Keltner Channel With User Selectable Moving AvgKeltner Channel with user options to calculate the moving average basis and envelopes from a variety of different moving averages.
The user selects their choice of moving average, and the envelopes automatically adjust. The user may select a MA that reacts faster to volatility or slower/smoother.
Added additional options to color the envelopes or basis based on the current trend and alternate candle colors for envelope touches. The script has a rainbow gradient by default based on RSI.
Options (generally from slower/smoother to faster/more responsive to volatility):
SMMA,
SMA,
Donchian, (Note: Selecting Donchian will just convert this indicator to a regular Donchian Channel)
Tillson T3,
EMA,
VWMA,
WMA,
EHMA,
ALMA,
LSMA,
HMA,
TEMA
Value Added:
Allows Keltner Channel to be calculated from a variety of moving averages other than EMA/SMA, including ones that are well liked by traders such as Tillson T3, ALMA, Hull MA, and TEMA.
Glossary:
The Hull Moving Average ( HMA ), developed by Alan Hull, is an extremely fast and smooth moving average . In fact, the HMA almost eliminates lag altogether and manages to improve smoothing at the same time.
The Exponential Hull Moving Average is similar to the standard Hull MA, but with superior smoothing. The standard Hull Moving Average is derived from the weighted moving average ( WMA ). As other moving average built from weighted moving averages it has a tendency to exaggerate price movement.
Weighted Moving Average: A Weighted Moving Average ( WMA ) is similar to the simple moving average ( SMA ), except the WMA adds significance to more recent data points.
Arnaud Legoux Moving Average: ALMA removes small price fluctuations and enhances the trend by applying a moving average twice, once from left to right, and once from right to left. At the end of this process the phase shift (price lag) commonly associated with moving averages is significantly reduced. Zero-phase digital filtering reduces noise in the signal. Conventional filtering reduces noise in the signal, but adds a delay.
Least Squares: Based on sum of least squares method to find a straight line that best fits data for the selected period. The end point of the line is plotted and the process is repeated on each succeeding period.
Triple EMA (TEMA) : The triple exponential moving average (TEMA) was designed to smooth price fluctuations, thereby making it easier to identify trends without the lag associated with traditional moving averages (MA). It does this by taking multiple exponential moving averages (EMA) of the original EMA and subtracting out some of the lag.
Running (SMoothed) Moving Average: A Modified Moving Average (MMA) (otherwise known as the Running Moving Average (RMA), or SMoothed Moving Average (SMMA)) is an indicator that shows the average value of a security's price over a period of time. It works very similar to the Exponential Moving Average, they are equivalent but for different periods (e.g., the MMA value for a 14-day period will be the same as EMA-value for a 27-days period).
Volume-Weighted Moving Average: The Volume-weighted Moving Average (VWMA) emphasizes volume by weighing prices based on the amount of trading activity in a given period of time. Users can set the length, the source and an offset. Prices with heavy trading activity get more weight than prices with light trading activity.
Tillson T3: The Tillson moving average a.k.a. the Tillson T3 indicator is one of the smoothest moving averages and is both composite and adaptive.
Least Squares Moving Average (LSMA)
Buy and Sell with Master_in_chart-ind. [V1]This script indicates the Buy and Sell positions on your chart. In addition, it shows entry price , stop loss and possible targets on the chart. The same information are shown in a table where you can find the position type (long/short) in green and red color, entry point, stop-loss (always in red) and targets.
The targets are defined by Risk to Reward ratios 1:1, 1:1.5 and 1:2.
the labels appears when the all conditions are satisfied.
Interesting part of the script is the alert function. Here one can set the script for different
securities and activate alert in TV.
In summary, one can change and tune the setting of the indicator easily by clicking on the gear icon. In the setting, there are four sections. First section sets the slop-loss. Second section activates and shows the super trend indicator. Third section is designed to tune the signals. Finally, you can apply the script on five different symbols at different time-frames. Here you can set alarm to alert you the signals.
I hope you enjoy it!
Moving Average PanelThis indicator calculates many different moving averages and displays whether they are increasing or decreasing as a panel/table instead of a plot. Rows/columns can be removed from the table as needed in the options menu, there is also a mobile friendly/compact option as well as a location option.
Note: This script is large and may take a few moments to load.
Note: If there is not enough data, will default to bearish/decreasing.
Value Added
This is the most complete and transparent moving average panel/table indicator. Unlike things such as the Technical Ratings, you can see what components are increasing or decreasing.
There may be some advantage in judging if a trend is likely to reverse or not based on the MA's with less lag.
Good for quick screening of charts.
Triple Colored Least Squares Moving Average + Crossover AlertsThis script is forked from the ‘ Double Colored Least Squares Moving Average + Crossover Alerts ‘ from @IronKnightmare.
