Database Normalization

Database normalization in the context of backtesting involves structuring historical data in a consistent format that allows for accurate, error-free analysis. This includes cleaning data to remove outliers, filling in missing price points, and adjusting for events like token splits or rebrandings.

In crypto, where data sources are often inconsistent and noisy, normalization is a significant technical challenge. Without a clean, standardized database, backtests will be riddled with errors that lead to false conclusions.

Normalization ensures that the backtesting engine interprets price and volume data correctly across different assets and timeframes. It is a foundational step in quantitative research, providing the reliable data foundation required for any meaningful analysis of trading strategies.