Ehlers Dominant Cycle Tuned Bypass Filter [CC]The Dominant Cycle Tuned Bypass Filter was created by John Ehlers (Stocks & Commodities V. 26:3 (16-22)) and this is a particularly unique indicator because this does a pretty good job at predicting the future stock movements. If the blue line crosses over the red then a few bars from now the stock price will most likely go up and if the blue line crosses below the red then a few bars from now the stock price should go down. Since this is such a unique indicator to use with entry and exit points, I don't have them color coded but try this out and let me know what you think.
This was a special request so let me know what other scripts you would like to see me publish or if you want anything custom done!
Note: I'm republishing this because the original script couldn't be found in searches so this will fix that.
Filter
Ehlers Swiss Army Knife Indicator [CC]The Swiss Army Knife Indicator was created by John Ehlers (Stocks & Commodities V. 24:1 (28-31, 50-53)) and it is 9 different filters in one big mega indicator! This is my first attempt at allowing you all to select different timeframes, to choose if you allow repainting or not, or by letting you choose which indicator you want to see on the chart. I know this may cause problems so feel free to send me a pm if you are stuck or if you have any questions!
This was a custom request so please let me know if you want to see me publish any other scripts or if you want something custom done!
Note: I'm republishing this because the original script couldn't be found in searches so this will fix that.
[LunaOwl] Swing Filter作品: 擺盪濾波器 (Swing Filter)
This is a Swing Filter, the function is to remind you that you do not need to trade during the neutral period, only buy or long when the series is higher than the high-level you set, when the series is lower than the low-level you set, you need to short or hedge. Hope your to use it happily. In a larger time frame, the length can be set smaller, the default value is 100.
這是一個擺盪濾波器，它的功能是提醒您在中性時期不用交易，僅在市場高水平的時候買進持有或做多，當來到低水平時需要做空或對沖。希望使用愉快。此外，在較大的時間框架，期數設定小一點，預設是100。
Long Wick TrialI've created this as a confirmation indicator to help know when market conditions are favorable to enter a trade. It measures volume, volatility, and ATR. It is not intended to tell you when to enter/exit the market, but use it with another indicator such as the mirror macd to filter out many losses and avoid entering the market during low volume or excessive volatility that may trip your stop loss.
Green = Favorable Market conditions
Yellow = Enter with caution, the market is moving sideways but is slightly trending
Orange = Enter with caution, the market is trending but extremely volatile and may trip stop loss early
Black = Shouldn't enter market here, market is moving sideways and volume is also low.
Filter Information Box - PineCoders FAQWhen designing filters it can be interesting to have information about their characteristics, which can be obtained from the set of filter coefficients (weights). The following script analyzes the impulse response of a filter in order to return the following information:
Lag
Smoothness via the Herfindahl index
Percentage Overshoot
Percentage Of Positive Weights
The script also attempts to determine the type of the analyzed filter, and will issue warnings when the filter shows signs of unwanted behavior.
DISPLAYED INFORMATION AND METHODS
The script displays one box on the chart containing two sections. The filter metrics section displays the following information:
- Lag : Measured in bars and calculated from the convolution between the filter's impulse response and a linearly increasing sequence of value 0,1,2,3... . This sequence resets when the impulse response crosses under/over 0.
- Herfindahl index : A measure of the filter's smoothness described by Valeriy Zakamulin. The Herfindahl index measures the concentration of the filter weights by summing the squared filter weights, with lower values suggesting a smoother filter. With normalized weights the minimum value of the Herfindahl index for low-pass filters is 1/N where N is the filter length.
- Percentage Overshoot : Defined as the maximum value of the filter step response, minus 1 multiplied by 100. Larger values suggest higher overshoots.
- Percentage Positive Weights : Percentage of filter weights greater than 0.
Each of these calculations is based on the filter's impulse response, with the impulse position controlled by the Impulse Position setting (its default is 1000). Make sure the number of inputs the filter uses is smaller than Impulse Position and that the number of bars on the chart is also greater than Impulse Position . In order for these metrics to be as accurate as possible, make sure the filter weights add up to 1 for low-pass and band-stop filters, and 0 for high-pass and band-pass filters.
The comments section displays information related to the type of filter analyzed. The detection algorithm is based on the metrics described above. The script can detect the following type of filters:
All-Pass
Low-Pass
High-Pass
Band-Pass
Band-Stop
It is assumed that the user is analyzing one of these types of filters. The comments box also displays various warnings. For example, a warning will be displayed when a low-pass/band-stop filter has a non-unity pass-band, and another is displayed if the filter overshoot is considered too important.
HOW TO SET THE SCRIPT UP
In order to use this script, the user must first enter the filter settings in the section provided for this purpose in the top section of the script. The filter to be analyzed must then be entered into the:
f(input)
function, where `input` is the filter's input source. By default, this function is a simple moving average of period length . Be sure to remove it.
If, for example, we wanted to analyze a Blackman filter, we would enter the following:
f(input)=>
pi = 3.14159,sum = 0.,sumw = 0.
for i = 0 to length-1
k = i/length
w = 0.42 - 0.5 * cos(2 * pi * k) + 0.08 * cos(4 * pi * k)
sumw := sumw + w
sum := sum + w*input
sum/sumw
EXAMPLES
In this section we will look at the information given by the script using various filters. The first filter we will showcase is the linearly weighted moving average (WMA) of period 9.
