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.

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