Triple-Confluence RMSE Trend-Follower

Family: trend_following · Regime: trending · Complexity: medium · Asset classes: Equities, FX, Crypto · Timeframes: H1, H4, D1

Thesis

Market trends represent structural imbalances that persist longer than random walk theory suggests. By requiring three distinct trend-following methodologies (MA-extremes, ATR-trailing, and Momentum-midpoints) to align under conditions of low 'noise' (RMSE), we can identify high-probability trend legs while avoiding the 'choppy' volatility that typically degrades trend-following performance.

Components

• HiLo Activator (Gann Style) (regime) — Establishes the macro-regime by ensuring the price is structurally trending above or below moving averages of price extremes.
• SuperTrend (direction) — Confirms intermediate trend direction using ATR-based volatility bands to filter out minor price fluctuations.
• Ichimoku Kinko Hyo (entry) — Provides the execution trigger via the Tenkan-Sen/Kijun-Sen cross, representing a shift in short-term momentum within the broader trend.
• Auto Fibonacci (exit) — Determines objective profit-taking levels based on the 0.0 and -61.8 extension levels of the current price swing.
• Root Mean Squared Error (RMSE) (risk) — Acts as a secondary filter; entries are only permitted when RMSE is below its own trailing average, suggesting a 'smooth' trend with low prediction error.
• TMA Risk Panel (volatility_filter) — Calculates dynamic lot sizing and stop-loss placement based on ATR volatility to normalize risk across different assets.

Known failure conditions

• Hypothesis is invalidated if the strategy experiences a drawdown exceeding 20% during a sustained trending period (suggesting lag is too high).
• Invalidated if price repeatedly hits Fibonacci 23.6% retracements before reversing, bypassing the 0.0 target.

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.