T3 Slope Variation [Loxx]T3 Slope Variation is an indicator that uses T3 moving average to calculate a slope that is then weighted to derive a signal.
The center line
The center line changes color depending on the value of the:
Slope
Signal line
Threshold
If the value is above a signal line (it is not visible on the chart) and the threshold is greater than the required, then the main trend becomes up. And reversed for the trend down.
Colors and style of the histogram
The colors and style of the histogram will be drawn if the value is at the right side, if the above described trend "agrees" with the value (above is green or below zero is red) and if the High is higher than the previous High or Low is lower than the previous low, then the according type of histogram is drawn.
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
Alets
Signals
Bar coloring
Loxx's Expanded Source Types
M-oscillator
Multi HMA Slopes [Loxx]Multi HMA Slopes is an indicator that checks slopes of 5 (different period) Hull Moving Averages and adds them up to show overall trend. To us this, check for color changes from red to green where there is no red if green is larger than red and there is no red when red is larger than green. When red and green both show up, its a sign of chop.
What is the Hull Moving Average?
The Hull Moving Average (HMA) attempts to minimize the lag of a traditional moving average while retaining the smoothness of the moving average line. Developed by Alan Hull in 2005, this indicator makes use of weighted moving averages to prioritize more recent values and greatly reduce lag.
Included
Signals: long, short, continuation long, continuation short.
Alerts
Bar coloring
Loxx's expanded source types
Multi T3 Slopes [Loxx]Multi T3 Slopes is an indicator that checks slopes of 5 (different period) T3 Moving Averages and adds them up to show overall trend. To us this, check for color changes from red to green where there is no red if green is larger than red and there is no red when red is larger than green. When red and green both show up, its a sign of chop.
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
Signals: long, short, continuation long, continuation short.
Alerts
Bar coloring
Loxx's expanded source types
Cumulative Delta Volume RSI-8 CandlesThis script combines Cumulative delta volume information and the RSI set to an 8 period look back to show momentum in the market. It is displayed using a color overlay with 3 colors. Green candles indicate positive market momentum along with positive delta and positive price movement. Red candles indicate negative market momentum along with negative delta and negative price movement. Yellow candles indicate possible ranging conditions or the start of a pullback in either direction. There is also a moving average built into the indicator to help with trend direction.
Combined with price action strategies or even simple moving averages this indicator can be used as a powerful confirmation or confluence in any trading system. Works nicely to confirm breakout strategies as well.
Can be used on any market or time frame though for price action strategies it works best on time frames H1 and under.
Zero-line Volatility Quality Index (VQI) [Loxx]Originally volatility quality was invented by Thomas Stridsman, and he uses it in combination of two averages.
This version:
This doesn't use averages for trend estimation, but instead uses the slope of the Volatility quality. In order to lessen the number of signals (which can be enormous if the VQ is not filtered), some versions similar to this are using pips filters. This version is using % ATR (Average True Range) instead. The reason for that is that :
Using fixed pips value as a filter will work on one symbol and will not work on another
Changing time frames will render the filter worthless since the ranges of higher time frames are much greater than those at lower time frames, and, when you set your filter on one time frame and then try it on another, it is almost certain that it will have to be adjusted again
Additionally, this version is made to oscillate around zero line (which makes the potential levels, which are even in the original Stridsman's version doubtful, unnecessary)
Usage:
You can use the color change as signals when using this indicator
T3 PPO [Loxx]T3 PPO is a percentage price oscillator indicator using T3 moving average. This indicator is used to spot reversals. Dark red is upward price exhaustion, dark green is downward price exhaustion.
What is Percentage Price Oscillator (PPO)?
The percentage price oscillator (PPO) is a technical momentum indicator that shows the relationship between two moving averages in percentage terms. The moving averages are a 26-period and 12-period exponential moving average (EMA).
The PPO is used to compare asset performance and volatility, spot divergence that could lead to price reversals, generate trade signals, and help confirm trend direction.