First release & notes : 2021-11-03.
Overview:
The Least Squares Moving Average is used mainly as a crossover signal to identify bullish or bearish trends. When a shorter duration line cross a longer one a trend can be identified. When multiple lines or the price action cross a longterm trend the confirmation can be further validated. Tradingview contains already some indicators with 1 or two LSMA trendlines that can be configured and toggled.
The original script that I forked had two LSMA lines that could be plotted with other valuable functions, I added a third for further confirmation as some trading systems will use three lines or some combination of those for validation.
Usage:
In inputs
- You will see LSMA 1, LSMA 2 & LSMA 3. The default values are 40, 100 & 400 representing the number of periods plotted by that line : fast, medium and slow changing trendlines will be plotted. The offset value and source are standard for most scripts.
In Style
- You can toggle LSMA 1, 2 or 3 and any combination of those. There are much more possibilities this way.
- For each LSMA, Color 0 & Color 1 are for coloring the slope of the trendline,
- Color 0 for rising slope,
- Color 1 for descending slope.
- The script will automatically color the rise or fall of the trendline accordingly. You can also set one identical color in both slopes for one unique color.
- The ‘ Long Crossover 1 on 2 ’ is a signal for when the LSMA 1 cross over the LSMA 2, usually a shorter periods trendline, more volatile, climbing over the medium term one. A Signal will be traced on the chart at that crossing, you can configure this. The ‘Short Crossover 1 on 2’ is when the LSMA 1 cross under the LSMA 2, a signal will be traced on the chart accordingly.
- The Long Crossover 1 on 3 & Short Crossover 1 on 3 act on the same principle, although the crossing of the fast LSMA on the long / slow LSMA are used. Both can be toggled.
- The ‘ Background Coloring Line 1 : 0-Neutral, 1-Up, 2-Down ’ is an optional background coloring for the LSMA1 line. This can provide additional information at a quick glance, especially if you combine the two other lines backgrounds, the partial transparency will compound.
Multi-Length Stochastic Average [LuxAlgo]This indicator returns the average of stochastic oscillators with periods ranging from 4 to length . This allows for a slightly more reactive oscillator as well as having information regarding the position of the price relative to rolling maximums/minimums of different periods.
We introduce settings that allow for pre and post-smoothing, with selectable smoothing methods and periods for both steps.
Settings
Length: Period of the indicator, determine the maximum period of the stochastic oscillator used in the average
Source: Source input of the indicator
Pre-Smoothing (1st Input): Degree of smoothing applied to the source input
Pre-Smoothing (2nd Input): Pre-Smoothing Method
Post-Smoothing (1st Input): Degree of smoothing applied to the final oscillator output
Post-Smoothing (2nd Input): Post-Smoothing Method
Smoothing methods include a simple moving average, a triangular moving average, and a least-squares moving average (this method can induce overshoots during the post-smoothing step). The user can also select "None".
Usages
The "multi-length" aspect of technical indicators is something that hasn't been deeply explored yet such indicators can give us information regarding both short-term and long-term information which was the motivation for the creation of the indicator.
The Multi-length Stochastic Average allows us to quantify the price position relative to a multitude of highest/lowest levels.
In the example above the oscillator returns the average of stochastic oscillators with periods ranging from 4 to 20, as well as multiple rolling minimums with periods ranging from 4 to 20. We can see that when the price is equal to all rolling minimums the oscillator is equal to 0, the oscillator would return 100 if the price were equal to all rolling maximums with periods in that same range.
The oscillator can be interpreted like any scaled oscillator and can be used to estimate trend direction as well as trend strength.
Here we only make of use pre-smoothing by using a period 20 simple moving average. The indicator graphical elements such as colors/circles can help us determine potential directions trends might take.
Circles are displayed when the oscillator crosses over/under the 20/80 level. Such conditions offer better timing than waiting for the oscillator to be greater/lower than 50 and are less subjective to noise than simply looking at the direction taken by the oscillator. However, it can suffer from potential retracements in a trend more easily, this is illustrated in the chart above.
Aroon Strategy long onlyThis is a simple long only strategy made of Aroon and Least Square moving average.
The rules are simple:
Long entry = crossover of upper part with the lower part from aroon and close of the candle is above the moving average
Long exit = crossunder of upper part with the lower part from aroon and close of the candle is below the moving average
IF you have any questions let me know !
Volume, Momentum and Volatility weighted moving averageMoving averages are filters on price data. This moving average creates a filter which factors in:
- the price RSI or it's Momentum
- the volume RSI
- the RVI or Volatility
Each factor is put through a least squares filter to smooth them first.
Then the factors are used to build a coefficient for an exponentially weighted average.
The chart above shows a comparison of standard average types with this script.
This is useful if you are looking for a moving average based trigger and do not wish to react to candle noise price action.
