Predictive Strength

Negligible
Dogecoin historically had -6.82% 30 days returns when Equities Market Breadth was▆ Very Low (0 - 0.2). It indicates negative expected returns.
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DOGE Price with Equities Market Breadth

Factor Plot

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

Predictive Strength

Negligible

Equities Market Breadth measures the number of the top 400 large cap companies participating in a market rally. The index provides insights into market sentiment.

Potential Edge

Risk Appetite Transmission Mechanism

Broad-based equity rallies (high market breadth) signal robust investor confidence and risk-on behavior. This sentiment often spills into crypto markets, particularly when institutional capital seeks asymmetric returns.

Data Collection Methodology

The Equities Market Breadth data is sourced by tracking daily price movements of the top 400 large-cap companies, calculating the ratio of advancing stocks to total stocks in this cohort. This ratio is then normalized relative to historical averages, with values scaled to 1 and 0. The index quantifies broad market sentiment by measuring how widely these large-cap stocks participate in rallies or declines.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Equities Market Breadth and DOGE 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