Alxuse Supertrend 4EMA Buy and Sell for tutorialAll abilities of Supertrend, moreover :
Drawing 4 EMA band & the ability to change values, change colors, turn on/off show.
Sends Signal Sell and Buy in multi timeframe.
The ability used in the alert section and create customized alerts.
To receive valid alerts the replay section , the timeframe of the chart must be the same as the timeframe of the indicator.
Supertrend with a simple EMA Filter can improve the performance of the signals during a strong trend.
For detecting the continuation of the downward and upward trend we can use 4 EMA colors.
In the upward trend , the EMA lines are in order of green, blue, red, yellow from bottom to top.
In the downward trend, the EMA lines are in order of yellow, red, blue, green from bottom to top.
How it works:
x1 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA3, MA4)
x2 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA2, MA3)
x3 = MA1 < MA2 and MA2 < MA3 and MA3 < MA4 and ta.crossunder(MA1, MA2)
y1 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA3, MA4)
y2 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA2, MA3)
y3 = MA4 < MA3 and MA3 < MA2 and MA2 < MA1 and ta.crossover(MA1, MA2)
Red triangle = x1 or x2 or x3
Green triangle = y1 or y2 or y3
Long = BUY signal and followed by a Green triangle
Exit Long = SELL signal
Short = SELL signal and followed by a Red triangle
Exit Short = BUY signal
It is also possible to get help from the Stochastic RSI and MACD indicators for confirmation.
For receiving a signal with these two conditions or more conditions, i am making a video tutorial that I will release soon.
Supertrend
Definition
Supertrend is a trend-following indicator based on Average True Range (ATR). The calculation of its single line combines trend detection and volatility. It can be used to detect changes in trend direction and to position stops.
The basics
The Supertrend is a trend-following indicator. It is overlaid on the main chart and their plots indicate the current trend. A Supertrend can be used with varying periods (daily, weekly, intraday etc.) and on varying instruments.
The Supertrend has several inputs that you can adjust to match your trading strategy. Adjusting these settings allows you to make the indicator more or less sensitive to price changes.
For the Supertrend inputs, you can adjust atrLength and multiplier:
the atrLength setting is the lookback length for the ATR calculation;
multiplier is what the ATR is multiplied by to offset the bands from price.
When the price falls below the indicator curve, it turns red and indicates a downtrend. Conversely, when the price rises above the curve, the indicator turns green and indicates an uptrend. After each close above or below Supertrend, a new trend appears.
Summary
The Supertrend helps you make the right trading decisions. However, there are times when it generates false signals. Therefore, it is best to use the right combination of several indicators. Like any other indicator, Supertrend works best when used with other indicators such as MACD, Parabolic SAR, or RSI.
Exponential Moving Average
Definition
The Exponential Moving Average (EMA) is a specific type of moving average that points towards the importance of the most recent data and information from the market. The Exponential Moving Average is just like it’s name says - it’s exponential, weighting the most recent prices more than the less recent prices. The EMA can be compared and contrasted with the simple moving average.
Similar to other moving averages, the EMA is a technical indicator that produces buy and sell signals based on data that shows evidence of divergence and crossovers from general and historical averages. Additionally, the EMA tries to amplify the importance that the most recent data points play in a calculation.
It is common to use more than one EMA length at once, to provide more in-depth and focused data. For example, by choosing 10-day and 200-day moving averages, a trader is able to determine more from the results in a long-term trade, than a trader who is only analyzing one EMA length.
It’s best to use the EMA when for trending markets, as it shows uptrends and downtrends when a market is strong and weak, respectively. An experienced trader will know to look both at the line the EMA projects, as well as the rate of change that comes from each bar as it moves to the next data point. Analyzing these points and data streams correctly will help the trader determine when they should buy, sell, or switch investments from bearish to bullish or vice versa.
Short-term averages, on the other hand, is a different story when analyzing Exponential Moving Average data. It is most common for traders to quote and utilize 12- and 26-day EMAs in the short-term. This is because they are used to create specific indicators. Look into Moving Average Convergence Divergence (MACD) for more information. Similarly, the 50- and 200-day moving averages are most common for analyzing long-term trends.
Moving averages can be very useful for traders using technical analysis for profit. It is important to identify and realize, however, their shortcomings, as all moving averages tend to suffer from recurring lag. It is difficult to modify the moving average to work in your favor at times, often having the preferred time to enter or exit the market pass before the moving average even shows changes in the trend or price movement for that matter.
All of this is true, however, the EMA strives to make this easier for traders. The EMA is unique because it places more emphasis on the most recent data. Therefore, price movement and trend reversals or changes are closely monitored, allowing for the EMA to react quicker than other moving averages.
Limitations
Although using the Exponential Moving Average has a lot of advantages when analyzing market trends, it is also uncertain whether or not the use of most recent data points truly affects technical and market analysis. In addition, the EMA relies on historical data as its basis for operating and because news, events, and other information can change rapidly the indicator can misinterpret this information by weighting the current prices higher than when the event actually occurred.
Summary
The Exponential Moving Average (EMA) is a moving average and technical indicator that reflects and projects the most recent data and information from the market to a trader and relies on a base of historical data. It is one of many different types of moving averages and has an easily calculable formula.
The added features to the indicator are made for training, it is advisable to use it with caution in tradings.
Cerca negli script per "averages"
Adaptive MA-Bollinger HistogramVisualize two of your favorite moving averages in a fun new way.
This script calculates the distance (or difference) between the price and two moving averages of your choosing and then creates two histograms.
The two histograms are plotted inversely, so if the price is over both moving averages, one will be positive above the centerline while the other still positive will be below the centerline.
(In a future update you will have the option to have them both positive at the same time)
Next, what it does is apply Bollinger Bands (optional) to each of the histograms.
This creates a very interesting effect that can highlight areas of interest you may miss with other indicators.
You have plenty of options for coloring, the type of moving average, Bollinger Band length, and toggling features on and off.
Give it a few minutes of your time to study, and see what information you can learn from watching this indicator by comparing it with the chart.
Here is a full user guide:
Adaptive MA-Bollinger Histogram Indicator User Guide
Welcome to the user guide for the **Adaptive MA-Bollinger Histogram** indicator. This custom indicator is designed to help traders analyze trends and potential reversals in a financial instrument's price movements. The indicator combines two Moving Averages (MA) and Bollinger Bands to provide valuable insights into market conditions.
### Indicator Overview
The Adaptive MA-Bollinger Histogram indicator comprises the following components:
1. **Moving Averages (MA1 and MA2):** The indicator uses two moving averages, namely MA1 and MA2, to track different time periods. MA1 has a user-defined length (default: 50) and MA2 has a longer user-defined length (default: 100). These moving averages can be calculated using different methods such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Volume Weighted Moving Average (VWMA), or Smoothed Moving Average (RMA).
2. **Histograms:** The indicator displays histograms based on the differences between the price source and the respective moving averages. Positive values of the histogram for MA1 are plotted in one color (default: green), while negative values are plotted in another color (default: red). Similarly, positive values of the histogram for MA2 are plotted in one color (default: blue), while negative values are plotted in another color (default: yellow). It's important to note that the histogram for MA1 is plotted positively, while the histogram for MA2 is plotted inversely.
3. **Bollinger Bands:** The indicator also features Bollinger Bands calculated based on the differences between the price source and the respective moving averages (dist1 and dist2). Bollinger Bands consist of three lines: the middle band, upper band, and lower band. These bands help visualize the potential volatility and overbought/oversold levels of the instrument's price.
### Understanding the Indicator
- **Histograms:** The histograms highlight the divergence between the price and the two moving averages. When the histogram for MA1 is positive, it indicates that the price is above the MA1. Conversely, when the histogram for MA1 is negative, it suggests that the price is below the MA1. Similarly, the histogram for MA2 is plotted inversely.
- **Bollinger Bands:** The Bollinger Bands consist of three lines. The middle band represents the moving average (MA1 or MA2), while the upper and lower bands are calculated based on the standard deviation of the differences between the price source and the moving average. The bands expand during periods of higher volatility and contract during periods of lower volatility.
### Possible Trading Ideas
1. **Trend Confirmation:** When the histograms for both MA1 and MA2 are consistently positive, it may indicate a strong bullish trend. Conversely, when both histograms are consistently negative, it may suggest a strong bearish trend.
2. **Divergence:** Divergence between price and the histograms could signal potential reversals. For example, if the price is making new highs while the histogram is declining, it might indicate a bearish divergence and a possible upcoming trend reversal.
3. **Bollinger Bands Squeeze:** A narrowing of the Bollinger Bands indicates lower volatility and often precedes a significant price movement. Traders might consider a potential breakout trade when the bands start to expand again.
