Oracle Trade Optimizer
Proprietary optimization engine that identifies optimal trade entry and exit points using dynamic programming. Generates the training labels for the ensemble classifier.
Why AlgoZilla Uses Oracle Trade Optimizer
This is the foundation of AlgoZilla’s supervised learning approach. The optimizer creates high-quality labels from historical data, which the ensemble then learns to predict forward.
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.
Variants: 4 variants including trade density and quality scores
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.