As we can see, its lag is 2.6667, which is indeed correct as the closed form of the lag of the WMA is equal to (period-1)/3 , which for period 9 gives (9-1)/3 which is approximately equal to 2.6667. The WMA does not have overshoots, this is shown by the the percentage overshoot value being equal to 0%. Finally, the percentage of positive weights is 100%, as the WMA does not possess negative weights.
Lets now analyze the Hull moving average of period 9. This moving average aims to provide a low-lag response.
Here we can see how the lag is way lower than that of the WMA. We can also see that the Herfindahl index is higher which indicates the WMA is smoother than the HMA. In order to reduce lag the HMA use negative weights, here 55% (as there are 45% of positive ones). The use of negative weights creates overshoots, we can see with the percentage overshoot being 26.6667%.
The WMA and HMA are both low-pass filters. In both cases the script correctly detected this information. Let's now analyze a simple high-pass filter, calculated as follows:
input - sma(input,length)
Most weights of a high-pass filters are negative, which is why the lag value is negative. This would suggest the indicator is able to predict future input values, which of course is not possible. In the case of high-pass filters, the Herfindahl index is greater than 0.5 and converges toward 1, with higher values of length . The comment box correctly detected the type of filter we were using.
Let's now test the script using the simple center of gravity bandpass filter calculated as follows:
wma(input,length) - sma(input,length)
The script correctly detected the type of filter we are using. Another type of filter that the script can detect is band-stop filters. A simple band-stop filter can be made as follows:
input - (wma(input,length) - sma(input,length))
The script correctly detect the type of filter. Like high-pass filters the Herfindahl index is greater than 0.5 and converges toward 1, with greater values of length . Finally the script can detect all-pass filters, which are filters that do not change the frequency content of the input.
WARNING COMMENTS
The script can give warning when certain filter characteristics are detected. One of them is non-unity pass-band for low-pass filters. This warning comment is displayed when the weights of the filter do not add up to 1. As an example, let's use the following function as a filter:
sum(input,length)
Here the filter pass-band has non unity, and the sum of the weights is equal to length . Therefore the script would display the following comments:
We can also see how the metrics go wild (note that no filter type is detected, as the detected filter could be of the wrong type). The comment mentioning the detection of high overshoot appears when the percentage overshoot is greater than 50%. For example if we use the following filter:
5*wma(input,length) - 4*sma(input,length)
The script would display the following comment:
We can indeed see high overshoots from the filter:
@alexgrover for PineCoders
Look first. Then leap.
Filter Amplitude Response Estimator - A Simple CalculationIn digital signal processing knowing how a system interact with the frequency content of an input signal is extremely important, the mathematical tool that give you this information is called "frequency response". The frequency response regroup two elements, the amplitude response, and the phase response. The amplitude response tells you how the system modify the amplitude of the frequency components in the input signal, the phase response tells you how the system modify the phase of the frequency components in the signal, each being a function of the frequency.
The today proposed tool aim to give a low resolution representation of the amplitude response of any filter.
What Is The Amplitude Response Of A Filter ?
Remember that filters allow to interact with the frequency content of a signal by amplifying, attenuating and/or removing certain frequency components in the input signal, the amplitude (also called magnitude) response of a filter let you know exactly how your filter change the amplitude of the frequency components in the input signal, another way to see the amplitude response is as a tool that tell you what is the peak amplitude of a filter using a sinusoid of a certain frequency as input signal.
For example if the amplitude response of a filter give you a value of 0.9 at frequency 0.5, it means that the filter peak amplitude using a sinusoid of frequency 0.5 is equal to 0.9.
There are several ways to calculate the frequency response of a filter, when our filter is a FIR filter (the filter impulse response is finite), the frequency response of the filter is the absolute value of the discrete Fourier transform (DFT) of the filter impulse response.
If you are curious about this process, know that the DFT of a N samples signal return N values, so if our FIR filter coefficients are composed of only 5 values we would get a frequency response of 5 values...which would not be useful, this is why we "pad" our coefficients with zeros, that is we add zeros to the start and end of our series of coefficients, this process is called "zero-padding", so if our series of coefficients is: (1,2,3,4,5), applying zero padding would give (0,0...1,2,3,4,5,...0,0) while keeping a certain symmetry. This is related to the concept of resolution, a low resolution amplitude response would be composed of a low number of values and would not be useful, this is why we use zero-padding to add more values thus increasing the resolution.
Making a Fourier transform in Pinescript is not doable, as you need the complex number i for computing a DFT, but thats not even the only problem, a DFT would not be that useful anyway (as the processes to make it useful in a trading context would be way too complex) . So how can we calculate a filter amplitude response without using a DFT ? The simple answer is by taking the peak amplitude of a filter using a sinusoid of a certain frequency as input, this is what the proposed tool do.
Using The Tool
The proposed tool give you a 50 point amplitude response from frequency 0.005 to 0.25 by default. the setting "Range Divisor" allow you to see the amplitude response by using a different range of frequency, for example if the range divisor is equal to 2 the filter amplitude response will be evaluated from frequency 0.0025 to 0.125.
In the script, filt hold the filter you want to see the frequency response, by default a simple moving average.
The position of the frequency response is defined by the "Show Amplitude Response At Bar Number" setting, if you want the frequency response to start at bar number 5000 then enter 5000, by default 10000. If you are not a premium set the number at 4000 and it should work.
amplitude response of a simple moving average of period 14, res = 2.
By default the amplitude response use an amplitude scale, a value of 1 represent an unchanged amplitude. You can use Dbfs (decibel full scale) instead by checking the "To Decibels (Full Scale)" setting.
