Options Data API (2026): 7 Providers Compared

For intraday and tick-level historical options data, Massive, Databento and ORATS offer the most detail, while EOD Historical Data and Alpha Vantage are simpler choices for daily chains. Massive has a free plan and paid plans from $29 a month, and OptionMetrics prices on request (checked on 7 Nov 2026). Pull long histories from an always-on QuantVPS options trading VPS, not a laptop. The seven APIs:

• Massive Options API (formerly Polygon.io): All US options tickers, a free plan and paid plans from $29 to $199 a month, with trades, quotes and up to 5+ years of history.
• Databento OPRA: Tick-level US options data since 2013, paid per GB or through a $199 monthly plan.
• IVolatility Historical Options Data: End-of-day and minute snapshots, Greeks and implied volatility, with US data back to 2005.
• OptionMetrics Historical Options Data: Institutional end-of-day data for US equity and index options since 1996, priced on request.
• EOD Historical Data Options API: End-of-day options data for 6,000+ US stocks with two years of history, for $29.99 a month.
• ORATS: End-of-day options data since 2007 and one-minute data since August 2020, with API plans from $199 a month.
• Alpha Vantage Options Data: Daily historical options chains with implied volatility and Greeks, covering 15+ years, on premium plans.

Quick Comparison (checked on 7 Nov 2026)

Selecting the right API depends on your needs: granularity, historical range and pricing transparency are the key factors. For intraday and tick-level work, Massive, Databento and ORATS offer the most detail, while EOD Historical Data and Alpha Vantage are simpler choices for daily chains. Always review documentation and pricing before committing.

1. Massive Options Market Data API

Market Coverage

Massive, the company formerly known as Polygon.io, covers all US options tickers on the major options markets, including Cboe, NYSE and Nasdaq. Its options chain data includes Greeks, implied volatility and daily open interest, plus contract reference data.

Historical Depth and Granularity

Massive advertises 7+ years of options history, and each plan unlocks part of it: 2 years on Basic and Starter, 4 years on Developer and 5+ years on Advanced. Developer adds trade-level data and Advanced adds quotes, both with nanosecond timestamps, alongside second and minute aggregates. Pulling tick-level history is the kind of job best run from an always-on algo trading VPS rather than a laptop.

For scheduled data pulls, a Linux trading VPS runs your scripts around the clock, with 24/7 US-based support. Its 99.999% uptime keeps those jobs on schedule.

Pricing Model

Massive sells flat monthly options plans (checked on 7 Nov 2026): Options Basic is free (5 API calls a minute, end-of-day data), Options Starter costs $29 a month, Options Developer $79 and Options Advanced $199 (real-time data, individual use only). Paid plans include unlimited API calls, flat files and WebSockets, and annual billing saves 20%.

Polygon.io now operates as Massive, so the Polygon historical options API is the same product described here. Data comes as JSON or CSV through REST, WebSocket, S3 flat files or SQL, with client libraries for Python, JavaScript, Go and Java.

2. Databento Options Data (OPRA)

Databento sells historical and real-time US equity options data from OPRA, the consolidated feed for all US options exchanges, through the same API it uses for futures and stocks.

Historical Depth and Granularity

OPRA history starts in 2013. Data comes in several schemas: tick-by-tick trades, top of book (trades and quotes), bar aggregates and intraday and end-of-day statistics, plus reference data such as expirations and open interest. Databento does not publish pre-calculated implied volatility or Greeks; you compute them from its prices with your own pricing model.

Pricing Model

Databento bills like a utility (checked on 7 Nov 2026): pay as you go per GB of historical data, or a Standard subscription at $199 a month for unlimited access to frequently used data. New accounts get $125 in free credits, and client libraries cover Python, C++ and Rust. If you stream its feeds all day, a Databento VPS keeps the connection running.

3. IVolatility Historical Options Data

IVolatility sells options data for listed equity, index and ETF options across the USA, Canada, Europe and Asia, with US history back to 2005. Datasets include NBBO bid and ask quotes, OHLC, volume, open interest, and implied volatility and Greeks for every strike and expiration.

Granularity is end of day, plus 1, 5, 15, 30 and 60 minute snapshots where available, delivered by API, FTP, Snowflake or cloud buckets. API plans cost $63 (Builder), $159 (Quant) or $239 (Lab) a month billed annually, each with a 7-day free trial, and the Data Download tool charges per use with no subscription (checked on 7 Nov 2026).

4. OptionMetrics Historical Options Data

OptionMetrics provides historical options data for institutional and academic research through its IvyDB databases. US equity and index options have complete end-of-day history from 1996, with daily high, low and closing prices, implied volatility and Greeks (delta, gamma, vega, theta) and a constant-maturity volatility surface; IvyDB also covers Europe and Asia-Pacific. OptionMetrics does not publish prices, so you request a quote.

5. EOD Historical Data Options API

The EOD Historical Data (EODHD) US Stock Options Data API provides end-of-day options data for over 6,000 top-traded US stocks, with two years of history. Each record has 40+ fields, including bid and ask, volume, open interest, implied volatility, Delta, Gamma, Theta and Vega, delivered as JSON. It is an add-on listed at $29.99 a month, down from $39.99 (checked on 7 Nov 2026), and suits traders who rely on daily summaries.

Next up, let’s look at an API designed for more in-depth historical analysis.

6. ORATS Options Data API

ORATS (Option Research and Technology Services) sells US equity options data with its own smoothed implied volatilities and Greeks (SMV), plus hundreds of derived indicators.

