Predictive Strength

Negligible
Dogecoin historically had 2.53% 30 days returns when Active Addresses was▆ Low (0.2 - 0.4). It indicates average expected returns.
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DOGE Price with Active Addresses

Factor Plot

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

Predictive Strength

Negligible

Active Addresses measures the number of unique addresses interacting with the network. The index provides insights into network activity and user engagement.

Potential Edge

Network Adoption as Leading Demand Signal

Active Addresses directly measure organic network usage, acting as a proxy for real-world adoption. Sustained growth in unique interacting addresses often precedes price appreciation, as seen in Ethereum's 2020-2022 cycle where active addresses expansion correlated with a 5,560% ETH rally. This metric filters out noise from inactive "zombie wallets" (addresses holding assets but not transacting), focusing instead on engaged participants likely to influence market liquidity.

Speculative Activity Detection

Sharp spikes in Active Addresses frequently signal retail-driven speculation:

  • 93% correlation between ETH price peaks and Active Addresses Ratio spikes since 2018
  • Bitcoin's March 2025 active address surge to 912,300 preceded a 22% price reversal within 72 hours

The metric's 100-day SMA provides a smoothed view of speculative pressure, with sustained breaks above historical norms (>1.2x 5-year average) indicating overheating.

Data Collection Methodology

Active Addresses data is sourced by analyzing blockchain transactions to identify unique addresses participating as senders or receivers, excluding duplicates and the null address. The metric aggregates these addresses over specified time intervals (daily, weekly, monthly) and normalizes values relative to historical averages for comparative analysis.

Read more about our methodology

Track this predictive factor on your dashboard

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Scatter plot - Active Addresses 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