Dbfs amplitude response, a value of 0 represent an unchanged amplitude.
Some Amplitude Responses
In order to prove the accuracy of the proposed tool we can compare the amplitude response given by the proposed tool with the mathematical function of the amplitude response of a simple moving average, that is:
abs(sin(pi*f*length)/(length*sin(pi*f)))
In cyan the amplitude response given by the proposed tool and in blue the above function. Below are the amplitude responses of some moving averages with period 14.
Amplitude response of an EMA, the EMA is a IIR filter, therefore the amplitude response can't be made by taking the DFT of the impulse response (as this ones has infinite length), however our tool can give its frequency response.
Amplitude response of the Hull MA, as you can see some frequencies are amplified, this is common with low-lag filters.
Gaussian moving average (ALMA), with offset = 0.5 and sigma = 6.
Simple moving average high-pass filter amplitude response
Center of gravity bandpass filter amplitude response
Center of gravity bandreject filter.
IMPORTANT!: The amplitude response of adaptive moving averages is not stationary and might change over time.
Conclusion
A tool giving the amplitude response of any filter has been presented, of course this method is not efficient at all and has a low resolution of 50 points (the common resolution is of 512 points) and is difficult to work with, but has the merit to work on Tradingview and can give the frequency response of IIR filters, if you really need to see the frequency response of a filter then i recommend you to use the function freqz from the scipy package.
I still hope you will enjoy using this tool to have a look at the amplitude responses of your favorite moving averages.
I'am aware of the current situation, however i'am somehow feeling left out from the pinescript community, let me know via PM if i have done something to you and i'll do my best to fix any problems i might have caused (or i might be being parano xD)
Sequential Filter - An Original Filtering ApproachRemoving irregular variations in the closing price remain a major task in technical analysis, indicators used to this end mostly include moving averages and other kind of low-pass filters. Understanding what kind of variations we want to remove is important, irregular (noisy) variations have mostly a short term period, fully removing them can be complicated if the filter is not properly selected, for example we might want to fully remove variations with a period of 2 bars and lower, if we select an arithmetic moving average the filter output might still contain such variations because of the ripples in the frequency response passband, all it would take is a variation of high amplitude for that variation to be clearly visible.
Although all it would take for better filtering is a filter with better performance in the frequency domain (gaussian, Butterworth, Bessel...) we can design innovative approaches that does not rely on the model of classical moving averages, today a new technical indicator is proposed, the technical indicator fully remove variations lower than the selected period.
The Indicator Approach
In order for the indicator output to change the closing price need to produce length consecutive up's/down's, length control the variation threshold of the indicator, variations lower than length are fully removed. Lets see a visual example :
Here length = 3, the closing price need to make 3 consecutive up's/down's, when the sequence happen the indicator output is equal to src , here the closing price, else the indicator is equal to its precedent value, hence removing other variations. The value of 3 is the value by default, this is because i have seen in the past that the average smallest variations period where in general of 3 bars.
Because the indicator focus only on the variation sign, it totally ignore the amplitude of the movement, this provide an effective way to filter short term retracement in a fluctuation as show'n below :
The candle option of the indicator allow the indicator to only focus on the body color of a candle, thus ignoring potential gaps, below is an example with the candle option off :
If we activate the "candle" option we end up with :
Note that the candle option is based on the closing and opening price, if you use the indicator on another indicator output make sure to have the candle option off.
Length and Indicator Color
The closing price is infected by noise, and will rarely make a large sequence of consecutive up's/down's, the indicator can therefore be useful to detect consecutive sequence of length period, here 6 is selected on BTCUSD :
A consecutive up's/down's of period 6 can be considered a relatively rare event.
It is important to note that the color of the indicator used by default has nothing to do with the consecutive sequence detected, that is the indicator turning red doesn't necessarily mean that a consecutive down's sequence has occurred, but only that this sequence has occurred at a lower value than the precedent detected sequence. This is show'n below :
In order to make the indicator color based on the detected sequence check the "Color Based On Detected Sequence" option.
Conclusion
An original approach on filtering price variations has been proposed, i believe the indicator code is elegant as well as relatively efficient, and since high values of length can't really be used the indicator execution speed will remain relatively fast.
Thanks for reading !
Grand Trend Forecasting - A Simple And Original Approach Today we'll link time series forecasting with signal processing in order to provide an original and funny trend forecasting method, the post share lot of information, if you just want to see how to use the indicator then go to the section "Using The Indicator".
Time series forecasting is an area dealing with the prediction of future values of a series by using a specific model, the model is the main tool that is used for forecasting, and is often an expression based on a set of predictor terms and parameters, for example the linear regression (model) is a 1st order polynomial (expression) using 2 parameters and a predictor variable ax + b . Today we won't be using the linear regression nor the LSMA.
In time series analysis we can describe the time series with a model, in the case of the closing price a simple model could be as follows :
Price = Trend + Cycles + Noise
The variables of the model are the components, such model is additive since we add the component with each others, we should be familiar with each components of the model, the trend represent a simple long term variation of high amplitude, the cycles are periodic fluctuations centered around 0 of varying period and amplitude, the noise component represent shorter term irregular variations with mean 0.
As a trader we are mostly interested by the cycles and the trend, altho the cycles are relatively more technical to trade and can constitute parasitic fluctuations (think about retracements in a trend affecting your trend indicator, causing potential false signals).