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.
QQE of Parabolic-Weighted Velocity [Loxx]QQE of Parabolic-Weighted Velocity is a QQE indicator that takes as its input parabolic-weighted velocity calculation. This version can help in determining trend. Adjust the calculating period to your trading style: longer - to trend traders, shorter - for scalping.
What is Qualitative Quantitative Estimation (QQE)?
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index ( RSI ) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR).
There are many indicators for many purposes. Some of them are complex and some are comparatively easy to handle. The QQE indicator is a really useful analytical tool and one of the most accurate indicators. It offers numerous strategies for using the buy and sell signals. Essentially, it can help detect trend reversal and enter the trade at the most optimal positions.
Included:
Loxx's Expanded Source Types
Alerts
Signals
Bar coloring
J_TPO Velocity VariationThis one is a very random indicator but with an excellent concept. Unfortunately, I don't know much about the origin of this indicator or who made it. Still, the first appearance was around 2004 on a Meta Trader forum. There are a lot of variations of the J_TPO indicator. One of them is the J_TPO Velocity. The difference from the original version is that it uses the price range of the latest candles to change the magnitude of the indicator value, but the concept is the same.
More info here
In its original form, an oscillator between -1 and +1 is a nonparametric statistic quantifying how well the prices are ordered in consecutive ups (+1) or downs (-1), or intermediate cases. The velocity variation adds the price range, and this script variation adds a baseline as a filter for the indicator. This indicator will work as a confirmation indicator. Using it with the trend filter will work as an entry indicator.
Besides the columns representing the indicator's values, 2 more signals will be printed on the chart. One is the middle cross, the other the kicking middle cross. The first will print a signal when the J_TPO crosses the middle line (0) in favor of the trend. A diamond will be printed when the baseline is above 0, and the cross is upwards. The inverse for crosses downwards. The other signal is the Kicking middle cross which will appear when the cross comes after an opposite cross. This will give only one signal per cross in the same direction, which may help identify earlier the trend direction.
Autocorrelative Power Oscillator (APO) [SpiritualHealer117]This indicator is very strong in identifying short-term trends, and was made for trading stocks and commodities. When it is green, it indicates an uptrend, and red indicates a downtrend. The transparency of the columns illustrates the strength of the trend, with transparent columns indicating weakness, while solid columns indicate strength.
Basic Explanation of the Indicator
This indicator calculates an asset's Pearson's R coefficient when compared with several different lags of the stock's price. After that, the oscillator checks whether the indicator is in the green or red compared to those correlations, and takes the sum of the correlative periods to predict which direction the market should go based on the relationship of the current price with its past correlations.
MACD frontSide backSide + TTM Squeeze by bangkokskaterDark Mode is enabled by default for black theme
disable Dark Mode for white theme
MACD frontSide backSide
===================
an elegant, much better way to use MACD
for trend following momentum ( aka momo) style
MACD with default settings of 12/26 smoothing of 9
✔️ but without histogram
✔️ only has MACD and signal "lines"
green = frontSide momentum impulse
take longs only
red = backSide momentum impulse
take shorts only
black area = exit (once green or red is no longer showing)
or keep holding till next bigger TP
PS: credits to Warrior Trading Ross Cameron for this idea
youtu.be
TTM Squeeze
===================
white dots = incoming pump / dump (monitor for entry)
PS: credits to John Carter's TTM Squeeze & Greeny for PineScript adaptation
Andean ScalpingAndean Scalping Implementation - BETA
- Uses Andean Oscillator: alpaca.markets
- Implements a threshold moving average (SMA 1000) on the Andean Signal line at 1.1 factor to filter out small moves
- TP/SL using ATR bands at 3x multiplier
TheATR: Aroon Oscillator.Aroon Oscillator (AO).
The Aroon Oscillator, was developed by Tushar Chande, in 1995, to highlight the start of a new trend and to measure trend strength.
