Log-Normalized Cyclical Momentum Hybrid

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Forex, Crypto · Timeframes: H1, H4, D1

Thesis

Markets exhibit 'energy' bursts where volatility and trend direction align. By using Log-transformation to normalize these bursts and a Bandpass Filter to isolate the cycle of price, we can identify pullbacks (via Stochastic) within high-momentum regimes (Frankenstein ATM) while scaling risk based on how 'erratic' the price is relative to its mean (RAE).

Components

Known failure conditions

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