If you are curious, in signal processing combining components has a specific name, "synthesis" , here we are dealing with additive synthesis, other type of synthesis are more specific to audio processing and are relatively more complex, but could be used in technical analysis.
So what to do with our components ? If we want to trade the trend, we should estimate right ? Estimating the trend component involve removing the cycle and noise component from the price, if you have read stuff about filters you should know where i'am going, yep, we should use filters, in the case of keeping the trend we can use a simple moving average of relatively high period, and here we go.
However the lag problem, which is recurrent, come back again, we end up with information easier to interpret (here the trend, which is a simple fluctuation such as a line or other smooth curve) at the cost of decision timing, that is unfortunate but as i said the information, here the moving average output, is relatively simple, and could be easily forecasted right ? If you plot a moving average of high period it would be easier for you to forecast its future values. And thats what we aim to do today, provide an estimate of the trend that should be easy to forecast, and should fit to the price relatively well in order to produce forecast that could determine the position of future closing prices observations.
Estimating And Forecasting The Trend
The parameter of the indicator dealing with the estimation of the trend is length , with higher values of length attenuating the cycle and noise component in the price, note however that high values of length can return a really long term trend unlike a simple moving average, so a small value of length, 14 for example can still produce relatively correct estimate of trend.
here length = 14.
The rough estimate of the trend is t in the code, and is an IIR filter, that is, it is based on recursion. Now i'll pass on the filter design explanation but in short, weights are constants, with higher weights allocated to the previous length values of the filter, you can see on the code that the first part of t is similar to an exponential moving average with :
t(n) = 0.9t(n-length) + 0.1*Price
However while the EMA only use the precedent value for the recursion, here we use the precedent length value, this would just output a noisy and really slow output, therefore in order to create a better fit we add : 0.9*(t(n-length) - t(n-2length)) , and this create the rough trend estimate that you can see in blue. On the parameters, 0.9 is used since it gives the best estimate in my opinion, higher values would create more periodic output and lower values would just create a rougher output.
The blue line still contain a residual of the cycle/noise component, this is why it is smoothed with a simple moving average of period length. If you are curious, a filter estimating the trend but still containing noisy fluctuations is called "Notch" filter, such filter would depending on the cutoff remove/attenuate mid term cyclic fluctuations while preserving the trend and the noise, its the opposite of a bandpass filter.
In order to forecast values, we simply sum our trend estimate with the trend estimate change with period equal to the forecasting horizon period, this is a really really simple forecasting method, but it can produce decent results, it can also allows the forecast to start from the last point of the trend estimate.
Using The Indicator
We explained the length parameter in the precedent section, src is the input series which the trend is estimated, forecast determine the forecasting horizon, recommend values for forecast should be equal to length, length/2 or length*2, altho i strongly recommend length.
here length and forecast are both equal to 14 .
The corrective parameter affect the trend estimate, it reduce the overshoot and can led to a curve that might fit better to the price.
The indicator with the non corrective version above, and the corrective one below.
The source parameter determine the source of the forecast, when "Noisy" is selected the source is the blue line, and produce a noisy forecast, when "Smooth" is selected the source is the moving average of t , this create a smoother forecast.
The width interval control...the width of the intervals, they can be seen above and under the forecast plot, they are constructed by adding/subtracting the forecast with the forecast moving average absolute error with respect to the price. Prediction intervals are often associated with a probability (determining the probability of future values being between the interval) here we can't determine such probability with accuracy, this require (i think) an analysis of the forecasting distribution as well as assumptions on the distribution of the forecasting error.
Finally it is possible to see historical forecasts, that is, forecasts previously generated by checking the "Show Historical Forecasts" option.
Examples
Good forecasts mostly occur when the price is close to the trend estimate, this include the following highlighted periods on AMD 15TF with default settings :
We can see the same thing at the end of EURUSD :
However we can't always obtain suitable fits, here it is isn't sufficient on BTCUSD :
We can see wide intervals, we could change length or use the corrective option to get better results, another option is to use a log scale.
We will end the examples with the log SPX, who posses a linear trend, so for example a linear model such as a linear regression would be really adapted, lets see how the indicator perform :
Not a great fit, we could try to use an higher length value and use "Smooth" :
Most recent fits are quite decent.
Conclusions
A forecasting indicator has been presented in this post. The indicator use an original approach toward estimating the trend component in the closing price. Of course i should have given statistics related to the forecasting error, however such analysis is worth doing with better methods and in more advanced environment allowing for optimization.
But we have learned some stuff related to signal processing as well as time series analysis, seeing a time series as the sum of various components is really helpful when it comes to make sense of chaotic and noisy series and is a basic topic in time series analysis.
You can see that in this new year i work harder on the visual of my indicators without trying to fall in the label addict trap, something that i wasn't really doing before, let me know what do you think of it.
Thanks for reading !
Adaptive Bandpass Filter [ChuckBanger]An band-pass filtered Oscillator that has adaptive buy and sell thresholds. You buy or sell when the blue line is under or over the pink line. The histogram is there to show how big the momentum is on a potential pump or dump.
Time Series Lag Reduction Filter by CryptorhythmsTime Series Lag Reduction Filter by Cryptorhythms
Description
A little filter to reduce lag on any time series data. Here we use an EMA to demonstrate how it works, but you could use it in many different ways/appications.
This method can cause overshoot if you get too aggressive with the "lagReduce" setting. In this case lower the lagReduce variable.
