Quick scan for drift🙏🏻
ML based algorading is all about detecting any kind of non-randomness & exploiting it, kinda speculative stuff, not my way, but still...
Drift is one of the patterns that can be exploited, because pure random walks & noise aint got no drift.
This is an efficient method to quickly scan tons of timeseries on the go & detect the ones with drift by simply checking wherther drift < -0.5 or drift > 0.5. The code can be further optimized both in general and for specific needs, but I left it like dat for clarity so you can understand how it works in a minute not in an hour
^^ proving 0.5 and -0.5 are natural limits with no need to optimize anything, we simply put the metric on random noise and see it sits in between -0.5 and 0.5
You can simply take this one and never check anything again if you require numerous live scans on the go. The metric is purely geometrical, no connection to stats, TSA, DSA or whatever. I've tested numerous formulas involving other scaling techniques, drift estimates etc (even made a recursive algo that had a great potential to be written about in a paper, but not this time I gues lol), this one has the highest info gain aka info content.
The timeseries filtered by this lil metric can be further analyzed & modelled with more sophisticated tools.
Live Long and Prosper
P.S.: there's no such thing as polynomial trend/drift, it's alwasy linear, these curves you see are just really long cycles
P.S.: does cheer still work on TV? @admin
Gradient
Gradient Value Overlay
This script helps with identifying certain conditions without cluttering too much of the candles.
Some use cases:
It helps identify rsi low and high values.
Directional price movement becoming difficult.
low and high volume.
it uses a percent rank to distinguish low and high values.
It then uses a gradient to match the percentile rank to heatmap type colors.
i.e. dark blue for lowest volume, white for highest volume.
Current options are:
max bars to use.
approximate color - This value will attempt to give an approximation of what the color might be for the candle close.
e.g. If you're on the 1-hour chart, and only 30 minutes have past, it will multiple the current volume by 1.5. As time passes, if no volume comes in eventually, it will multiply current volume by 1.
This approximate value is only set to work with volume-based options.
option - select the type of value you'd like to see the gradient for.
timeframe - get values from a different chart timeframe.
on/off - turns the gradient on or off.
Gradient type - color wheel or heatmap. Currently these are the only two gardient options.
color wheel's colors for low to high values:
color wheel's current colors:
dark blue
purple
pink
red
orange
yellow
green
teal
white
heatmap's current colors from low values to high values:
dark blue
purple
pink
red
orange
yellow
white
reverse gradient - will reverse the colors so dark blue will be the high value and white will be the low value. Some charts based on previous data; you might need to switch the gradient colors.
moving average length while inside timeframe - an exponential moving average is applied to the values. At 1, there is no moving average applied.
Use case for this is to smooth out the gradient.
An example use case - if your currently on the 1-hour chart, you can set the timeframe to 1 minute and then the moving average length inside timeframe to 60. You will then be seeing the color sixty 1-minute bars.
current timeframe moving average length - an exponential moving average applied to current gradient (helps with smoothing gradient).
Smooth, further smooths values.
There is no set rule for what moving average lengths to use. Adjust timeframe, and moving average lengths to get an insight.
Keltner Channels Bands (RMA)Keltner Channel Bands
These normally consist of:
Keltner Channel Upper Band = EMA + Multiplier ∗ ATR
Keltner Channel Lower Band = EMA − Multiplier ∗ ATR
However instead of using ATR we are using RMA
This gives us a much smoother take of the KCB
We are also using 2 sets of bands built on 1 Moving average, this is a common set up for mean reversion strategies.
This can often be paired with RSI for lower timeframe divergences
Divergence
This is using the RSI to calculate when price sets new lows/highs whilst the RSI movement is in the opposite direction.
The way this is calculated is slightly different to traditional divergence scripts. instead of looking for pivot highs/lows in the RSI we are logging the RSI value when price makes it pivot highs/lows.
Gradient Bands
The Gradient Colouring on the bands is measuring how long price has been either side of the MA.
As Keltner bands are commonly used as a mean reversion strategy, I thought it would be useful to see how long price has been trending in a certain direction, the stronger the colours get,
the longer price has been trending that direction which could suggest we are looking for a retrace soon.
Alerts
Alerts included let you choose whether you want to receive an alert for the inside, outside or both band touches.
To set up these alerts, simply toggle them on in the settings, then click on the 3 dots next to the indicators name, from there you click 'Add Alert'.
