MACD Normalized [ChartPrime]Overview of MACD Normalized Indicator
The MACD Normalized indicator, serves as an asset for traders seeking to harness the power of the moving average convergence divergence (MACD) combined with the advantages of the stochastic oscillator. This novel indicator introduces a normalized MACD, offering a potentially enhanced flexibility and adaptability to numerous market conditions and trading techniques.
This indicator stands out by normalizing the MACD to its average high and average low, also factoring in the deviation of the high-low position from the mean. This approach incorporates the high and low in the calculations, providing the benefits of stochastic without its common drawbacks, such as clipping problems. As a result, the indicator becomes exceptionally versatile and suitable for various trading strategies, including both faster and slower settings.
The MACD Normalized Indicator boasts a variety of options and settings. The features include:
Enable Ribbon: Toggle the display of the ribbon accompanying the MACD Normalized, as desired.
Fast Length: Determine the movement speed of the fast line to receive advance notice of potential market opportunities.
Slow Length: Control the movement pace of the slow line for smoother signals and a comprehensive outlook on market trends.
Average Length: Specify the length used to calculate the high and low averages, providing greater control over the indicator's granularity.
Upper Deviation: Establish the extent to which the high and low values deviate from the mean, ensuring adaptability to diverse market situations.
Inner Band (Middle Deviation): Adjust the balance between the high and low deviations to create an inner band signal, giving traders a secondary level of market analysis and decision-making support.
Enable Candle Color: Enable the coloring of candles based on the MACD Normalized value for effortless visualization of trading potential.
Use Cases for the MACD Normalized Indicator
In addition to analyzing market trends and identifying potential trading opportunities, ChartPrime's MACD Normalized Indicator offers a range of applications for traders. These use cases encompass distinct trading scenarios and strategies:
Overbought and Oversold Regions
One of the key applications of the MACD Normalized Indicator is identifying overbought and oversold regions. Overbought refers to a situation where an asset's price has risen significantly and is expected to face a downturn, while oversold indicates a price drop that may subsequently lead to a reversal.
By adjusting the indicator's parameters, such as the upper and inner deviation levels, traders can set precise boundaries to determine overbought and oversold areas. When the MACD moves into the upper region, it may signal that the asset is overbought and due for a price correction. Conversely, if the MACD enters the lower region, it possibly indicates an oversold condition with the potential for a price rebound.
Signal Line Crossovers
The MACD Normalized Indicator displays two lines: the fast line and the slow line (inner band). A common trading strategy involves observing the intersection of these two lines, known as a crossover. When the fast line crosses above the slow line, it may signify a bullish trend or a potential buying opportunity. Conversely, a crossover with the fast line moving below the slow line typically indicates a bearish trend or a selling opportunity.
Divergence and Convergence
Divergence occurs when the price movement of an asset does not align with the corresponding MACD values. If the price establishes a new high while the MACD fails to do the same, a bearish divergence emerges, suggesting a potential downtrend. Similarly, a bullish divergence takes place when the price forms a new low but the MACD does not follow suit, hinting at an upcoming uptrend.
Convergence, on the other hand, is represented by the MACD lines moving closer together. This movement signifies a potential change in the trend, providing traders with a timely opportunity to enter or exit the market.
Ribbons
T3 Striped [Loxx]Theory:
Although T3 is widely used, some of the details on how it is calculated are less known. T3 has, internally, 6 "levels" or "steps" that it uses for its calculation.
This version:
Instead of showing the final T3 value, this indicator shows those intermediate steps. This shows the "building steps" of T3 and can be used for trend assessment as well as for possible support / resistance values.
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
Alerts
Signals
Bar coloring
Loxx's Expanded Source Types
R-squared Adaptive T3 Ribbon Filled Simple [Loxx]R-squared Adaptive T3 Ribbon Filled Simple is a T3 ribbons indicator that uses a special implementation of T3 that is R-squared adaptive.
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.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
Included:
Alerts
Signals
Loxx's Expanded Source Types
MA Ribbon AnnualThis script starts with the 252 E(S)MA and steps it down 21 days per MA. The starting ribbon (Annual Moving Avg Length) and step-downs (Monthly Step Downs) are adjustable.
The goal of this script is to quickly see if prices respect a certain MA length more than another. Monthly lengths were chosen because of their relation to earnings and other reports.
