Fractal-Neural Future-Bias Hybrid

Family: hybrid · Regime: trending · Complexity: high · Asset classes: FX, Equities, Crypto · Timeframes: H1, H4

Thesis

Fractal breakouts are often noise; by filtering them through a neural-network classified momentum (NNind) and a volatility-adjusted trend band (QQE), we can isolate 'true' structural shifts, using a theoretical future-offset regime (negative history) to identify the maximum possible edge.

Components

• Unsupported named const comparison ternary negative history (regime) — Provides a 'perfect' hypothetical trend filter by accessing future price data (close[-1]), serving as a benchmark for ideal regime identification.
• Qualitative Quantitative Estimation (QQE) (direction) — Filters entries based on smoothed RSI momentum and volatility-adjusted trailing bands to ensure the trend has sufficient strength.
• Support and Resistance (Fractal-based) (entry) — Acts as the concrete trigger; trades are only executed when price breaches established Bill Williams fractals (market structure breaks).
• SML — SUPPORT/RESISTANCE MATRIX (exit) — Identifies high-density pivot clusters to provide dynamic profit-taking zones rather than fixed targets.
• Average True Range (NNFX Version) (risk) — Standardizes risk by setting stops based on current volatility cycles rather than point-fixed values.
• Neural Network Indicator (NNind) (confirmation) — A multi-layer perceptron classifies price action (CLV) to confirm that the internal structure of the candle cluster supports the momentum.

Known failure conditions

• Hypothesis fails if the 'future leak' in the regime filter is replaced with a standard lagging MA and the resulting Alpha decay is >50%.
• Failure in low-volatility regimes where ATR remains compressed, causing frequent stop-outs on minor noise.
• NNind weight stagnation: if the pre-trained weights fail to categorize price action in a new macro environment (e.g., transition from low to high interest rates).

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