From there you can customise the alert settings but make sure to leave the 2 top boxes which control the alert conditions. They will be default selected onto your correct settings, the rest you may want to change.
Once you create the alert, it will then trigger as soon as price touches your chosen inside/outside band.
Suggestions
Please feel free to offer any suggestions which you think could improve the script
Disclaimer
The default settings/parameters were shared by Jimtalbott, feel free to play about with the and use this code to make your own strategies.
Stochastic of Two-Pole SuperSmoother [Loxx]Stochastic of Two-Pole SuperSmoother is a Stochastic Indicator that takes as input Two-Pole SuperSmoother of price. Includes gradient coloring and Discontinued Signal Lines signals with alerts.
What is Ehlers ; Two-Pole Super Smoother?
From "Cycle Analytics for Traders Advanced Technical Trading Concepts" by John F. Ehlers
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter.Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Market data contain noise, and removal of noise is the reason for using smoothing filters. In fact, market data contain several kinds of noise. I’ll group one kind of noise as systemic, caused by the random events of trades being exercised. A second kind of noise is aliasing noise, caused by the use of sampled data. Aliasing noise is the dominant term in the data for shorter cycle periods.
It is easy to think of market data as being a continuous waveform, but it is not. Using the closing price as representative for that bar constitutes one sample point. It doesn’t matter if you are using an average of the high and low instead of the close, you are still getting one sample per bar. Since sampled data is being used, there are some dSP aspects that must be considered. For example, the shortest analysis period that is possible (without aliasing)2 is a two-bar cycle.This is called the Nyquist frequency, 0.5 cycles per sample.A perfect two-bar sine wave cycle sampled at the peaks becomes a square wave due to sampling. However, sampling at the cycle peaks can- not be guaranteed, and the interference between the sampling frequency and the data frequency creates the aliasing noise.The noise is reduced as the data period is longer. For example, a four-bar cycle means there are four samples per cycle. Because there are more samples, the sampled data are a better replica of the sine wave component. The replica is better yet for an eight-bar data component.The improved fidelity of the sampled data means the aliasing noise is reduced at longer and longer cycle periods.The rate of reduction is 6 dB per octave. My experience is that the systemic noise rarely is more than 10 dB below the level of cyclic information, so that we create two conditions for effective smoothing of aliasing noise:
1. It is difficult to use cycle periods shorter that two octaves below the Nyquist frequency.That is, an eight-bar cycle component has a quantization noise level 12 dB below the noise level at the Nyquist frequency. longer cycle components therefore have a systemic noise level that exceeds the aliasing noise level.
2. A smoothing filter should have sufficient selectivity to reduce aliasing noise below the systemic noise level. Since aliasing noise increases at the rate of 6 dB per octave above a selected filter cutoff frequency and since the SuperSmoother attenuation rate is 12 dB per octave, the Super- Smoother filter is an effective tool to virtually eliminate aliasing noise in the output signal.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
FDI-Adaptive Non-Lag Moving Average [Loxx]FDI-Adaptive Non-Lag Moving Average is a Fractal Dimension Index adaptive Non-Lag moving Average. This acts more like a trend coloring indictor with gradient coloring.
What is the Fractal Dimension Index?
The goal of the fractal dimension index is to determine whether the market is trending or in a trading range. It does not measure the direction of the trend. A value less than 1.5 indicates that the price series is persistent or that the market is trending. Lower values of the FDI indicate a stronger trend. A value greater than 1.5 indicates that the market is in a trading range and is acting in a more random fashion.
Included
Bar coloring
Loxx's Expanded Source Types
RSI - colour fillThis script showcases the new (overload) feature regarding the fill() function! 🥳
2 plots could be filled before, but with just 1 colour per bar, now the colour can be a gradient type.
In this example we have 2 plots
- rsiPlot , which plots the rsi value
- centerPlot, which plots the value 50 ('centre line')
Explanation of colour fill in the zone 50-80
Default when rsi > 50
- a bottom value is set at 50 (associated with colour aqua)
- and a top value is set at 80 (associated with colour red)
This zone (bottom -> top value) is filled with a gradient colour (50 - aqua -> 80 - red)
When rsi is towards 80, you see a red coloured zone at the rsi value,
while when rsi is around 50, you'll only see the colour aqua
The same principle is applied in the zone 20-50 when rsi < 50
Cheers!
Educational: FillThis script showcases the latest feature of colour fill between lines with gradient
There are 17 ema's, all with adjustable lengths.