4. **Overbought/Oversold Levels:** Prices touching or exceeding the upper Bollinger Band could suggest overbought conditions, while prices touching or falling below the lower Bollinger Band could indicate oversold conditions. Traders might look for reversals or corrections in such scenarios.
### Customization
- You can adjust the parameters such as MA lengths, Bollinger Bands length, width, and colors to suit your preferences and trading strategy.
### Conclusion
The **Adaptive MA-Bollinger Histogram** indicator provides a comprehensive view of price trends, divergences, and potential reversal points. Traders can use the information from this indicator to make informed decisions in their trading strategies. However, like any technical tool, it's recommended to combine this indicator with other forms of analysis and risk management techniques for optimal results.
Universal MA Trend(Republishing in Open source)
Hello traders,
Many existing moving average indicators have not been satisfactory in terms of the number, types, and length adjustments of moving averages.
Feeling the inconvenience, I created a moving average indicator and collected numerous famous moving averages.
Fortunately, there was a PineCoder "andre_007" who had already compiled various Moving Averages,
so I was able to find a new Moving Average and combine it with the indicator. Here is the link below
Among these, for the JMA, which has not been publicly disclosed, I utilized the source code from TradingView Wizard everget:
For VIDYA, I also used everget's source code:
And also MAMA / FAMA Coded from Pinescript Wizard everget :
Ehlers MESA Adaptive Moving Averages (MAMA & FAMA)
For Frama, I used the code from nemozny's source code :
Thanks to all these Pinecoders.
---
By using these excellent moving averages together, I found that the simultaneous Up/Down changes of various moving averages with different characteristics tend to be maintained for quite a long time.
Therefore, this indicator not only collects various moving averages but also displays areas with simultaneous trends as background.
An example can be found here:
Furthermore, to prevent the up/down changes of the moving averages due to factors like whipsaws, a smoothing filter has been introduced.
And Also, Alert is able when trend changes.
---
(오픈소스화 후 재발행)
안녕하세요 트레이더여러분.
기존의 이동평균선 지표들은, 이동평균선의 갯수, 종류, 길이조절 등에서 만족스럽지 못한 점들이 많았습니다.
불편함을 느끼고 직접 이동평균선 지표를 만들면서, 유명한 수 많은 이동평균선들을 모았습니다.
그리고 이미 이러한 수많은 이동평균선을 손수 모아서 정리해주신 고마우신 파인코더(andere_007 님)가 있어서, 그 분의 코드를 많이 이용했습니다. 링크는 아래와 같습니다.
이 중 소스가 공개되지 않은 이동평균선 중 JMA는 트레이딩뷰 위자드이신 everget의 소스코드를 이용했습니다.
VIDYA 역시 everget의 소스코드를 이용했습니다.
MAMA와 FAMA의 코드 역시 everget님의 코드를 가져왔습니다.
Ehlers MESA Adaptive Moving Averages (MAMA & FAMA)
Frama는 nemozny님의 코드를 이용했습니다.
의 코드를 이용했습니다.
이 자리를 빌어 위의 파인코더님들께 감사의 말씀을 전합니다.
---
이러한 좋은 이동평균선을 모아서 사용해보니, 다양한 특성을 갖고 있는 이동평균선의 동시적인 Up/Down 변화는 꽤 오랫동안 유지된다는 점을 발견했습니다.
그래서 이 지표는, 위의 여러가지 이동평균선을 모아놓은 것 뿐만 아니라,
그것에서 동시적인 트랜드가 나오는 곳을 배경화면으로 표시해두었습니다.
예시는 다음과 같습니다.
나아가 휩쏘 등으로 이동평균선의 up/down이 바뀌는 것을 막고자, Smoothing 필터도 도입했습니다.
또한 트랜드가 바뀔 때 얼러트가 울리도록, 얼러트 기능을 설정해놓을 수 있게 해놓았으며, 현재 이동평균선과 상태를 보기 쉽도록 테이블을 만들어놓았습니다.
R-squared Adaptive T3 [Loxx]R-squared Adaptive T3 is an R-squared adaptive version of Tilson's T3 moving average. This adaptivity was originally proposed by mladen on various forex forums. This is considered experimental but shows how to use r-squared adapting methods to moving averages. In theory, the T3 is a six-pole non-linear Kalman filter.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis. Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD, Momentum, Relative Strength Index) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA (simple moving average) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA(n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA.
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE/2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE/2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE/2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA, popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE/2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA(3) has lag 1, and EMA(11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA(3) through itself 5 times than if I just take EMA(11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA(3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA(7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA(n) = EMA(n) + EMA(time series - EMA(n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA. The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA(n) + EMA(time series - EMA(n))*.7;
This is algebraically the same as:
EMA(n)*1.7-EMA(EMA(n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD(n,v) = EMA(n)*(1+v)-EMA(EMA(n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA, and when v=1, GD is DEMA. In between, GD is a cooler DEMA. By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD(GD(GD(n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA(n)) to correct themselves. In Technical Analysis, these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Overlay Indicators (EMAs, SMAs, Ichimoku & Bollinger Bands)This is a combination of popular overlay indicators that are used for dynamic support and resistance, trade targets and trend strength.
Included are:
-> 6 Exponential Moving Averages
-> 6 Simple Moving Averages
-> Ichimoku Cloud
-> Bollinger Bands
-> There is also a weekend background marker ideal for cryptocurrency trading
Using all these indicators in conjunction with each other provide great confluence and confidence in trades and price targets.
An explanation of each indicator is listed below.
What Is an Exponential Moving Average (EMA)?
"An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average (SMA), which applies an equal weight to all observations in the period.
What Does the Exponential Moving Average Tell You?
The 12- and 26-day exponential moving averages (EMAs) are often the most quoted and analyzed short-term averages. The 12- and 26-day are used to create indicators like the moving average convergence divergence (MACD) and the percentage price oscillator (PPO). In general, the 50- and 200-day EMAs are used as indicators for long-term trends. When a stock price crosses its 200-day moving average, it is a technical signal that a reversal has occurred.
Traders who employ technical analysis find moving averages very useful and insightful when applied correctly. However, they also realize that these signals can create havoc when used improperly or misinterpreted. All the moving averages commonly used in technical analysis are, by their very nature, lagging indicators."
Source: www.investopedia.com
Popular EMA lookback periods include fibonacci numbers and round numbers such as the 100 or 200. The default values of the EMAs in this indicator are the most widely used, specifically for cryptocurrency but they also work very well with traditional.
EMAs are normally used in conjunction with Simple Moving Averages.
" What Is Simple Moving Average (SMA)?
A simple moving average (SMA) calculates the average of a selected range of prices, usually closing prices, by the number of periods in that range.
Simple Moving Average vs. Exponential Moving Average
The major difference between an exponential moving average (EMA) and a simple moving average is the sensitivity each one shows to changes in the data used in its calculation. More specifically, the EMA gives a higher weighting to recent prices, while the SMA assigns an equal weighting to all values."
Source: www.investopedia.com
In this indicator, I've included 6 popular moving averages that are commonly used. Most traders will find specific settings for their own personal trading style.
Along with the EMA and SMA, another indicator that is good for finding confluence between these two is the Ichimoku Cloud.
" What is the Ichimoku Cloud?
The Ichimoku Cloud is a collection of technical indicators that show support and resistance levels, as well as momentum and trend direction. It does this by taking multiple averages and plotting them on the chart. It also uses these figures to compute a "cloud" which attempts to forecast where the price may find support or resistance in the future.
The Ichimoku cloud was developed by Goichi Hosoda, a Japanese journalist, and published in the late 1960s.1 It provides more data points than the standard candlestick chart. While it seems complicated at first glance, those familiar with how to read the charts often find it easy to understand with well-defined trading signals."
More info can be seen here: www.investopedia.com
I have changed the default settings on the Ichimoku to suit cryptocurrency trading (as cryptocurrency is usually fast and thus require slightly longer lookbacks) to 20 60 120 30.
Along with the Ichimoku, I like to use Bollinger Bands to not only find confluence for support and resistance but for price discovery targets and trend strength.
" What Is a Bollinger Band®?
A Bollinger Band® is a technical analysis tool defined by a set of trendlines plotted two standard deviations (positively and negatively) away from a simple moving average (SMA) of a security's price, but which can be adjusted to user preferences.
Bollinger Bands® were developed and copyrighted by famous technical trader John Bollinger, designed to discover opportunities that give investors a higher probability of properly identifying when an asset is oversold or overbought."
This article goes into great detail of the complexities of using the Bollinger band and how to use it.
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This indicator combines all these powerful indicators into one so that it is easier to input different settings, turn specific tools on or off and can be easily customised.
