About Volatility

The Volatility Dashboard provides an institutional-grade framework for dissecting the structural mechanics of market movements. Rather than tracking absolute price action, it isolates variance, entropy, and statistical distribution to reveal whether an asset is storing kinetic energy or violently expending it. This suite moves beyond basic oscillators by deploying physics-inspired models and multi-dimensional composites. Metrics such as Market Entropy apply information theory to classify regimes as ordered or purely random, whilst Volatility Directional Bias (VDB) separates the **Standard Deviation** of upside versus downside momentum. The Consolidation Index mathematically maps transitional resting phases, warning analysts before high-velocity breakouts occur. Finally, the Return Distribution module aggregates the entire historical dataset to map the exact statistical shape of daily performance. By quantifying skew and excess kurtosis, it explicitly defines the probability of 'fat tail' outlier events, allowing investors to properly size positions adjusted for structural risk.

Signal Zones

Trading Signals by Regime

How It Is Calculated

Frequently asked questions

What does Market Entropy actually measure?

Market Entropy applies information theory to price action. Low entropy means the market is highly ordered and trending predictably. High entropy means the market is acting randomly, heavily punishing trend-following strategies.

How does Volatility Directional Bias (VDB) differ from standard momentum?

Standard momentum indicators strictly track absolute price changes. VDB isolates the **Standard Deviation** of up-days versus down-days, revealing whether the actual variance of the market structurally leans bullish or bearish.

What is the significance of the Consolidation Index?

Markets spend the majority of their time ranging. The Consolidation Index mathematically defines these resting phases by evaluating narrowing bands and contracting volatility, flagging exactly when the asset is storing energy for a breakout.

Why are skew and kurtosis important in the Return Distribution?

Skew reveals whether extreme days lean statistically positive (right-skewed) or negative (left-skewed). Kurtosis measures fat tails, meaning a high kurtosis warns that extreme outlier price movements occur far more frequently than a normal bell curve would predict.