Matrix-Validated S&D Mean Reversion

Family: mean_reversion · Regime: ranging · Complexity: high · Asset classes: FX, Crypto, Equities · Timeframes: H1, H4, D1

Thesis

The hypothesis is that price exhibits structural memory at supply/demand zones, and mean reversion from these zones can be timed using momentum oscillators if and only if the underlying price-change matrix maintains stochastic properties (validating a stable probabilistic regime). Risk can be anchored to the most recent local price extrema (fractals) to maximize R:R.

Components

• Matrix Predicate Property Tester (regime) — Used as a regime filter to ensure computational integrity and stability; specifically, it ensures the market matrix is non-zero/stochastic before allowing signal execution.
• Supply & Demand Zones (NNFX) (direction) — Provides the directional bias; we only look for longs at demand zones and shorts at supply zones.
• Stochastic Oscillator (ta.stoch) (entry) — Acts as the timing trigger, identifying local exhaustion (oversold/overbought) within the established S&D zones.
• UTBot Alerts (exit) — Serves as the trend-exhaustion exit mechanism, using its ATR-based trailing stop to lock in profits or exit when the micro-trend reverses.
• FractalScanner (risk) — Defines the structural risk anchor; stop losses are placed at the most recent fractal high/low.

Known failure conditions

• Price consistently ignores identified Supply/Demand zones due to high fundamental volatility.
• The Stochastic Oscillator remains 'embedded' in oversold/overbought territories during strong parabolic trends.
• ATR levels in UTBot become so narrow that spread/slippage costs exceed the average winning trade.

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