KNN-Confluence Darvas Breakout Strategy

Family: trend_following · Regime: trending · Complexity: high · Asset classes: Equities, Crypto, Major Forex Pairs · Timeframes: H1, H4, D1

Thesis

The hypothesis is that trend-following success is maximized when a structural breakout (Darvas Box) occurs during a regime of historically validated volume-price synergy (KNN SuperTrend) and is confirmed by a broad spectrum of technical indicators (IAE). By requiring confluence across volume, structure, and momentum, we filter for institutional-led moves rather than retail noise.

Components

• Volume SuperTrend AI (Expo) (regime) — Filters for high-conviction regimes by using KNN to classify price action relative to volume-weighted volatility bands.
• Heiken Ashi (direction) — Provides a noise-filtered directional bias; ensures entry is aligned with immediate smoothed momentum.
• IAE — Technical Confluence Score (entry) — Acts as the final 'go/no-go' trigger by requiring at least 7 layers of technical agreement before committing capital.
• Risk Reward Indicator Tool (exit) — Standardizes the exit logic using a fixed mathematical expectancy relative to the structural risk defined by the Darvas Box.
• Darvas Boxes MetaTrader 5 (risk) — Defines the structural breakout level and provides a logical, market-structure based anchor for stop losses.

Known failure conditions

• Price consistently breaks Darvas Box boundaries only to revert (low volatility noise).
• KNN Classification remains static during rapid trend reversals due to window lag.
• IAE Confluence score remains high (>70) while price undergoes a deep retracement, indicating divergence.

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.