Predictive Strength
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Predictive Strength
Measures the number of new users on-chain.
New user growth often precedes price rallies as it reflects grassroots adoption before institutional capital arrives. Historical patterns show networks like Ethereum and Solana experienced large user growth spikes 3-6 months before major price appreciation cycles
This metric captures first-mover activity – retail investors and developers testing new protocols – which often seeds liquidity and narrative momentum.
Blockchain value accrual follows Metcalfe's Law (network value ∝ users²). Key dynamics:
Unlike lagging price metrics, user growth directly measures protocol health.
Blockchain nodes are connected via polling (active block queries) to capture transaction data. The metric is derived by counting unique wallet addresses appearing for the first time, then normalizing against historical averages to create a relative index.
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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