Lorentzian-Chan Structural Pivot System
Family: hybrid · Regime: mixed · Complexity: high · Asset classes: Equities, Forex, Crypto · Timeframes: H1, H4
Thesis
Market turns occur at specific structural 'hubs' (Chan Lun) when the current non-linear momentum signature (Lorentzian) aligns with historical high-probability pivots, provided the move is verified by volume-price efficiency (EOM). By using KNN to classify local states, we can filter structural signals that occur in noisy regimes.
Components
- Lorentzian Classification (regime) — Acts as the primary regime filter by finding historical price-feature clusters similar to current conditions using a non-Euclidean distance metric.
- Chan Lun Engine (MT4 DLL version) (direction) — Identifies high-probability structural setups (2nd/3rd type signals) which represent local trend exhaustions and hub breakouts within the Zen Theory framework.
- Volume SuperTrend AI (Expo) (entry) — Provides the tactical execution trigger by combining volume-weighted trend tracking with a local KNN state classifier.
- Ichimoku Kinko Hyo (exit) — The Kijun-sen (Base Line) serves as a dynamic equilibrium level for trailing exits, ensuring profits are locked when the trend mean-reverts.
- ATR Heiken Ashi (risk) — Determines volatility-adjusted stop-loss levels and position sizing using smoothed synthetic price data to avoid noise-induced stops.
- Ease of Movement (EOM) (confirmation) — Confirms that the directional move is backed by volume-price efficiency, filtering out low-conviction structural breaks.
Known failure conditions
- Structural labels from the Chan Lun Engine frequently disappear/reappear (excessive repainting) during high-volatility events.
- The Lorentzian classifier converges to a 50/50 win rate across all feature combinations during extended sideways consolidation.
- Execution latency exceeds signal generation time due to nested loop overhead in the KNN/Lorentzian components.
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