Wavelet-Bandpass Trend Pulse

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

Thesis

Market trends are not linear but composed of cyclical pulses. By using a Discrete Wavelet Transform to isolate signal energy and a Bandpass Filter to time the entry of these pulses, we can enter a trend at the moment of maximum cyclical momentum, provided transaction costs (spread) do not exceed the signal's expected amplitude.

Components

• Bill Williams Alligator (regime) — Acts as the primary regime filter to ensure the market is in an 'active' state (lines fanned out) rather than 'sleeping' (lines intertwined).
• MultiKAMA (TyphooN) (direction) — Provides the directional bias. Its adaptive nature filters out minor price noise while responding to true trend shifts faster than static averages.
• Bandpass Filter (BPF) (entry) — Isolates cyclical momentum within a specific frequency band, providing precise entry triggers when the cycle aligns with the trend.
• Average True Range (ATR) (exit) — Used to calculate volatility-adjusted stop losses and take profit targets to ensure exits are proportional to current market range.
• Spread Display and Alert (risk) — Serves as a hard cost-filter; entries are blocked if the bid-ask spread exceeds a percentage of the ATR, preventing cost-erosion.
• Discrete Wavelet Transform (DWT) (volatility_filter) — Used as a volatility/noise filter. It decomposes the price to ensure the signal-to-noise ratio is high enough (energy in the detail coefficients) to trade.

Known failure conditions

• The market enters a prolonged 'sawtooth' regime where volatility is high but net displacement is zero.
• Realized spreads during high-impact news events consistently exceed the ATR-based profit targets.
• The dominant market cycle shifts outside of the 10-20 period Bandpass window, causing the BPF to lead or lag significantly.

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