👍 We hope you enjoyed this indicator and find it useful! We post free crypto analysis, strategies and indicators regularly. This is our 76th script on Tradingview!
(16) DRAGON-X VS-148The Dragon is an experimental indicator that is currently still under development. I called this indicator the Dragon because, not unlike the movie and book; “How to train your Dragon”, you must adjust or dial in this indicator (train it) to get good entry/exit signals out of it, for each individual equity you want to examine. That is not nearly as convenient as all of my other indicators, but the extra work can be worth the effort. The benefits of this indicator are its responsive nature and it forecasting ability. In the inputs the algorithm allows you to select a forecasting option. Forecasting in this instance merely means shifting the resulting indicator projections forward by altering the algorithm to be looser. It can fairly accurately forecast 1 to 3 bars forward. The more forward you set the adjustment the less accurate it becomes. John Ehlers was the first person to transform Dr. Voss’s algorithm into an equity trading indicatory. His observations about forecasting are important. While the Voss filter “can’t it really look into the future, it can provide signals in advance of signals used by other traders – and that may be enough to create a successful trading edge.”
As the image below demonstrates the Dragon does indeed get you into and our of trades in advance of even our best indicator, Genie-Cycles, shown below the Dragon.
The second issue regarding this indicator is, it’s not easy to understand the rational behind it. The Dragon filter is a direct derivative of the Voss Predictive Filter. Dr. Voss describes this filter as “A filter for universal real-time prediction of band-limited signals” This algorithm was developed to provide greater resolution and insight into a wide class of signals generated by deterministic or stochastic systems. It attempts to remove group and phase delays from the Weighted Moving Average output. One of Dr. Voss’s fields of endeavor is working to make MRI images clearer. This is done by extracting the first harmonic of the output using a bandpass filter and then applying a "negative-delay" formula to it. Forecasting financial time series is regarded as one of the most challenging applications of time series prediction due to their dynamic nature.
We have more information on our website describing this indicator as well as three links to reference articles that describe the scientific concept underpinning this indicator.
In the image below, the Dragon Indicator is plotted below the price chart so you can see the correlation between the two. If you examine the last two entry signals you can clearly see that the Dragon flags an entry position very early in the turning point transition shift. Actually, at points in the chart that do not in any way look like the end of the last down leg of the cycle. This get you into a trade before most of the rest of the other market competitors.
We consider the Dragon to still be under development. It requires a narrow band width of input data, for the output to generate reliably accurate signals. Market data has unlimited bandwidth.
Our future development of this indicator will take two center of gravity filters and first narrow that resulting bandwidth by utilizing a pass band filter. We will than use this data as an input to the Voss algorithm. We will advise all of our user when this updated version is available. Currentely this experimental version is only available to our unlimited members.
Access this Genie indicator for your Tradingview account, through our web site. (Links Below) This will provide you with additional educational information and reference articles, videos, input and setting options and trading strategies this indicator excels in.
Roofing Filter [DW]This is an experimental study built on the concept of using roofing filters on price data proposed by John Ehlers.
Roofing filters are a type of bandpass filter conventionally used in HF radio receivers in the first IF stage to limit the frequency spectrum passed on to later stages in the receiver.
The goal in applying roofing filters to a price signal is to simultaneously attenuate high frequency noise and low frequency distortion to pass an oscillating signal with a nearly zero mean for analysis and/or further calculation.
In this study, there are three filter types to choose from:
-> Ehlers Roofing Filter, which passes data through a 2 pole high pass filter, then through a Super Smoother filter.
-> Gaussian Roofing Filter, which passes data through a 2 pole Gaussian high pass filter, then through a 2 pole Gaussian low pass filter.
-> Butterworth Roofing Filter, which passes data through a 2 pole Butterworth high pass filter, then through a 2 pole Butterworth low pass filter.
Each filter type has different amplitude and delay characteristics, so play around with each type and see which response suits your needs best.
There is an option to normalize the scale of the output as well. The normalization process in this script is computed by comparing positive and negative outputs to the filter's moving RMS value.
The resulting oscillator can be fed through numerous conventional indicators including Stochastic Oscillator, RSI, CCI, etc. to generate smoother, less distorted indicators for a clearer view of turning points.
Alternatively, it can also act as an indicator itself, as implied by the corresponding color scheme included in the script.
Although roofing filters are not conventionally used in the analysis of market data, applying such spectral analysis techniques may prove to be quite useful for the design of more efficient indicators and more reliable predictions.
Low Pass Channel [DW]This is an experimental study designed to attenuate higher frequency oscillations in price and volatility with minimal lag.
In this study, a single pole low pass filter is used. The low pass filter's cutoff period is determined either by a fixed user input, or by using an Instantaneous Frequency Measurement (IFM) algorithm.
Most radar warning, electronic countermeasures, and electronic intelligence systems employ IFM to identify threats, map the electronic battlefield, and implement deceptive countermeasures.
The IFM technique used for this study was devised by John Ehlers. It calculates In Phase and Quadrature (IQ) components using the Hilbert Transform and uses them to determine the dominant price cycle.
To generate the channel, the same filter approach is applied to true range then added to and subtracted from the price filter.
Custom bar colors are included for simple wave and trend indication.
Damped Sine Wave Weighted FilterIntroduction
Remember that we can make filters by using convolution, that is summing the product between the input and the filter coefficients, the set of filter coefficients is sometime denoted "kernel", those coefficients can be a same value (simple moving average), a linear function (linearly weighted moving average), a gaussian function (gaussian filter), a polynomial function (lsma of degree p with p = order of the polynomial), you can make many types of kernels, note however that it is easy to fall into the redundancy trap.
