Linear Cycle Convergence Strategy

Family: trend_following · Regime: trending · Complexity: high · Asset classes: Equities, Crypto, Forex · Timeframes: H1, H4, D1

Thesis

The strategy's edge is based on the hypothesis that profitable trend-following opportunities occur when market noise (measured by Standard Error) is low and momentum is synchronized with the underlying cyclical frequency of the asset. By filtering out 'Random Walk' price movements using the RWI, we ensure that the trend has statistical persistence. The Bullish Pivot Detector provides a structural 'trigger' that verifies the trend's resumption from a local pullback, allowing for tight, objective risk management.

Components

• Standard Error of Regression (STDERR) (regime) — Acts as the regime filter; low STDERR values indicate price is tightly hugging a linear trend, reducing the probability of 'noise' triggers.
• Smoothed Adaptive Momentum (direction) — Provides the primary directional bias by adapting to the market's dominant cycle, ensuring momentum is measured relative to current volatility.
• Ichimoku Kinko Hyo (entry) — Used as a structural entry trigger; ensures price is in a confirmed breakout above the Kumo (cloud) with momentum support (Tenkan > Kijun).
• Stochastic Oscillator (exit) — Provides a mean-reversion exit signal to capture profits before the adaptive cycle (SAM) fully rolls over.
• Bullish Pivot Detector (risk) — Handles specific entry confirmation and dynamic risk placement (Stop Loss) based on local price structure.
• Random Walk Index (RWI BTF) (volatility_filter) — Filters out 'random walk' price action; entry only occurs when the RWI High is significantly above 1.0, indicating persistent trending behavior.

Known failure conditions

• The strategy is invalidated if the Homodyne Discriminator fails to converge on a dominant cycle (SAM stays near zero).
• Invalidated if the Bullish Pivot Detector produces more than 3 consecutive 'liquidity_grab' (marker_dead) signals in a single cloud-trend.
• High-frequency whipsaws in the Stochastic %K indicate a regime shift from trending to noisy ranging where linear regression (STDERR) fails.

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