Kalman Box-Breakout Stat-Arb

Family: mean_reversion · Regime: ranging · Complexity: high · Asset classes: Crypto (ETH/BTC), Major FX Pairs (AUD/NZD, EUR/GBP) · Timeframes: 15m, 1h

Thesis

Statistical arbitrage via Kalman-filtered spreads generates alpha in non-trending regimes; however, it often fails during 'drifting' trends or low-volatility traps. By filtering for low ADX (non-trending) but requiring a Boxline breakout (volatility expansion), we isolate high-probability mean-reversion entries where price is actually moving back toward its statistical equilibrium rather than oscillating in a dead zone.

Components

• Average Directional Index (regime) — Used to ensure the market is not in a strong directional trend, as statistical arbitrage/mean reversion underperforms in trending regimes.
• QQE (Qualitative Quantitative Estimation) (direction) — Confirms momentum in the direction of the mean-reversion before entry to avoid catching a 'falling knife'.
• AI Pair Trading System v1.0 (entry) — The core entry signal; uses Kalman-filtered Z-scores and ML weighting to identify statistical mispricings between paired assets.
• Restu Kaioh Scalping Indicator (exit) — Uses stochastic exhaustion to exit trades when the mean reversion has likely reached local momentum maturity.
• Pivot Points Reversal Levels (risk) — Provides dynamic support/resistance levels based on local two-bar reversals to set tight, market-structure-based stop losses.
• Boxline (Consolidation Zones) (volatility_filter) — Acts as a volatility expansion filter; entries only occur after a box breakout to ensure sufficient price movement for the mean-reversion signal.

Known failure conditions

• Pair correlation coefficient drops below 0.7 over the lookback period.
• ADX stays above 35 for sustained periods, indicating a regime shift to trend-following.
• Frequent SL triggers at Pivot Levels without reaching the mean, suggesting the Kalman mean is lagging price action significantly.

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