ACF-SML Persistence Hybrid

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

Thesis

The strategy operates on the hypothesis that markets transition between random walks and persistent trends. By using the Autocorrelation Function (ACF) to isolate periods of positive serial correlation, we enter trades only when momentum is statistically likely to continue. We enter at structural pivot clusters (SML) confirmed by institutional walls (Q-Levels), assuming these levels represent major liquidity blocks where big players defend positions. The zero-lag MCNMA serves as a rapid exit to mitigate the 'fat-tail' risk of sudden regime shifts back to mean reversion.

Components

Autocorrelation Function (ACF) (regime) — Identifies trend persistence; only trades when the series shows positive serial correlation (momentum) rather than mean reversion.
ProfitRobots Dashboard Template (direction) — Aggregates trend direction across multiple timeframes to ensure trade alignment with broader market flow.
SML — SUPPORT/RESISTANCE MATRIX (entry) — Pinpoints entry locations at high-confluence pivot zones where structural liquidity is likely to reside.
McNicholl EMA (MCNMA) (exit) — Acts as a zero-lag trend follower to provide a fast exit signal before profit erosion occurs during a reversal.
Q-Levels V2.2 (risk) — Uses external institutional data (Gamma walls/Expected moves) to define hard stops and calculate risk-based position sizing.

Known failure conditions

ACF remains below 0 for extended periods, indicating a permanent shift to high-noise/mean-reversion regime.
Price consistently ignores Q-Level institutional walls, suggesting the external data source is stale or inaccurate.
MCNMA triggers premature exits in high-volatility whipsaw environments without price reaching the SL.

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