MACD Enhanced [DCAUT]█ MACD Enhanced
📊 ORIGINALITY & INNOVATION
The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently.
This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies.
The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms.
📐 MATHEMATICAL FOUNDATION
The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines.
Available Algorithms:
The implementation supports a comprehensive spectrum of technical analysis algorithms:
Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting)
Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive)
Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential)
Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility)
Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation)
Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing)
Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Histogram Interpretation:
Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities
Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities
Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift
Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration
Advanced Signal Recognition:
Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals
Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup
Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones
🎯 STRATEGIC APPLICATIONS
Comprehensive Trend Confirmation Strategies:
Primary Trend Validation Protocol:
Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line
Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening
Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation
Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend)
Entry Timing Techniques:
Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero
Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns
Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals
Advanced Divergence Trading Systems:
Regular Divergence Recognition:
Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals
Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management
Hidden Divergence Strategies:
Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks
Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies
Multi-Timeframe Coordination Framework:
Three-Timeframe Analysis Structure:
Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction
Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions
Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias
Timeframe Synchronization Rules:
Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions
Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions
Conflicting signals between timeframes = Wait for alignment or use smaller position sizes
1H MACD signals only valid when aligned with both higher timeframes
Algorithm Considerations by Market Type:
Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions
Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market
Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required
Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise
Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches.
📋 DETAILED PARAMETER CONFIGURATION
Comprehensive Source Selection Strategy:
Price Source Analysis and Optimization:
Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows
HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions
HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth
OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis
Parameter Configuration Principles:
Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness.
Length Parameter Considerations:
Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment
Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings
Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics
Comprehensive Algorithm Selection Framework:
MACD Line Algorithm Decision Matrix:
EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior
SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes
HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise
KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio
Signal Line Algorithm Optimization Strategies:
Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics
Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals
Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA)
Parameter Sensitivity Considerations:
Impact of Parameter Changes:
Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics
Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms
Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Response Characteristics by Algorithm:
Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise
Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability
Highest Stability: SMA, RMA, TMA - reduced noise but increased lag
Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions
Noise Filtering Capabilities:
Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings.
Market Condition Adaptability:
Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility.
Comparative Performance vs Traditional MACD:
Algorithm Flexibility: 21 algorithms vs 1 fixed EMA
Signal Quality: Reduced false signals through noise filtering algorithms
Market Adaptability: Optimizable for any market condition vs fixed behavior
Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching
Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
ANDROMEDA - TrendSyncANDROMEDA - TrendSync
Pedro Canto - Portfolio Manager | CGA/CGE
OVERVIEW
Trend Sync is a multi-layered trend-following indicator designed to help traders identify high-probability trend continuation setups while avoiding low-quality entries caused by overbought or oversold market conditions.
This indicator combines the power of Moving Averages (MA), MACD , and a visual RSI-based filter to validate both trend direction and timing for entries. It's goal is simple: filter out noise and highlight only the most technically relevant buy and sell signals based on objective momentum and trend criteria.
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WALKTHROUGH
This indicator is built for traders seeking to operate in the direction of established trends. It's core principle is to identify and validate current trend conditions, and then signal entry opportunities during pullbacks to key moving averages.
Trend identification is achieved through the alignment of two moving averages. When these MAs are crossed and angled in the same direction, they confirm that a trend is in progress. To double-confirm trend direction, the MACD histogram is used—only. When both the MAs and MACD are aligned in the same direction, then the trend is considered valid.
Once all trend criteria are met, a dynamic coloring system is activated to visually reinforce the trend across the candles and moving averages.
To avoid poor entries during market exhaustion, an RSI-based filter is used. This short-term RSI highlights overbought or oversold zones, helping traders filter trades in extreme price conditions.
Only when the trend is validated and price pulls back to one of the MAs will a buy/sell signal be triggered, aligning momentum, price action and timing into a single actionable setup.
This combination ensures that each component plays a specific role:
i) Moving Averages define the trend
ii) MACD validates it
iii) RSI filters noise
iv) Intrabar price action triggers entries
This synchronism helps improve decision-making and entry timing, especially for swing and intraday traders.
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USE CASES
- Identifying trend continuation setups
- Filtering false signals during consolidation phases
- Avoiding trades in overbought or oversold zones
- Enhancing entry timing for both swing and intraday strategies
- Providing visual confirmation of trend strength and momentum alignment
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KEY FEATURES
1. Dual Moving Average Setup
The indicator allows full customization of two moving averages (MA1 and MA2), supporting both EMA and SMA types. The slope of the longer MA (MA2) acts as an essential trend filter, ensuring signals are only generated when the market shows clear directional bias.
2. MACD Histogram Trend Confirmation
A classic MACD Histogram calculation is used to validate the momentum of the prevailing trend.
- Bullish Trend: Histogram > 0
- Bearish Trend: Histogram < 0
This step filters out counter-trend signals and ensures trades are aligned with momentum.
3. Intrabar Price Trigger
Unlike standard crossover systems, this indicator waits for intrabar price action to trigger entries:
- Buy Signal: Price crosses below one of the MAs during an uptrend (dip-buy logic)
- Sell Signal: Price crosses above one of the MAs during a downtrend (rally-sell logic)
This intrabar trigger improves entry timing and helps capture retracement-based opportunities.
4. RSI Visual Filter
A short-term RSI is plotted and color-coded to visually highlight overbought and oversold conditions, acting as a discretionary filter for users to avoid low-probability trades during exhaustion points.
5. Dynamic Coloring System
Bar Colors:
- Blue: Bullish trend
- Red: Bearish trend
- Orange: RSI Overbought/Oversold zones
MA Colors:
- Blue for bullish conditions
- Red for bearish conditions
- Gray for neutral/no-trend phases
6. Signal Markers and Alerts
Clear visual buy and sell markers are plotted directly on the chart.
Additionally, the indicator includes real-time alerts for both Buy and Sell signals, helping traders stay informed even when away from the screen.
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INPUTS AND CUSTOMIZATION OPTIONS
- Moving Average Types: EMA or SMA for both MA1 and MA2.
- MACD Settings: Customizable fast, slow, and signal periods.
- RSI Settings: Source, length, and overbought/oversold levels fully adjustable.
- Color Customization: Adjust RSI zone colors to suit your chart theme.
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DISCLAIMER
This indicator is a technical analysis tool designed for educational and informational purposes only. It should not be used as a standalone trading system. Always combine it with sound risk management, price action analysis, and, where applicable, fundamental context.
Past performance does not guarantee future results.
Super Arma Institucional PRO v6.3Super Arma Institucional PRO v6.3
Description
Super Arma Institucional PRO v6.3 is a multifunctional indicator designed for traders looking for a clear and objective analysis of the market, focusing on trends, key price levels and high liquidity zones. It combines three essential elements: moving averages (EMA 20, SMA 50, EMA 200), dynamic support and resistance, and volume-based liquidity zones. This integration offers an institutional view of the market, ideal for identifying strategic entry and exit points.
How it Works
Moving Averages:
EMA 20 (orange): Sensitive to short-term movements, ideal for capturing fast trends.
SMA 50 (blue): Represents the medium-term trend, smoothing out fluctuations.
EMA 200 (red): Indicates the long-term trend, used as a reference for the general market bias.
Support and Resistance: Calculated based on the highest and lowest prices over a defined period (default: 20 bars). These dynamic levels help identify zones where the price may encounter barriers or supports.
Liquidity Zones: Purple rectangles are drawn in areas of significantly above-average volume, indicating regions where large market participants (institutional) may be active. These zones are useful for anticipating price movements or order absorption.
Purpose
The indicator was developed to provide a clean and institutional view of the market, combining classic tools (moving averages and support/resistance) with modern liquidity analysis. It is ideal for traders operating swing trading or position trading strategies, allowing to identify:
Short, medium and long-term trends.
Key support and resistance levels to plan entries and exits.
High liquidity zones where institutional orders can influence the price.
Settings
Show EMA 20 (true): Enables/disables the 20-period EMA.
Show SMA 50 (true): Enables/disables the 50-period SMA.
Show EMA 200 (true): Enables/disables the 200-period EMA.
Support/Resistance Period (20): Sets the period for calculating support and resistance levels.
Liquidity Sensitivity (20): Period for calculating the average volume.
Minimum Liquidity Factor (1.5): Multiplier of the average volume to identify high liquidity zones.
How to Use
Moving Averages:
Crossovers between the EMA 20 and SMA 50 may indicate short/medium-term trend changes.
The EMA 200 serves as a reference for the long-term bias (above = bullish, below = bearish).
Support and Resistance: Use the red (resistance) and green (support) lines to identify reversal or consolidation zones.
