QuanTAlib/lib/trends_IIR/_index.md

Trends (IIR)

"Recursion trades memory for computation. Single coefficient replaces entire window. But feedback loop carries risk: instability lurks in coefficient choices that FIR designers never face."

Trend indicators based on Infinite Impulse Response (IIR) filters. Recursive architecture uses previous outputs to compute current values, enabling lower lag with fewer coefficients than equivalent FIR filters.

Indicator	Full Name	Description
DECYCLER	Ehlers Decycler	Ehlers Decycler — complementary HP filter that subtracts high-frequency components from price.
DEMA	Double Exponential MA	Reduces lag by applying double exponential smoothing, enhancing responsiveness while maintaining signal quality.
DSMA	Deviation-Scaled MA	Adaptive IIR filter that adjusts smoothing factor based on market volatility, increasing responsiveness during high-deviation periods.
EMA	Exponential MA	Applies exponentially decreasing weights to price data, balancing responsiveness and stability.
FRAMA	Ehlers Fractal Adaptive Moving Average	Adapts smoothing based on fractal dimension analysis, minimizing lag in trends and maximizing smoothing in consolidation.
HEMA	Hull Exponential MA	EMA-domain Hull analog using half-life timing and de-lagged EMA cascade.
HTIT	Ehlers Hilbert Transform Instantaneous Trend	Utilizes Hilbert Transform to isolate instantaneous trend component, providing zero-lag trendline with hybrid FIR-in-IIR design.
JMA	Jurik MA	Adaptive filter achieving high noise reduction and low phase delay through multi-stage volatility normalization and dynamic parameter optimization.
KAMA	Kaufman Adaptive MA	Automatically adjusts sensitivity based on market volatility using Efficiency Ratio, balancing responsiveness and stability.
MAMA	Ehlers MESA Adaptive Moving Average	Applies Hilbert Transform for phase-based adaptation, using dual-line system (MAMA/FAMA) for cycle-sensitive smoothing.
MGDI	McGinley Dynamic Indicator	Adjusts speed based on market volatility using dynamic factor, aiming to hug prices closely.
MMA	Modified MA	Combines simple and weighted components, emphasizing central values for balanced smoothing.
QEMA	Quad Exponential MA	Zero-lag filter with four cascaded EMAs using geometrically ramped alphas and minimum-energy weights for DC lag elimination.
REMA	Regularized Exponential MA	Applies regularization to EMA using lambda parameter, balancing smoothing and momentum-based prediction.
RGMA	Recursive Gaussian MA	Approximates Gaussian smoothing by recursively applying EMA filters multiple times (passes), controlled by adjusted period.
RMA	wildeR MA	Wilder's smoothing average using specific alpha (1/period), designed for indicators like RSI and ATR.
T3	Tillson T3 MA	Six-stage EMA cascade with optimized coefficients based on volume factor for reduced lag and superior noise reduction.
TEMA	Triple Exponential MA	Triple-cascade EMA architecture with optimized coefficients (3, -3, 1) for further lag reduction compared to DEMA.
VAMA	Volatility Adjusted MA	Dynamically adjusts moving average length based on ATR volatility ratio, shortening during high volatility and lengthening during low volatility.
VIDYA	Variable Index Dynamic Average	Adjusts smoothing factor based on market volatility using Volatility Index (ratio of short-term to long-term standard deviation).
YZVAMA	Yang-Zhang Volatility Adjusted MA	Adjusts MA length based on percentile rank of short-term YZV, providing context-aware volatility adaptation for gap-prone markets.
ZLDEMA	Zero-Lag Double Exponential MA	Combines zero-lag preprocessing with dual EMA cascade (DEMA) for faster response than DEMA with moderate smoothing.
ZLEMA	Zero-Lag Exponential MA	Reduces lag by estimating future price based on current momentum, using dynamically calculated lag period.
ZLTEMA	Zero-Lag Triple Exponential MA	Combines zero-lag preprocessing with triple EMA cascade (TEMA) for maximum smoothness with minimal lag.