Stationarity Testing

Stationarity testing is a statistical procedure used to determine whether a time series has constant mean, variance, and autocorrelation over time. A stationary series is predictable, whereas a non-stationary series, like the price of a volatile crypto asset, can drift unpredictably, making standard forecasting models unreliable.

Traders perform these tests to see if their data is suitable for mean-reversion strategies or if they need to apply transformations like differencing to make the data usable. If a series is non-stationary, the results of regression models can be spurious, leading to incorrect trading decisions.

Ensuring stationarity is a prerequisite for robust quantitative research in finance, as it confirms that the statistical relationships observed in the past are likely to hold in the future. It is a foundational step in building any reliable algorithmic trading system.