Liquidity Zones: The purple rectangles highlight areas of high volume, where the price may react (reversal or breakout). Consider these zones to place orders or manage risks.
Adjust the parameters according to the asset and timeframe to optimize the analysis.
Notes
The chart should be configured only with this indicator to ensure clarity.
Use on timeframes such as 1 hour, 4 hours or daily for better visualization of liquidity zones and support/resistance levels.
Avoid adding other indicators to the chart to keep the script output easily identifiable.
The indicator is designed to be clean, without explicit buy/sell signals, following an institutional approach.
This indicator is perfect for traders who want a visually clear and powerful tool to trade based on trends, key levels and institutional behavior.
[blackcat] L3 Dynamic CrossOVERVIEW
The L3 Dynamic Cross indicator is a powerful tool designed to assist traders in identifying potential buy and sell opportunities through the use of dynamic moving averages. This versatile script offers a wide range of customizable options, allowing users to tailor the moving averages to their specific needs and preferences. By providing clear visual cues and generating precise crossover signals, it helps traders make informed decisions about market trends and potential entry/exit points 📈💹.
FEATURES
Multiple Moving Average Types:
Simple Moving Average (SMA): Provides a straightforward average of prices over a specified period.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it responsive to new information.
Weighted Moving Average (WMA): Assigns weights to all prices within the look-back period, giving more importance to recent prices.
Volume Weighted Moving Average (VWMA): Incorporates volume data to provide a more accurate representation of price movements.
Smoothed Moving Average (SMMA): Averages out fluctuations to create a smoother trend line.
Double Exponential Moving Average (DEMA): Reduces lag by applying two layers of exponential smoothing.
Triple Exponential Moving Average (TEMA): Further reduces lag with three layers of exponential smoothing.
Hull Moving Average (HullMA): Combines weighted moving averages to minimize lag and noise.
Super Smoother Moving Average (SSMA): Uses a sophisticated algorithm to smooth out price data while preserving trend direction.
Zero-Lag Exponential Moving Average (ZEMA): Eliminates lag entirely by adjusting the calculation method.
Triangular Moving Average (TMA): Applies a double smoothing process to reduce volatility and enhance trend identification.
Customizable Parameters:
Length: Adjust the period for both fast and slow moving averages to match your trading style.
Source: Select different price sources such as close, open, high, or low for more nuanced analysis.
Visual Representation:
Fast MA: Displayed as a green line representing shorter-term trends.
Slow MA: Shown as a red line indicating longer-term trends.
Crossover Signals:
Generate buy ('BUY') and sell ('SELL') labels based on crossover events between the fast and slow moving averages 🏷️.
Clear visual cues help traders quickly identify potential entry and exit points.
Alert Functionality:
Receive real-time notifications when crossover conditions are met, ensuring timely action 🔔.
Customizable alert messages for personalized trading strategies.
Advanced Trade Management:
Support for pyramiding levels allows traders to manage multiple positions effectively.
Fine-tune your risk management by setting the number of allowed trades per signal.
HOW TO USE
Adding the Indicator:
Open your TradingView chart and go to the indicators list.
Search for L3 Dynamic Cross and add it to your chart.
Configuring Settings:
Choose your desired Moving Average Type from the dropdown menu.
Adjust the Fast MA Length and Slow MA Length according to your trading timeframe.
Select appropriate Price Sources for both fast and slow moving averages.
Monitoring Signals:
Observe the plotted lines on the chart to track short-term and long-term trends.
Look for buy and sell labels that indicate potential trade opportunities.
Setting Up Alerts:
Enable alerts based on crossover conditions to receive instant notifications.
Customize alert messages to suit your trading plan.
Managing Positions:
Utilize the pyramiding feature to handle multiple entries and exits efficiently.
Keep track of your position sizes relative to the defined pyramiding levels.
Combining with Other Tools:
Integrate this indicator with other technical analysis tools for confirmation.
Use additional filters like volume, RSI, or MACD to enhance decision-making accuracy.
LIMITATIONS
Market Conditions: The effectiveness of the indicator may vary in highly volatile or sideways markets. Be cautious during periods of low liquidity or sudden price spikes 🌪️.
Parameter Sensitivity: Different moving average types and lengths can produce varying results. Experiment with settings to find what works best for your asset class and timeframe.
False Signals: Like any technical indicator, false signals can occur. Always confirm signals with other forms of analysis before executing trades.
NOTES
Historical Data: Ensure you have enough historical data loaded into your chart for accurate moving average calculations.
Backtesting: Thoroughly backtest the indicator on various assets and timeframes using demo accounts before deploying it in live trading environments 🔍.
Customization: Feel free to adjust colors, line widths, and label styles to better fit your chart aesthetics and personal preferences.
EXAMPLE STRATEGIES
Trend Following: Use the indicator to ride trends by entering positions when the fast MA crosses above/below the slow MA and exiting when the opposite occurs.
Mean Reversion: Identify overbought/oversold conditions by combining the indicator with oscillators like RSI or Stochastic. Enter counter-trend positions when the moving averages diverge significantly from the mean.
Scalping: Apply tight moving average settings to capture small, quick profits in intraday trading. Combine with volume indicators to filter out weak signals.
Advanced MVRV Trend AnalyzerThe "Advanced MVRV Trend Analyzer" is a sophisticated trading tool designed for the TradingView platform that enhances traditional Market Value to Realized Value (MVRV) analysis. It provides a multi-timeframe perspective of market valuation dynamics by comparing the current market price to the realized price across short-term, mid-term, and long-term cohorts. This indicator is particularly useful for cryptocurrency traders and investors who seek deeper insights into potential overvaluation or undervaluation conditions in the market.
Key Features
Multiple Timeframes:
Analyzes market conditions across three distinct timeframes: short-term (14 days), mid-term (50 days), and long-term (200 days).
Moving Averages: Includes moving averages for each MVRV ratio to smooth out short-term fluctuations and highlight longer-term trends.
Dynamic Thresholds: Provides dynamic color-coded backgrounds that highlight overvalued and undervalued market conditions based on predefined thresholds.
How to Use
Adding the Indicator:
Open your TradingView chart.
Click on "Indicators" at the top of your screen.
Search for "Advanced MVRV Trend Analyzer" and add it to your chart.
Interpreting the Indicator:
MVRV Lines: Each of the three MVRV lines (short-term, mid-term, long-term) reflects how much higher or lower the current market price is compared to the average price at which coins were last moved. A value above 1 indicates that the current price is higher than the realized price, suggesting overvaluation. Conversely, a value below 1 suggests undervaluation.
Moving Averages: The moving averages of the MVRV ratios help identify the underlying trend. If the MVRV line deviates significantly from its moving average, it might indicate a potential reversal or continuation of the current trend.
Color-coded Backgrounds:
Red background indicates an overvalued condition where the MVRV ratio exceeds 1.5, suggesting caution as the market may be overheated.
Green background indicates an undervalued condition where the MVRV ratio is below 0.5, potentially signaling a buying opportunity.
Trading Strategies:
Overvalued Zones: Consider taking profits or setting stop-loss orders when the indicator shows a prolonged red background, especially if supported by other bearish signals.
Undervalued Zones: Look for buying opportunities when the indicator shows a prolonged green background, especially if other bullish signals are present.
Combining with Other Indicators:
Enhance your analysis by combining the "Advanced MVRV Trend Analyzer" with other technical indicators such as RSI, MACD, or volume-based tools to confirm trends and signals.
Conclusion
The "Advanced MVRV Trend Analyzer" offers a nuanced view of market dynamics, providing traders with valuable insights into when a market may be approaching extremes. By utilizing this indicator, traders can better time their entries and exits, manage risk, and align their strategies with underlying market trends.
Multi-Sector Trend AnalysisThis script, titled "Multi-Sector Trend Analysis: Track Sector Momentum and Trends," is designed to assist traders and investors in monitoring multiple sectors of the stock market simultaneously. It leverages technical analysis by incorporating trend detection and momentum indicators like moving averages and the Relative Strength Index (RSI) to offer insights into the price action of various market sectors.
Core Features:
1. Sector-Based Analysis: The script covers 20 major sectors from the NSE (National Stock Exchange) such as Auto, Banking, Energy, FMCG, IT, Pharma, and others. Users can customize which sectors they wish to analyze using the available input fields.
Technical Indicators: The script uses two core technical indicators to detect trends and momentum:
2. Moving Averages: The script calculates both fast and slow exponential moving averages (EMAs). These are critical for identifying short- and long-term price trends and crossovers, helping detect shifts in momentum.
3. Relative Strength Index (RSI): A well-known momentum indicator that shows whether a stock is overbought or oversold. This script uses a 14-period RSI to gauge the strength of each sector.