In the settings there are 3 options: '1' , '2' , and '1 & 2' :
Option '1'
Here the highest - lowest lines are filled with a gradient colour,
dependable where the 3rd highest/lowest ema is situated in regard of these 2 lines:
Option '2'
Here the colour fill is applied between every ema and the one next to it.
The gradient colour is dependable where the ema is situated in regard of the highest - lowest line:
Option '1 & 2'
A combination of both options:
The setting 'switch colours at ema x' regulates the switch between bullish and bearish colours.
When close is above the chosen ema -> bullish colours, when below -> bearish colours.
Examples of other settings of 'switch colours at ema x' :
Colour switch when close above/below:
ema 14
ema 11
ema 8
ema 5
ema 2
The colours can be set below, both for option '1' and '2'
Cheers!
T3 Velocity [Loxx]T3 Velocity is a simple velocity indicator using T3 moving average that uses gradient colors to better identify trends.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Standard-Deviation Adaptive Smoother MA [Loxx]Standard-Deviation Adaptive Smoother MA is a Smoother moving average with standard deviation adaptivity.
What is the Smoother Moving Average?
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Included:
Bar coloring
Moving Average with Dynamic Color Gradient (WaveTrend Momentum)Similar scripts exist but I haven't seen one using WaveTrend and I haven't seen one that hand picks evenly divided colors between GREEN-YELLOW-RED.
The green is exact green, the yellow is exact yellow, and the red is exact red.
Not complicated, just useful.
Green to Red Gradient for Dynamic / Color Changing IndicatorsI have evenly divided every color between green and red.
This gradient is useful for pine coders who are creating color changing, dynamic, or gradient indicators.
RGB color check tool with RSI [DM]Greetings colleagues.
Here I share a tool that uses the color gradient provided by PineCoders and lucf.
This tool was made for the reason that whenever we start with an idea for a script, we end up consuming a lot of time in selecting suitable colors.
An RSI was taken as a reference for the signal
You have multiple switches for axes, fill, background and colors
You can also change the background so that you can check the contrast with the signals.
The mix of colors with 8 boxes (4 channels for each color as detailed below) for each color since RGB has been defined
Red= 0 a 255
Green= 0 a 255
Blue= 0 a 255
Transparency= 0 a 100
I hope you enjoy it. [ ;-)
Trend Gradient Moving Average This moving average uses a gradient function which calculates the number of advances/declines of the moving average to change the intensity of the colors, meaning a longer trend in either direction will show a stronger color. You can choose 3 colors to build the gradient: a bullish, bearish & neutral/transition color. The number of steps chosen will change the speed of color change, with a lower number of steps meaning a faster transition and viceversa.
Furthermore, you can choose between many different types of moving averages:
-SMA (Simple Moving Average)
-EMA (Exponential Moving Average)
-RMA (Rolling Moving Average)
-WMA (Weighted Moving Average)
-HMA (Hull Moving Average)
-VWMA (Volume Weighted Moving Average)
-TMA (Triangular Moving Average)
Enjoy!
Color Gradient Framework [PineCoders]█ OVERVIEW
This indicator shows how you can use the new color functions in Pine to generate color gradients. We provide functions that will help Pine coders generate gradients for multiple use cases using base colors for bull and bear states.
█ CONCEPTS
For coders interested in maximizing the use of color in their scripts, TradingView has added new color functions and new functionality to existing functions. For us coders, this translates in the ability to generate colors on the fly and use dynamic colors ("series color") in more places.
New functions allow us to:
• Generate colors dynamically from calculated RGBA components ("A" is the Alpha channel, known to Pine coders as the "transparency"). See color.rgb() .
• Extract RGBA components from existing colors. See color.r() , color.g() , color.b() and color.t() .
• Generate linear gradients between two colors. See color.from_gradient() .
Improvements to existing color/plotting functions allow more flexible use of color:
• plotcandle() now accepts a "series color" argument for its `wickcolor` and `bordercolor` parameters.
• plotarrow() now accepts a "series color" argument for its `colorup` and `colordown` parameters.
Gradients are not only useful to make script visuals prettier; they can be used to pack more information in your displays. Our gradient #4 goes overboard with the concept by using a different gradient for the source line, its fill, and the background.
