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3.8 KiB
3.8 KiB
Trends
Trend indicators help identify the direction and strength of a market trend. Moving averages are the most common type of trend indicator, smoothing out price data to create a clearer picture of the underlying direction.
| Indicator | Full Name | Description |
|---|---|---|
| ALMA | Arnaud Legoux MA | Uses Gaussian distribution weights to balance smoothness and responsiveness. |
| BESSEL | Bessel Filter | |
| BILATERAL | Bilateral Filter | |
| BLMA | Blackman Window MA | |
| BPF | Ehlers Bandpass Filter | |
| BUTTER | Butterworth Filter | |
| BWMA | Bessel-Weighted MA | |
| CHEBY1 | Chebyshev Type I Filter | |
| CHEBY2 | Chebyshev Type II Filter | |
| CONV | Convolution Indicator | Applies a custom kernel (weights) to the data window. |
| DEMA | Double Exponential Moving Average | Reduces lag by placing more weight on recent data than a standard EMA. |
| DSMA | Deviation-Scaled MA | |
| DWMA | Double Weighted MA | Applies WMA smoothing twice to reduce noise further. |
| ELLIPTIC | Elliptic (Cauer) Filter | |
| EMA | Exponential Moving Average | Weighted average giving more importance to recent price data. |
| EPMA | Endpoint MA | |
| FRAMA | Fractal Adaptive MA | |
| GAUSS | Gaussian Filter | |
| GWMA | Gaussian-Weighted MA | |
| HAMMA | Hamming Window MA | |
| HANN | Hann FIR Filter | |
| HANMA | Hanning Window MA | |
| HEMA | Hull Exponential MA | |
| HMA | Hull Moving Average | Developed by Alan Hull to reduce lag while improving smoothing. |
| HP | Hodrick-Prescott Filter | |
| HPF | Ehlers Highpass Filter | |
| HTIT | Hilbert Transform Instantaneous Trend | |
| HWMA | Holt Weighted MA | |
| JMA | Jurik MA | |
| KAMA | Kaufman Adaptive MA | Adapts to market volatility by adjusting its smoothing factor based on an Efficiency Ratio. |
| KF | Kalman Filter | |
| LOESS | LOESS/LOWESS Smoothing | |
| LSMA | Least Squares MA | Calculates the linear regression line for a specified period. |
| LTMA | Linear Trend MA | |
| MAMA | MESA Adaptive MA | Adapts to market cycles using Hilbert Transform phase measurement. |
| MEDIAN | Median Filter | |
| MGDI | McGinley Dynamic Indicator | |
| MMA | Modified MA | |
| NOTCH | Notch Filter | |
| PWMA | Pascal Weighted MA | |
| QEMA | Quadruple Exponential MA | |
| REMA | Regularized Exponential MA | |
| RGMA | Recursive Gaussian MA | |
| RMA | wildeR MA (SMMA, MMA) | Exponential moving average with alpha = 1/N. |
| SGF | Savitzky-Golay Filter | |
| SGMA | Savitzky-Golay MA | |
| SINEMA | Sine-weighted MA | |
| SMA | Simple Moving Average | The unweighted mean of the previous n data. |
| SSF | Ehlers Super Smooth Filter | |
| T3 | Tillson T3 Moving Average | A smooth moving average that uses a smoothing factor to reduce lag. |
| TEMA | Triple Exponential Moving Average | Designed to smooth price fluctuations and filter out volatility. |
| TRIMA | Triangular Moving Average | A double-smoothed SMA that gives more weight to the middle of the data window. |
| USF | Ehlers Ultrasmooth Filter | |
| VAMA | Volatility Adjusted MA | |
| VIDYA | Variable Index Dynamic Average | Adapts smoothing based on volatility using the Chande Momentum Oscillator (CMO). |
| WIENER | Wiener Filter | |
| WMA | Weighted Moving Average | Assigns a heavier weighting to more current data points since they are more relevant. |
| YZVAMA | Yang-Zhang Volatility Adjusted MA | |
| ZLDEMA | Zero-Lag Double Exponential MA | |
| ZLEMA | Zero-Lag Exponential MA | |
| ZLTEMA | Zero-Lag Triple Exponential MA |