4. Trend Detection: The script identifies whether the current market trend is "Up" or "Down" based on the relationship between the fast and slow EMAs (i.e., whether the fast EMA is above or below the slow EMA). It highlights this trend visually in a table format, allowing quick and easy trend recognition.
5. Gain/Loss Tracking: This feature calculates the percentage gain or loss since the last EMA crossover (a key point in trend change), giving users a sense of how much the price has moved since the trend shifted.
6. Customizable Table for Display: The script displays the analyzed data in a table format, where users can view each sector's:
Symbol
Trend (Up or Down)
RSI Value
Gain/Loss Since the Last EMA Crossover
This table is customizable in terms of size and color theme (dark or light), providing flexibility in presentation for different charting styles.
How It Works:
Sector Selection: Users can input up to 20 different sector symbols for analysis.
Moving Averages: Users can define the period lengths for both the fast and slow EMAs to suit their trading strategies.
Table Options: Choose between different table sizes and opt for a dark theme to enhance the visual appearance on charts.
How to Use:
Select the symbols (sectors) that you want to track. The script includes pre-configured symbols for major sectors on the NSE, but you can modify these to suit your needs.
Adjust the fast and slow EMA lengths to your preference. A common setting would be 3 for the fast EMA and 4 for the slow EMA, but more conservative traders might opt for higher values.
Customize the table size and theme based on your preference, whether you want a compact table or a larger one for easier readability.
Why Use This Script:
This script is ideal for traders looking to:
Monitor multiple market sectors simultaneously.
Identify key trends across sectors quickly.
Understand momentum and detect potential reversals through RSI and EMA crossovers.
Stay informed on sector performance using a clear visual table that tracks gains or losses.
By using this script, traders can gain better insights into sector-based trading strategies, improve their sector rotation tactics, and stay informed about the broader market environment. It provides a powerful yet easy-to-use tool for both beginner and advanced traders.
SMA/EMA/RSImagic 36.963 by IgorPlahutaTwo Elements in this script:
Alerts: These are notifications that draw your attention to specific market conditions. There are two types:
RSI Higher Lows or Lower Highs: This alert triggers when the Relative Strength Index (RSI) forms higher lows or lower highs.
RSI Exiting 30 (Up) or RSI Exiting 70 (Down): These alerts activate when the RSI crosses the 30 threshold upwards or the 70 threshold downwards.
ALL BUY/SELL: to catch both of them with one setting
To Set Up an Alert: To configure an alert, select the one relevant to your trading strategy, choose the "Greater than" option, and input a value of "0" (this essentially activates the alert). Adjust other settings as per your requirements.
Please note that these alerts should be used in conjunction with a system you trust for confirmation.
Moving Averages: This involves monitoring several moving averages:
SMA12, SMA20, EMA12, EMA20: These moving averages are highlighted with background colors to help you quickly identify changes or crossovers. They are superimposed on each other for easy comparison.
SMA 50, SMA200: These moving averages are also highlighted with background colors to spot crossovers, and their lines change color depending on their direction (falling in red or rising in green).
Enjoy using these tools in your trading endeavors!
[blackcat] L1 Mel Widner Rainbow ChartNOTE: Because the originally released script failed to comply with the House Rule in the description, it was banned. After revising and reviewing the description, it is republished again. Please forgive the inconvenience caused.
Level: 1
Background
The Rainbow Charts indicator is a technical analysis tool that follows trend. It helps traders to visualize a full spectrum of trends in the market. Mel Widner developed the indicator and elaborated it in the 1997 issue of Technical Analysis of Stocks and Commodities magazine. It uses 10 simple moving averages and hence, it is a very interesting take on a simple moving average.
Function
The basis of the Rainbow Charts indicator are 10 moving averages. The first Rainbow Moving Average is a 2-period simple moving average. It applies recursive smoothing to this first SMA. The first moving average is the base of nine other simple Rainbow Moving Averages of different lengths. Each SMA bases on the previous SMA. The application of the recursive smoothing enables the indicator to create a full spectrum of the current trends in the market. As we know that the financial markets are full of wonders and surprises and we have an indicator that also surprises us. Yes, it is none other than the Rainbow Charts indicator that presents information on the charts in the form of a rainbow. That is the reason that it is known as the Rainbow Charts indicator.
The interpretation of the Rainbow Charts indicator is quite straightforward. The Rainbow Moving Average with the least recursive smoothing stays at the very top of the Rainbow during a bullish trend in the market. Conversely, the moving average with the most recursive smoothing stays at the bottom of the Rainbow.
On the other hand, the positions of the least and the most smoothed moving averages reverses during a bearish trend in the market. Now the least smoothed moving average stays at the bottom while the most smoothed moving average stays at the top of the Rainbow.
The Rainbow Charts indicator’s moving averages track the uptrend or downtrend in the market. The moving averages track the trend as it progresses and cross each other in a sequential order. The distancing of the price from the Rainbow indicates the continuation of the current market trend. Conversely, if the price moves closer to the Rainbow, it suggests that a potential trend reversal is imminent.
The use of the indicator is also quite simple. Traders should look for initiating a buy position as soon as a strong positive move starts. Similarly, they should look for opening a sell position at the very beginning of a strong negative trend. It is important to note that the angle of the moving averages helps to identify the strength of a trend. The steeper curve suggests a stronger trend and vice versa.
Traders can also use the tool in combination with other technical analysis tools as a trend-following indicator. Traders can enter a buy position when indicators suggest a strong bullish trend. They can initiate a sell position when indicators indicate a bearish trend. Technical analysts and experts always suggest to use the Rainbow Charts indicator in combination with other technical analysis tools for successful trading.
Key Signal
Plot a1~c4 --> 10 Rainbow Moving Averages.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
Percentage Of Rising MA'sReturn the percentage of rising moving averages with periods in a custom range from min to max , with the possibility of using different types of moving averages.
Settings
Minimum MA Length Value : minimum period of the moving average.
Maximum MA Length Value : maximum period of the moving average.
Smooth : determine the period of an EMA using the indicator as input, 1 (no smoothing) by default.
Src : source input for the moving averages.
Type : type of the moving averages to be analyzed, available options are "SMA", "WMA" and "TMA", by default "SMA".
Usages
The indicator can return information about the main direction of a trend as well as its overall strength. A value of the indicator above 50 implies that more than 50% of the moving averages from period min to max are rising, this would suggest an uptrend, while a value inferior to 50 would suggest a down-trend.
On the chart, a ribbon consisting of simple moving averages from period 14 to 19, with a color indicating their direction, below the indicator with min = 14 and max = 19
The strength of a trend can be determined by how close the indicator is to 0 or 100, a value of 100 would imply that 100% percent of the moving averages are rising, this indicates a strong up-trend, while a value of 0 would suggest a strong down-trend.
Using different types of moving averages can allow to have more reactive or on the contrary, less noisy results.
Here the type of moving average used by both the ribbon and the indicator is the WMA, the WMA is more reactive than the SMA at the cost of providing less amount of filtering. On the other hand, using a triangular moving average (TMA) provide more filtering at the cost of being less reactive.
Finally, irregularities in the indicator output can be removed by using the smooth setting.
Above smooth = 50.
Details
The indicator is based upon a for loop, this implies that both the sma, wma or change functions are not directly usable, fortunately for us, it is possible to get the first difference of both the SMA, WMA and TMA without relying on a loop by using simple calculations.
The first difference of an SMA of period p is simply a momentum oscillator of period p divided by p , there are two ways to explain why this is the case, first, simple math can prove this, the first difference of an SMA is given by:
(x + x + ... + x )/p - (x + x + ... + x )/p
The repeating terms cancel each other out, as such, we end up with
(x - x )/p
which is simply a momentum oscillator divided by p , since this division doesn't change the sign of the output we can leave it out. We can also use impulses responses to prove this, the impulse response of a simple moving average is rectangular, taking the first difference of this impulse response will give the impulse response of a momentum oscillator, with the only difference being that the non-zero values of the result will be equal to 1/p instead of 1.
The same thing applies to the WMA
above the impulse response of the first difference of a WMA, we can see it is extremely similar to the one of a high pass SMA, only 1 bar longer, as such we can have the first difference of a WMA quite easily. The TMA is simply a 2 pass SMA (the SMA of an SMA), as such the solution is also simple.
Smart MAThe Smart MA indicator is a tool designed for traders seeking insights into market trends, with its foundation rooted in moving averages. It offers two distinctive color options, with "Crossing" as the default choice and "Direction" as an alternative. Let's delve deeper into these options:
1. "Crossing" Color Option (Default):
Key Features:
Utilizes the interaction between fast and slow moving averages.
