Predictive Strength

Negligible
Cardano historically had 23.64% 30 days returns when BTC Market Dominance was▆ Very High (0.8 - 1). It indicates higher than average expected returns.
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ADA Price with BTC Market Dominance

Factor Plot

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

Predictive Strength

Negligible

Measures the market capitalization of Bitcoin as a percentage of all the total crypto market capitalization.

Potential Edge

Sentiment Amplification Mechanism

BTC Dominance acts as a real-time gauge of risk appetite. When it rises above key thresholds (e.g., 60% in 2025), it reflects institutional and retail investors treating Bitcoin as a "digital gold" safe haven during market uncertainty, at least relative to other crypto assets. Conversely, sustained elevated levels historically precede altcoin rallies as capital seeks higher beta plays. This self-reinforcing dynamic creates predictable liquidity waves.

Data Collection Methodology

Bitcoin Dominance data is sourced by aggregating real-time price and circulating supply data from cryptocurrency exchanges, calculating the ratio of Bitcoin's market capitalization to the total crypto market cap, and normalizing the result against historical averages to create a relative index.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - BTC Market Dominance and ADA 30 and 90 Day Average Returns

Backtest - Strategy Performance

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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