Coming Soon · Q3 2026Institutional

Volatility Mean Reversion Engine

Systematic signals for when volatility is due to revert to its long-run average

Volatility is mean-reverting by nature — periods of extreme fear or extreme calm are always temporary. The Mean Reversion Engine quantifies exactly how far current volatility has deviated from its long-run equilibrium and estimates the probability and timeline for reversion. This creates high-conviction trade signals for both premium sellers (when IV is elevated) and volatility buyers (when IV is suppressed).

What's Included

Module capabilities

Ornstein-Uhlenbeck Model

Uses the Ornstein-Uhlenbeck stochastic process — the standard academic model for mean-reverting processes — to estimate the speed and magnitude of IV reversion for each ticker.

Reversion Probability Bands

For any current IV level, shows the probability that IV will revert to within 10%, 20%, or 30% of its long-run mean within 30, 60, or 90 days.

Half-Life Estimation

Calculates the statistical half-life of the current IV deviation — the expected time for IV to close half the gap to its mean. Critical for selecting the right expiration.

Regime Detection

Classifies the current volatility regime (low, normal, elevated, extreme) using a Hidden Markov Model, and adjusts all signals for the current regime.

Macro Overlay

Overlays macro volatility drivers (VIX term structure, credit spreads, macro event calendar) to distinguish regime-driven IV from idiosyncratic spikes.

Portfolio-Level Signals

Aggregates mean-reversion signals across your entire watchlist to identify when broad portfolio volatility is at an extreme — the ideal time to add or reduce premium-selling exposure.

Workflow

How it works

1

The engine fits an OU process to each ticker's IV history

The Ornstein-Uhlenbeck model is calibrated to the last 3 years of daily IV data for each stock. This gives three parameters: the long-run mean (theta), the speed of mean reversion (kappa), and the volatility of volatility (sigma).

2

It calculates the current deviation from equilibrium

The deviation is expressed as a standardized score: how many OU standard deviations the current IV sits above or below the estimated long-run mean. This is more precise than a simple Z-score because it accounts for the mean-reverting dynamics of the process.

3

It estimates the reversion timeline

Using the kappa parameter (speed of mean reversion), the engine calculates the expected half-life: the time for IV to close half the gap to its mean. A half-life of 15 days suggests a 30-45 DTE trade; a half-life of 45 days suggests a longer-dated position.

4

The regime detector adjusts the signal

If the macro environment is in a high-volatility regime (VIX above 25, credit spreads widening), the engine reduces confidence in reversion signals because regime persistence can override mean reversion. Signals are color-coded by confidence level.

5

You receive a ranked list of high-conviction reversion trades

The final output is a ranked list of tickers where mean reversion is both statistically likely and macro-consistent, with the suggested strategy, expiration, and position size.

Tutorial

See it in action

Volatility Anomaly — Platform Overview

A complete walkthrough of the Volatility Anomaly platform: the screener, backtester, research library, and all Professional modules.

1:12

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Institutional Feature

Volatility Mean Reversion Engine

Systematic signals for when volatility is due to revert to its long-run average

Unlock institutional-grade portfolio optimization and quantitative modeling tools.

Institutional Plan

$199/moor $1,990/yr
  • Everything in Professional
  • Volatility Mean Reversion Engine (Q3 2026)
  • Premium-Selling Portfolio Optimizer (Q4 2026)
  • API access for automated trading
  • Dedicated onboarding call
  • White-glove support
FAQ

Frequently asked questions

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