The color of the base moving average (MA) line dynamically changes based on crossovers between these moving averages.
Offers real-time visual signals for potential shifts in market sentiment.
Interpretation:
With the "Crossing" color option as the default setting, the base MA line's color responds to the interaction of the fast and slow moving averages.
A crossover where the fast MA crosses above the slow MA may prompt the base MA line to change to a bullish color (e.g., teal), indicating a potential bullish trend.
Conversely, if the fast MA crosses below the slow MA, the base MA line's color may alter to represent a bearish sentiment (e.g., red). This color shift provides a visual marker for a potential bearish trend, potentially guiding traders towards shorting opportunities.
2. "Direction" Color Option:
Key Features:
Focuses on the directional trend of the base moving average (MA).
The color of the base MA line signifies the direction in which the base MA is moving.
Aids in quickly identifying the prevailing market trend.
Interpretation:
Uptrend - Bullish Direction: When the base MA slopes upward, indicating an average price increase over the chosen base MA length, the base MA line's color may shift to a bullish hue (e.g., teal). This visual cue signals a potential uptrend, suggesting favorable long positions.
Downtrend - Bearish Direction: If the base MA slopes downward, signifying an average price decrease over the selected base MA length, the base MA line could change to a bearish shade (e.g., red). This color shift acts as an indicator of a potential downtrend, implying possible opportunities for shorting.
Customization:
Both color options allow traders to adjust the indicator's parameters, including base MA length, MA type, fast MA length, and slow MA length, to align with their trading strategies and preferred timeframes.
In summary, the Smart MA indicator, based on moving averages, provides traders with two color options: the default "Crossing" and "Direction" as an alternative. The "Crossing" option leverages fast and slow moving averages to offer real-time visual cues for dynamic market shifts. The "Direction" option simplifies trend analysis by focusing on the directional trend of the base MA. The choice between these options depends on your trading style and the depth of analysis you require. With the Smart MA indicator, you're equipped to make informed trading decisions in today's financial markets.
MA Correlation CoefficientThis script helps you visualize the correlation between the price of an asset and 4 moving averages of your choice. This indicator can help you identify trendy markets as well as trend-shifts.
Disclaimer
Bear in mind that there is always some lag when using Moving-Averages, hence the purpose of this indicator is as a trend identification tool rather than an entry-exit strategy.
Working Principle
The basic idea behind this indicator is the following:
In a trendy market you will find high correlation between price and all kinds of Moving-Averages. This works both ways, no matter bull or bear trend.
In sideways markets you might find a mix of correlations accross timeframes (2018) or high correlation with Low-Timeframe averages and low correlation with High-Timeframe averages (2021/2022).
Trend shifts might be characterised by a 'staircase' type of correlation (yellow), where the asset regains correlation with higher timeframe averages
Indicator Options
1. Source : data used for indicator calculation
1. Correlation Window : size of moving window for correlation calculation
2. Average Type :
Simple-Moving-Average (SMA)
Exponential-Moving-Average (EMA)
Hull-Moving-Average (HMA)
Volume-Weighted-Moving-Average (VWMA)
3. Lookback : number of past candles to calculate average
4. Gradient : modify gradient colors. colors relate to correlation values.
Plot Explanation
The indicator plots, using colors, the correlation of the asset with 4 averages. For every candle, 4 correlation values are generated, corresponding to 4 colors. These 4 colors are stacked one on top of the other generating the patterns explained above. These patterns may help you identify what kind of market you're in.
Logarithmic Moving Average [TusSensei]Logarithmic moving averages involve mathematical modification of classical moving averages(EMA-RMA-SMA). Logarithmic modified averages deviate high over short time periods. For long time periods, it behaves exactly like the original moving averages. Its basic formulation is (MovingAverage x (1 + (1 / log(length))).
The most important reason for the operability of logarithmic moving averages is the time periods they use. The values used are 21-55-149-404-1098-2981. These numbers are the consecutive powers of the number "e", which is the base of the natural logarithm (rounded up to an integer).
In this script you will also see another moving average called SQRT. This moving average is equal to the square root of the product of the EMA and the RMA. In other words, it is a moving average that is the geometric mean of two averages. In this script, you can use all of the EMA-RMA-SQRT and SMA averages in the classical and modified way. For formulaic modification, it is sufficient to select "mEMA", "mRMA" forms from the setting section.
Thanks everyone!
Quantum Market Analyzer X7Quantum Market Analyzer X7 - Complete Study Guide
Table of Contents
1. Overview
2. Indicator Components
3. Signal Interpretation
4. Live Market Analysis Guide
5. Best Practices
6. Limitations and Considerations
7. Risk Disclaimer
________________________________________
Overview
The Quantum Market Analyzer X7 is a comprehensive multi-timeframe technical analysis indicator that combines traditional and modern analytical methods. It aggregates signals from multiple technical indicators across seven key analysis categories to provide traders with a consolidated view of market sentiment and potential trading opportunities.
Key Features:
• Multi-Indicator Analysis: Combines 20+ technical indicators
• Real-Time Dashboard: Professional interface with customizable display
• Signal Aggregation: Weighted scoring system for overall market sentiment
• Advanced Analytics: Includes Order Block detection, Supertrend, and Volume analysis
• Visual Progress Indicators: Easy-to-read progress bars for signal strength
________________________________________
Indicator Components
1. Oscillators Section
Purpose: Identifies overbought/oversold conditions and momentum changes
Included Indicators:
• RSI (14): Relative Strength Index - momentum oscillator
• Stochastic (14): Compares closing price to price range
• CCI (20): Commodity Channel Index - cycle identification
• Williams %R (14): Momentum indicator similar to Stochastic
• MACD (12,26,9): Moving Average Convergence Divergence
• Momentum (10): Rate of price change
• ROC (9): Rate of Change
• Bollinger Bands (20,2): Volatility-based indicator
Signal Interpretation:
• Strong Buy (6+ points): Multiple oscillators indicate oversold conditions
• Buy (2-5 points): Moderate bullish momentum
• Neutral (-1 to 1 points): Balanced conditions
• Sell (-2 to -5 points): Moderate bearish momentum
• Strong Sell (-6+ points): Multiple oscillators indicate overbought conditions
2. Moving Averages Section
Purpose: Determines trend direction and strength
Included Indicators:
• SMA: 10, 20, 50, 100, 200 periods
• EMA: 10, 20, 50 periods
Signal Logic:
• Price >2% above MA = Strong Buy (+2)
• Price above MA = Buy (+1)
• Price below MA = Sell (-1)
• Price >2% below MA = Strong Sell (-2)
Signal Interpretation:
• Strong Buy (6+ points): Price well above multiple MAs, strong uptrend
• Buy (2-5 points): Price above most MAs, bullish trend
• Neutral (-1 to 1 points): Mixed MA signals, consolidation
• Sell (-2 to -5 points): Price below most MAs, bearish trend
• Strong Sell (-6+ points): Price well below multiple MAs, strong downtrend
3. Order Block Analysis
Purpose: Identifies institutional support/resistance levels and breakouts
How It Works:
• Detects historical levels where large orders were placed
• Monitors price behavior around these levels
• Identifies breakouts from established order blocks
Signal Types:
• BULLISH BRK (+2): Breakout above resistance order block
• BEARISH BRK (-2): Breakdown below support order block
• ABOVE SUP (+1): Price holding above support
• BELOW RES (-1): Price rejected at resistance
• NEUTRAL (0): No significant order block interaction
4. Supertrend Analysis
Purpose: Trend following indicator based on Average True Range
Parameters:
• ATR Period: 10 (default)
• ATR Multiplier: 6.0 (default)
Signal Types:
• BULLISH (+2): Price above Supertrend line
• BEARISH (-2): Price below Supertrend line
• NEUTRAL (0): Transition period
5. Trendline/Channel Analysis
Purpose: Identifies trend channels and breakout patterns
Components:
• Dynamic trendline calculation using pivot points
• Channel width based on historical volatility
• Breakout detection algorithm
Signal Types:
• UPPER BRK (+2): Breakout above upper channel
• LOWER BRK (-2): Breakdown below lower channel
• ABOVE MID (+1): Price above channel midline
• BELOW MID (-1): Price below channel midline
6. Volume Analysis
Purpose: Confirms price movements with volume data
Components:
• Volume spikes detection
• On Balance Volume (OBV)
• Volume Price Trend (VPT)
• Money Flow Index (MFI)
• Accumulation/Distribution Line
Signal Calculation: Multiple volume indicators are combined to determine institutional activity and confirm price movements.
