Kalman-KNN Statistical Reversion Strategy

Family: hybrid · Regime: ranging · Complexity: high · Asset classes: Crypto (ETH/BTC Pair), Forex (Correlated Pairs) · Timeframes: 15m, 1H

Thesis

Temporary price dislocations between highly correlated assets (like ETH and BTC) represent statistical anomalies. These anomalies are most likely to mean-revert when local trend predictions (KNN) and momentum (CCI) align with the direction of the reversion, while higher-timeframe confluence (Dashboard) ensures the trade isn't fighting a macro trend.

Components

• AI Pair Trading System v1.0 (regime) — Identifies statistical extremes in the relative value between two assets using Kalman-filtered mean reversion.
• AI Trend Navigator [K-Neighbor] (direction) — Uses non-parametric KNN prediction to determine the likely local trend direction based on historical price state similarities.
• CCI Arrows (entry) — Provides the discrete timing trigger when momentum crosses the zero-line threshold.
• Bollinger Bands (exit) — Provides volatility-defined targets and mean-reversion exit points.
• ATR Fib (risk) — Establishes structural stop-loss levels based on session volatility and Fibonacci extensions.
• ProfitRobots Dashboard Template (confirmation) — Filters for higher-timeframe confluence and cross-symbol correlation health.

Known failure conditions

• The correlation between the two assets in the Pair Trading System breaks down permanently (cointegration failure).
• Static ATR input in ATR Fib fails to account for a sudden 2x increase in market volatility.
• KNN prediction becomes a 'coin-flip' in low-volatility ranging environments.

Explore the full interactive blueprint, parameter ranges and evidence on WOBR StrategyVerse, or generate this strategy as an MT4/MT5 Expert Advisor with QuantMogul AI Engine.