Moving Average ToolThe Moving Average Tool is the only indicator you will ever need to plot MA lines. It comes loaded with 9 different types of moving averages so traders can lay down any line at any length. There is also an option to plot a trigger line. Features: SMA , SMMA, EMA, LSMA, ZLSMA, HULL, LWMA, VWMA and ALMA. Simply pick an average type and enter the desired length.
LSMA CrossoverThis is a simple script designed to help filter out bad trades. LSMA is a trend king and by using the 21,200 and 1000 length lines traders can get a clear view of where price action is travelling. This indicator is the perfect companion to the LSMA Wave Rider indicator. Once a pullback is discovered (price action crosses under blue or white line) Traders can use LSMA Wave Rider to locate perfect entry point.
Least Squares Moving Average follows these rules:
When price crosses over it signals a bull trend.
When price crosses under it signals bear trend.
When price stays close or on the line sideways action is to be expected.
The direction of the line shows the direction of the trend.
Here is an example of finding good trades. Price action pulls below white or blue line.
Another example of what a pullback looks like.
This example shows how to find trend using crossovers.
Another example how trend can be found but by using line direction.
LSMA Wave Rider can be found here:
LSMA Wave RiderThe LSMA Wave Rider uses Least Squares Moving Average to make a fast oscillator ideal for scalping lower timeframe charts. Upper and lower bands contract during pullback and expand as it "booms". The perfect entry is the first crossover after bands expand. This is a great tool for entering trades.
The above image shows two examples of perfect entries:
1. The upper and lower bands contract getting tighter as it pulls back.
2. The upper and lower bands then begin to expand as it gets ready to fly.
3. The oscillator crosses over showing entry point.
* Please note that this strategy may not work during major downtrends. *
Oscillator 2 is used to detect diversions. Reduce the number to pick up shorter diversions and increase to 200 to pickup larger diversions.
PSAR using Moving Linear Regression (LSMA)Works exactly as the standard PSAR with the only difference that a Moving Linear Regression Line (=Least Squares Moving Average, LSMA) is used as input.
So the PSAR flip is triggered not by price itself but by the LSMA line.
StochasticLSMAIntroducing the worlds first StochasticLSMA. A powerful Stochastic that shows trends and highlights market tops/bottoms. This may be the ultimate tool in locating tops and bottoms on any timeframe.
How to tune the settings:
Depending on what chart you use and what timeframe you are on it may be necessary to dial in the settings to correctly locate the tops and bottoms you wish to trade. Here are some settings to try:
32 < Finds longer term Tops and bottoms.
21 < Great for longer term tops and bottoms on hourly and daily charts.
19 < If 21 is not accurate enough.
17 < If 19 is not accurate enough.
13 <Great setting for short to mid range tops and bottoms and lower timeframes. (default)
11
9
6 < Excellent for finding shorter term tops and bottoms on all timeframes.
*Changing the “Stochastic Price” setting to “hl3” or “low” can help hone in on lows and highs.*
It can be very useful to to use 2 StochasticLSMA indicators with different settings. Here we have two examples how to use multiple indicators on the ETHUSD Daily chart. One set on 13 and the other on 6. A trader can enter on the 6 length indicator and exit on the 13. It also shows how it has the potential to filter out “bad entries” by matching the bottoms.
Example of different length settings.
Alerts:
Overbought: K line crosses over overbought line (Red Dot)
Oversold: K line crosses under oversold line (Green Dot)
Buy Signal: K line is under oversold line and trends up (Green Up Arrow)
Sell Signal: K line is over oversold line and trends down (Red Down Arrow)
Last Chance Sell Signal: As K line leaves overbought line (Yellow Dot)
Last Chance Buy Signal: As K line leaves oversold line (Yellow Dot)
Example of alert signals and trigger enabled in settings
macZLSMA - Overlay**Overlay Version** Macd that shows instantaneous trend using ZLSMA. This crossover has the ability to reveal trend directions before it happens. With multi time frame option.
Above image shows 1 hour timeframe using 12hour setting on indicator
Example with 1 hour timeframe:
Example with 1 hour timeframe using daily setting on indicator:
Non overlay version available here:
macZLSMAMacd that shows instantaneous trend using ZLSMA. This crossover has the ability to reveal trend directions before it happens.
ZLSMA - Zero Lag LSMAAn almost zero lag version of the LSMA (Least Squares Moving Average)
Gives instant linear regression of current price action.
This line works with the same rules as its "laggy" counterpart the LSMA:
When price crosses over it signals a bull trend.
When price crosses under it signals bear trend.
When price stays close or on the line sideways action is to be expected.
The direction of the line shows the direction of the trend.
