mean-reversion

You test if a time series is mean-reverting by applying statistical tests like the ADF, Hurst exponent, and Variance Ratio to your price data using Python libraries such as pandas and scipy.

You calculate the half-life of mean reversion by estimating Ornstein-Uhlenbeck process coefficients from your time series data, which helps tune your trading strategy parameters.

Yes, you can generate pairs trading signals by calculating z-score deviations from the rolling mean of an asset spread, triggering buy or sell actions when deviations become significant.

Stationarity testing in statistical arbitrage uses the Augmented Dickey-Fuller (ADF) test, Hurst exponent, and Variance Ratio tests to confirm mean-reverting characteristics in time series data.

Yes, you need Python libraries including pandas, numpy, scipy, and httpx to perform the statistical computation and data manipulation required for mean reversion analysis.

You should use mean reversion strategies instead of trend following in ranging markets, capitalizing on the statistical tendency for prices or spreads to revert to their long-term average.