Linear Regression Confluence & Cyclic Balance Strategy

Family: trend_following · Regime: trending · Complexity: high · Asset classes: Forex, Equities · Timeframes: H1, D1

Thesis

Stable trends are characterized by a high degree of linearity (low STDERR) and directional consensus across multiple technical dimensions (IAE). By entering only when price exhibits low regression dispersion and exiting when the balance of cyclic momentum (CG) shifts, a trader can capture the meat of a trend while avoiding the 'noise' of high-volatility range contractions. The edge relies on the behavioral tendency of markets to persist in a linear path once technical and fundamental (currency strength) confluence is achieved.

Components

• Standard Error of Regression (STDERR) (regime) — Defines the regime: low STDERR indicates a stable, high-confidence linear trend, while high STDERR indicates breakdown or excessive noise.
• Frankenstein Ultimate Pro - ATM Logic (direction) — Determines core directionality by aligning TEMA timing with LSMA trend and ATR-based volatility expansion.
• IAE — Technical Confluence Score (entry) — Acts as a high-conviction trigger; requires a multi-layer technical consensus (>70 score) to validate entry.
• Ehlers Center of Gravity (CG) (exit) — Identifies the balance point of the current cycle to time exits before momentum exhaustion.
• Q-Levels V2.2 (risk) — Provides structural price levels for hard stop-loss placement and risk-adjusted position sizing based on distance to key gamma/SR levels.
• Currency Strength Matrix (All) (confirmation) — Confirms that the underlying asset strength is supported by broader currency flow (e.g., Long USD/JPY requires USD > JPY in the matrix).
• Vertical Horizontal Filter (VHF) (volatility_filter) — Filters out choppy/congestion phases where linear regression and trend-following signals typically fail.

Known failure conditions

• Hypothesis fails if VHF remains above 0.4 but price action stays within a 1-ATR range for >10 periods (false breakout regime).
• Fails if the correlation between LSMA and price breaks down during high-volatility macro events.

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