I re-branded a bit the whole thing, If you are familiar with how this Oscillator usually is, you are going to notice the differences.
Aroon Oscillator Components.
1 - Aroon Up -> Bullish Directional Component, highlighted in blue.
2 - Aroon Down -> Bearish Directional Component, highlighted in purple.
We also have the Oscillators static thresholds, which are:
- 0 Line.
- 100 Line.
- Exit Signal Line Level.
How to read the Aroon Oscillator.
The AO main goal, is to identify the trend from its first stages, to then come up with how strong that trend is.
So, classic interpretation for the AO would be:
-Aroon Up>Aroon Down = Bull Scenario.
-Aroon Up<Aroon Down = Bear Scenario.
There's also a filter I added, called "Weak Spots Filter". It's purpose goes alongside with how the Aroon present its signals, which a big spike, that usually reaches the top range of the Oscillator, for both Long and Short cases.
So, if the momentum of the market fails to push the Aroon up to a specific level (Exit Signal Line Level), the Filter says market's not strong, and therefore signal is not valid.
The same level (Exit Signal Line Level) allows the user to set Exit Signals for the AO.
I found Exit Signal extremely powerful in this oscillator, as they way they're structured aims to capture the slow down of the trend, which may be followed by the market reversing.
TheATR Documentation regarding TheATR: Aroon Oscillator.
I pretty much already say what I love about the Aroon: It's Exit Signals.
Those are the most valuable part of the Oscillator, by far, in my opinion.
Also, I noticed it gives nice trend recognition when the lenght it's set to
big number (from 150 to 200, for ex).
But. I would never use JUST the Aroon, to decide when to enter and exit the market.
I think it may be an outstanding player if its in a team, where it should play a defensive role.
But that's just my way of using it. I wish you find profitable ways too!
Thanks for reading,
TheATR.
QG-Relative Strength Rank MTF DSL
Relative strength rank is a momentum indicator based on combination of short and long term strength combined with ATR to adjust for current volatility.
The Multi timeframe version long with signals only above or below +1 and -1 provide quite reliable signals and entries for pullback levels.
The RSR signal has been smoothed with EMA.
RSI TrendRSI Hull Trend is a hybrid indicator with RSI of HULL Signal. The Hull MA is combined with RSI to see if the Hull MA Buy/Sell Signal is in overbought or oversold condition. Buy Sell Signals are plotted based on settings of OB/OS or RSI. This indicator is very useful to see if the Trend is in Exhaustion or Beginning of a Trend. Entry and Exit conditions can be more precise based on OB/OS condition of price action. In addition normal RSI trend is plotted with trend color from Hull MA. Best Performance with Heiken Ashi Candles.
OB/OS Settings provided
Hull Buy/Sell Signals plotted
Double RSI FAST and DEFAULT signal with crossover
Bar Color applied based on Hull RSI Trend
Hull Trend + RSI + Price Action
TDI - Traders Dynamic Index [Goldminds] with DIV RSI AlertsOriginally from Goldminds. Later modified by Jakub a Babo. I just added RSI DIV alerts. You're welcome. :)
Instruction: once you have have this indicator and press Alt + A to create alert.
Tom Joseph MACD 5-35 for Elliot WavesThis oscillator for the Elliott Theory has been invented by Tom Joseph and it's useful to correctly count the impulsive and corrective waves.
Its difference compared to a simple MACD is the peculiarity to use the ratio between the Fast SMA (default period set to 5) and the Slow SMA (default period se to 35).
The used formula is as below:
( (fast_SMA / slow_SMA) -1 ) * 100
Hope you could find it useful! 😉
Currency Strength V2An update to my original Currency Strength script to include a 2nd timeframe for more market context.
Changed the formatting slightly for better aesthetics, as the extra column and colors became unsightly.