Multiple Moving Averages for Heikin Ashi I want to give credits to @QuantNomad, i got the heikin ashi part of the script from this open script /0iKy7lyG-QuantNomad-Heikin-Ashi-PSAR-Strategy/;
and to the other guy that provided a 17 type of moving average script open source but i forgot his name, if someone remember please tell me.
My idea was to see how the different types of moving averages behaves in a Heinkin Ashi chart, you can change to more than 15 types of Moving Average and use it the way you want it.
For the source of the moving averages i used a simple moving average of 1 period using the high of the heikin ashi candle, low of it and divided by 2 as the source of the different types of moving averages.
Different types of Moving Averages
Moving Average Types
SMA ---> Simple
WMA ---> Weighted
VWMA ---> Volume Weighted
EMA ---> Exponential
DEMA ---> Double EMA
ALMA ---> Arnaud Legoux
HMA ---> Hull MA
SMMA ---> Smoothed
LSMA ---> Least Squares
KAMA ---> Kaufman Adaptive
TEMA ---> Triple EMA
ZLEMA ---> Zero Lag
FRAMA ---> Fractal Adaptive
VIDYA ---> Variable Index Dynamic Average
JMA ---> Jurik Moving Average
T3 ---> Tillson
TRIMA ---> Triangular
The type of moving average you select will appear in a separated chart with Heikin Ashi candles, like in the image above.
Multi Timeframe Moving Average [xdecow]This indicator plots a moving average of 4 different timeframes.
The types of averages available are: SMA, EMA, DEMA, VWMA, RMA, WMA.
Polynomial Regression Bands + Channel [DW]This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support.
This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs).
I also designed this study with the intent of showcasing some of the capabilities and potential applications of TV's fantastic new array functions.
Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of nth degree (order).
For clarification, linear regression can also be described as a first order polynomial regression. The process of deriving linear, quadratic, cubic, and higher order polynomial relationships is all the same.
In addition, although deriving a polynomial regression equation results in a nonlinear output, the process of solving for polynomials by least squares is actually a special case of multiple linear regression.
So, just like in multiple linear regression, polynomial regression can be solved in essentially the same way through a system of linear equations.
In this study, you are first given the option to smooth the input data using the 2 pole Super Smoother Filter from John Ehlers.
I chose this specific filter because I find it provides superior smoothing with low lag and fairly clean cutoff. You can, of course, implement your own filter functions to see how they compare if you feel like experimenting.
Filtering noise prior to regression calculation can be useful for providing a more stable estimation since least squares regression can be rather sensitive to noise.
This is especially true on lower sampling lengths and higher degree polynomials since the regression output becomes more "overfit" to the sample data.
Next, data arrays are populated for the x-axis and y-axis values. These are the main datasets utilized in the rest of the calculations.
To keep the calculations more numerically stable for higher periods and orders, the x array is filled with integers 1 through the sampling period rather than using current bar numbers.
This process can be thought of as shifting the origin of the x-axis as new data emerges.
This keeps the axis values significantly lower than the 10k+ bar values, thus maintaining more numerical stability at higher orders and sample lengths.
The data arrays are then used to create a pseudo 2D matrix of x power sums, and a vector of x power*y sums.
These matrices are a representation the system of equations that need to be solved in order to find the regression coefficients.
Below, you'll see some examples of the pattern of equations used to solve for our coefficients represented in augmented matrix form.
For example, the augmented matrix for the system equations required to solve a second order (quadratic) polynomial regression by least squares is formed like this:
(∑x^0 ∑x^1 ∑x^2 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 | ∑(x^2)y)
The augmented matrix for the third order (cubic) system is formed like this:
(∑x^0 ∑x^1 ∑x^2 ∑x^3 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 ∑x^4 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 ∑x^5 | ∑(x^2)y)
(∑x^3 ∑x^4 ∑x^5 ∑x^6 | ∑(x^3)y)
This pattern continues for any n ordered polynomial regression, in which the coefficient matrix is a n + 1 wide square matrix with the last term being ∑x^2n, and the last term of the result vector being ∑(x^n)y.
Thanks to this pattern, it's rather convenient to solve the for our regression coefficients of any nth degree polynomial by a number of different methods.
In this script, I utilize a process known as LU Decomposition to solve for the regression coefficients.
Lower-upper (LU) Decomposition is a neat form of matrix manipulation that expresses a 2D matrix as the product of lower and upper triangular matrices.
This decomposition method is incredibly handy for solving systems of equations, calculating determinants, and inverting matrices.
For a linear system Ax=b, where A is our coefficient matrix, x is our vector of unknowns, and b is our vector of results, LU Decomposition turns our system into LUx=b.
We can then factor this into two separate matrix equations and solve the system using these two simple steps:
1. Solve Ly=b for y, where y is a new vector of unknowns that satisfies the equation, using forward substitution.
2. Solve Ux=y for x using backward substitution. This gives us the values of our original unknowns - in this case, the coefficients for our regression equation.
