Sigmoid-Logit Volatility Arb

Family: hybrid · Regime: high_vol · Complexity: high · Asset classes: Crypto (ATOM Focus), Equities · Timeframes: 15m

Thesis

Price movement is most predictable at the edges of momentum saturation (measured by Sigmoid) when accompanied by high statistical volatility (StDev). By filtering these moments through an asset-specific logit model (ATOM Coefficient) and catching exhaustion crossovers (ADX Inversion), we can exploit short-term structural imbalances before price mean-reverts or trends further, using accelerating risk management (PSAR) to protect gains.

Components

• Logistic Function (SIGMOID) (regime) — Filters for high-conviction momentum cycles (near 1 or 0) to avoid mid-range noise.
• ADX Crossing INGM (direction) — Identifies the exhaustion and reversal of directional strength (using the specific inverted logic provided).
• ATOM Coefficient Indicator (logit) (entry) — Provides a ML-derived entry probability based on local market structure features.
• MACD (exit) — Detects momentum fading or crossing against the trade direction for early exit.
• Parabolic SAR (risk) — Provides a non-linear, accelerating trailing stop that tightens as the trend progresses.
• Hull Moving Average (HMA) (confirmation) — Acts as a fast-response trend filter to confirm the immediate price action alignment.
• Standard Deviation (volatility_filter) — Ensures trades only occur during periods of elevated volatility (above 80th percentile) to increase move potential.

Known failure conditions

• The ATOM Coefficient model exhibits significant drift due to the hardcoded intercept and coefficients.
• Persistent low-volatility regimes prevent the 80th percentile StDev threshold from ever being met.
• The inverted ADX logic results in entry during momentum acceleration rather than exhaustion.

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