Today a low-lag filter who weight the price with a damped sine wave is proposed, the filter characteristics are discussed below.
A Damped Sine Wave
A damped sine wave is a like a sine wave with the difference that the sine wave peak amplitude decay over time.
A damped sine wave
Used Kernel
We use a damped sine wave of period length as kernel.
The coefficients underweight older values which allow the filter to reduce lag.
Step Response
Because the filter has overshoot in the step response we can conclude that there are frequencies amplified in the passband, we could have reached to this conclusion by simply seeing the negative values in the kernel or the "zero-lag" effect on the closing price.
Enough ! We Want To See The Filter !
I should indeed stop bothering you with transient responses but its always good to see how the filter act on simpler signals before seeing it on the closing price. The filter has low-lag and can be used as input for other indicators
Filter with length = 100 as input for the rsi.
The bands trailing stop utility using rolling squared mean average error with length 500 using the filter of length 500 as input.
Approximating A Least Squares Moving Average
A least squares moving average has a linear kernel with certain values under 0, a lsma of length k can be approximated using the proposed filter using period p where p = k + k/4 .
Proposed filter (red) with length = 250 and lsma (blue) with length = 200.
Conclusions
The use of damping in filter design can provide extremely useful filters, in fact the ideal kernel, the sinc function, is also a damped sine wave.
Keltner Channel with signals [ChuckBanger]This is Keltner Channel where I added Bull and Bear signals. It has a lot of settings to play around with. Have fun...
For more information on Keltner Channel: www.investopedia.com
Decaying Rate of Change Non Linear FilterThis is a potential solution to dealing with the inherent lag in most filters especially with instruments such as BTC and the effects of long periods of low volatility followed by massive volatility spikes as well as whipsaws/barts etc.
We can try and solve these issues in a number of ways, adaptive lengths, dynamic weighting etc. This filter uses a non linear weighting combined with an exponential decay rate.
With the non linear weighting the filter can become very responsive to sudden volatility spikes. We can use a short length absolute rate of change as a method to improve weighting of relative high volatility.
c1 = abs(close - close ) / close
Which gives us a fairly simple filter :
filter = sum(c1 * close,periods) / sum(c1,periods)
At this point if we want to control the relative magnitude of the ROC coefficients we can do so by raising it to a power.
c2 = pow(c1, x)
Where x approaches zero the coefficient approaches 1 or a linear filter. At x = 1 we have an unmodified coefficient and higher values increase the relative magnitude of the response. As an extreme example with x = 10 we effectively isolate the highest ROC candle within the window (which has some novel support resistance horizontals as those closes are often important). This controls the degree of responsiveness, so we can magnify the responsiveness, but with the trade off of overshoot/persistence.
So now we have the problem whereby that a highly weighted data point from a high volatility event persists within the filter window. And to a possibly extreme degree, if a reversal occurs we get a potentially large "overshoot" and in a way actually induced a large amount of lag for future price action.
This filter compensates for this effect by exponentially decaying the abs(ROC) coefficient over time, so as a high volatility event passes through the filter window it receives exponentially less weighting allowing more recent prices to receive a higher relative weighting than they would have.
c3 = c2 * pow(1 - percent_decay, periods_back)
This is somewhat similar to an EMA, however with an EMA being recursive that event will persist forever (to some degree) in the calculation. Here we are using a fixed window, so once the event is behind the window it's completely removed from the calculation
I've added Ehler's Super Smoother as an optional smoothing function as some highly non linear settings benefit from smoothing. I can't remember where I got the original SS code snippet, so if you recognize it as yours msg me and I'll link you here.
First time coding - a 5min forex Scalping strategy This is my first attempt at producing a strategy in Pine Script.
I am NOT a professional coder. I'm not even a good coder at that. I've only started Pine Script coding since September 2019. I am teaching myself.
This script is far from finished. I need to tweak a number of things about this script. Namely:
Add a validity window to the 'trigger bar' condition. Ie, I want to shut down the condition when the price closes above EMA21
Change the order entry so they are stop orders, using the stop entry price derived from the signals
Make changes to lot sizing
Add a trailing stop condition
Comments welcome, but do not expect me to reply to any questions or requests. In fact, don't expect any replies from me. I consider myself notoriously bad at replies.
I do welcome any feedback from any seasoned coders out there, as I am still a novice coder, and have so much to learn!
As to anyone who wants to criticise me - constructive and helpful criticism are most welcome, criticism to make yourself feel superior to me - you kind can eat a dk.
For the strategy rules, google the user ForexSignals TV account and look for the video "SIMPLE and PROFITABLE Forex Scalping Strategy".
Share, learn, prosper
Peace to y'all
Serialhenry
6/11/19
Fast/Slow Degree OscillatorIntroduction
The estimation of a least squares moving average of any degree isn't an interesting goal, this is due to the fact that lsma of high degrees would highly overshoot as well as overfit the closing price, which wouldn't really appear smooth. However i proposed an estimate of an lsma of any degree using convolution and a new sine wave series, all the calculation are described in the paper : "Pierrefeu, Alex (2019): A New Low-Pass FIR Filter For Signal Processing."
Today i want to make use of this filter as an oscillator providing fast entry points. The oscillator would be similar to the MACD in the sense that is consist on the difference between two filters, with one faster than the other, however unlike the MACD which use two moving averages of different length, here i'll use two filters of same length but different degrees.
