[blackcat] L1 Leavitt Convolution SlopeLevel 1
Background
First of all, I would like to thank @ashok1961 for his donation. Second, he made an interesting request: can I write a pine version of LeavittConvSlope.
Function
The indicator uses linear regression of price data to derive slope and acceleration information that helps traders spot trends and turning points. After trying this metric myself, I think it works better with the divergence detector. So I added it. Let me know what you think of this divergence detector.
Remarks
Feedbacks are appreciated.
M-oscillator
+ Dynamic Fibo-Donchian ChannelsThis is my second Donchian Channels indicator (and will probably be my last because how many does one really need). This version is different from my other one in that, well, it's 'dynamic' which simply means that it self adjusts based on the same formula that my Ultimate Moving Average does. What does that mean? It just means that the script takes an average of 8 different length, in this case, highest highs and lowest lows. The user doesn't need to pick a lookback/length/period/what-have-you. The indicator does it all itself. This, I think, makes for a very nice baseline or bias indicator to fit within a system that utilizes something like that. I also think it makes for a more accurate gauge of higher highs and lower lows within a timeframe, because honestly what does it mean to make a lower low over 20 periods or 8 periods or 50 periods? I don't know. What I do know is that traditional Donchian Channels never made much sense to me, but this does.
Additionally, I've kept (I guess that's not 'additionally') the fibonacci retracement levels from my other Donchian Channels indicator. These are calculated off the high and the low of the Donchian Channels themselves. You will see that there are only three retracement levels (.786, .705, .382), one of which is not a fib level, but what some people call the 'OTE,' or 'optimal trade entry.'' If you want more info on the OTE just web search it. So, why no .618 or .236? Reason being that the .618 overlaps the .382, and the .236 is extremely close to the .786. This sounds confusing, but the retracement levels I'm using are derived from the high and low, so it was unnecessary to have all five levels from each. I could have just calculated from the high, or just from the low, and used all the levels, but I chose to just calculate three levels from the high and three from the low because that gives a sort of mirror image balance, and that appeals to me, and the utility of the indicator is the same.
The plot lines are all colored, and I've filled certain zones between them. There is a center zone filled between both .382 levels, an upper and lower zon filled between the .786 and either the high or the low, and a zone between the .705 and .785
If you like the colored zones, but don't like the plots because they cause screen compression, turn off the plots under the "style" tab, or much more simply right click on the price scale and click 'scale price chart only.' Voila! No more screen compression due to a moving average or some other annoyance.
Besides that basis being a nice baseline indicator the various fib bands (or just the high and low bands) make for excellent mean reversion extremes in ranging environments.
There are alerts for candle closes across every line.
Below is an image of the indicator at default settings.
Below is an image of the indicator with the center .382 channel turned off.
Below is an image of the indicator with just the .786/.705 channel showing .
Stochastic GuppyDerived from TradingView's built-in Stochastic indicator. Switched from SMA to EMA and applied Guppy (GMMA) indicator short and long term periods.
RSI mid partition color changeWhen RSI is above 50 our default bias is on buy side and when below 50 our bias is on sell side.
Therefore created 2 zones for easy identification.
Kase Peak Oscillator w/ Divergences [Loxx]Kase Peak Oscillator is unique among first derivative or "rate-of-change" indicators in that it statistically evaluates over fifty trend lengths and automatically adapts to both cycle length and volatility. In addition, it replaces the crude linear mathematics of old with logarithmic and exponential models that better reflect the true nature of the market. Kase Peak Oscillator is unique in that it can be applied across multiple time frames and different commodities.
As a hybrid indicator, the Peak Oscillator also generates a trend signal via the crossing of the histogram through the zero line. In addition, the red/green histogram line indicates when the oscillator has reached an extreme condition. When the oscillator reaches this peak and then turns, it means that most of the time the market will turn either at the present extreme, or (more likely) at the following extreme.
This is both a reversal and breakout/breakdown indicator. Crosses above/below zero line can be used for breakouts/breakdowns, while the thick green/red bars can be used to detect reversals
The indicator consists of three indicators:
The PeakOscillator itself is rendered as a gray histogram.
Max is a red/green solid line within the histogram signifying a market extreme.
Yellow line is max peak value of two (by default, you can change this with the deviations input settings) standard deviations of the Peak Oscillator value
White line is the min peak value of two (by default, you can change this with the deviations input settings) standard deviations of the PeakOscillator value
The PeakOscillator is used two ways:
Divergence: Kase Peak Oscillator may be used to generate traditional divergence signals. The difference between it and traditional divergence indicators lies in its accuracy.
PeakOut: The second use is to look for a Peak Out. A Peak Out occurs when the histogram breaks beyond the PeakOut line and then pulls back. A Peak Out through the maximum line will be displayed magenta. A Peak Out, which only extends through the Peak Min line is called a local Peak Out, and is less significant than a normal Peak Out signal. These local Peak Outs are to be relied upon more heavily during sideways or corrective markets. Peak Outs may be based on either the maximum line or the minimum line. Maximum Peak Outs, however, are rarer and thus more significant than minimum Peak Outs. The magnitude of the price move may be greater following the maximum Peak Out, but the likelihood of the break in trend is essentially the same. Thus, our research indicates that we should react equally to a Peak Out in a trendy market and a Peak Min in a choppy or corrective market.
Included:
Bar coloring
Alerts
HMA Slope Variation [Loxx]HMA Slope Variation is an indicator that uses HMA 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 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
Alets
Signals
Bar coloring
Loxx's Expanded Source Types
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
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.
________________________________________________________________________________
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.
