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

Leverage Databento's real-time stock API to get tick data with full order book depth (MBO). Offering seamless intraday market replay in a single API call.

Intraday 1 minute sample data for Micro E Mini S&P MES timestamped in Chicago time

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An example of TRTH intraday top-of-book transaction data for a single Johannesburg Stock Exchange (JSE) listed equity. The data is for teaching, learning and research projects sourced from the legacy Tick History v1 SOAP API interface from https://tickhistory.thomsonreuters.com/TickHistory in May 2016. Related raw data and similar data-structures can now be accessed using Tick History v2 and the REST API https://hosted.datascopeapi.reuters.com/RestApi/v1.

Configuration control: the test dataset contains 16 CSV files with names: "

Attributes: The data set is for the ticker: AGLJ.J from May 2010 until May 2016. The files include the following attributes: RIC, Local Date-Time, Event Type, Price at the Event, Volume at the Event, Best Bid Changes, Best Ask Changes, and Trade Event Sign: RIC, DateTimeL, Type, Price, Volume, L1 Bid, L1 Ask, Trade Sign. The Local Date-Time (DateTimeL) is a serial date number where 1 corresponds to Jan-1-0000, for example, 736333.382013 corresponds to 4-Jan-2016 09:10:05 (or 20160104T091005 in ISO 8601 format). The trade event sign (Trade Sign) indicates whether the transaction was buyer (or seller) initiated as +1 (-1) and was prepared using the method of Lee and Ready (2008).

Disclaimer: The data is not up-to-date, is incomplete, it has been pre-processed; as such it is not fit for any other purpose than teaching and learning, and algorithm testing. For complete, up-to-date, and error-free data please use the Tick History v2 interface directly.

Research Objectives: The data has been used to build empirical evidence in support of hierarchical causality and universality in financial markets by considering price impact on different time and averaging scales, feature selection on different scales as inputs into scale dependent machine learning applications, and for various aspects of agent-based model calibration and market ecology studies on different time and averaging scales.

Acknowledgements to: Diane Wilcox, Dieter Hendricks, Michael Harvey, Fayyaaz Loonat, Michael Gant, Nicholas Murphy and Donovan Platt.

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