gavindiaz/QuanTAlib
1
0
Fork 0
mirror of https://github.com/mihakralj/QuanTAlib.git synced 2026-08-25 05:48:06 +00:00
Code Issues Packages Projects Releases Wiki Activity

feat: Enhance volume indicators with ADOSC and SSF implementation and validation

Browse Source
This commit is contained in:
Miha Kralj
2025-12-20 15:08:07 -08:00
parent 5549c7329a
commit d21fea3c18
85 changed files with 5144 additions and 3954 deletions
Download Patch File Download Diff File Expand all files Collapse all files
lib/trends/_index.md
+29 -24
View File
@@ -1,11 +1,17 @@
# Trends
# Trend Indicators
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.
> "The trend is your friend, until it bends at the end and liquidates your position."
Trend indicators are the bread and butter of technical analysis—and often just as stale. They attempt to smooth out the chaotic noise of market data to reveal the underlying direction. Most fail, introducing so much lag that by the time they signal "buy," the smart money is already shorting.
We don't do "laggy" here. We do mathematically rigorous, zero-allocation smoothing that respects the physics of market momentum.
## The Collection
| Indicator | Full Name | Description |
| :--- | :--- | :--- |
| ALLIGATOR | Williams Alligator | |
| [ALMA](alma/Alma.md) | Arnaud Legoux MA | Uses Gaussian distribution weights to balance smoothness and responsiveness. |
| [ALMA](alma/Alma.md) | Arnaud Legoux MA | Gaussian distribution weights for the perfect balance of smoothness and responsiveness. |
| AMAT | Archer Moving Averages Trends | |
| BESSEL | Bessel Filter | |
| BILATERAL | Bilateral Filter | |
@@ -15,13 +21,12 @@ Trend indicators help identify the direction and strength of a market trend. Mov
| BWMA | Bessel-Weighted MA | |
| CHEBY1 | Chebyshev Type I Filter | |
| CHEBY2 | Chebyshev Type II Filter | |
| [CONV](conv/Conv.md) | Convolution MA with any kernel | Applies a custom kernel (weights) to the data window. |
| [CONV](conv/Conv.md) | Convolution MA | Applies a custom kernel to the data window. For the signal processing purists. |
| [DEMA](dema/Dema.md) | Double Exponential MA | Reduces lag by placing more weight on recent data than a standard EMA. |
| DSMA | Deviation-Scaled MA | |
| [DWMA](dwma/Dwma.md) | Double Weighted MA | Applies WMA smoothing twice to reduce noise further. |
| [DWMA](dwma/Dwma.md) | Double Weighted MA | Applies WMA smoothing twice. Because sometimes once isn't enough. |
| ELLIPTIC | Elliptic (Cauer) Filter | |
| [EMA](ema/Ema.md) | Exponential MA | Weighted average giving more importance to recent price data. |
| EPMA | Endpoint MA | |
| [EMA](ema/Ema.md) | Exponential MA | The classic. Weighted average giving more importance to recent price data. |
| FRAMA | Ehlers Fractal Adaptive MA | |
| GAUSS | Gaussian Filter | |
| GWMA | Gaussian-Weighted MA | |
@@ -29,43 +34,43 @@ Trend indicators help identify the direction and strength of a market trend. Mov
| HANMA | Hanning Window MA | |
| HANN | Hann FIR Filter | |
| HEMA | Hull Exponential MA | |
| [HMA](hma/Hma.md) | Hull MA | Developed by Alan Hull to reduce lag while improving smoothing. |
| [HMA](hma/Hma.md) | Hull MA | Alan Hull's attempt to eliminate lag using weighted averages of weighted averages. |
| HP | Hodrick-Prescott Filter | |
| HPF | Ehlers Highpass Filter | |
| [HTIT](htit/Htit.md) | Ehlers Hilbert Transform Instantaneous Trend | Uses Hilbert Transform to measure the dominant cycle period and compute an instantaneous trendline. |
| [HTIT](htit/Htit.md) | Hilbert Transform Instantaneous Trend | Uses the Hilbert Transform to extract the dominant cycle and compute the trend. |
| HT_TRENDMODE | Ehlers Hilbert Transform Trend Mode | |
| HWMA | Holt Weighted MA | |
| ICHIMOKU | Ichimoku Cloud | |
| [JMA](jma/Jma.md) | Jurik Moving Average | Adaptive moving average that adjusts to market volatility for superior smoothing with minimal lag. |
