Hurst Exponent
Measures the long-term memory of a time series. H > 0.5 = trending, H < 0.5 = mean-reverting, H = 0.5 = random walk.
Why AlgoZilla Uses Hurst Exponent
The Hurst exponent helps the model decide between momentum and contrarian strategies for each coin in each period.
Feature Variants
AlgoZilla expands every base indicator into multiple variants: raw values, delta (rate of change over 8/12/24 bars), divergences, and multi-timeframe computations across 2H, 4H, 8H, 12H, and 24H horizons. This is what sets AlgoZilla apart: 170+ features, each retrained every two weeks per coin.
Variants: 1 variant (raw value)
Part of a Bigger Picture
No single indicator drives AlgoZilla decisions. This is one of 170+ features feeding into a machine learning ensemble, retrained every two weeks per coin. The model learns which features matter in each market regime.