The Indicator
The indicator consist in 3 elements, one main line (in green) the trigger line (in orange) and the histogram which is the difference between the green line and the red one. The main line is made from the difference between two filters of both period length and different degrees (fast, slow), fast should always be higher than slow. The signal line is just the exponential moving average of the main line, the period of the exponential moving average can be adjusted from the settings.
Both fast/slow determine the degree of the filters, higher values will create a faster filter.
For those who are curious, the filter use a kernel who estimate a polynomial function, this is how an lsma work, the kernel of an lsma of degree p is a polynomial of degree p . I achieved this estimation using a sine wave series.
When fast = 1 and slow = 0, the oscillator appear less periodic, this equivalent to : lsma - sma
Using 2/1 allow the indicator to highlight cycles more easily without being uncorrelated with the price. This is equivalent to qlsma - lsma, where qlsma is a quadratic least squares moving average. This is similar to my old indicator "Linear Quadratic Convergence Divergence Oscillator".
By default the indicator use 3 for fast and 2 for slow, but you can increase both values, here 4/3 :
In general higher values of fast/slow will create way more cyclical results, but they can be uncorrelated with the market price.
Conclusion
This indicator was rather made to show the filter calculation rather than proposing something interesting. However it can be funny to see how the difference between low lag filters create more cyclical outputs, it often allow indicators to have more predictive capabilities.
I invite you to read the paper made about the filter, codes for both pinescript and python are provided.
Smart Envelope - Running Away From The TrendIntroduction
Envelopes indicators consist in displaying one upper and one lower extremity on the price chart. They are most of the time built by adding/subtracting a volatility estimator (rolling stdev, atr, range...etc) to a central tendency estimator (SMA, EMA, LSMA...etc) . Their interpretation is often subject to debate amongst technical analyst, some will use a support and resistance methodology, where price will start a downtrend once it cross the upper extremity, and a down trend once it cross the lower one. Others will prefer a breakout methodology, where price will reach higher highs once it cross the upper extremity, and lower lows when it cross the lower one. Because of price non stationarity its hard to select the best methodology, the support and resistance one will mostly work on ranging markets, while the breakout methodology mostly work on trending ones.
Therefore new methods where proposed, instead of using moving averages with a high lag, faster filters where used, such as the least squares moving average or zero lag exponential moving average, other band indicators where also created using adaptive filters, but improvements remain relatively low. The most difficult task would be to make extremities with the ability to return accurate support and resistances levels, and today i want to provide a new way to construct such extremities by using the recursive bands framework that allow extremely creative and efficient indicators.
The Main Idea
With classical bands indicators, the upper and lower extremity will still be correlated with the main trend, the problem behind such method is that we can't use a support and resistance methodology with trending markets, the fact that reversals exist tells us that our extremities will always be crossed by the main trend, here is an example :
Here the support is correlated with the main trend, in order for it to be accurate we must assume the trend will go on for ever, and will only detect higher lows, this is what we expect with the orange line, but we can see that a severe down trend totally destroy our plan.
In short we need to give some headroom to our extremities, and thus one extremity can't be correlated with the main trend.
The proposed Indicator
We want to minimize the correlation between the extremities, so if the upper extremity rise, the lower one must fall. This allow to give some headroom and allow the user to anticipate larger movements, this is how bands seeking to give support and resistances points should work.
The indicator has a length setting that control the wideness of the extremities, unlike other indicators low values such as 14 can still create really wide bands, take that into account.
length = 5. Lower length values allow for more motion from the extremities, but does not necessarily involve detecting shorter terms support and resistances levels. The factor setting is not that important, but it allow to return extremities with more motion when high, and really wide bands when below 1 and greater than 0.
Central Tendency Estimator
Something fun with the recursive band framework is that the bands are no longer based on the central tendency estimator but its the central tendency estimator who is based on the bands. The central tendency estimator can also provide support and resistances points with the price, like classical moving averages, altho its lack of motion is this time a downside.
Conclusion
Altho the extremities are more accurate than other band indicators, the problem remain the same, larger trend will always break the extremities and continue creating higher/lower highs/lows, at this point our stop loss would certainly be triggered. This is a huge downsides of contrarian strategy, we sure might anticipate reversals earlier, but we are exposed to larger price movements, therefore the risk is extreme.
But the proposed methodology might still prove useful to develop more robust support and resistances levels based on envelopes indicators.
Thanks for reading !
Custom Profitable Moving Average CrossoverA custom version of Profitable Moving Average Crossover that shows the last estimations on profit for each crossover type and allows to set date range for analysis.
G-Channels - Efficient Calculation Of Upper/Lower ExtremitiesIntroduction
Channels indicators are widely used in technical analysis, they provide lot of information. In general, technical indicators giving upper/lower extremities are calculated by adding/subtracting a volatility component to a central tendency estimator. This is the case with Bollinger bands, using the rolling standard deviation as volatility estimator and the simple moving average as central tendency estimator, or the Keltner channels using the exponential moving average and the average true range.
Lots and lots and lots (i can go on) of those indicators have been made, they only really need a central tendency estimator, which can be obtained from pretty much any filter, however i find interesting to focus on the efficiency of those indicators, therefore i propose a super efficient channel indicator using recursion. The average resulting from the upper/lower extremity of the indicator provide a new efficient filter similar to the average highest/lowest.
