Advanced Smoothers

Advanced Smoothers is an indicator library consisting of cutting edge smoothers and smoother-related indicators. A smoother is an indicator that can be applied to source data to produce a smoothed version. These indicators go beyond the traditional Simple Moving Average (SMA), Exponential Moving Average (EMA), and the other standard smoothers. They deliver outstanding smoothing as well as low lag. The indicator library includes:

Smoothers

• AdaptiveLaguerre (Adaptive Laguerre by John Ehlers) - An adaptive Laguerre smoothing filter based on John Ehlers’ work, dynamically adjusting its smoothing factor according to recent price change to reduce lag while maintaining strong noise suppression. The result behaves like an adaptive moving average that responds quickly during trends and smooths aggressively during consolidation.
• ALMA (Armand Legoux Moving Average) - A Gaussian-weighted moving average designed to minimize lag while maintaining smoothness. The weighting curve is shifted forward using an offset to emphasize recent prices, and an optional percentage filter can suppress minor fluctuations for additional noise reduction.
• Butterworth - A two/three-pole Butterworth filter to the source series, providing smooth price filtering with minimal phase distortion. This digital signal–processing filter attenuates high-frequency noise while preserving the underlying trend more effectively than simple moving averages.
• DEMA (Double Exponential Moving Average) - A low-lag smoothing technique that reduces the delay of traditional moving averages by combining a single EMA with a second EMA of that EMA. The result is a smoother yet more responsive trend line, well suited for tracking price direction with reduced noise.
• ELF (Ehlers Roofing Filter) - An Ehlers band-pass filter that removes long-term trend with a high-pass filter and short-term noise with a SuperSmoother, isolating the dominant market cycle.
• GD (Generalized DEMA) - Extends the Double Exponential Moving Average by introducing a volume factor that adjusts the weighting between the first and second EMAs, allowing finer control over responsiveness versus smoothness. Higher values increase responsiveness with less lag, while lower values emphasize smoothing.
• Hampel - A robust, non–look-ahead smoothing filter that removes outliers by comparing each value to the median of a trailing window.
• HMA (Hull Moving Average) - A low-lag smoother designed to track price more closely than traditional moving averages by combining weighted moving averages and a square-root period adjustment. The result is a smooth yet highly responsive trend line.
• InstantaneousTrendLine2 (by John Ehlers) - A filter designed to extract the dominant trend while minimizing lag and suppressing high-frequency noise. The filter adapts smoothly to price movement, producing a responsive trend line that reacts faster than traditional moving averages while remaining stable.
• Kalman - A filter that estimates the current value by combining recent price levels with their rate of change, producing a responsive yet stable trend line. The filter adapts dynamically to price movement, helping reduce noise while anticipating short-term direction changes.
• KAMA (Kaufman Adaptive Moving Average) - Dynamically adjusts its smoothing factor based on market efficiency to reduce whipsaws during consolidation and respond more quickly during strong trends. The average becomes smoother in noisy markets and more responsive when price movement is directional.
• T3 (Tim Tilson's T3 indicator) - A filter that epeatedly applies the Generalized DEMA to produce an extremely smooth, low-lag moving average. The volume factor controls the balance between smoothness and responsiveness, making T3 well suited for trend identification in noisy markets.
• TEMA (Triple Exponential Moving Average) - A low-lag smoothing technique that further reduces delay by combining three exponential moving averages. The result is a smoother and more responsive trend line than traditional EMAs, well suited for tracking price direction with minimal noise.
• VIDYA (Volatility Index Dynamic Average) - An adaptive moving average that adjusts its responsiveness based on market conditions. By using the Efficiency Ratio to gauge price movement quality, the average becomes more responsive during trending periods and smoother during consolidation.
• VWMA (Volume Weighted Moving Average) - A volume-weighted moving average, where each price value is weighted by its corresponding trading volume, giving greater influence to periods of higher activity. This helps emphasize price levels where participation is strongest and can improve trend confirmation compared to simple moving averages.
• WLMA (Wealth-Lab Moving Average) - An adaptive moving average whose effective length is determined dynamically from recent price swing structure using the Adaptive Lookback. The average automatically shortens during fast market rhythms and lengthens during slower conditions, producing a market-driven trend line that adapts to changing behavior.
• ZLEMA (Zero-Lag EMA) - A zero-lag exponential moving average that compensates for EMA delay by removing a lagged price component, producing a more responsive trend line.

Smoother Related

• AdaptiveLookback - Determines an adaptive lookback period by analyzing recent price swing structure, allowing indicators to automatically adjust their effective length based on current market rhythm. The lookback is derived from the spacing between swing highs and lows, with optional fast or precise swing detection and a parameterless mode for fully adaptive behavior.
• TTI (Trend Thrust Index) - Measures trend thrust using volume-weighted price dynamics by enhancing the spread between fast and slow VWMAs with a volume-based multiplier. The indicator emphasizes moves where price trend and volume expansion align, helping identify strong, conviction-backed trends.