Statistical Fat-Tail Breakout (SFTB) Strategy

Family: breakout · Regime: trending · Complexity: medium · Asset classes: FX, Equities, Crypto · Timeframes: H1, H4

Thesis

Market prices do not follow a normal distribution during trend inception; they exhibit 'fat tails' and non-normality. By using the Jarque-Bera test to identify these statistical anomalies during a break of the previous day's price range (DHL), we can enter momentum trades with high conviction. The use of MultiKAMA and Midpoint ensures that the breakout is aligned with both adaptive trend direction and recent price range centers, while the Gator Oscillator captures the cycle from awakening to exhaustion.

Components

MultiKAMA (TyphooN) (regime) — Establishes the macro-regime; the efficiency ratio ensures we are only entering when the trend has adaptive momentum.
MIDPOINT: Rolling Midpoint (direction) — Acts as a directional filter; price must be on the 'correct' side of the recent price range center to confirm momentum.
Daily High Low (entry) — Provides the structural breakout trigger. We enter when price breaches the previous day's extremes.
Gator Oscillator (exit) — Signals momentum exhaustion (the 'sated' phase) where the triple-SMMA lines begin to converge.
Simple Panel Template (risk) — Used for manual execution parameters and visual stop-loss management based on the Midpoint levels.
Jarque-Bera Test (JB) (volatility_filter) — Filters for non-normal price distributions. We only want to trade when returns exhibit fat-tails (high JB values), indicating a significant breakout rather than noise.

Known failure conditions

Jarque-Bera values remain below 5.991 during extended trending moves, leading to missed opportunities (Gaussian trends).
High frequency of 'sleeping' Gator phases during tight consolidation leading to multiple whipsaws on DHL breakouts.

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