After solving for the regression coefficients, the values are then plugged into our regression equation:
Y = a0 + a1*x + a1*x^2 + ... + an*x^n, where a() is the ()th coefficient in ascending order and n is the polynomial degree.
From here, an array of curve values for the period based on the current equation is populated, and standard deviation is added to and subtracted from the equation to calculate the channel high and low levels.
The calculated curve values can also be shifted to the left or right using the "Regression Offset" input
Changing the offset parameter will move the curve left for negative values, and right for positive values.
This offset parameter shifts the curve points within our window while using the same equation, allowing you to use offset datapoints on the regression curve to calculate the LSMA and bands.
The curve and channel's appearance is optionally approximated using Pine's v4 line tools to draw segments.
Since there is a limitation on how many lines can be displayed per script, each curve consists of 10 segments with lengths determined by a user defined step size. In total, there are 30 lines displayed at once when active.
By default, the step size is 10, meaning each segment is 10 bars long. This is because the default sampling period is 100, so this step size will show the approximate curve for the entire period.
When adjusting your sampling period, be sure to adjust your step size accordingly when curve drawing is active if you want to see the full approximate curve for the period.
Note that when you have a larger step size, you will see more seemingly "sharp" turning points on the polynomial curve, especially on higher degree polynomials.
The polynomial functions that are calculated are continuous and differentiable across all points. The perceived sharpness is simply due to our limitation on available lines to draw them.
The approximate channel drawings also come equipped with style inputs, so you can control the type, color, and width of the regression, channel high, and channel low curves.
I also included an input to determine if the curves are updated continuously, or only upon the closing of a bar for reduced runtime demands. More about why this is important in the notes below.
For additional reference, I also included the option to display the current regression equation.
This allows you to easily track the polynomial function you're using, and to confirm that the polynomial is properly supported within Pine.
There are some cases that aren't supported properly due to Pine's limitations. More about this in the notes on the bottom.
In addition, I included a line of text beneath the equation to indicate how many bars left or right the calculated curve data is currently shifted.
The display label comes equipped with style editing inputs, so you can control the size, background color, and text color of the equation display.
The Polynomial LSMA, high band, and low band in this script are generated by tracking the current endpoints of the regression, channel high, and channel low curves respectively.
The output of these bands is similar in nature to Bollinger Bands, but with an obviously different derivation process.
By displaying the LSMA and bands in tandem with the polynomial channel, it's easy to visualize how LSMAs are derived, and how the process that goes into them is drastically different from a typical moving average.
The main difference between LSMA and other MAs is that LSMA is showing the value of the regression curve on the current bar, which is the result of a modelled relationship between x and the expected value of y.
With other MA / filter types, they are typically just averaging or frequency filtering the samples. This is an important distinction in interpretation. However, both can be applied similarly when trading.
An important distinction with the LSMA in this script is that since we can model higher degree polynomial relationships, the LSMA here is not limited to only linear as it is in TV's built in LSMA.
Bar colors are also included in this script. The color scheme is based on disparity between source and the LSMA.
This script is a great study for educating yourself on the process that goes into polynomial regression, as well as one of the many processes computers utilize to solve systems of equations.
Also, the Polynomial LSMA and bands are great components to try implementing into your own analysis setup.
I hope you all enjoy it!
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NOTES:
- Even though the algorithm used in this script can be implemented to find any order polynomial relationship, TV has a limit on the significant figures for its floating point outputs.
This means that as you increase your sampling period and / or polynomial order, some higher order coefficients will be output as 0 due to floating point round-off.
There is currently no viable workaround for this issue since there isn't a way to calculate more significant figures than the limit.
However, in my humble opinion, fitting a polynomial higher than cubic to most time series data is "overkill" due to bias-variance tradeoff.
Although, this tradeoff is also dependent on the sampling period. Keep that in mind. A good rule of thumb is to aim for a nice "middle ground" between bias and variance.
If TV ever chooses to expand its significant figure limits, then it will be possible to accurately calculate even higher order polynomials and periods if you feel the desire to do so.
To test if your polynomial is properly supported within Pine's constraints, check the equation label.
If you see a coefficient value of 0 in front of any of the x values, reduce your period and / or polynomial order.
- Although this algorithm has less computational complexity than most other linear system solving methods, this script itself can still be rather demanding on runtime resources - especially when drawing the curves.
In the event you find your current configuration is throwing back an error saying that the calculation takes too long, there are a few things you can try:
-> Refresh your chart or hide and unhide the indicator.
The runtime environment on TV is very dynamic and the allocation of available memory varies with collective server usage.
By refreshing, you can often get it to process since you're basically just waiting for your allotment to increase. This method works well in a lot of cases.
-> Change the curve update frequency to "Close Only".
