ATOM Logit-Regression Hybrid System

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Crypto (ATOMUSDT.P) · Timeframes: 15m

Thesis

The hypothesis is that ATOMUSDT price action follows identifiable logistic patterns based on market structure (FVGs and liquidity) which are more predictive when the trend is 'tight' (low STDERR). By using RCI to time the entry during a synchronized cycle and Ichimoku to define structural support, we can filter out the low-AUC noise (0.52) of the standalone logit model. Volume-scaled risk (RVOL) ensures we capitalize on institutional momentum.

Components

• Standard Error of Regression (STDERR) (regime) — Filters for 'stable' trend environments; when price dispersion around the regression line is low, momentum signals are less likely to be noise.
• ATOM Coefficient Indicator (logit) (direction) — Provides the primary directional bias using a pre-trained logistic model sensitive to ATOM-specific liquidity and FVG structures.
• Ichimoku Kinko Hyo (entry) — Acts as the execution trigger; the Kumo cloud provides a structural 'breakout' validation for the logit signal.
• SuperTrend (exit) — Provides a volatility-adjusted trailing stop and final exit signal to capture extended trends.
• RVOL (Relative Volume) (risk) — Ensures the move is backed by institutional participation; scales position size based on relative activity.
• Rank Correlation Index (confirmation) — Confirms that price is moving in a synchronized cycle (high correlation between price rank and time rank) before entry.

Known failure conditions

• ATOM Coefficient Indicator AUC drops below 0.50 in live testing.
• Market regime shifts to high-dispersion/parabolic (STDERR spikes) where linear regression models fail.
• Exchange volume data becomes unreliable or highly manipulated, invalidating RVOL.

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