| [KAMA](kama/Kama.md) | Kaufman Adaptive MA | Adapts to market volatility by adjusting its smoothing factor based on an Efficiency Ratio. |
| [JMA](jma/Jma.md) | Jurik Moving Average | The gold standard of adaptive smoothing. Minimal lag, maximum noise reduction. |
| [KAMA](kama/Kama.md) | Kaufman Adaptive MA | Adapts smoothing based on an Efficiency Ratio. Smart, but moody. |
| KF | Kalman Filter | |
| LOESS | LOESS/LOWESS Smoothing | |
| [LSMA](lsma/Lsma.md) | Least Squares Moving Average | Calculates the linear regression line for a specified period. |
| [LSMA](lsma/Lsma.md) | Least Squares MA | Calculates the linear regression line for every point. Computationally expensive, visually satisfying. |
| LTMA | Linear Trend MA | |
| [MAMA](mama/Mama.md) | Ehlers MESA Adaptive MA | Adapts to market cycles using Hilbert Transform phase measurement. |
| [MGDI](mgdi/Mgdi.md) | McGinley Dynamic Indicator | A moving average that adjusts for shifts in market speed to minimize lag and whipsaws. |
| [MAMA](mama/Mama.md) | MESA Adaptive MA | John Ehlers' masterpiece. Adapts to market cycles using phase measurement. |
| [MGDI](mgdi/Mgdi.md) | McGinley Dynamic | A moving average that adjusts for shifts in market speed to minimize lag and whipsaws. |
| MMA | Modified MA | |
| NOTCH | Notch Filter | |
| [PWMA](pwma/Pwma.md) | Parabolic Weighted MA | Uses parabolic weighting ($i^2$) to give more weight to recent data. |
| [PWMA](pwma/Pwma.md) | Parabolic Weighted MA | Uses parabolic weighting ($i^2$) to aggressively favor recent data. |
| QEMA | Quadruple Exponential MA | |
| REMA | Regularized Exponential MA | |
| RGMA | Recursive Gaussian MA | |
| [RMA](rma/Rma.md) | wildeR MA (SMMA, MMA) | Exponential moving average with alpha = 1/N. |
| [RMA](rma/Rma.md) | wildeR MA | The "Wilder" moving average. An EMA with $\alpha = 1/N$. Simple, robust, slow. |
| SGF | Savitzky-Golay Filter | |
| SGMA | Savitzky-Golay MA | |
| SINEMA | Sine-weighted MA | |
| [SMA](sma/Sma.md) | Simple MA | The unweighted mean of the previous n data. |
| SSF | Ehlers Super Smooth Filter | |
| [SUPER](super/Super.md) | SuperTrend | Trend-following indicator using ATR to define upper and lower bands acting as a trailing stop. |
| [T3](t3/T3.md) | Tillson T3 MA | A smooth moving average that uses a smoothing factor to reduce lag. |
| [TEMA](tema/Tema.md) | Triple Exponential MA | Designed to smooth price fluctuations and filter out volatility. |
| [TRIMA](trima/Trima.md) | Triangular MA | A double-smoothed SMA that gives more weight to the middle of the data window. |
| [SMA](sma/Sma.md) | Simple MA | The unweighted mean. The vanilla ice cream of indicators. |
| [SSF](ssf/Ssf.md) | Ehlers Super Smooth Filter | A 2-pole Butterworth filter using complex conjugate poles for maximal flatness in the passband. |
| [SUPER](super/Super.md) | SuperTrend | Trend-following indicator using ATR bands as a trailing stop. |
| [T3](t3/T3.md) | Tillson T3 MA | Tim Tillson's smooth operator. Uses a smoothing factor to reduce lag. |
| [TEMA](tema/Tema.md) | Triple Exponential MA | Three EMAs in a trench coat, trying to cancel out lag. |
| [TRIMA](trima/Trima.md) | Triangular MA | A double-smoothed SMA. Heavily weighted towards the center. Very smooth, very laggy. |
| TTM | TTM Trend | |
| USF | Ehlers Ultrasmooth Filter | |
| VAMA | Volatility Adjusted Moving Average | |
| [VIDYA](vidya/Vidya.md) | Variable Index Dynamic Average | Adapts smoothing based on volatility using the Chande Momentum Oscillator (CMO). |
| [VIDYA](vidya/Vidya.md) | Variable Index Dynamic Average | Chande's adaptive average using the CMO for volatility adjustments. |
| WIENER | Wiener Filter | |
| [WMA](wma/Wma.md) | Weighted MA | Assigns a heavier weighting to more current data points since they are more relevant. |
| [WMA](wma/Wma.md) | Weighted MA | Linear weighting. More relevant than SMA, less neurotic than EMA. |
| YZVAMA | Yang-Zhang Volatility Adjusted MA | |
| ZLDEMA | Zero-Lag Double Exponential MA | |
| ZLEMA | Zero-Lag Exponential MA | |