Whale Momentum Wave Oscillator//Credits: @Noldo - Whale Trading System @rumpypumpydumpy - ALMA Ribbons @QuantNomad - Elastic Volume Weighted Moving Average
Composite Indicator, created by taking QuantNomad's EVWMA and using that as input for a variation of rumpypumpydumpy's ALMA Ribbons. Each Ribbon had its sub ribbons summed up and then averaged. The averages were then fed through the ta.rsi and the ta.mom functions giving us our momentum waves. Signal line created from the close value being fed through the ta. ema into the ta.rsi then ta.wma then ta.mom function. Why those in that order? No reason in particular just what I stumbled upon after many variations. I then overlayed Noldo's Whale Trading System to view what "whales" were doing, giving us a good view of when capital is flowing into and out the asset which often contradicts the momentum waves prior to trend changes. Provides a nice visual for how capital is moving along with momentum. Can see when smart money is buying up a big dip or of they seem to still be waiting on the sidelines.
Fibonacci RibbonThe Fibonacci Ribbon (FibRib) creates two Fibonacci moving averages: essentially a Fibonacci WMA and a Fibonacci EMA.
The ribbon colors based on which is higher and where PA is relative to the ribbon.
Optional colored candles shades candles the same color as the ribbon.
Any RibbonThis indicator displays a ribbon of two individually configured Fast and Slow and Moving Averages for a fixed time frame. It also displays the last close price of the configured time frame, colored green when above the band, red below and blue when interacting. A label shows the percentage distance of the current price from the band, (again red below, green above, blue interacting), when the price is within the band it will show the percentage distance from median of the band.
The Fast and Slow Moving Averages can be set to:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Volume-Weighted Moving Average (VWMA)
Hull Moving Average (HMA)
Exponentially Weighted Moving Average (RMA) (SMMA)
Linear regression curve Moving Average (LSMA)
Double EMA (DEMA)
Double SMA (DSMA)
Double WMA (DWMA)
Double RMA (DRMA)
Triple EMA (TEMA)
Triple SMA (TSMA)
Triple WMA (TWMA)
Triple RMA (TRMA)
Symmetrically Weighted Moving Average (SWMA) ** length does not apply **
Arnaud Legoux Moving Average (ALMA)
Variable Index Dynamic Average (VIDYA)
Fractal Adaptive Moving Average (FRAMA)
I wrote this script after identifying some interesting moving average bands with my AMACD indicator and wanting to see them on the price chart. As an example look at the interactions between ETHBUSD 4hr and the band of VIDYA 32 Open and VIDYA 39 Open. Or start from the good old BTC Bull market support band, Weekly EMA 21 and SMA 20 and see if you can get a better fit. I find the Double RMA 22 a better fast option than the standard EMA 21.
CoRA Ribbon - Multiple Compound Ratio Weighted Moving AveragesWhat distinguishes this indicator?
A Compound Ratio Weighted Moving Average ("CoRA") is a Moving Average that, regardless of its length, has very little lag and that can be relied on to accurately track price movements and fluctuations - compared to other types of Moving Averages.
By combining multiple Compound Ratio Weighted Moving Averages you can identify the trend better and more reliably . This is where "CoRA Ribbon" comes in.
The original study, which supported one CoRA Wave, comes from RedKTrader and was introduced as "RedK Compound Ratio Moving Average (CoRa_Wave)” . Thanks to him for the great work!
What was improved or added to this version of the indicator?
With this version of the indicator, up to 5 waves of Compound Ratio Moving Averages with different lengths can be combined and output to one "CoRA Ribbon".
Alerts were implemented. You can be notified e.g. in the event of
changes in direction of each single CoRA Wave
a trend change, which is determined on the basis of all 5 CoRA Waves
A CoRA Wave compared to other Moving Averages - CoRa Waves are less lagging behind
A suggestion for interpretation of “CoRA Ribbon”:
Since CoRA Ribbon can help you to identify the trend better and more reliably, this indicator provides a good baseline for your strategy, but should always be used in conjunction with other indicators or market analysis.
By adjusting the length of each individual wave, you can adapt "CoRA Ribbon" to your trading style - whether it is more aggressive or more cautious.
The following general rules can be formulated:
If the Ribbon changes its color to green, this can be interpreted as a buy signal.
If the Ribbon changes its color to red, this can be interpreted as a sell signal.
Good to know: The default settings have been selected for timeframe lower than 15 minutes. Adjust them and the indicator will do a great job on higher timeframes too. Please remember to test carefully after every change before the changes are applied to your live trading.
Background “Compound Ratio Weighted Average” - provided by "RedKTrader"
A Compound Ratio Weighted Average is a moving average where the weights increase in a "logarithmically linear" way - from the furthest point in the data to the current point.