________________________________________
Signal Interpretation
Overall Summary Signals
The indicator aggregates all component signals into an overall market sentiment:
Signal Score Range Interpretation Action
STRONG BUY 10+ Overwhelming bullish consensus Consider long positions
BUY 4-9 Moderate to strong bullish bias Look for long opportunities
NEUTRAL -3 to 3 Mixed signals, consolidation Wait for clearer direction
SELL -4 to -9 Moderate to strong bearish bias Look for short opportunities
STRONG SELL -10+ Overwhelming bearish consensus Consider short positions
Progress Bar Interpretation
• Filled bars indicate signal strength
• Green bars: Bullish signals
• Red bars: Bearish signals
• More filled bars = stronger conviction
________________________________________
Live Market Analysis Guide
Step 1: Initial Assessment
1. Check Overall Summary: Start with the main signal
2. Verify with Component Analysis: Ensure signals align
3. Look for Divergences: Identify conflicting signals
Step 2: Timeframe Analysis
1. Set Appropriate Timeframe: Use 1H for intraday, 4H/1D for swing trading
2. Multi-Timeframe Confirmation: Check higher timeframes for trend context
3. Entry Timing: Use lower timeframes for precise entry points
Step 3: Signal Confirmation Process.
For Buy Signals:
1. Oscillators: Look for oversold conditions (RSI <30, Stoch <20)
2. Moving Averages: Price should be above key MAs
3. Order Blocks: Confirm bounce from support levels
4. Volume: Check for accumulation patterns
5. Supertrend: Ensure bullish trend alignment.
For Sell Signals:
1. Oscillators: Look for overbought conditions (RSI >70, Stoch >80)
2. Moving Averages: Price should be below key MAs
3. Order Blocks: Confirm rejection at resistance levels
4. Volume: Check for distribution patterns
5. Supertrend: Ensure bearish trend alignment.
Step 4: Risk Management Integration
1. Signal Strength Assessment: Stronger signals = larger position size
2. Stop Loss Placement: Use Order Block levels for stops
3. Take Profit Targets: Based on channel analysis and resistance levels
4. Position Sizing: Adjust based on signal confidence
________________________________________
Best Practices
Entry Strategies
1. High Conviction Entries: Wait for STRONG BUY/SELL signals
2. Confluence Trading: Look for multiple components aligning
3. Breakout Trading: Use Order Block and Trendline breakouts
4. Trend Following: Align with Supertrend direction.
Risk Management
1. Never Risk More Than 2% Per Trade: Regardless of signal strength
2. Use Stop Losses: Place at invalidation levels
3. Scale Positions: Stronger signals warrant larger (but still controlled) positions
4. Diversification: Don't rely solely on one indicator.
Market Conditions
1. Trending Markets: Focus on Supertrend and MA signals
2. Range-Bound Markets: Emphasize Oscillator and Order Block signals
3. High Volatility: Reduce position sizes, widen stops
4. Low Volume: Be cautious of breakout signals.
Common Mistakes to Avoid
1. Signal Chasing: Don't enter after signals have already moved significantly
2. Ignoring Context: Consider overall market conditions
3. Overtrading: Wait for high-quality setups
4. Poor Risk Management: Always use appropriate position sizing
________________________________________
Limitations and Considerations
Technical Limitations
1. Lagging Nature: All technical indicators are based on historical data
2. False Signals: No indicator is 100% accurate
3. Market Regime Changes: Indicators may perform differently in various market conditions
4. Whipsaws: Possible in choppy, sideways markets.
Optimal Use Cases
1. Trending Markets: Performs best in clear trending environments
2. Medium to High Volatility: Requires sufficient price movement for signals
3. Liquid Markets: Works best with adequate volume and tight spreads
4. Multiple Timeframe Analysis: Most effective when used across different timeframes.
When to Use Caution
1. Major News Events: Fundamental analysis may override technical signals
2. Market Opens/Closes: Higher volatility can create false signals
3. Low Volume Periods: Signals may be less reliable
4. Holiday Trading: Reduced participation affects signal quality
________________________________________
Risk Disclaimer
IMPORTANT LEGAL DISCLAIMER FROM aiTrendview
WARNING: TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This Quantum Market Analyzer X7 indicator ("the Indicator") is provided for educational and informational purposes only. By using this indicator, you acknowledge and agree to the following terms:
No Investment Advice
• The Indicator does NOT constitute investment advice, financial advice, or trading recommendations
• All signals generated are based on historical price data and mathematical calculations
• Past performance does not guarantee future results
• No representation is made that any account will achieve profits or losses similar to those shown.
Risk Acknowledgment
• TRADING CARRIES SUBSTANTIAL RISK: You may lose some or all of your invested capital
• LEVERAGE AMPLIFIES RISK: Margin trading can result in losses exceeding your initial investment
• MARKET VOLATILITY: Financial markets are inherently unpredictable and volatile
• TECHNICAL ANALYSIS LIMITATIONS: No technical indicator is infallible or guarantees profitable trades.
User Responsibility
• YOU ARE SOLELY RESPONSIBLE for all trading decisions and their consequences
• CONDUCT YOUR OWN RESEARCH: Always perform independent analysis before making trading decisions
• CONSULT PROFESSIONALS: Seek advice from qualified financial advisors
• RISK MANAGEMENT: Implement appropriate risk management strategies
No Warranties
• The Indicator is provided "AS IS" without warranties of any kind
• aiTrendview makes no representations about the accuracy, reliability, or suitability of the Indicator
• Technical glitches, data feed issues, or calculation errors may occur
• The Indicator may not work as expected in all market conditions.
Limitation of Liability
• aiTrendview SHALL NOT BE LIABLE for any direct, indirect, incidental, or consequential damages
• This includes but is not limited to: trading losses, missed opportunities, data inaccuracies, or system failures
• MAXIMUM LIABILITY is limited to the amount paid for the indicator (if any)
Code Usage and Distribution
• This indicator is published on TradingView in accordance with TradingView's house rules
• UNAUTHORIZED MODIFICATION or redistribution of this code is prohibited
• Users may not claim ownership of this intellectual property
• Commercial use requires explicit written permission from aiTrendview.
Compliance and Regulations
• VERIFY LOCAL REGULATIONS: Ensure compliance with your jurisdiction's trading laws
• Some trading strategies may not be suitable for all investors
• Tax implications of trading are your responsibility
• Report trading activities as required by law
Specific Risk Factors
1. False Signals: The Indicator may generate incorrect buy/sell signals
2. Market Gaps: Overnight gaps can invalidate technical analysis
3. Fundamental Events: News and economic data can override technical signals
4. Liquidity Risk: Some markets may have insufficient liquidity
5. Technology Risk: Platform failures or connectivity issues may prevent order execution.
Professional Trading Warning
• THIS IS NOT PROFESSIONAL TRADING SOFTWARE: Not intended for institutional or professional trading
• NO REGULATORY APPROVAL: This indicator has not been approved by any financial regulatory authority
• EDUCATIONAL PURPOSE: Designed primarily for learning technical analysis concepts
FINAL WARNING
NEVER INVEST MONEY YOU CANNOT AFFORD TO LOSE
Trading financial instruments involves significant risk. The majority of retail traders lose money. Before using this indicator in live trading:
1. Practice on paper/demo accounts extensively
2. Start with small position sizes
3. Develop a comprehensive trading plan
4. Implement strict risk management rules
5. Continuously educate yourself about market dynamics
By using the Quantum Market Analyzer X7, you acknowledge that you have read, understood, and agree to this disclaimer. You assume full responsibility for all trading decisions and their outcomes.
Contact: For questions about this disclaimer or the indicator, contact aiTrendview through official TradingView channels only.
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This study guide and indicator are published on TradingView in compliance with TradingView's community guidelines and house rules. All users must adhere to TradingView's terms of service when using this indicator.
Document Version: 1.0
Publisher: aiTrendview
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Disclaimer
The content provided in this blog post is for educational and training purposes only. It is not intended to be, and should not be construed as, financial, investment, or trading advice. All charting and technical analysis examples are for illustrative purposes. Trading and investing in financial markets involve substantial risk of loss and are not suitable for every individual. Before making any financial decisions, you should consult with a qualified financial professional to assess your personal financial situation.
Waldo RSI :oWaldo RSI :o Indicator Guide
The Waldo RSI :o indicator is designed to complement the "Waldo RSI Overlay :o" by providing an RSI-based analysis on TradingView, focusing on macro shifts in market trends. Here's a comprehensive guide on how to use this indicator:
Key Features:
RSI Settings:
RSI Source: Choose from ON RSI, ON HIGH, ON LOW, ON CLOSE, or ON OPEN to determine how RSI calculates pivots.
RSI Settings:
Source: Default is (H+L)/2, but you can select any price for RSI calculation.
Length: Default RSI length is 7, which can be adjusted for sensitivity.
