Gaussian Kernel Smoothing EMAGaussian Kernel Smoothing EMA
The Gaussian Kernel Smoothing EMA integrates the exponential moving average with kernel smoothing techniques to refine the trend tool. Kernel smoothing is a non-parametric technique used to estimate a smooth curve from a set of data points. It is particularly useful in reducing noise and capturing the underlying structure of data. The smoothed value at each point is calculated as a weighted average of neighboring points, with the weights determined by a kernel function.
The Gaussian kernel is a popular choice in kernel smoothing due to its properties of being smooth, symmetric, and having infinite support. This function gives higher weights to data points closer to the target point and lower weights to those further away, resulting in a smooth and continuous estimate. Since price isn't normally distributed a logarithmic transformation is performed to remove most of its skewness to be able to fit the Gaussian kernel.
This indicator also has a bandwidth, which in kernel smoothing controls the width of the window over which the smoothing is performed. It determines how much influence nearby data points have on the smoothed value. In this indicator, the bandwidth is dynamically adjusted based on the standard deviation of the log-transformed prices so that the smoothing adapts to the underlying variability and potential volatility.
Bandwidth Factor: The bandwidth factor in this indicator is used to adjust the degree of the smoothing applied to the MA. In kernel smoothing, Bandwidth controls the width of the window over which the smoothing is applied. It determines how many data points around a central point are considered when calculating a smooth value. A smaller bandwidth results in less smoothing, while a larger bandwidth smooths out more noise, leading to a broader, more general trend.

# Smoothedmovingaverage

SMA Cross Dashboard | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Simple Moving Average (SMA) Cross Dashboard! This dashboard let's you select a source for the calculation of the SMA of it, then let's you enter 2 lengths for up to 5 timeframes, plotting their crosses in the chart.
Features of the new SMA Cross Dashboard :
Shows SMA Crosses Across Up To 5 Different Timeframes.
Select Any Source, Including Other Indicators.
Customizable Dashboard.
📌 HOW DOES IT WORK ?
SMA is a widely used indicator within trading community, it simply works by taking the mathematical average of a source by desired length. Crosses of SMA lines can be helpful to determine strong bullish & bearish movements of an asset. This indicator shows finds crosses across 5 different timeframes in a dashboard and plots them in your chart for ease of use.
🚩UNIQUENESS
This dashboard cuts through the hassle of manual SMA cross calculations and plotting. It offers flexibility by allowing various data sources (even custom indicators) and customization through enabling / disabling individual timeframes. The clear visualization lets you see SMA crosses efficiently.
⚙️SETTINGS
1. Timeframes
You can set up to 5 timeframes & 2 lenghts to detect crosses for each timeframe here. You can also enable / disable them.
2. General Configuration
SMA Source -> You can select the source for the calculation of the SMA here. You can select sources from other indicators as well as more general sources like close, high and low price.

AIR Vortex ADXThis project started as an effort to improve the user interface of the hybrid indicator ADX of Vortex, which is, as per the name, a blend of ADX and Vortex Indicator. Plotting both indicators on the same polarity and normalising the vortex, a better interpretation of the interaction between the two is possible, and trend becomes apparent.
Basically, the Vortex provides the bright punch and ADX the continuation of the trend and momentum.
A range mixer has been added to the vortex, comprising both true and interpercentile ranges (see my previous script for a desrciption of interpercentile range). Users can activate and add amounts of each as they see fit.
Finally, there is an RSI filter, the idea of which is to filter out ranging (flat) markets, where no distinct direction is yet emerging.

Higher TimeFrame Smooth Moving AveragesScript is designed for those who dislike how plotting a moving average from a higher timeframe on a lower timeframe chart results in a choppy zigzag line when using the standard request.security(syminfo.ticker,"x",ta.sma(src,len)) method.
My more elegant solution was to translate the chart's current timeframe, and the selected higher timeframe into seconds, then check if selected timeframe is Larger than chart timeframe, but not so large that too many bars would be necessary. Then the quotient is calculated by dividing the chosen timeframe (value in seconds) by the chart's timeframe (value in seconds).
Then take that quotient and multiply it by the chosen length. This gives us how many bars of the chart's timeframe would be used in calculating the higher timeframe Moving Average
Use the value to calculate a moving average of choice (SMA,EMA,WMA,LRC,DEMA,TEMA,TRIMA,FRAMA) thanks to @TradingView 's ta library () and @alexgrover 's () for their functions supporting series as length, making this possible.
Basically, get how many of the current chart's bars are in the higher timeframe moving average and use that as the length for calculation using chart's timeframe.
If the higher timeframe relative is too large relative to chart's timeframe, due to bar referencing limits some combinations may not be possible under current limitations, but most will work by either moving chart's timeframe higher or higher timeframe lower assuming you aren't trying to do something too extreme like plotting a weekly moving average onto a 30 second chart etc.

VHF Adaptive Linear Regression KAMAIntroduction
Heyo, in this indicator I decided to add VHF adaptivness, linear regression and smoothing to a KAMA in order to squeeze all out of it.
KAMA:
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
VHF:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Linear Regression Curve:
A line that best fits the prices specified over a user-defined time period.
This is very good to eliminate bad crosses of KAMA and the pric.
Usage
You can use this indicator on every timeframe I think. I mostly tested it on 1 min, 5 min and 15 min.
Signals
Enter Long -> crossover(close, kama) and crossover(kama, kama )
Enter Short -> crossunder(close, kama) and crossunder(kama, kama )
Thanks for checking this out!
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Credits to
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@LucF – Gradient

Multiple Moving AveragesThis script plots up to five Moving Averages , either Simple or Exponential (9, 20, 50, 100 and 200 days period by default).