Predictive Strength
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Predictive Strength
Measures the development effort of a project by counting the number of commits, merges, and lines of code added or removed from the codebase.
Active development correlates strongly with future protocol improvements and ecosystem expansion. Chains with sustained developer activity (measured by commits, unique contributors, and codebase complexity) tend to deploy upgrades that drive user adoption.
Development Effort is calculated by tracking codebase activity metrics such as commit frequency, merge events and issues. These metrics are sourced from version control systems like GitHub, then normalized against historical averages to assess current development intensity relative to past activity.
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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.