Recursive-ML Volatility Force Trader

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

Thesis

This strategy hypothesizes that significant trend moves are preceded by synchronized expansions in volatility (BBForce) and filtered by non-linear machine learning parameters (ML SuperTrend). By using a recursive moving average (RMAU), we can enter trends after noise is smoothed out, while using Fractal Chaos Bands to define the structural boundary of market 'noise' for risk placement. The 'Supported Map' provides a historical anchor to ensure price has progressed beyond previous localized distribution levels.

Components

Known failure conditions

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


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