█ OUR SCRIPT
The script presents four functions to generate gradients:
f_c_gradientRelative(_source, _min, _max, _c_bear, _c_bull)
f_c_gradientRelativePro(_source, _min, _max, _c_bearWeak, _c_bearStrong, _c_bullWeak, _c_bullStrong)
f_c_gradientAdvDec(_source, _center, _c_bear, _c_bull)
f_c_gradientAdvDecPro(_source, _center, _steps, _c_bearWeak, _c_bearStrong, _c_bullWeak, _c_bullStrong)
The relative gradient functions are useful to generate gradients on a source that oscillates between known upper/lower limits. They use the relative position of the source between the `_min` and `_max` levels to generate the color. A centerline is derived from the `_min` and `_max` levels. The source's position above/below that centerline determines if the bull/bear color is used, and the relative position of the source between the centerline and the max/min level determines the gradient of the bull/bear color.
The advance/decline gradient functions are useful to generate gradients on a source for which min/max levels are unknown. These functions use source advances and declines to determine a gradient level. The `f_c_gradientAdvDec()` version uses the historical maximum of advances/declines to determine how many correspond to the strongest bull/bear colors, making its gradients adaptive. The `f_c_gradientAdvDecPro()` version requires the explicit number of advances/declines that correspond to the strongest bull/bear colors. This is useful when coloring chart bars, for example, where too many gradient levels are difficult to distinguish. Using the Pro version of the function allows you to limit the number of gradient levels to 5, for example, so that transitions are fewer, but more obvious. The `_center` parameter of the advance/decline functions allows them to determine which of the bull/bear colors to use.
Note that the custom `f_colorNew(_color, _transp)` function we use in our script should soon no longer be necessary, as changes are under way to allow color.new() to accept series arguments.
Inputs
The script's inputs demonstrate one way you can allow users to choose base bull/bear colors. Because users can modify any of the colors, only two are technically needed: one for bull, one for bear, as we do for the configuration of the bull/bear colors for the background in the gradient #4 configuration. Providing a few presets from which users can choose can be useful for color-challenged script users, but that type of inputs has the disadvantage of not rendering optimally in all OS/Browser environments.
You can use the inputs to select one of eight gradient demonstrations to display.
█ THANKS
Thanks to the PineCoders team for validating the code and description of this publication.
Thanks also to the many TradingView devs from multiple teams who made these improvements to Pine colors possible.
Look first. Then leap.
Volume Profile [LuxAlgo]Displays the estimate of a volume profile, with the option to show a rolling POC (point of control). Users can change the lookback, row size, and various visual aspects of the volume profile.
Settings
Basic:
Lookback: Number of most recent bars to use for the calculation of the volume profile
Row Size: Determines the number of rows used for the calculation of the volume profile
Show Rolling POC: Determines whether to display the rolling POC of the volume profile
Style:
Width (% of the box): Determines the length of the bars relative to the Lookback value
Bar Width: Width of each bar
Flip Histogram: Flips the histogram, when enabled, the histogram base will be located at the most recent candle
Gradient: Allows to color the volume profile bars with a gradient, with a color intensity determined by the length of each bar
Rows Solid Color: Color of each bar when 'Gradient' is disabled
POC Solid Color: Color of the POC when 'Gradient' is disabled
Usage
It is very common to display volume over time in order to visualize the trading activity made over a specific candle, however this is not the only way to display volume and it can be interesting to put it in relation with the price, which is what volume profiles do.
Volume profiles are displayed as price relative histograms showing the accumulated volume within certain price areas, the number of areas are determined by the row size of the volume profile. Knowing which price's area accumulated the most volume allow highlighting areas of interest to market participants.
Most accumulated volume will be encountered in zones of equilibrium between buyers and sellers; that is zones of local price stationarity. These zones are highlighted by high volume nodes in the volume profile. Imbalance between buyers and sellers are highlighted by thinner zones of the volume profile.
The price level with the most accumulated volume is highlighted by the "point of control" (POC), displayed by the dotted line in the indicator.
The POC is often considered an important level, commonly used as support/resistance by traders. One can verify the accuracy of this use case by using the rolling POC (assuming one would use the POC over time as SR).
Indicator Limitations
Volume profiles are calculated using tick data, which is not the case of this estimate, as such you won't have an accurate representation of an actual volume profile.
The rolling POC can introduce time outs in the script computation, use lower lookback and row size value to display it.
200 Week Moving Average HeatmapСolors part of the SMA depending on the change in % (delta %) to the previous value. From blue(none to low increase) through green(moderate increase) to red(high increase).
