https://optionmetrics.com/contact/https://optionmetrics.com/contact/
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.
https://optiondata.org/about.htmlhttps://optiondata.org/about.html
Free historical options data, dataset files in CSV format.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Downloading the Options IV SP500 Dataset
This document will guide you through the steps to download the Options IV SP500 dataset from Hugging Face Datasets. This dataset includes data on the options of the S&P 500, including implied volatility. To start, you'll need to install Hugging Face's datasets library if you haven't done so already. You can do this using the following pip command: !pip install datasets
Here's the Python code to load the Options IV SP500 dataset from Hugging… See the full description on the dataset page: https://huggingface.co/datasets/gauss314/options-IV-SP500.
Access real-time and historical US equity options data included as part of Databento's OPRA data feed. The NASDAQ Options Market offers immediate and automatic price improvement to orders. Orders designated to use the options routing feature are routed to other markets to ensure orders get the best price available. The NASDAQ Options Market also links to and complies with the obligations of the Options InterMarket Linkage.
We offer three easy-to-understand packages to fit your business needs. Visit intrinio.com/pricing to compare packages.
Bronze
The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD options prices sourced from OPRA.
When you’re ready for launch, it’s a seamless transition to our Silver package for delayed options prices, Greeks and implied volatility, and unusual options activity, plus delayed equity prices.
Exchange Fees & Requirements:
This package requires no paperwork or exchange fees.
Bronze Benefits:
Silver
The Silver package is ideal for clients that want delayed options data for their platform, or for startups in the development and testing phase. You’ll get 15-minute delayed options data, Greeks, implied volatility, and unusual options activity, plus the latest EOD options prices and delayed equity prices.
You can easily move up to the Gold package for real-time options and equity prices, additional access methods, and premium support options.
Exchange Fees & Requirements:
If you subscribe to the Silver package and will not display the data outside of your firm, you’ll need to fill out a simplified exchange agreement and send it back to us. There are no exchange fees and we can provide immediate access to the data.
If you subscribe to the Silver package and will display the data outside of your firm, we’ll work with your team to submit the correct paperwork to OPRA for approval. Once approved, OPRA will bill exchange fees directly to your firm – typically $600-$2000/month depending on your use case. These fees are the same no matter what data provider you use. Per-user reporting is not required, so there are no variable per user fees.
Silver Benefits:
Gold
The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers real-time options prices, Greeks and implied volatility, and unusual options activity, as well as the latest EOD options prices and real-time equity prices.
You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.
Exchange Fees & Requirements:
If you subscribe to the Gold package, we’ll work with your team to submit the correct paperwork to OPRA for approval. Once approved, OPRA will bill exchange fees directly to your firm – typically $600-$2000/month depending on your use case. These fees are the same no matter what data provider you use. Per-user reporting is required, with an associated variable per user fee.
Gold Benefits:
Platinum
Don’t see a package that fits your needs? Our team can design a premium custom package for your business.
Consolidated last sale, exchange BBO and national BBO across all US equity options exchanges. Includes single name stock options (e.g. TSLA), options on ETFs (e.g. SPY, QQQ), index options (e.g. VIX), and some indices (e.g. SPIKE and VSPKE). This dataset is based on the newer, binary OPRA feed after the migration to SIAC's OPRA Pillar SIP in 2021. OPRA is notable for the size of its data and we recommend users to anticipate several TBs of data per day for the full dataset in its highest granularity (MBP-1).
Access real-time and historical US equity options data included as part of Databento's OPRA data feed. BOX is an electronic trading exchange that holds a price-time priority model, with the exception of certain options classes using a pro rata priority model. BOX is the first options market to offer a Price Improvement Period which allows investors the potential for price improvements through an electronic auction process.
https://optiondata.org/about.htmlhttps://optiondata.org/about.html
Historical option data in the last 24 years, dataset files in CSV format.
Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Cboe C2 is an all-electronic options exchange that holds a maker-taker and pro-rata allocation model, as well as a price-time priority model for specific options being traded. C2 was partially designed to compete against companies with multiple exchanges and various pricing structures.
https://optiondata.org/about.htmlhttps://optiondata.org/about.html
Historical option EOD data in 2021, dataset files in CSV format.
Cryptocurrency options markets have grown increasingly sophisticated, requiring reliable data infrastructure to support trading and analysis. Our platform gives you direct access to comprehensive crypto options data through straightforward API connections.
We capture the complete options chain across major crypto derivatives exchanges, delivering real-time and historical cryptocurrency market data that shows exactly what's happening in these complex markets. Each options contract is tracked with precision - strikes, expiration dates, premiums, open interest, and volume metrics all accessible through our standardized data feeds.
The data is available through multiple integration methods depending on your needs. Use our REST API for flexible queries and historical analysis, WebSocket for real-time market monitoring, or FIX protocol for institutional-grade connectivity with minimal latency.
Why work with us?
Market Coverage & Data Types: - Real-time and historical data since 2010 (for chosen assets) - Full order book depth (L2/L3) - Tick-by-tick data - OHLCV across multiple timeframes - Market indexes (VWAP, PRIMKT) - Exchange rates with fiat pairs - Spot, futures, options, and perpetual contracts - Coverage of 90%+ global trading volume
Technical Excellence: - 99% uptime guarantee - Multiple delivery methods: REST, WebSocket, FIX, S3 - Standardized data format across exchanges - Ultra-low latency data streaming - Detailed documentation - Custom integration assistance
When options traders need reliable market intelligence, they don't leave it to chance. That's why trading desks across five continents, quantitative hedge funds managing billions, and fintech innovators building tomorrow's trading platforms all rely on our data infrastructure. We've established ourselves as a dependable source in a market where accuracy isn't just preferred - it's essential. While others promise comprehensive coverage, we deliver it consistently, trade after trade, day after day.
Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Cboe was the first options exchange to launch in the United States. It currently operates a hybrid system offering electronic and floor-based trading, and holds a pro rata allocation model.
https://www.lseg.com/en/policies/website-disclaimerhttps://www.lseg.com/en/policies/website-disclaimer
Explore Options Price Reporting Authority (OPRA) through LSEG. OPRA collects, consolidates and disseminates information for US Options.
ODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
License information was derived automatically
The data set contains the following columns: Date Symbol Strike Option Name Close price Volume Interest Delta Gamma Vega Theta Rho riskfree rate Expiry Time-to-Expiry (in trading days) Asset price
This repository contains data collected from the bitcoin options exchange Deribit and the R codes used in the following paper: Lai T. Hoang, Dirk G. Baur, 2020, Forecasting Bitcoin Volatility: Evidence from the Options Market.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Financial bubbles arise when the underlying asset's market price deviates from its fundamental value. Unlike other bubble tests that use time series data and assume a reduced-form price process, we infer the existence of bubbles nonparametrically using option price data. Under no-arbitrage and acknowledging data constraints, we can partially identify asset price bubbles using a cross section of European option prices. In the empirical analysis, we obtain interval estimates of price bubbles embedded in the S&P 500 Index. The estimated index bubbles are then used to construct profitable momentum trading strategies that consistently outperform a buy-and-hold trading strategy.
https://optiondata.org/about.htmlhttps://optiondata.org/about.html
Historical option data in 2019 to 2021, 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
https://www.kappasignal.com/p/legal-disclaimer.htmlhttps://www.kappasignal.com/p/legal-disclaimer.html
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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
Dataset cantains spot,future and options Data for Nifty and Banknifty from 2020 jan to 2024 october.
https://optionmetrics.com/contact/https://optionmetrics.com/contact/
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.