The calculation - How Does It Works
Efficiency is often associated to recursion, this would allow us to use past output values as input, so how does the indicator is calculated? Lets look at the upper band calculation :
a := max(src,nz(a(1))) - nz(a(1) - b(1))/length
src is the closing price, a is upper extremity, b is the lower one. Here we only need 3 values, the previous values of a and b and the closing price. Basically a := max(src,nz(a(1))) mean :
if the closing price is greater than the precedent value of a then output the closing price, else output the precedent value of a
therefore a will never be inferior to its precedent value, this is useful for getting the maximum price value in our dataset however its not useful to make an upper band, therefore we subtract this to a correction factor defined as the difference between a and b , this force the upper band to have lower values thus acting like a band without loosing its "upper" property, a similar process is done with the lower band.
Of course we could only use 2 values for making the indicator, thus ending with :
a := max(src,nz(a(1))) - nz(abs(close - a(1))/length
In fact this implementation is the same as the one proposed in my paper "Recursive Bands - A New Indicator For Technical Analysis", its also what i used for making the indicator "Adaptive Trailing Stop", this would be more efficient but i used the difference between the upper and lower extremities for a reason.
The Central tendency Estimator
This is the reason why i didn't implemented a more efficient version. Basically this central tendency estimator is just the average between the upper and lower extremities, it behave like the average of the highest/lowest over length period, its central plot in the Donchian channel indicator. Below is a comparison of both with length = 100 :
But why is our average so "boxy"? The extremities are not boxy, so why the average is sometimes equal to its previous value? Explain!
Its super easy to understand, imagine two lines, if their absolute change is the same and they follow an opposite direction, then their average is constant.
the average of the green and red line is the orange line. If both lines follow the same direction then their average will also follow this direction.
When both extremities follow the same direction, the average will also do the same, when both follow an opposite direction then the average will be equal to its precedent value, this is also due to the fact that both extremities are based on the same correction factor (a-b) , else the average wouldn't act that way, now you understand why i made this choice.
Conclusion
I proposed an efficient implementation of a channel indicator that provide an interesting central tendency estimator. This simple implementation would allow for tons of interesting concepts, some of my indicators use a similar approach and allow for great outputs, you'll see them soon enough. I hope this indicator find its use in the community, remember to ask before using this indicator in a script you want to publish.
Thanks for reading !
If you want to discuss about anime stuff send me a pm but don't do it in the commend section.
Blackman Filter - The Smoother The BetterIntroduction
Who doesn't like smooth things? I'd like a smooth market price for christmas! But i can't get it, instead its so noisy...so you apply a filter to smooth it, such filters are called low-pass filters, they smooth and its great but they have lag, so nobody really use them, but they are pretty to look at.
Its on a childish note that i will introduce this indicator, so what it is all about? I propose a new FIR filter using a blackman function as filter kernel for financial time-series smoothing, do you prefer the childish tone ? Fear not its surprisingly easy!
The Blackman Function
The blackman function look like a bell shaped curve, look:
The blackman function will produce such curve. This function is called a cosine sum function because she is based on the sum of cosine functions, here only 2.
0.42 - 0.5 * cos(2 * pi * k) + 0.08 * cos(4 * pi * k)
Originally you use this function for windowing , what does it means? In signal processing you have a function called sync function , if you use this function as filter kernel you would get the ideal frequency domain response filter, sometime called brickwall filter, it would be extremely smooth.
Above the optimal low pass filter frequency response.
However the sync function has no ending values and goes on forever, therefore we can't use it for convolution, expect if we apply windowing. Filters using windowing are called windowed-sinc filters, i will describe the procedure below :
1 - Create a sync function = sin(pi*n)/(pi*n)
2 - Truncate it = I only keep the first length points of the sync function.
This create a abrupt end, the frequency of a filter using step 1 as kernel would contain ripples in the pass band and stop band, this is bad! The frequency response would look like this :
3 - I multiply my values of step 2 by a window function, it can the blackman window, i no longer have an abrupt end, its smooth!
The frequency response of the filter using this kernel would no longer have ripples! This is the power of windowing functions.
Here we are not using such thing, but we could in the future. Here instead we use the blackman function as filter kernel, because this function is bell shaped this mean that the filter will certainly be smooth (symmetrical weighting is a rule of thumb for kernels when we want really smooth filters).
The Filter
This filter is quite smooth, unlike the gaussian filter this filter give less weights to recent and past values, this is because the blackman function has fatter tails than the gaussian one. I could make a comparison of both, however they are quite alike, if you often use a gaussian filter its up to you to decide which one you prefer.
The filter can do a better job than the moving average when it comes to preserve the frequency components that constitute the cycles/trend.
We can see that the filter has a greater performance when it comes to keep the shape of the market price, thus it has a slightly better fit.
Conclusion
Ok so in this post you learned a bit about the sync function and windowing, those are basic subjects in signal processing, they allow us to approximate the filter with the ideal frequency response, i also showed you that those windowing function could be used as kernel and that they where pretty smooth on their own, there are many others, but the one i prefer is the blackman windowing function.
I know what you are thinking, "we want trailing stops, alerts, colors, arrows!", and i understand you pal, but sometimes its cool to take a break from all this stuff. However i can tell that i'am working on a side project that aim to estimate rolling maximum/minimum as fast as possible, any experiments will be published here, and i can ensure you that those indicators will make your day quite brighter, we will see that soon.
I hope you learned something from this post! I'am a bit tired (look i'am disappearing !)
Thanks for reading !