If you've tried refreshing multiple times and still have the error, your configuration may simply be too demanding of resources.
v4 drawing objects, most notably lines, can be highly taxing on the servers. That's why Pine has a limit on how many can be displayed in the first place.
By limiting the curve updates to only bar closes, this will significantly reduce the runtime needs of the lines since they will only be calculated once per bar.
Note that doing this will only limit the visual output of the curve segments. It has no impact on regression calculation, equation display, or LSMA and band displays.
-> Uncheck the display boxes for the drawing objects.
If you still have troubles after trying the above options, then simply stop displaying the curve - unless it's important to you.
As I mentioned, v4 drawing objects can be rather resource intensive. So a simple fix that often works when other things fail is to just stop them from being displayed.
-> Reduce sampling period, polynomial order, or curve drawing step size.
If you're having runtime errors and don't want to sacrifice the curve drawings, then you'll need to reduce the calculation complexity.
If you're using a large sampling period, or high order polynomial, the operational complexity becomes significantly higher than lower periods and orders.
When you have larger step sizes, more historical referencing is used for x-axis locations, which does have an impact as well.
By reducing these parameters, the runtime issue will often be solved.
Another important detail to note with this is that you may have configurations that work just fine in real time, but struggle to load properly in replay mode.
This is because the replay framework also requires its own allotment of runtime, so that must be taken into consideration as well.
- Please note that the line and label objects are reprinted as new data emerges. That's simply the nature of drawing objects vs standard plots.
I do not recommend or endorse basing your trading decisions based on the drawn curve. That component is merely to serve as a visual reference of the current polynomial relationship.
No repainting occurs with the Polynomial LSMA and bands though. Once the bar is closed, that bar's calculated values are set.
So when using the LSMA and bands for trading purposes, you can rest easy knowing that history won't change on you when you come back to view them.
- For those who intend on utilizing or modifying the functions and calculations in this script for their own scripts, I included debug dialogues in the script for all of the arrays to make the process easier.
To use the debugs, see the "Debugs" section at the bottom. All dialogues are commented out by default.
The debugs are displayed using label objects. By default, I have them all located to the right of current price.
If you wish to display multiple debugs at once, it will be up to you to decide on display locations at your leisure.
When using the debugs, I recommend commenting out the other drawing objects (or even all plots) in the script to prevent runtime issues and overlapping displays.
Every single moving average (ALMA, EMA, HMA, KAMA, RMA, SMA...)So you may be looking at the graph and thinking "this is a mess", and I agree.
The purpose of this script is to plot in the same graph every single type of moving average that I could think of, so you can find the ones that are better for your timeframe and for your asset. Once you add it, disable those types that doesn't seem to serve your purpose, until you can select one you like.
The average types are: ALMA, EMA, HMA, KAMA, RMA, SMA, SWMA, VIDYA, VWAP, VWMA, and WMA. Each one is ploted two times (except SWMA and VWAP), one with a short period, and another with a long, both of which you can configure.
Bull vs Bear Power by DGTElder-Ray Bear and Bull Power
Dr. Alexander Elder cleverly named his first indicator Elder-Ray because of its function, which is designed to see through the market like an X-ray machine. Developed in 1989, the Elder-Ray indicator can be applied to the chart of any security and helps traders determine the strength of competing groups of bulls and bears by gazing under the surface of the markets for data that may not immediately be ascertainable from a superficial glance at prices
The Elder-Ray indicator is comprised by three elements – Bear Power, Bull Power and a 13-period Exponential Moving Average.
As the high price of any candle shows the maximum power of buyers and the low price of any candle shows the maximum power of sellers, Elder uses the 13-period EMA in order to present the average consensus of price value. Bull power shows whether buyers are capable of pushing prices above the average consensus of value. Bear power shows whether sellers are capable of pushing prices below the average consensus of value. Mathematically, Bull power is the result of subtracting the 13-period EMA from the high price of the day, and Bear power is the result of subtracting the 13-period EMA from the low price of the day.
What does this study implements
Attempts to customize interpretation of Alexander Elder's Elder-Ray Indicator (Bull and Bear Power) by
• adding additional insights to support/confirm Elder’s strategy with different indicators related with the Elder’s concept
• providing different options of visualization of the indicator
• providing smoothing capability
Other Indicators to support/confirm Elder-Ray Indicator:
Colored Directional Movement Index (CDMI) , a custom interpretation of J. Welles Wilder’s Directional Movement Index (DMI) , where :
DMI is a collection of three separate indicators ( ADX , +DI , -DI ) combined into one and measures the trend’s strength as well as its direction
CDMI is a custom interpretation of DMI which presents ( ADX , +DI , -DI ) with a color scale - representing the trend’s strength, color density - representing momentum/slope of the trend’s strength, and triangle up/down shapes - representing the trend’s direction. CDMI provides all the information in a single line with colored triangle shapes plotted on the top. DMI can provide quality information and even trading signals but it is not an easy indicator to master, whereus CDMI simplifies its usage.
