FOMO Score
Detects Fear Of Missing Out conditions: rapidly rising prices + increasing volume + greed gauge. Identifies potential tops and overextended rallies.
Why AlgoZilla Uses FOMO Score
FOMO conditions often precede sharp corrections. The model becomes more conservative with entries and tightens exit timing during FOMO periods.
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.
