Indicator
paigep.llc - SuperMASuperMA is a multi-layered moving-average and candle-coloring system that combines SMA, EMA, and optional HMA logic to help traders visualize trend shifts, pullbacks, and momentum changes in a clean, structured way.
The script includes multiple modules: trend-based moving averages, pullback signals, exit logic, and an optional HMA cross engine.
📌 Core Features
1. Full SMA + EMA Framework
The indicator plots multiple moving averages (8, 9, 13, 20, 50, 200) using both SMA and EMA calculations. Each line automatically colors bullish or bearish based on its relationship to the 200-period baseline.Users can toggle SMAs and EMAs independently for clearer chart control.
2. Main Trend Entry & Exit Logic (8×200 and 8×20)
Built-in crossover logic detects:
Main Entry: SMA 8 crossing above/below EMA 200
Main Exit: SMA 8 and SMA 20 cross (with an option to choose which SMA is treated as the “fast” leg)
A “first exit only” option allows the script to ignore additional exit signals until a new trend regime begins.
3. Pullback Module (20 SMA Interaction)
Pullback entries and exits occur when price crosses the 20 SMA during existing trend conditions.
This includes:
Pullback entries through the 20 SMA
Pullback exits back across the 20 SMA
Labels and candle colors are available for all pullback events.
4. Optional HMA Cross Module
A separate module allows traders to use two Hull Moving Averages (HMA) with customizable:
Lengths
Independent timeframes
Line colors
Cross-based entries and exits
This module has its own events, labels, and optional candle coloring.
5. Advanced Candle Coloring System
Candle coloring is layered in priority order, based on:
Main trend entries
Main exits
HMA entries
HMA exits
Pullback entries
Pullback exits
Trend-only candles (based on SMA 8 relative to EMA 200)
Users may also independently color wicks and borders.
6. Configurable Alerts (Fully Decoupled from Visuals)
Alerts are available for all major events, including:
Main Entries (8×200)
Main Exits (8×20)
Pullback Entries and Exits
HMA Entries and Exits
Bull or Bear Trend candles
Any colored candle event
Alerts can fire on bar close only or intrabar, depending on user preference.
📌 Use Cases
SuperMA helps traders visualize:
Trend direction using SMA/EMA structure
Momentum shifts through HMA crosses
Pullback zones around the 20 SMA
Early regime transitions based on the 8×200 relationship
Candle-level context through color-coded bars
The indicator works across all markets and timeframes.
⚠️ Note
This tool is for visual and analytical assistance only. It does not guarantee future performance and should be combined with additional analysis and risk management. Indicator
EMA/RMA clouds by AlpachinoRE-UPLOAD
The indicator is designed for faster trend determination and also provides hints about whether the trend is strong, weaker, or if a range is expected.
It consists of an exponential moving average (EMA) and a slower smoothed moving average (RMA). I chose these because EMA is the fastest and is respected by the market, while I discovered through practice that the market often respects RMA, and in some cases, even more than EMA. Their combination is necessary because I want to take advantage of the best qualities of both averages. Displaying averages based solely on the close values creates a simple line that the market might respect. However, this is often not the case. Market makers know that many traders still believe in the theory that closing above/below an EMA signals a valid new trend. They commonly apply this belief to EMA200. Traders think that if the market closes below EMA, it signals a downtrend. That’s not necessarily true. This misconception often traps inexperienced traders.
For this reason, my indicator does not include a separate line.
I use what are called envelopes. In other words, for both EMA and RMA, the calculation uses the high and low of the selected period, which can be chosen as an input in the indicator.
Why did I choose high and low?
To stabilize price fluctuations as much as possible, especially to allow enough space for the price to react to the moving average. This reaction occurs precisely between the high and low.
Modes:
EMA Cloud – This is the most common envelope in terms of averages. It shows the best reactions with a period of 50.
What should you observe: the alignment of the envelope or its slope.
Usage:
Breakouts through the entire envelope tend to be strong, which signals that the trend may change. However, what interests you most is that the first test of the envelope after a breakout is the most successful entry point for trades in the breakout direction.
In an uptrend, the first support will be the high of the envelope, and the second (let’s call it the "ultimate support") will be the low of the envelope.
If, during an uptrend, the market closes below the low, be cautious, as the trend may reverse.
If the envelope is broken, trade the retest of the envelope.
In general, if the price is above the envelope, focus on long trades; if it’s below the envelope, focus on short trades.
Double Cloud – Since we already know that highs and lows are more relevant for price respect, I utilized this in the double cloud. Here, I use calculations for EMA and RMA highs and EMA and RMA lows.
The core idea is that since the price often reacts more to RMA than EMA, I wanted to eliminate attempts by market makers to lure you into incorrect directions. By creating more space for the price to react to the highs or lows, I made the cloud fill the area between EMA and RMA highs. This serves as the last zone where the price can hold. If the price breaks above this high cloud during a return, this doesn’t happen randomly—you should pay attention, as it’s likely signaling a range or a trend change.
The same applies to the low cloud for EMA and RMA.
The advantage of the double cloud is that you can see two clouds that may move sideways. This can resemble two walls—and they really act as such.
Usage:
Let’s say we have a downtrend. The market seems to be experiencing a downtrend exhaustion. Here's the behavior you might observe:
The price returns to the EMA/RMA low; the first reaction may still have some strength, but each subsequent return will move higher and higher into the cloud with increasingly smaller rejections downward. This indicates the absorption of selling pressure by bullish pressure. Eventually, the price may close above the cloud, significantly disrupting the downtrend and potentially signaling a reversal.
A confirmation of the reversal is usually seen with a retest of the cloud and a bounce upward into an uptrend.
The second scenario, which you’ll often see, involves sharp and significant moves through both envelopes. This kind of move is the strongest signal of a trend change. However, do not jump into trades immediately—wait for the first retest, which is usually successful. Additional tests may not work, as the breakout might not signify a trend change but rather a range.
When the clouds are far apart, it signals a weak trend or that the market is in a range. You will see that this is generally true. When the clouds cross or overlap, their initial point of contact signals the start of a stronger trend. The steeper the slope, the stronger the trend.
Indicator
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. Indicator
Indicator
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. Indicator
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 @PulseWire '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.
Indicator
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
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