Alexander Elder considers the slope of the EMA, which gives insight into the recent trend whether is up or down, and CDMI adds additional insight of verifying/confirming the trend as well as its strength
Note : educational content of how to read CDMI can be found in ideas section named as “Colored Directional Movement Index”
different usages of CDMI can be observed with studies “Candlestick Patterns in Context by DGT", “Ichimoku Colored SuperTrend + Colored DMI by DGT”, “Colored Directional Movement and Bollinger Band's Cloud by DGT”, and “Technical Analyst by DGT”
Price Convergence/Divergence , if we pay attention to mathematical formulations of bull power, bear power and price convergence/divergence (also can be expressed as price distance to its ma) we would clearly observe that price convergence/divergence is in fact the result of how the market performed based on the fact that we assume 13-period EMA is consensus of price value. Then, we may assume that the price convergence/divergence crosses of bull power, or bear power, or sum of bull and bear power could be considered as potential trading signals
Additionally, price convergence/divergence visualizes the belief that prices high above the moving average or low below it are likely to be remedied in the future by a reverse price movement
Alternatively, Least Squares Moving Average of Price Convergence/Divergence (also known as Linear Regression Curve) can be plotted instead of Price Convergence/Divergence which can be considered as a smoothed version of Price Convergence/Divergence
Note : different usages of Price Convergence/Divergence can be observed with studies “Trading Psychology - Fear & Greed Index by DGT”, “Price Distance to its MA by DGT”, “P-MACD by DGT”, where “Price Distance to its MA by DGT” can also be considered as educational content which includes an article of a research carried on the topic
Options of Visualization
Bull and Bear Power plotted as two separate
• histograms
• lines
• bands
Sum of Bull and Bear Power plotted as single
• histogram
• line
• band
Others
Price Convergence/Divergence displayed as Line
CDMI is displayed as single colored line of triangle shapes, where triangle shapes displays direction of the trend (triangle up represents bull and triangle down represent bear), colors of CDMI displays the strength of the trend (green – strong bullish, red – strong bearish, gray – no trend, yellow – week trend)
In general with this study, color densities also have a meaning and aims to displays if the value of the indicator is falling or growing, darker colors displays more intense move comparing to light one
Note : band's upper and lower levels are calculated by using standard deviation build-in function with multiply factor of 0.236 Fibonacci’s ratio (just a number for our case, no any meaning)
Smoothing
No smoothing is applied by default but the capability is added in case Price Convergence/Divergence Line is assumed to be used as a signal line it will be worth smoothing the bear, bull or sum of bear and bull power indicators
Interpreting Elder-Ray Indicator, according to Dr. Alexander Elder
Bull Power should remain positive in normal circumstances, while Bear Power should remain negative in normal circumstances. In case the Bull Power indicator enters into negative territory, this implies that sellers have overcome buyers and control the market. In case the Bear Power indicator enters into positive territory, this indicates that buyers have overcome sellers and control the market. A trader should not go long at times when the Bear Power indicator is positive and he/she should not go short at times when the Bull Power indicator is negative.
13-period EMAs slope can be used in order to identify the direction of the major trend. According to Elder, the most reliable buy signals are generated, when there is a bullish divergence between the Bear Power indicator and the price (Bear Power forms higher lows, while the market forms lower lows). The most reliable sell signals are generated, when there is a bearish divergence between the Bull Power indicator and the price (Bull Power forms lower highs, while the market forms higher highs).
There are four basic conditions, required to go long or short, with the use of the Elder-Ray method alone.
In order to go long:
1. The market is in a bull trend, as indicated by the 13-period EMA
2. Bear Power is in negative territory, but increasing
3. The most recent Bull Power top is higher than its prior top
4. Bear Power is going up from a bullish divergence
The last two conditions are optional that fine-tune the buying decision
In order to go short:
1. The market is in a bear trend, as indicated by the 13-period EMA
2. Bull Power is in positive territory, but falling
3. The most recent Bear Power bottom is lower than its prior bottom
4. Bull Power is falling from a bearish divergence
The last two conditions are optional, they provide a stronger signal for shorting but they are not absolutely essential
If a trader is willing to add to his/her position, he/she needs to:
1. add to his/her long position, when the Bear Power falls below zero and then climbs back into positive territory
2. add to his/her short position, when the Bull Power increases above zero and then drops back into negative territory.
note : terminology of the definitions used herein are as per TV dictionary
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Rolling Linear Regression ChannelCompute a rolling linear regression channel, the value of the bands at a precise point in time is equal to the last value of the corresponding extremity of a regression channel of equal length and mult at that point. The bands are made by adding/subtracting the RMSE of a linear regression to a least-squares moving average.
