Predictive Strength

Negligible
Bitcoin historically had 2.53% 30 days returns when Instantenous Volatility was▆ Low (0.2 - 0.4). It indicates average expected returns.
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BTC Price with Instantenous Volatility

Factor Plot

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▆ Very Low▆ Low▆ Moderate▆ High▆ Very High

Predictive Strength

Negligible

Instantenous Volatility Factor is a proprietary measure of the volatility of an asset, calculated based on a range different lookback periods.

Potential Edge

Market Microstructure Sensitivity

Captures latent liquidity dynamics through directional-change thresholds that act as "circuit breakers" for trend exhaustion. Unlike time-based metrics, this approach identifies regime shifts when price movements reverse beyond specified percentages (e.g., 0.5%-2% thresholds common in FX), making it particularly effective in crypto's fragmented liquidity environment where order book imbalances create asymmetric volatility patterns.

Structural Break Robustness

The measure's intrinsic time normalization mitigates false persistence signals from exogenous shocks. By decoupling volatility measurement from physical time sequences, it avoids overfitting to temporary market anomalies like exchange hacks or regulatory announcements that distort traditional GARCH/HAR models.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Instantenous Volatility and BTC 30 and 90 Day Average Returns

Backtest - Strategy Performance

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1.00
100.00%
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To understand a predictive factors predictive power, we create a simple long/short strategy and simulate its past performance (with daily rebalancing):

  • 100% Long when the predictive factor is close to 1, with a position size equivalent to the predictive factor value.
  • Flat when the predictive factor is close to 0, with a position size equivalent to the predictive factor value.

The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.

API

Get started by validating the historical performance of the strategy with our transparent code snippets.
Copy and paste the code snippets below into your Python environment or download the files below.

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Our Methodology