The formula to calculate these weights work in a similar way to how "compound ratio" works: you start with an initial amount, then add a consistent "ratio of the cumulative prior sum" each period until you reach the end amount. The result is the "step ratio" between the weights is consistent - This is not the case with linear-weighted “Moving Average Weighted” (WMA) or “Exponential Moving Average” (EMA)
For example, if you consider a Weighted Moving Average ( WMA ) of length 5, the weights will be (from the furthest point towards the most current) 1, 2, 3, 4, 5 -- we can see that the ratio between these weights are inconsistent. in fact, the ratio between the 2 furthest points is 2:1, but the ratio between the most recent points is 5:4. the ratio is inconsistent, and in fact, more recent points are not getting the best weights they should get to counter-act the lag effect. Using the Compound Ratio approach addresses that point.
A key advantage here is that we can significantly reduce the "tail weight" - which is "relatively" large in other Moving Averages.
A Compound Ratio Weighted Moving Average is a moving average that has very little lag and that can be relied on to accurately track price movements and fluctuations.
Use or modify the code, invite us for a coffee, ... most importantly: have a lot of fun and success with this indicator
The code is commented - please don't hesitate to use it as needed or customize it further ... and if you are satisfied and even successful with this indicator, maybe buy us a coffee ;-)
The original developer ( RedKTrader ) and I ( consilus ) are curious to see how our indicators will develop through further ideas - so please keep us updated.
{Gunzo} Heiken Ashi RibbonsHeiken Ashi Ribbons is a trend-following indicator which gives entry and exit points for short-term, medium-term and long term trading (using Exponential Moving Averages and Heiken Ashi formulas).
OVERVIEW :
The Heiken Ashi Ribbons indicator is composed of 3 moving average ribbons (slow, normal and fast) that are computed using the Heiken Ashi formulas. The 3 ribbons give a clear vision of the current trend as they use moving averages that smooth out the price and filter noise from short term fluctuations. In a simplified way, you can consider each ribbon as a moving average with a larger body size.
If the price is above the slow ribbon, we consider the asset as trending up in the short term (trending down otherwise). If the price is above the fast ribbon, we consider the asset as trending up in the long term (trending down otherwise).
CALCULATION :
First of all, to compute a ribbon for this indicator we calculate a moving average (EMA by default) for common sources (OHLC) :
EMA (open), EMA (high), EMA (low), EMA (close)
We then apply the Heiken Ashi formulas to the moving averages calculated previously.
HA (open) = HA (open) previous + HA (close) previous
HA (close) = ( EMA (open) + EMA (high) + EMA (low) + EMA (close) ) / 4
HA (high) = max( EMA (open), EMA (close), EMA (high) )
HA (low) = min ( EMA (open), EMA (close), EMA (low) )
The ribbon displayed (by default) on the chart is the area between HA (open) and HA (close).
SETTINGS :
1st Moving average length : Length of the slow moving average
2nd Moving average length : Length of the normal moving average
3rd Moving average length : Length of the fast moving average
Moving average method : Moving average calculation method (EMA : Exponential Moving Average, SMA : Simple Moving Average, WMA : Weighted Moving Average)
Ribbon type : standard ribbon uses the area between HA (open) and HA (close). Large ribbon uses the area between HA (low) and HA (high)
Display ribbon as candles : change the type of visualization between area and candles
Display short term buy/sell signals : Display short term buy/sell signals (crosses) when the fast moving average and normal moving average are crossing
Display long term buy/sell signals : Display long buy/sell signals (circles) when the fast moving average and slow moving average are crossing
Display ribbon trending up signals : Display ribbon direction change (triangle up) when the trend of the ribbon changes to trending up
Display ribbon trending down signals : Display ribbon direction change (triangle down) when the trend of the ribbon changes to trending down
VISUALIZATIONS :
This indicator has 2 possible visualizations :
Ribbons : the ribbons can be considered as enhanced moving averages for trading purposes. They represent the area between the Heiken Ashi of the moving average of the open and closing price. The color of the moving average line is green when the ribbon is trending up and red when the ribbon is trending down.
Signals : Various signals can be displayed at the bottom of the chart (Buy/Sell signals, Ribbon direction changes signals).
USAGE :
This indicator can be used in many strategies, just like when you are using multiple moving averages. You should test these strategies and use the one that best fits your trading style.