Also added a new setting for "Flat Color", which changes the value background to a simple green/red for above or below 50, rather than using the Color Scale that increases color intensity the further it gets from 50.
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This script measures the strength of the 6 major currencies USD, EUR, GBP, CAD, AUD and JPY.
Simply, it averages the RSI values of a currency vs the 5 other currencies in the basket, and displays each average RSI value in a table with color coding to quickly identify the strongest and weakest currencies over the past 14 bars (or user defined length).
The arrow in the current RSI column shows the difference in average RSI value between current and X bars back (user defined), telling you whether the combined RSI value has gone up or down in the last X bars.
Using the average RSI allows us to get a sense of the currency strength vs an equally weighted basket of the other majors, as opposed to using Indexes which are heavily weighted to 1 or 2 currencies.
The additional security calls for the extra timeframe make this slower to load than the original, but this was a user request so hopefully it will prove worthwhile for some people.
Those who find the loading too slow when switching between charts may be better off still using the original, which is why this is posted as a separate script and not an update to the original.
This is the table with Flat Color option enabled.
GDM Price Power & Under CurrentPrice Power & Under Current.
This is an updated version of the script I had shared earlier namely 'GDM Power Cross'
I have added under current and have removed upper an lower bands.
How to Trade:
Similar to various Moving Average cross over strategies; this indicator can be used to trade crossovers of Price Powers.
Lengths I have used as default are 7, 9 and 21. So when smaller Power Crosses above the Higher Powers; it is a Bullish Crossover and vice versa.
It is observed that most times we do get opportunity to enter in the price range of the candle where cross over has taken place.
Under Current: These are moving averages of above mention Price Powers. it is found that cross overs of Power lines work better when under currents are already in Bullish or Bearish mode. e.g. Bullish Cross Over of Power Lines will work better when Under Current lines are already Bullish or say Smaller Length Under Current is already above the Higher Length Under Current.
Indicator works better for bigger time frames... recommended time frame is 1 day.
Please feel free to Post your views.
Best Regards
Girish Mane
Re-Lions Trading Academy
+91 8080755813
Revolver Oscillator Strategy 1.2 (RSI+UO+MFI)ROS (Revolver Oscillator Strategy)
Version 1.2
Description
This script combines three popular oscillators (RSI, Ultimate Oscillator and MFI) to accurately determine the price momentum of an asset.
Context
- RSI (Relative Strength Index) is a momentum oscillator that measures the speed and change of price movements over a period of time (14).
- Ultimate Oscillator uses three different periods (7, 14, and 28) to represent short, medium, and long-term market trends.
- Money Flow Index (MFI) is a momentum indicator that measures the flow of money into and out over a period of time. It is related to the Relative Strength Index (RSI) but incorporates volume, whereas the RSI only considers price
How does it work?
When a RED bar appears, it means that the three oscillators have exceeded the set thresholds, and it is a SELL signal.
When a GREEN bar appears, it means that the three oscillators are below the set thresholds, and it is a BUY signal.
I recommend leaving the default settings.
Rsi/W%R/Stoch/Mfi: HTF overlay mini-plotsOverlay mini-plots for various indicators. Shows current timeframe; and option to plot 2x higher timeframes (i.e. 15min and 60min on the 5min chart above).
The idea is to de-clutter chart when you just want real-time snippets for an indicator.
Useful for gauging overbought/oversold, across timeframes, at a glance.
~~Indicators~~
~RSI: Relative strength index
~W%R: Williams percent range
~Stochastic
~MFI: Money flow index
~~Inputs~~
~indicator length (NB default is set to 12, NOT the standard 14)
~choose 2x HTFs, show/hide HTF plots
~choose number of bars to show (current timeframe only; HTF plots show only 6 bars)
~horizontal position: offset (bars); shift plots right or left. Can be negative
~vertical position: top/middle/bottom
~other formatting options (color, line thickness, show/hide labels, 70/30 lines, 80/20 lines)
~~tips~~
~should be relatively easy to add further indicators, so long as they are 0-100 based; by editing lines 9 and 11
~change the vertical compression of the plots by playing around with the numbers (+100, -400, etc) in lines 24 and 25
MFI StrategyThis indicator is based on MFI25 and EMA55 and optimized for 1 day graph and slow swing trading to show large moves and tops and bottoms.