RK's Framework 01 - Auto Color GradientThis started as a personal arrays study, but after a few tests I decided to made a framework to get my own scripts simplest, lighter and faster.
And now I'm sharing with you guys.
Is very simple to use:
Copy evething inside "RK's Auto Color Gradient Framework" block;
Paste anywhere before the plotting;
Declare the color variable name calling the function "f_autocolor(___, ___)" with the source you gonna plot and the size of the scale do you want to use to compare the data.
Feel free to use.
Hope brings some profits for you guys!!
A Useful MA Weighting Function For Controlling Lag & SmoothnessSo far the most widely used moving average with an adjustable weighting function is the Arnaud Legoux moving average (ALMA), who uses a Gaussian function as weighting function. Adjustable weighting functions are useful since they allow us to control characteristics of the moving average such as lag and smoothness.
The following moving average has a simple adjustable weighting function that allows the user to have control over the lag and smoothness of the moving average, we will see that it can also be used to get both an SMA and WMA.
A high-resolution gradient is also used to color the moving average, makes it fun to watch, the plot transition between 200 colors, would be tedious to make but everything was made possible using a custom R script, I only needed to copy and paste the R console output in the Pine editor.
Settings
length : Period of the moving average
-Lag : Setting decreasing the lag of the moving average
+Lag : Setting increasing the lag of the moving average
Estimating Existing Moving Averages
The weighting function of this moving average is derived from the calculation of the beta distribution, advantages of such distribution is that unlike a lot of PDF, the beta distribution is defined within a specific range of values (0,1). Parameters alpha and beta controls the shape of the distribution, with alpha introducing negative skewness and beta introducing positive skewness, while higher values of alpha and beta increase kurtosis.
Here -Lag is directly associated to beta while +Lag is associated with alpha . When alpha = beta = 1 the distribution is uniform, and as such can be used to compute a simple moving average.
Moving average with -Lag = +Lag = 1 , its impulse response is shown below.
It is also possible to get a WMA by increasing -Lag , thus having -Lag = 2 and +Lag = 1 .
Using values of -Lag and +Lag equal to each other allows us to get a symmetrical impulse response, increasing these two values controls the heaviness of the tails of the impulse response.
Here -Lag = +Lag = 3 , note that when the impulse response of a moving average is symmetrical its lag is equal to (length-1)/2 .
As for the gradient, the color is determined by the value of an RSI using the moving average as input.
I don't promise anything but I will try to respond to your comments
Trading With Colors7 hours ago
Hello friends. This is simply a moving average ribbon, per se. The values for the colored ribbon can have their length calculated to fit their chosen resolution on the current one. This solved problems for me, but it was my own solution. Maybe I'll learn something new from sharing this.
To everybody else who is learning as well, this script essentially serves to introduce other time-frame moving averages. This intends to helps traders find the scope of relevance and not get lost in the current time-frame.
Besides the colored moving averages (2 sets, different resolutions, great zoomed in our out), I included optional check-boxes to allow comparison of sets of moving averages at will, so that the most important to the individual trader can be compared and selected specifically.
I kept the default options set to keep it clean. It likely won't be the only indicator on one's chart, so it's naturally best to reduce indicator noise from one, as to not subtract from the benefit of the other indicators.
I integrated tons of acquired knowledge into this, so I hope somebody finds a missing piece to their collection or a solution to a coding problem within. I also hope this provides a new insight and helps others on their path to financial freedom.
Best wishes.
PS: I left some old code in comments in case it helps to understand the evolution of my code. I'll update this again once it works on the Daily. You might figure it out before I do, in wish case, do share :)
[LunaOwl] Rainbow Color Gradient作品: 色彩漸變－彩虹帶
This script is about rainbow color gradient, there are 35 colors from red to purple. The color changes with the time series, from light to dark, and then to light, repeat. Maybe it's visually beautiful. Compared to practicality, I prefer to use it as a color gradient reference. for the later works.
色彩漸變彩虹帶，從紅到紫有35種色調，隨著時間序列進行而變化，由淺到深再到淺，或許視覺上很美。
相比於實用性，我覺得應該當成色彩漸層的參考，對於以後作品可以使用上。
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The changing process of the rainbow (resolution = 63).
彩虹帶的時序變化過程，分辨度是63格，充滿 (✪ω✪) 圖表藝術！
