KNN Fractal MTF Breakout System

Family: breakout · Regime: trending · Complexity: high · Asset classes: XAU/USD, EUR/USD, Major FX Pairs · Timeframes: M5, M15

Thesis

Price momentum is most reliable when local machine-learning smoothing (KNN) aligns with institutional structural levels (MTF MAs). By entering on a breakout of fractal chaos, we capture the transition from market noise to directional intent, exiting before momentum reaches the mean-reversion exhaustion point indicated by overextended stochastic levels.

Components

• AI Trend Navigator [K-Neighbor] (regime) — Determines the probabilistic trend regime by comparing current price vectors to historical clusters.
• MA(x6) Multi-Timeframe Visualizer (direction) — Provides structural bias by ensuring the entry occurs above/below major timeframe support/resistance levels.
• Fractal Chaos Bands (FCB) (entry) — Filters market noise and provides a breakout trigger when price exceeds the confirmed fractal range.
• Restu Kaioh Scalping Indicator (exit) — Identifies momentum exhaustion points for timely exits in a scalping context.
• Boxline (Dynamic Range Breakout) (risk) — Sets dynamic stop-loss levels based on the most recent market structure 'box' boundaries.

Known failure conditions

• Consecutive failures of the KNN prediction to lead to FCB breakouts (regime mismatch).
• Price oscillating across the MTF MA levels (low-volatility ranging).
• Boxline failure to print a new box for extended periods, leading to stale stop losses.

Explore the full interactive blueprint, parameter ranges and evidence on WOBR StrategyVerse, or generate this strategy as an MT4/MT5 Expert Advisor with QuantMogul AI Engine.