Adaptive Darvas-RLS Trend System

Family: trend_following · Regime: trending · Complexity: medium · Asset classes: Equities, Forex, Crypto · Timeframes: H1, H4

Thesis

Market price Discovery occurs in discrete steps (boxes); an adaptive filter (RLS) can distinguish between noise and a true structural shift out of these boxes more efficiently than static indicators. By aligning daily structural boundaries (Daily H/L) with intraday volatility triggers (SuperTrend), the strategy captures high-probability expansion phases while exiting as soon as average momentum (Heikin-Ashi) signals exhaustion.

Components

• Darvas Boxes Modern/Classic (regime) — Establishes the regime; trades are only taken when price has successfully broken out of a consolidation box, ensuring we aren't trading in noise.
• Recursive Least Squares Adaptive Filter (RLS) (direction) — Provides the directional bias; unlike static EMAs, RLS adapts to current volatility to minimize lag in identifying the primary trend slope.
• SuperTrend & Pivots (entry) — The tactical trigger; uses volatility-adjusted levels (SuperTrend) and structural levels (Pivots) to pinpoint the entry moment within the established regime.
• Heikin-Ashi Candles (exit) — Trend-following exit; HA smoothing prevents premature exits on minor LTF pullbacks, closing only when the average price momentum reverses color.
• Daily High Low MTF (risk) — Provides hard structural boundaries for Stop Loss placement based on previous day's extreme volatility ranges.

Known failure conditions

• Extended periods of low-volatility 'drift' where Darvas boxes are small and frequent.
• Mean-reverting environments where price repeatedly touches the Daily Pivot but fails to trend.
• Recursive instability in RLS during extreme 'black swan' gaps.

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.