KNN-SAK Structural Convergence Trader

Family: hybrid · Regime: trending · Complexity: high · Asset classes: FX, Indices, Commodities · Timeframes: H1, H4

Thesis

Structural reversals (Head & Shoulders) are most reliable when they align with both a local machine-learning smoothed trend (KNN) and a long-term cumulative price anchor (UDF EMA). By filtering these entries through a BandPass filter (SAK), we isolate the specific frequency of the move, while the DSP cycle-based exit captures momentum before typical trend-following lag destroys profit. The edge relies on the convergence of geometric price patterns and multi-scale frequency filters.

Components

• AI Trend Navigator [K-Neighbor] (regime) — Provides a locally-adaptive trend regime by comparing current price action to historical clusters, filtering out noise that traditional moving averages might miss.
• Unsupported UDF Final Switch Const Reassignment Qualifier (direction) — Serves as a macro-trend 'anchor'. Because the length scales with bar_index, it represents a cumulative mean that prevents trading against the multi-year primary direction.
• HS Indicator (Head & Shoulders) (entry) — Identifies high-conviction structural reversal points based on price and time symmetry.
• Ehlers Detrended Synthetic Price (DSP) (exit) — Exits trades when the cyclic component of price reaches exhaustion, regardless of the overarching trend.
• Williams Fractals (risk) — Provides objective, non-discretionary support/resistance levels for stop-loss placement, accounting for the 2-bar confirmation lag.
• Swiss Army Knife Indicator (SAK) (confirmation) — Acts as a signal-to-noise filter using a BandPass setting to confirm momentum is moving in the direction of the H&S breakout.
• Sunday Opening Gap (volatility_filter) — Prevents entry during the high-volatility, low-liquidity period immediately following the weekly open to avoid slippage.

Known failure conditions

• The UDF EMA length grows so large that it becomes a horizontal line, failing to provide any directional bias.
• Market volatility drops below the H&S MinPatternSizeATR threshold for extended periods.
• The KNN algorithm enters a 'loop' of high computational lag in assets with very deep historical data.

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