ML-Enhanced Gap Momentum Explorer

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

Thesis

This strategy hypothesizes that price gaps are not merely noise but represent 'information shocks' when they align with both a deep-learning validated regime (ML SuperTrend) and a high efficiency-to-noise ratio (ER). By using the Sherif Hilo channel as a directional anchor, we enter only when the gap confirms the established trend. The edge lies in the filtering of gaps: most gaps in ranging markets are mean-reverting, but gaps in 'efficient' trending markets (ER > 0.25) tend to be breakaway signals that precede a momentum surge.

Components

Known failure conditions

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