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.

Free historical options data, dataset files in CSV format.

Historical option data in the last 24 years, dataset files in CSV format.

This dataset offers both live (delayed) prices and End Of Day time series on equity options

1/ Live (delayed) prices for options on European stocks and indices including: Reference spot price, bid/ask screen price, fair value price (based on surface calibration), implicit volatility, forward Greeks : delta, vega Canari.dev computes AI-generated forecast signals indicating which option is over/underpriced, based on the holders strategy (buy and hold until maturity, 1 hour to 2 days holding horizon...). From these signals is derived a "Canari price" which is also available in this live tables.
Visit our website (canari.dev ) for more details about our forecast signals.

The delay ranges from 15 to 40 minutes depending on underlyings.

2/ Historical time series: Implied vol Realized vol Smile Forward
See a full API presentation here : https://youtu.be/qitPO-SFmY4 .

These data are also readily accessible in Excel thanks the provided Add-in available on Github: https://github.com/canari-dev/Excel-macro-to-consume-Canari-API

If you need help, contact us at: contact@canari.dev

User Guide: You can get a preview of the API by typing "data.canari.dev" in your web browser. This will show you a free version of this API with limited data.

Here are examples of possible syntaxes:

For live options prices: data.canari.dev/OPT/DAI data.canari.dev/OPT/OESX/0923 The "csv" suffix to get a csv rather than html formating, for example: data.canari.dev/OPT/DB1/1223/csv For historical parameters: Implied vol : data.canari.dev/IV/BMW

data.canari.dev/IV/ALV/1224

data.canari.dev/IV/DTE/1224/csv

Realized vol (intraday, maturity expressed as EWM, span in business days): data.canari.dev/RV/IFX ... Implied dividend flow: data.canari.dev/DIV/IBE ... Smile (vol spread between ATM strike and 90% strike, normalized to 1Y with factor 1/√T): data.canari.dev/SMI/DTE ... Forward: data.canari.dev/FWD/BNP ...

List of available underlyings: Code Name OESX Eurostoxx50 ODAX DAX OSMI SMI (Swiss index) OESB Eurostoxx Banks OVS2 VSTOXX ITK AB Inbev ABBN ABB ASM ASML ADS Adidas AIR Air Liquide EAD Airbus ALV Allianz AXA Axa BAS BASF BBVD BBVA BMW BMW BNP BNP BAY Bayer DBK Deutsche Bank DB1 Deutsche Boerse DPW Deutsche Post DTE Deutsche Telekom EOA E.ON ENL5 Enel INN ING IBE Iberdrola IFX Infineon IES5 Intesa Sanpaolo PPX Kering LOR L Oreal MOH LVMH LIN Linde DAI Mercedes-Benz MUV2 Munich Re NESN Nestle NOVN Novartis PHI1 Philips REP Repsol ROG Roche SAP SAP SNW Sanofi BSD2 Santander SND Schneider SIE Siemens SGE Société Générale SREN Swiss Re TNE5 Telefonica TOTB TotalEnergies UBSN UBS CRI5 Unicredito SQU Vinci VO3 Volkswagen ANN Vonovia ZURN Zurich Insurance Group

Historical option data in 2019 to 2021, dataset files in CSV format.

The latest closing stock price for Option Care Health as of July 03, 2025 is 31.16. An investor who bought $1,000 worth of Option Care Health stock at the IPO in 1996 would have $-377 today, roughly 0 times their original investment - a -1.62% compound annual growth rate over 29 years. The all-time high Option Care Health stock closing price was 90.00 on April 22, 2002. The Option Care Health 52-week high stock price is 35.53, which is 14% above the current share price. The Option Care Health 52-week low stock price is 21.39, which is 31.4% below the current share price. The average Option Care Health stock price for the last 52 weeks is 29.92. For more information on how our historical price data is adjusted see the Stock Price Adjustment Guide.

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.

PEAK Options & Futures Prediction

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

Historical option EOD data in 2021, dataset files in CSV format.

CoinAPI delivers Crypto Options Data and derivatives information from major exchanges. Access real-time and historical crypto market data to analyze volatility, pricing trends, and open interest across option chains.

Historical option sample data at 2022-08-24, dataset files in CSV format.

IF YOU FIND THIS CONTENT USEFUL, PLEASE LEAVE AN UPVOTE, COMMENT, AND/OR FOLLOW!

This dataset is a combination of four years of Tesla ($TSLA) options end of day quotes ranging from 01-2019 to 12-2022. Each row represents the information associated with one contract's strike price and a given expiration date.

Dates quotes are given in in Unix and in "YYYY-MM-DD HH:MM" formats. Quote frequency is daily at 4:00 pm EST, which corresponds with end of day market closure.

