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OIPD (Options Implied Probability Distribution) is a Python library that turns raw options chain data into risk-neutral probability distributions over future asset prices. It fits SVI volatility smiles to listed options, links maturities with total-variance interpolation, and transforms the fitted volatility into a full price-implied distribution.

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Capabilities

OIPD provides two tightly integrated capabilities in a single library:
  • Probability extraction — compute the full risk-neutral PDF and CDF over future asset prices, query tail probabilities (prob_below, prob_above), quantiles, skew, and kurtosis.
  • Volatility modeling — fit single-expiry SVI smiles and multi-expiry total-variance surfaces for pricing and risk work, evaluate implied volatilities, price options, and compute Greeks.

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Core objects

OIPD organises everything around four classes, arranged by scope (single expiry vs. a time horizon) and layer (volatility vs. probability):
ScopeVolatility layerProbability layer
Single future dateVolCurveProbCurve
Future time horizonVolSurfaceProbSurface
Use VolCurve and ProbCurve when you care about one expiry date. Use VolSurface and ProbSurface when you want to reason across multiple maturities simultaneously — for example, to build a probability fan chart over the next 12 months.

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Workflow

OIPD is similar to a scikit-learn workflow. Every OIPD estimator follows the same three-step lifecycle:
  1. Configure — instantiate the estimator object with any model parameters.
  2. Fit — call .fit(chain, market) (or the convenience factory from_chain) to calibrate the model to your data.
  3. Query — call methods on the fitted object to extract implied volatilities, probabilities, quantiles, plots, and diagnostics.
You never need to manage intermediate state manually; each fitted object is self-contained and queryable.

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Next steps

Quickstart

Compute your first market-implied probability distribution in under five minutes using live options data from yfinance.

Installation

Install OIPD with pip and understand the standard vs. minimal dependency sets.

Core concepts

Dive deeper into the four core objects, the volatility-to-probability pipeline, and the warning diagnostics system.

API reference

Full reference for every public method on OIPD’s classes, including parameters, return types, and examples.