Q-Level Intensity Cycler

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

Thesis

Institutional participation is most visible when volume accumulation (III) aligns with price breakouts from consolidation boxes. By filtering these moves through high-interest structural levels (Q-Levels) and a zero-lag cyclic momentum filter (CG), we can enter trends at points of maximum liquidity and momentum, using consolidated price ranges (Boxline) as a reliable structural invalidation point.

Components

• Sherif Hilo (regime) — Establishes the primary trend bias by looking at long-term price extremes to avoid counter-trend trades.
• Intraday Intensity Index (III) (direction) — Confirms that the price movement is backed by institutional volume distribution/accumulation within the daily range.
• Q-Levels V2.2 (entry) — Provides the tactical location for entry based on external market structure data (Gamma walls or high-interest levels).
• Pivot Points (Classic) (exit) — Provides objective, mathematical targets for profit taking that are independent of the entry indicators.
• Boxline (Dynamic Range Breakout) (risk) — Defines the structural 'safe zone' for stop loss placement based on the most recent price consolidation.
• Ehlers Center of Gravity (CG) (confirmation) — Acts as a zero-lag oscillator to time the entry once the price reaches a Q-Level within a trending regime.
• TMA Risk Panel (volatility_filter) — Ensures consistent position sizing across varying volatility regimes using ATR-based math.

Known failure conditions

• Price ignores Q-Levels consistently, suggesting the external data source is stale or inaccurate.
• CG Oscillator remains pinned to extremes for extended periods during trend runs, causing missed entries.
• Whipsaw in Sherif Hilo regime indicates a total loss of trend structure (sideways market).

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.