ORATS vs IVolatility: Options Analytics Compared

ORATS vs IVolatility: Options Analytics Compared

Two specialist options-data providers, compared on surfaces, Greeks, and earnings volatility, plus the universe gap they share.

What You Are Really Comparing

ORATS and IVolatility both specialize in options and volatility data, and both go well beyond raw chains into derived analytics. The practical comparison is about coverage, history, surface-construction methodology, and how each handles earnings volatility, since those determine which one fits a given options strategy. They are peers in the same niche rather than fundamentally different products.

A concrete example clarifies the choice. Suppose your strategy trades the volatility crush around earnings. ORATS, with its earnings-vol forecasts and skew analytics, packages the exact inputs that decision needs, and the comparison effectively ends there. Now suppose you are studying how the volatility term structure behaves across a full decade of market regimes. IVolatility's long, broad history of surfaces is the better fit, because the question depends on depth of history more than on earnings-specific analytics. The two are peers, and the right one follows from what your strategy actually keys on.

ORATS: Strengths and Trade-offs

ORATS is known for options analytics with a strong focus on earnings-related volatility, providing historical implied-volatility surfaces, Greeks, skew, and forecasts. Its strength is derived analytics oriented toward trading decisions, particularly around earnings, delivered through an API and flat files. For strategies that revolve around earnings vol and skew, ORATS packages exactly the inputs you want.

The trade-offs are scope and methodology dependence. ORATS is a specialist, so you would not use it for general market data, and you inherit its surface-construction and forecast choices. For options-centric research that aligns with its strengths, that focus is an advantage, as our ORATS alternatives guide discusses.

IVolatility: Strengths and Trade-offs

IVolatility offers a long history of implied-volatility surfaces, Greeks, and skew across a broad universe, with depth that suits research depending on extended historical vol data. Its strength is the breadth and length of its volatility history, which matters for studies that need many years of surfaces. For long-horizon volatility research, that depth is the draw.

The trade-offs are again specialization and methodology. Like ORATS, it is a vol-data specialist rather than a general provider, and its surface construction reflects its own methods. The choice between the two often comes down to coverage and how each builds the surface for your specific instruments.

Head-to-Head

Where Each Wins

ORATS wins when your strategy centers on earnings volatility, skew, and trading-oriented analytics, where its forecasts and earnings focus are directly useful. IVolatility wins when you need a long, broad history of surfaces and Greeks for research that depends on extended vol data. Both are benchmarked against the field in our roundup of the best volatility data providers.

For options data beyond the surface, including chains and prices, our guide to options data providers maps the broader options landscape, since a complete strategy usually needs more than vol analytics alone.

The Layer Both Omit

Both ORATS and IVolatility focus on the surface for contracts that existed. Neither tells you which underlyings were optionable on each historical date, which is the universe input a systematic options backtest needs to avoid trading names that had no options at the time.

Alphanume's historical optionable tickers dataset fills that gap with point-in-time eligibility, and a historical market cap dataset adds size context for liquidity filters. Paired with either vol provider, they close the universe gap that surface data leaves open.

Which Should You Choose?

Choose ORATS for earnings-vol and skew-driven strategies, and IVolatility for long-horizon research that needs deep historical surfaces, comparing both on coverage and methodology for your instruments. Either way, add a point-in-time optionable universe, because surface analytics and universe eligibility are different inputs and a systematic options backtest needs both.