Fractional Momentum Diagnostic Trend

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

Thesis

Market transitions from consolidation to trend are often preceded by micro-momentum bursts that are too fast for standard indicators to detect. By using a fractional MACD period (1.5), we can identify these 'pre-breakout' shifts. When confirmed by a volatility-based entry (UTBot) and a data-integrity check (Diagnostic tool), we enter the trend early and use accelerating stops (PSAR) to capture the meat of the move while protecting against sudden reversals.

Components

• Indicator Sample Logging Diagnostic (regime) — Acts as a data-integrity filter; ensures the strategy only executes when the market state is fully synchronized and new bar data is logged.
• Unsupported ta.macd Fast Length (Test Case) (direction) — Uses an ultra-short (fractional) fast EMA to detect micro-momentum shifts that occur before standard integer-based MACD signals.
• UTBot Alerts (entry) — Provides the specific execution trigger based on price crossing an ATR-calculated trailing stop, confirming volatility-backed moves.
• MACD (exit) — Used for trend exhaustion; the signal line crossover provides a mathematically smoothed exit point compared to the entry trigger.
• Parabolic SAR (risk) — Provides dynamic trailing stops and defines the risk-distance for position sizing.

Known failure conditions

• If the fractional MACD (1.5) is rounded to 1 or 2 by the execution engine, the micro-momentum edge is lost.
• Persistent logging errors in the Diagnostic indicator suggest unstable data feeds which invalidate the regime filter.
• Market conditions where price oscillates strictly between the UTBot signal and PSAR dots, causing constant SL hits.

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).