Lorentzian Volume-Momentum Hybrid
Family: hybrid · Regime: trending · Complexity: high · Asset classes: Equities, Forex, Crypto · Timeframes: 15M, 1H, 4H
Thesis
Market participants behave differently in similar 'feature-space' environments. By using Lorentzian Classification to find these historical parallels and confirming with Volume-Weighted AI trends, we identify windows of high-probability momentum. Laguerre RSI acts as the 'coiled spring' trigger, entering as price breaks from volatility-filtered consolidation zones, with risk defined by structural 'boxes' and profit taken at psychological grid levels.
Components
- Lorentzian Classification (regime) — Provides the primary regime filter by finding historical price-action similarities in a non-Euclidean feature space.
- Volume SuperTrend AI (Expo) (direction) — Combines volume-weighted trend detection with KNN classification to ensure the direction is supported by volume flow.
- Laguerre RSI (entry) — Acts as the momentum trigger; its low-lag polynomial structure captures entries precisely after minor pullbacks.
- Grid Points Utility (exit) — Defines objective Take-Profit targets based on psychological round numbers (fixed point intervals).
- Boxline (Consolidation Zones) (risk) — Defines the dynamic Stop-Loss based on the floor/ceiling of the most recent consolidation zone.
- On-Balance Volume (OBV) (confirmation) — Confirms that the price move is backed by cumulative volume accumulation/distribution.
- Modern Ichimoku Cloud [GBB] (volatility_filter) — Filters out 'dead zones' where ATR-normalized cloud thickness suggests insufficient volatility for the ML models to function.
Known failure conditions
- Lorentzian Classification returns 'Neutral' or oscillates rapidly between labels.
- Market remains in a 'Boxline' consolidation for >50 bars, indicating a breakdown of directional momentum.
- OBV diverges significantly from Price SuperTrend AI signals for more than 3 candles.
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