Settings
Length : Period of the indicator
Mult : Multiplication factor for the RMSE, determine the distance between the upper and lower extremities
Src : Input data for the indicator
Gradient : Determine if the area within the bands must be filled with a gradient, a color closer to blue indicates that src is close/superior to the upper band while a color closer to red indicates that src is close/inferior to the lower band. True by default, if false no filling is applied.
Usage
The indicator can be used like any other band indicator. Because the indicator makes use of the LSMA we can expect the bands to be more reactive to price changes, the indicator can also be more accurate when the bands must act as support and resistance as long as the underlying trend in the price is linear.
In blue/red the indicator, with the Bollinger bands in dark green with the same length/mult settings.
Since the indicator is derived from the linear regression channel indicator it can also be used to look at how drastically the regression channels changed over time, that is if the bands look linear, then it implies that the channel didn't change a lot with the arrival of new closing prices.
Details
As said the last value of each band is equal to the last value of the corresponding extremity of a linear regression channel.
In blue/red the indicator, with the linear regression channel in orange with the same length/mult settings, the last circle of the upper band is equal to the last value of the upper regression channel, same thing with the lower band, you can see this more clearly using the replay mode.
Notes
Thx to the twitter fans for their feedback and support, note that I often ask about feedback or about what kind of indicators I should do next on Twitter.
Computing The Linear Regression Using The WMA And SMAPlot a linear regression channel through the last length closing prices, with the possibility to use another source as input. The line is fit by using linear combinations between the WMA and SMA thus providing both an interesting and efficient method. The results are the same as the one provided by the built-in linear regression, only the computation differ.
Settings
length : Number of inputs to be used.
src : Source input of the indicator.
mult : Multiplication factor for the RMSE, determine the distance between the upper and lower level.
Usage
In technical analysis a linear regression can provide an estimate of the underlying trend in the price, this result can be extrapolated to have an estimate of the future evolution of the trend, while the upper and lower level can be used as support and resistance levels.
The slope of the fitted line indicates both the direction and strength of the trend, with a positive slope indicating an up-trending market while a negative slope indicates a down-trending market, a steeper line indicates a stronger trend.
We can see that the trend of the S&P500 in this chart is approximately linear, the upper and lower levels were previously tested and might return accurate support and resistance points in the future.
By using a linear regression we are making the following assumptions:
The trend is linear or approximately linear.
The cycle component has an approximately constant amplitude (this allows the upper and lower level to be more effective)
The underlying trend will have the same evolution in the future
In the case where the growth of a trend is non-linear, we can use a logarithmic scale to have a linear representation of the trend.
Details
In a simple linear regression, we want to the slope and intercept parameters that minimize the sum of squared residuals between the data points and the fitted line
intercept + x*slope
Both the intercept and slope have a simple solution, you can find both in the calculations of the lsma, in fact, the last point of the lsma with period length is equal to the last point of a linear regression fitted through the same length data points. We have seen many times that the lsma is an FIR filter with a series of coefficients representing a linearly decaying function with the last coefficients having a negative value, as such we can calculate the lsma more easily by using a linear combination between a WMA and SMA: 3WMA - 2SMA , this linear combination gives us the last point of our linear regression, denoted point B .
Now we need the first point of our linear regression, by using the calculations of the lsma we get this point by using:
intercept + (x-length+1)*slope
If we get the impulse response of such lsma we get
In blue the impulse response of a standard lsma, in red the impulse response of the lsma using the previous calculation, we can see that both are the same with the exception that the red one appears as being time inverted, the first coefficients are negative values and as such we also have a linear operation involving the WMA and SMA but with inverted terms and different coefficients, therefore the first point of our linear regression, denoted point A , is given by 4SMA - 3WMA , we then only need to join these two points thanks to "line.new".
The levels are simply equal to the fitted line plus/minus the root mean squared error between the fitted line and the data points, right now we only have two points, we need to find all the points of the fitted line, as such we first need to find the slope, which can be calculated by diving the vertical distance between B and A (the rise) with the horizontal distance between B and A (the run), that is
(A - B)/(length-1)
Once done we can find each point of our line by using
B + slope*i
where i is the position of the point starting from B, i=0 give B since B + slope*0 = B , then we continue for every i , we then only need to sum the squared distance between each closing prices at position i and the point found at that same position, we divide by length-1 and take the square root of the result in order to have the RMSE.
In Summary
The following post as shown that it was possible to compute a linear regression by using a linear combination between the WMA and SMA, since both had extremely efficient computations (see link at the end of the post) we could have a calculation for the linear regression where the number of operations is independent of length .
This post took me eons to make because it's related to the lsma, and I am rarely short on words when it comes to anything related to the lsma. Thx to LucF for the feedback and everything.
