Linear-Neural Momentum Convergence

Family: hybrid · Regime: trending · Complexity: high · Asset classes: Forex, Equities, Crypto · Timeframes: 15m, 1h, 4h

Thesis

The strategy hypothesizes that momentum signals are most reliable when the price path adheres to a high-fidelity linear regression model (low Standard Error). By using an ultra-fast MACD (1.5 period) for direction and a K-Nearest Neighbor algorithm for non-linear smoothing, we can enter 'clean' trends on stochastic pullbacks and exit at volatility extremes (Bollinger Bands) before the linear model breaks down.

Components

Known failure conditions

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