Strategy based on crossovers :
When the fast ribbon crosses above the normal ribbon, it is a short term buy signal (it is recommended to wait for a confirmation)
When the fast ribbon crosses under the normal ribbon, it is a short term sell signal (it is recommended to wait for a confirmation)
When the fast ribbon crosses above the slow ribbon, it is a long term buy signal
When the fast ribbon crosses over the slow ribbon, it is a long term buy signal
Strategy based on price position :
When the prices closes above the ribbon, it is a buy signal (long term if above slow ribbon, short term if above fast ribbon)
When the prices closes below the ribbon, it is a sell signal (long term if below slow ribbon, short term if below fast ribbon)
Strategy based on price bouncing :
When the price decreases and reaches the green long term ribbon, the price candles may not be able to cross the ribbon. If the price increases, we consider that move as a bounce on the ribbon, which is a buy signal
When the price increases and reaches a red long term ribbon, the price candles may not be able to cross the ribbon. If the price decreases, we consider that move as a bounce on the ribbon, which is a sell signal
Strategy based on ribbon direction :
When the direction of the ribbon changes, the trend of the asset is changing which may lead to a crossover to the next candles if the trend is continuing in that direction (it is recommended to validate the entry points with a second indicator as this strategy may have some false signals).
RK's 10 ∴ MA Types Ribbons (Fibonacci, Guppy and others)After some tips in my indicator
RK's 04 - Lots of MA Types Ribbon I Put some time and effort to make it better.
So, I'm sharing with you the results.
This is an up to 10 lines Moving Average Ribbon with an Auto Evaluate Length and a lots of options!!!
Type of Moving Average you can use:
SMA - Simple Moving Average
SMMA - Smoothed Moving Average
EMA - Exponential Moving Average
DEMA - Double Exponential Moving Average
TEMA - Triple Exponential Moving Average
WMA - Weighted Moving Average
HMA - Hull Moving Average
EHMA - Exponential Hull Moving Average
RMA - RSI Moving average
2PSS - Ehlers 2 Pole Super Smoother
3PSS - Ehlers 3 Pole Super Smoother
VWMA - Volume-Weighted Moving Average
ALMA - Arnaud Legoux Moving Average
STMA - Simple Triangular Moving Average
ETMA - Exponential Triangular Moving Average
LSMA - Least Squares Moving Average
ZSMA - Zero-Lag Simple Moving Average
ZEMA - Zero-Lag Exponential Moving Average
COVWMA - Coefficient of Variation Weighted Moving Average
COVWEMA - Coefficient of Variation Weighted Exponential Moving Average
FRAMA - Fractal Adaptive Moving Average
KAMA - Kaufman's Adaptive Moving Average
VIDYA - Variable Index Dynamic Average
If you want to change faster the MA type, in "Moving Average Setup:", Select "🤖 Use numbers to change MA Type", click inside the box in "🤖 Moving Average Type per Number:" and just scroll your mouse wheel. You can check what MA type you are using looking in the info panel label.
There is 4 automatic evaluate length:
Fibonacci Sequence
Arithmetic Progression
Geometric Progression
Guppy Multiple Moving Average (GMMA) without Lengths 03 and 05
And I already put a Manual Length, but I keep it inside the code, so if you want to use different lengths, just change the code, or ask me and I will put as an input.
And attending a request, this indicator can creates alerts when all the colors of the ribbons changes.
Hope you like it!
Any other good idea, just send me.
Hash Ribbons Backtest - Bitcoin Beats YT
Hello Hello Hello and welcome back to Bitcoin Beats!
This is a script written by capriole_charles
Go check out the original!
I have added leverage and stoploss % but also made it a strategy so we can look back at past trades to see patterns and profit.
Personally I feel this is not enough data to trade off as BTC is such a young asset. However I have seen other models similar to this for other assets that hold strong.
Trade safe!
Good bye from bitcoin beats!
Not Meant For The 1H! My Bad! higher timeframes are better!
The "Spring" is the confirmed Miner capitulation period:
The 1st "gray" circle is the start of Capitulation (1 month Hash Rate crosses UNDER 2 month Hash Rate)
Last "green" circle is the end of Capitulation (1 month Hash Rate crosses OVER 2 month Hash Rate)
The "greener" the spring gets (up until blue) represents Hash Rate recovery (it is increasing)
The "blue" circle is the first instance of positive momentum following recovery of Hash Rate (1m HR > 2m HR). This is historically a rewarding place to buy with limited downside.
35EMA Cross BuyAndSell Strategy + RIBBON [d3nv3r]This strategy allow the user to move the EMA which control the Buy&Sell Strategy and show the EMA ribbon that can be found in the Template area.
Buy showing the ribbon and letting the user to adjust the EMA signaling the B&S strat the user can create an elaborated strategy for buyPoint and sellPoint.
The 35EMA Cross is choosen by default but I recommend to move it to find best Sell point and best Buy point as you would not react on the same EMA for a Buy signal and a Sell Signal..
It would be good to have buy signal on a EMA and the sell signal on another but that's for another Strategy to be shared.
Let me know by commenting what you would like for the next one !