Historical Depth and Granularity

Near end-of-day history goes back to 2007, and one-minute intraday data covers more than 5,000 US equity option tickers from August 2020, with Greeks, theoretical values and implied volatility for every minute of the trading day.

Pricing Model

ORATS API plans cost $199 a month (Delayed Data API, 20,000 requests a month), $299 (Live), $599 (Live Intraday) or $899 (All-In), with optional Tools APIs at $99 or $299 each (checked on 7 Nov 2026). Historical data files are sold separately.

7. Alpha Vantage Options Data

Alpha Vantage's HISTORICAL_OPTIONS endpoint returns the full options chain for a symbol on a given date, covering 15+ years of history (any date since 1 Jan 2008), with implied volatility and Greeks (delta, gamma, theta, vega, rho). It is a premium endpoint: the free tier allows 25 requests a day, and premium plans cost $49.99 to $249.99 a month for 75 to 1,200 requests a minute (checked on 7 Nov 2026). Responses come as JSON or CSV, which makes it easy to pull a daily chain into a spreadsheet or backtest.

How to Connect APIs with Your Trading Infrastructure

Integrating historical options data APIs into your trading setup requires a combination of reliable hardware and efficient software solutions. A trading-focused VPS (Virtual Private Server) should feature a high-speed network interface to manage real-time data streams, sufficient CPU and RAM to process large datasets, and enough storage to handle historical data archives. Positioning servers close to major exchange data centers can further minimize latency, often reducing it to under 20ms.

Modern APIs provide flexibility with multiple access options for different workflows. RESTful APIs are ideal for retrieving bulk historical data, while WebSocket connections are better suited for real-time updates. For handling large datasets, options like flat file access through S3-compatible interfaces or direct SQL queries can be more efficient than repeated REST calls, helping to avoid I/O bottlenecks. Client libraries - especially those written in Python - make integration easier by allowing you to fetch and process data programmatically with minimal development effort. These tools support automated data pipelines.

Creating automated data pipelines is straightforward with these libraries. For instance, a trader can use a Python script with the Massive client library to fetch minute-level aggregates for a specific ticker over a defined date range. By scheduling this script using cron, regular data ingestion becomes a routine process. Since the data is delivered in standardized formats, it’s simple to parse, store, and feed directly into backtesting frameworks.

The data API is only half of the stack: if you also trade the strategies you test, our guide to choosing an options trading platform covers the execution side.

Conclusion

Choosing the right historical options data API comes down to matching your specific needs with the features that matter most. When backtesting intricate strategies, performing real-time analysis, or building advanced options models, the key factors to consider include the depth of historical data, its accuracy, and how easily the API integrates with your systems.

Access to detailed options chain data, including Greeks and implied volatility, is essential for precise backtesting. For strategies that require high accuracy, institutional-grade data sourced directly from exchanges is a must. Additionally, APIs offering multiple integration methods can save you from unnecessary technical hurdles, ensuring smooth incorporation into your existing tools.

Pricing and ease of integration are just as critical. Transparent, straightforward pricing models can help you manage costs effectively. Look for APIs that offer flat-rate monthly plans with unlimited access across asset classes, avoiding the pitfalls of complicated tiered pricing that might lead to unexpected expenses. The right API should also adapt to your growing needs without frequent plan changes.

Integration speed plays a big role too. APIs with clear documentation, client libraries, and support for standardized formats like JSON or CSV can significantly cut down development time, helping you establish an efficient workflow faster.

Ultimately, the best API for you will strike a balance between high-quality data, reliable infrastructure, and usability. Your choice should align with your specific goals - whether that's low-latency performance for high-frequency trading, extensive historical data for long-term research, or libraries and flexible access for automated systems. Combining top-tier data with dependable infrastructure is the foundation for executing advanced trading strategies effectively.

Running an options data pipeline

Pull a free sample from each API and check strikes, expiries, Greeks and implied volatility against a chain you trust before paying for history. Respect each provider's rate limits in code and save raw responses so you can rebuild datasets without paying twice. A VPS for stock and options data can run scheduled pulls on Ubuntu or Windows.

FAQs

What should I look for when selecting an API for historical options data?

When selecting an API for historical options data, it's crucial to prioritize data coverage, accuracy, and reliability. These factors ensure you’re working with datasets that are both complete and dependable. Additionally, pay attention to the latency and data format - these elements can significantly affect how efficiently you can access and utilize the data in your applications.

Cost is another key consideration. Choose an API that aligns with your budget while still delivering the features you require. Finally, look for APIs that offer integration into your workflows. This can save you valuable time and effort during the development process.

How do I integrate historical options data APIs into my trading systems?

To bring historical options data APIs into your trading systems, the first step is to get an API key from your chosen provider. Once you have access, you can use RESTful or WebSocket APIs to retrieve data in formats like JSON or CSV, depending on what suits your system best.

You can pull this data through direct API calls, flat files, or even SQL queries, based on your infrastructure. Make sure your setup is optimized for low-latency data retrieval - aim for under 20ms - and includes processes for data normalization to ensure accuracy. With this integration, you’ll be equipped to use the data for backtesting, developing strategies, and analyzing markets in real time.

Why is tick-level precision crucial for backtesting trading strategies?

Tick-level data lets a backtest use individual trade times and prices. It captures the precise timing and price of every trade, enabling you to assess how your strategy would hold up in real-world situations, even during periods of high volatility or rapid market shifts.

This granular data helps uncover potential risks, fine-tune execution timing, and enhance the overall performance of your strategy. Without tick-level data, backtesting can miss critical details, making the results less dependable for decision-making.