Data Survivorship Bias

Data survivorship bias occurs when a backtesting analysis only includes assets or strategies that are currently active, ignoring those that failed or were delisted. In the rapidly evolving crypto market, many projects and trading venues disappear, and excluding them from historical data leads to an overly optimistic view of historical performance.

This bias can cause traders to overestimate the success rate of strategies and underestimate the risk of total loss. To avoid this, datasets must include historical information on defunct protocols and delisted assets to ensure a representative sample.

Recognizing this bias is a critical step in professional quantitative analysis and strategy validation. Without accounting for it, the results of backtesting are fundamentally flawed and dangerous for capital allocation.

It ensures that the historical performance metrics reflect the true risk environment.