Lorentzian-Wavelet Confluence Engine

Family: trend_following · Regime: trending · Complexity: high · Asset classes: Equities, Crypto, Forex · Timeframes: 1H, 4H, Daily

Thesis

Market trends exhibit 'fractal momentum' where high-confluence technical signals (IAE) align with historical price geometry (Lorentzian) and non-linear trend intensity (TTF). By filtering out high-frequency noise using the Discrete Wavelet Transform and sizing positions based on Median Absolute Error (MdAE), we can isolate momentum clusters that have a higher probability of persistence than standard indicator crossovers.

Components

• IAE — Technical Confluence Score (regime) — Ensures broad-market technical alignment across 10 layers before considering a position.
• Trend Trigger Factor (TTF) with T3 Smoothing (direction) — Filters for high-velocity trend intensity by comparing current price ranges against historical buckets.
• Lorentzian Classification (entry) — Uses multi-dimensional distance to find historical analogs, triggering entry only when price geometry matches previous winning clusters.
• RSI Area (Histogram) (exit) — Provides a momentum-based exit signal when the trend reaches statistical exhaustion.
• Median Absolute Error (risk) — Calculates a robust, outlier-resistant volatility metric to define stop-loss distances based on typical 'prediction' error.
• Discrete Wavelet Transform (DWT) (volatility_filter) — Decomposes price to ensure the entry is aligned with the low-frequency 'approximation' (trend) and not a high-frequency 'detail' (noise) spike.

Known failure conditions

• TTF remains pinned at extremes while price oscillates, indicating a 'hanging' momentum signal in a bracketed market.
• IAE score falls into the 40-60 range for extended periods, causing frequent signal invalidation.
• MdAE values collapse to near-zero, leading to stops that are tighter than the spread.

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