You can change the MFI and EMA settings according to your style of trading.
The change from green to red and visa versa are the buying and selling moments.
The yellow line indicates that a change is possibly comming or not really sollid, so with yellow it's your own feeling that is leading.
Always combine this graph with others. F.i. a RSI14 to look for divergenses.
Ps. I'm not a professional or very experienced trader, but this indicator works perfect for me.
Succes.
Squeeze Momentum Indicator [LazyBear] added Alerting + webhookA modified version of Squeeze Momentum Indicator visualizing on Price Chart.
author: @LazyBear, modified by @KivancOzbilgic, and by @dgtrd
I took in all of the information as the script below is based on the V2 Script that @LazyBear posted and then added Alerting based on the math and the conditions that @dgtrd added.
Per the description here:
The Squeeze Indicator measures the relationship between Bollinger Bands and Keltner's Channels to help identify consolidations and signal when prices are likely to break out (whether up or down).
The Squeeze Indicator finds sections of the Bollinger Bands which fall inside the Keltner's Channels, and in this case, the market is said to be in a squeeze (indicator turns off, displayed with grey diamond shapes in this study).
When the volatility increases, so does the distance between the bands. Conversely, when the volatility declines, the distance also decreases, and in such cases, the squeeze is said to be released (indicator turns on, displayed with triangle up or triangle down shapes)
Taking the above information and what was in the script was able to base the alert conditions:
So when the condition:
Squeeze On or No Squeeze = In Squeeze
Squeeze Off = Squeeze Release Long or Squeeze Release Long based off conditions.
There are 2 separate alert Types.
1. App, Pop-up, eMail, play sound and Send email to SMS
2. It Is dedicated to Webhook for your various applications.
Alerting Options
i.imgur.com
App Notification
i.imgur.com
i.imgur.com
Webhook test into Discord
i.imgur.com
Polynomial Regression Derivatives [Loxx]Polynomial Regression Derivatives is an indicator that explores the different derivatives of polynomial position. This indicator also includes a signal line. In a later release, alerts with signal markings will be added.
Polynomial Derivatives are as follows
1rst Derivative - Velocity: Velocity is the directional speed of a object in motion as an indication of its rate of change in position as observed from a particular frame of reference and as measured by a particular standard of time (e.g. 60 km/h northbound). Velocity is a fundamental concept in kinematics, the branch of classical mechanics that describes the motion of bodies.
2nd Derivative - Acceleration: In mechanics, acceleration is the rate of change of the velocity of an object with respect to time. Accelerations are vector quantities (in that they have magnitude and direction). The orientation of an object's acceleration is given by the orientation of the net force acting on that object.
3rd Derivative - Jerk: In physics, jerk or jolt is the rate at which an object's acceleration changes with respect to time. It is a vector quantity (having both magnitude and direction). Jerk is most commonly denoted by the symbol j and expressed in m/s3 (SI units) or standard gravities per second (g0/s).
4th Derivative - Snap: Snap, or jounce, is the fourth derivative of the position vector with respect to time, or the rate of change of the jerk with respect to time. Equivalently, it is the second derivative of acceleration or the third derivative of velocity.
5th Derivative - Crackle: The fifth derivative of the position vector with respect to time is sometimes referred to as crackle. It is the rate of change of snap with respect to time.
6nd Derivative - Pop: The sixth derivative of the position vector with respect to time is sometimes referred to as pop. It is the rate of change of crackle with respect to time.
Included:
Loxx's Expanded Source Types
Loxx's Moving Averages
