KNN Volume SMC Fibonacci Hybrid
Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Indices (specifically Dow Jones/US30) · Timeframes: M15, H1
Thesis
Institutional trends established at the session open (Trepidity OR) and confirmed by AI-classified volume flow (Volume SuperTrend KNN) create reliable pullbacks to Fibonacci levels. By anchoring risk to macro 'Probable' levels and targeting session volatility extensions, one can capture the meat of a daily trend with institutional backing.
Components
- Volume SuperTrend AI (Expo) (regime) — Acts as the primary regime filter, ensuring that the trend is supported by both volume-weighted price action and historical KNN classification similarity.
- GOM KOLA SIDO — Full Integration (direction) — Identifies institutional direction via Order Blocks (OB) and KOLA Zones to ensure we are trading with market structure.
- Auto Fibonacci (entry) — Provides the specific entry trigger based on mean reversion to the 0.618 Golden Pocket within the established trend.
- Trepidity Opening Range with Extensions (exit) — Defines session-specific profit targets based on the volatility of the first 30 minutes of trading.
- Probable High Low (risk) — Sets the absolute risk boundaries; provides the hardcoded floor/ceiling for stop-loss placement based on macro price expectations.
- Chaikin Money Flow (NNFX Version) (confirmation) — Confirms that price movement is backed by actual accumulation/distribution rather than low-volume noise.
- Indicator Sample Skeleton (volatility_filter) — Acts as a data-integrity filter by ensuring a minimum bar count is present before the KNN and CMF logic can initialize.
Known failure conditions
- The asset price moves significantly away from the hardcoded Probable High/Low range (32889 - 33678), making the risk/reward ratio nonsensical.
- The KNN classification produces rapid oscillating labels in tight ranges, leading to excessive regime flipping.
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