Predictive Strength
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Predictive Strength
Active Addresses measures the number of unique addresses interacting with the network. The index provides insights into network activity and user engagement.
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.
Sharp spikes in Active Addresses frequently signal retail-driven speculation:
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.
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.
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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):
The strategy is rebalanced daily, on a continuous basis. There are 0.5% transaction costs applied on each position adjustment.
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.
Predictive Strength
Predictive Strength