Wavelet-MESA Cycle Breakout Strategy

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

Thesis

Market inefficiency exists in the overlap between macro trend regimes and micro cyclic corrections. By using Discrete Wavelet Transforms to isolate the macro trend and Ehlers MESA Stochastic to time the re-entry after noise-filtered corrections, we can enter high-probability breakouts confirmed by multi-factor momentum dashboards. The edge is maintained by using volatility-smoothed (Heiken Ashi) risk parameters and equilibrium-based (Ichimoku) exits.

Components

• Discrete Wavelet Transform (DWT) (regime) — Used as a regime filter to isolate the 'trend' approximation from high-frequency noise using the stationary wavelet transform.
• Unsupported ta.macd Fast Length (Test Case) (direction) — Provides a micro-momentum bias. Despite the float-length semantic error, it targets ultra-fast convergence/divergence detection.
• Ehlers MESA Stochastic (MSTOCH) (entry) — The primary timing mechanism. Uses a Roofing Filter to remove spectral dilation, allowing for cleaner cycle entries than standard Stochastics.
• Ichimoku Kinko Hyo (exit) — The Kijun-sen (base line) serves as a dynamic exit and trailing stop, representing the 26-period equilibrium price.
• ATR Heiken Ashi (risk) — Uses smoothed Heiken Ashi volatility to set stop-loss distances, reducing the impact of raw price spikes on SL placement.
• GFRMa Pivot HTF3 Dashboard (volatility_filter) — Acts as a multi-factor volatility filter ensuring the market is in a breakout state (Price > HSL Highs) with RSI confirmation.

Known failure conditions

• Persistent whipsaw in the DWT trend component due to Haar 'boxiness'.
• Execution failure if the platform cannot resolve the float-based MACD length.
• Total loss of cycle-periodicity in the asset, rendering Ehlers filters ineffective.

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