Wavelets Suite
See what traditional indicators miss
Decompose price action into distinct timescales using mathematically rigorous wavelet transforms. Powered by VectorWave, the Wavelets Suite brings institutional-grade multi-resolution analysis to your trading, revealing structure that traditional indicators simply cannot see.
Learn how wavelet indicators improve trend and cycle analysis →
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Standard
All studies included
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- Multi-scale analysis studies
- Trend & momentum studies
- Cycle detection studies
- Volatility studies
- Email support
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Studies + Signals
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- Everything in Standard
- Signal generation for all studies
- MotiveWave alerts integration
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- Automated trading strategies
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5 Professional Studies
Wavelet Analysis
Multi-scale decomposition
View 7 independent frequency bands (D1-D7) simultaneously using the MODWT transform with multiple wavelet families including Daubechies, Symlets, Coiflets, and Haar.
- • Intelligent denoising with BayesShrink and Universal thresholding
- • Auto-window sizing based on wavelet mathematical properties
- • Identify which timescale is driving current price action
Wavelet Trend Strength
Quantify trend quality, not just direction
Know whether a trend is worth trading by measuring the ratio of directional energy to market noise. Returns a normalized 0-1 score using percentile ranking across your lookback window.
- • Returns-based analysis eliminates price level bias
- • Multi-scale noise measurement considers chop at all frequencies
- • Signals: STRONG_TREND, WEAK_TREND, TREND_IMPROVING, TREND_DETERIORATING
Wavelet Cycle Detector
Identify turning points before they're obvious
Detect potential cycle highs and lows using wavelet zero-crossings with multi-level confluence scoring. Combines D3 (8-16 bars), D4 (16-32 bars), and D5 (32-64 bars) for high-probability signals.
- • Temporal alignment — crossings within configurable window count as aligned
- • Cycle length estimation tracks average period between turning points
- • Signals: CYCLE_HIGH, CYCLE_LOW, STRONG_HIGH, STRONG_LOW
Wavelet ATR (H-WATR)
Smarter volatility for better stops
A hybrid ATR that blends baseline magnitude with regime variance. Adapts faster to volatility changes while remaining stable for position sizing. Three profiles: Balanced, Magnitude Focused, and Regime Focused.
- • Built-in Moving Average, Bollinger Bands, and Percentile context
- • Detect volatility compression before breakouts ("coiling")
- • Signals: VOLATILITY_RISING, VOLATILITY_FALLING, SPIKE, COLLAPSE, EXTREME_HIGH, EXTREME_LOW
Wavelet ATR Bands
Dynamic volatility channels on price
Volatility-based price bands using the same hybrid H-WATR algorithm. Upper and lower bands at configurable multiples of WATR provide visual support/resistance and stop placement guides.
- • Same three volatility profiles as Wavelet ATR
- • Band width adapts to market regime automatically
- • Price overlay — see volatility context directly on chart
Technical Highlights
Real-Time Performance
O(1) live bar updates using streaming MODWT technology. Sub-millisecond updates during live trading while traditional implementations recalculate the entire transform on every tick.
Mathematically Sound
Built on MODWT — a shift-invariant transform that maintains data length at each decomposition level. No downsampling means no information loss. Crossings align with actual price turns.
Signal Generation
All studies generate actionable signals for automated alerts. Crossover-based detection prevents signal spam. Full integration with MotiveWave's signal and alert system.
Professional Integration
Native MotiveWave integration with consistent settings interface across all studies. Exportable values for strategy backtesting. Configurable visual appearance.
Why Wavelets?
Traditional indicators treat all price movements equally. Wavelets decompose price into distinct frequency bands, letting you:
- • See structure at multiple timescales simultaneously — without changing chart timeframes
- • Separate signal from noise mathematically — not with arbitrary smoothing
- • Detect regime changes faster — wavelets adapt to non-stationary data
- • Avoid lag — MODWT is shift-invariant; crossings align with actual price turns