REMEMBER: Tesla stock split on August 25th, 2022. This will be reflected in the data. Keep this in mind!

What is an option chain?

An option chain can be defined as the listing of all option contracts. It comes with two different sections: call and put. A call option means a contract that gives you the right but does not give you the obligation to buy an underlying asset at a particular price and within the option's expiration date. This means that in this dataset, there will be the entire option chain (all available option contracts for all expirations) for each business day between Q1 2019 and Q4 2022.

This dataset contains data for American options, which can be exercised on or before expiration data. This is unlike European options contracts, which can only be exercised on the expiration date.

I am also continuously working on the associated notebook to give a basic idea of how to load and explore the data. Stay tuned!

Similar Datasets: - $SPY Option Chains - $AAPL Option Chains - $NVDA Option Chains - $QQQ Option Chains

Futures data can be ordered as full month ranges. To control costs it is possible to order Nearest to Expiry (NTE) data with overlap between expiring future and the next future in the month of expiry or with overlap over more than 1 month is needed. Of course you can also select all active expiries if required.

The data is available at tick level with millisecond resolution as well as at regular intervals of 1 Min, 5 Min and so on.

Data is priced separately for Trades (Tx) and Quotes (Qt).

Tick level Tx data consists of a millisecond timestamp and trade price Tx with an option to include the Volume field. Tick level Qt data consists of millisecond timestamp and quote Qt with a flag to indicate whether it is a Bid or an Ask and optionally the Qt size field can be added.

Regular interval data is usually supplied as one of these sets: CloseTx CloseBid, CloseAsk OpenTx, HighTx, LowTx, CloseTx OpenBid, HighBid, LowBid, CloseBid OpenAsk, HighAsk, LowAsk, CloseAsk

Additional Fields: IntervalTxVolume, CloseBidSize, CloseAskSize and some others are available if required.

Timestamps are by default in GMT but data can be in any Time Zone requested.

Pricing depends on frequency and number of fields.

100s of papers in finance and economics have been written since 1986 onwards using our data and several reputed banks and hedge funds use our data for back testing and risk management.

Option Care Health stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

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.

NSE LEMONTREE Options & Futures Prediction

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

IF YOU FIND THIS CONTENT USEFUL, PLEASE LEAVE AN UPVOTE, COMMENT, AND/OR FOLLOW!

This dataset is a combination of three years of SPDR S&P 500 ETF Trust ($SPY) options end of day quotes ranging from 01-2020 to 12-2022. Each row represents the information associated with one contract's strike price and a given expiration date.

Dates quotes are given in in Unix and in "YYYY-MM-DD HH:MM" formats. Quote frequency is daily at 4:00 pm EST, which corresponds with end of day market closure.

What is an option chain?

An option chain can be defined as the listing of all option contracts. It comes with two different sections: call and put. A call option means a contract that gives you the right but does not give you the obligation to buy an underlying asset at a particular price and within the option's expiration date. This means that in this dataset, there will be the entire option chain (all available option contracts for all expirations) for each business day between Q1 2020 and Q4 2022.

This dataset contains data for American options, which can be exercised on or before expiration data. This is unlike European options contracts, which can only be exercised on the expiration date.

I am also continuously working on the associated notebook to give a basic idea of how to load and explore the data. Stay tuned!

Similar Datasets: - $TSLA Option Chains - $AAPL Option Chains - $NVDA Option Chains - $QQQ Option Chains

This data package contains all the information related to the economy of a country including price index, commodities values and info about NASDAQ members.

Explore Options Price Reporting Authority (OPRA) through LSEG. OPRA collects, consolidates and disseminates information for US Options.

Finland - Total financial sector liabilities: Financial derivatives and employee stock options was 13.90 % of GDP in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Total financial sector liabilities: Financial derivatives and employee stock options - last updated from the EUROSTAT on July of 2025. Historically, Finland - Total financial sector liabilities: Financial derivatives and employee stock options reached a record high of 90.10 % of GDP in December of 2011 and a record low of -0.40 % of GDP in December of 1995.

Japan OSE: Turnover: Value: Nikkei 225 Call and Put Options data was reported at 516.962 JPY bn in Nov 2018. This records a decrease from the previous number of 749.344 JPY bn for Oct 2018. Japan OSE: Turnover: Value: Nikkei 225 Call and Put Options data is updated monthly, averaging 228.702 JPY bn from Jun 1989 (Median) to Nov 2018, with 354 observations. The data reached an all-time high of 1,544.252 JPY bn in May 2013 and a record low of 44.521 JPY bn in Jul 2005. Japan OSE: Turnover: Value: Nikkei 225 Call and Put Options data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z016: Osaka Exchange Inc: Futures and Options.

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.

CBOE Volatility Index Options & Futures Prediction

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