Trend Lines:
Show Trend Lines: Option to display trend lines based on RSI pivot points.
Zigzag Length: Determines pivot point sensitivity.
Confirm Length: Validates pivot points (default is 3).
Colors: Customize colors for Higher Highs (HH), Lower Highs (LH), Higher Lows (HL), and Lower Lows (LL) on the RSI.
Label Size and Line Width: Adjust the appearance of labels and lines.
Divergences:
Classic Divergences:
Show Classic Div: Toggle to reveal divergences where RSI and price move in opposite directions.
Colors: Set different colors for bullish and bearish divergence indicators.
Transparency and Line Width: Control the visual impact of divergence signals.
Hidden Divergences:
Similar settings for identifying hidden divergences, suggest trend continuation.
Breakout/Breakdown:
Show Breakout/Breakdown: Generates signals for RSI breakouts or breakdowns, used by "Waldo RSI Overlay :o" for visual chart signals.
Overbought/Oversold Zones:
Show Overbought and OverSold Zones: Highlights when RSI goes above 70 (overbought) or below 30 (oversold).
Moving Averages on RSI:
The default Moving Average (MA) settings are tailored to capture macro shifts in market trends:
Show Moving Averages: Option to overlay two MAs on the RSI for trend confirmation:
Fast RSI MA:
RSI Period: 50 (this is the period over which the RSI is calculated).
MA Length: 50 (the number of periods used for the moving average of the RSI).
Slow RSI MA:
RSI Period: 50 (same as fast for consistency in RSI calculation).
MA Length: 200 (longer term for capturing broader trends).
Crossover Signals: The RSI changes color from red to green based on these moving average crossovers:
When the Fast MA (50 period) crosses above the Slow MA (200 period), the RSI turns green, indicating potential bullish conditions or momentum shift.
Conversely, when the Fast MA crosses below the Slow MA, the RSI turns red, suggesting bearish conditions or a shift back towards a downtrend.
This 50-period RSI crossover setting is used to identify overall macro shifts in the market, providing a clear visual cue for traders looking at longer-term trends.
Ghost Lines (Optional):
Ghost Lines: Option to limit how far RSI trend lines extend, helping to keep the chart less cluttered.
How to Use the Indicator:
Setup:
Configure RSI by choosing the source and setting the length to match your trading style.
Set the zigzag and confirm lengths for appropriate pivot detection.
Trend Analysis:
Monitor the RSI for trend changes using the colored trend lines and labels.
Divergence Detection:
Look for RSI and price divergences to anticipate potential reversals or continuations.
Breakout/Breakdown:
Use these signals in conjunction with "Waldo RSI Overlay :o" for price action confirmation.
Overbought/Oversold:
Identify when the market might be due for a correction or continued momentum.
Moving Averages:
Focus on the color changes in RSI to understand macro trend shifts with the default 50/200 period setup.
Ghost Lines:
Enable for a cleaner chart if you don't need trend lines extending indefinitely.
Usage Tips:
Combine with other indicators for confirmation, as no single tool is foolproof.
Adjust settings to suit different market conditions or trading timeframes.
Use in tandem with "Waldo RSI Overlay :o" for a full trading signal system.
Remember, trading involves significant risk, and historical data does not guarantee future performance. Use this indicator as part of a broader trading strategy.
Moving Average Crossover Strategy with Take Profit and Stop LossThe Moving Average Crossover Strategy is a popular trading technique that utilizes two moving averages (MAs) of different periods to identify potential buy and sell signals. By incorporating take profit and stop loss levels, traders can effectively manage their risk while maximizing potential returns. Here’s a detailed explanation of how this strategy works:
Overview of the Moving Average Crossover Strategy
Moving Averages:
A short-term moving average (e.g., 50-day MA) reacts more quickly to price changes, while a long-term moving average (e.g., 200-day MA) smooths out price fluctuations over a longer period.
The strategy generates trading signals based on the crossover of these two averages:
Buy Signal: When the short-term MA crosses above the long-term MA (often referred to as a "Golden Cross").
Sell Signal: When the short-term MA crosses below the long-term MA (known as a "Death Cross").
Implementing Take Profit and Stop Loss
1. Setting Take Profit Levels
Definition: A take profit order automatically closes a trade when it reaches a specified profit level.
Strategy:
Determine a realistic profit target based on historical price action, support and resistance levels, or a fixed risk-reward ratio (e.g., 2:1).
For instance, if you enter a buy position at $100, you might set a take profit at $110 if you anticipate that level will act as resistance.
2. Setting Stop Loss Levels
Definition: A stop loss order limits potential losses by closing a trade when the price reaches a specified level.
Strategy:
Place the stop loss just below the most recent swing low for buy orders or above the recent swing high for sell orders.
Alternatively, you can use a percentage-based method (e.g., 2-3% below the entry point) to define your stop loss.
For example, if you enter a buy position at $100 with a stop loss set at $95, your maximum loss would be limited to $5 per share.
Example of Using Moving Average Crossover with Take Profit and Stop Loss
Entry Signal:
You observe that the 50-day MA crosses above the 200-day MA at $100. You enter a buy position.
Setting Take Profit and Stop Loss:
You analyze historical price levels and set your take profit at $110.
You place your stop loss at $95 based on recent swing lows.
Trade Management:
If the price rises to $110, your take profit order is executed, securing your profit.
If the price falls to $95, your stop loss is triggered, limiting your losses.
Buy/Sell IndicatorBuy/Sell Indicator
Overview
The Buy/Sell Indicator is designed to help traders identify potential entry and exit points in the market using a combination of Simple Moving Averages (SMA) and the Relative Strength Index (RSI). This indicator plots buy and sell signals directly on the chart, making it easier to make informed trading decisions.
Inputs
Fast MA Length: The period for the fast-moving average. Default is 9.
Slow MA Length: The period for the slow-moving average. Default is 21.
RSI Length: The period for the RSI calculation. Default is 14.
RSI Overbought Level: The RSI level considered overbought. Default is 70.
RSI Oversold Level: The RSI level considered oversold. Default is 30.
How It Works
Moving Averages:
The indicator calculates two SMAs: a fast-moving average (fastMA) and a slow-moving average (slowMA).
The fast MA reacts more quickly to price changes, while the slow MA reacts more slowly.
RSI:
The RSI is calculated to measure the momentum of price movements.
It helps identify overbought and oversold conditions in the market.
Buy and Sell Conditions:
Buy Signal: A buy signal is generated when the fast MA crosses above the slow MA and the RSI is below the overbought level.
Sell Signal: A sell signal is generated when the fast MA crosses below the slow MA and the RSI is above the oversold level.
Plotting
Buy Signals: Displayed as green labels below the bars where the buy condition is met.
Sell Signals: Displayed as red labels above the bars where the sell condition is met.
Moving Averages: The fast MA is plotted in blue, and the slow MA is plotted in orange.
Flying Buddha Inside Bars Indicator v1 by JustUncleLDescription:
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This indicator plots MAs and paints Triggered Alert Arrows base on Flying Buddha candle patterns.
The “Flying Buddha” Pattern is defined as:
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A candlestick chart with two moving averages: the 5 period exponential moving average (fast EMA) and the 10 period simple moving average (slow SMA), both applied to the closing price. The default “Flying Buddha” pattern is any candlestick which:
1. Has a LOW above the fast EMA, when the fast EMA is above the slow SMA (a bearish “Flying Buddha”); or
2. Has a HIGH below the fast EMA, when the fast EMA is below the slow SMA (a bullish “Flying Buddha”).
Alert Trigger:
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A Flying Buddha Alert is triggered on the first candle that is a non-flying Buddha candle after a Flying Buddha Pattern candle sequence. Flying Buddhas can optionally be filtered by InsideBars and PinBars.
The Alert Trigger is optionally filtered by the Directional MA (default=EMA 89), and/or by Minimum Sequence length of Flying Buddhas.
Moving Averages:
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You can select between 11 different types of moving averages, for each MA line in Flying Buddha MAs (fastMA and slowMA) and the Directional Filter MA:
SMA = Simple Moving Average.
EMA = Exponential Moving Average.
WMA = Weighted Moving Average
VWMA = Volume Weighted Moving Average
SMMA = Smoothed Simple Moving Average.
DEMA = Double Exponential Moving Average
TEMA = Triple Exponential Moving Average.
HullMA = Hull Moving Average
SSMA = Ehlers Super Smoother Moving average
ZEMA = Near Zero Lag Exponential Moving Average.
TMA = Triangular (smoothed) Simple Moving Average.
NOTE: This is a concept indicator, I also intend to release a trading BOT suitable for Autoview, based on this concept indicator.
