Relative Strength VWAP Reversion Strategy
Family: hybrid · Regime: trending · Complexity: medium · Asset classes: Forex · Timeframes: H1, M15
Thesis
This strategy assumes that significant currency trends are driven by aggregate strength across all pairs of a specific currency, and that prices within these trends periodically revert to institutional fair value (VWAP). By filtering for environments where a linear model (RAE) outperforms the mean and adjusting risk based on logarithmic variance (MSLE), the strategy aims to capture high-probability pullbacks while avoiding erratic market noise.
Components
- SuperTrend MT5 (FxGeek) (regime) — Defines the macro trend regime and provides a volatility-adjusted baseline for price action.
- Currency Strength Template (direction) — Ensures the trade is supported by aggregate currency strength rather than idiosyncratic pair noise.
- VWAP (entry) — Provides an institutional fair-value entry point, capturing pullbacks within the established regime.
- MultiKAMA (TyphooN) (exit) — Acts as an adaptive trailing stop or exit trigger that tightens during high efficiency and widens during noise.
- Mean Squared Logarithmic Error (MSLE) (risk) — Determines position sizing and risk by measuring the variance of log returns relative to the mean; high MSLE suggests unstable price action requiring smaller sizes.
- Relative Absolute Error (RAE) (volatility_filter) — Filters out environments where the price is effectively a random walk (RAE > 1.0) to prevent trading in non-trending noise.
Known failure conditions
- Persistent RAE > 1.2 indicating price is more volatile than a simple mean-reversion model can handle.
- Base/Quote currency strength convergence where both lines oscillate around zero, indicating lack of market leadership.
- SuperTrend and MultiKAMA provide conflicting signals for more than 5 consecutive bars in a high-volatility environment.
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.