updated monthly, averaging 8,299.190 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 12,041.730 NA in Nov 2024 and a record low of 4,936.030 NA in Aug 2013. United States New York Stock Exchange: Index: S&P 500 Low Volatility Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: New York Stock Exchange: S&P: Monthly.

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.

Is Volatility King of the S&P 500 Index?

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

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.

Volatility Gauge Signals Stability for S&P 500 VIX Index

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

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.

Is the S&P 500 VIX Index Signaling Market Volatility?

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

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.

Is Volatility Primed for a Spike in S&P 500?

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

United States US Composite: Index: Volatility S&P 500 Index data was reported at 24.700 NA in Apr 2025. This records an increase from the previous number of 22.280 NA for Mar 2025. United States US Composite: Index: Volatility S&P 500 Index data is updated monthly, averaging 16.305 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 53.540 NA in Mar 2020 and a record low of 9.510 NA in Sep 2017. United States US Composite: Index: Volatility S&P 500 Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: US Composite (NYSE, NASDAQ): Monthly.

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.

Volatility May Rise, Signaling Uncertainty for S&P 500 VIX index.

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

US Composite：指数：CBOE S&P 500 Volatility Index (VIX)在04-01-2025达24.700NA，相较于03-01-2025的22.280NA有所增长。US Composite：指数：CBOE S&P 500 Volatility Index (VIX)数据按月更新，01-01-2012至04-01-2025期间平均值为16.305NA，共160份观测结果。该数据的历史最高值出现于03-01-2020，达53.540NA，而历史最低值则出现于09-01-2017，为9.510NA。CEIC提供的US Composite：指数：CBOE S&P 500 Volatility Index (VIX)数据处于定期更新的状态，数据来源于Exchange Data International Limited，数据归类于全球数据库的美国 – Table US.EDI.SE: US Composite (NYSE, NASDAQ): Monthly。