Ehlers Triangle Moving Average [CC]The Triangle Moving Average is the last of custom scripts converting Ehlers Window Indicators to Moving Averages. As you can see this is actually very similar to the Hamming Moving Average and the Hann Moving Average so I would recommend to test this one out with different settings and see what works best for you. As far as the formula calculation, it is a custom weighted moving average that determines how close the price is compared to the middle of the length period and gives a custom weight to that price. For example it will assign heavier weights according to how close the price is to the beginning of the loop (which is the most recent data) and lighter weights, the further the price is away from the recent prices. I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish!
Windowing
Ehlers Hamming Moving Average [CC]The Hamming Moving Average is a custom script I made to attempt to create a moving average using Ehler's Hamming Window Indicator . Let me stress that this is extremely experimental considering the original indicator works by creating a sine wave by adjusting the Pedestal value. Change the Pedestal value to anything 5 or higher and you will see what I mean. I think this is a fun experiment so let me know what you think. I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators or scripts you would like to see me publish!
[blackcat] L2 Hann Ehanced DMILevel: 2
Background
Among the many indicators, it can be said that DMI is the only "super turning" indicator. This indicator can alone send out risk warning signals when extreme market conditions occur in the stock market, helping us to solve some problems.
If we can operate according to the instructions of DMI, firstly, we can avoid the mistake of buying stocks at the head. Secondly, in the process of falling fear of the market, we can follow the direction signal sent by DMI and catch every time on the way down. Opportunity to rebound to unwind.
If you look at the diagram of the DMI, you will think it is very complicated, because there are four lines in its diagram, and they are intertwined, and it is difficult to distinguish the complex signals in it. But don't worry about its complex structure, we will fully dissect this indicator.
Function
These four lines are: PDI, MDI, ADX and ADXR. The scale of the table is from 0-100, which means from very weak to very strong. The PDI curve and MDI curve on some software are called +DI curve and -DI curve , all have the same meaning.
PDI: Represents the position of multiple parties in the market.
In market movements, the higher the PDI, the stronger the current market. On the contrary, it is a weak market. The A-share market is easy to go to extremes. Therefore, we can see that in the past A-share market, the PDI sometimes fell to near zero, and at this time, it often indicated that a rebound and uptrend was about to start.
MDI: Represents the position of the bears in the market.
In the market movement, the higher the MDI goes, the weaker the current market is, and vice versa, it is a strong market. Before a big bull market comes, we can see the MDI drop to a position close to zero, and at this time, the bears in the market have no power to fight back.
The relationship between PDI and MDI:
In the operation of the market, PDI and MDI are intertwined with each other. If the PDI is above the MDI, the market at this time is a strong market. The MDI is above the PDI, which is a bear market. The closer the distance between the two, the market is in a stalemate of consolidation. On the contrary, the further apart the two lines are, the more obvious the unilateral nature of the market is, whether it is a bull market or a bear market. The so-called unilateral market means that there is no midway adjustment when it rises, and there is no rebound correction when it falls.
ADX: Fast steering pullback.
The difference between ADX and other analysis indicators is that whether it is rising or falling, as long as there is a unilateral market, it runs upwards, not like other indicators, the strong market runs upwards and the weak market runs downwards.
The thread is almost entwined with PDI and MDI in general market movement, which makes no sense at this time. However, once the market breaks out of the market and starts to go to extremes, whether the market is rising or falling, ADX will start to run upwards. At this time, ADX has a clear meaning, because DMI has begun to issue early warning of impending turn!
ADXR: slow pull back.
This line is matched to ADX and is a moving average of ADX values. When ADX goes up, ADXR goes up with it, just slower.
When a round of rapid decline ends, it usually needs to be corrected by a rebound, and ADX will take the lead in turning up. Once it crosses with ADXR, it is regarded as an effective breakthrough.
Numerical division. I set an input threshold for HEDMI, and users can set the optimal threshold to buy and sell according to different TFs.
When PDI crosses the threshold, no matter how strong the bull market is, we must beware of risks from happening at any time.
In order to distinguish more clearly, I slightly modified the formula of the system, and when this happens, the indicator will issue a green warning label, so as to avoid risks in time.
Comprehensive use of four lines:
If the four lines in the steering indicator DMI are intertwined below 50, it usually means that the market is in a state of mild consolidation at this time. The DMI indicator at this time is useless because it does not generate a strong pullback force. Don't worry about an unexpected turnaround in the market. As for the consolidation, it's not a turnaround, it's a breakout.
When PDI and MDI gradually separate, at this time, ADX and ADXR will also rise. At this time, the DIM that is usually messy like twine will be clearly separated. When rising, PDI rises along with ADX and ADXR, while MDI sinks weakly. On the contrary, when the market starts to fall, MDI will rise along with ADX and ADXR, and PDI will sink helplessly. At this time, the DMI will be like a "tiger's mouth", gradually opening its bloody mouth. The bigger the opening, the more lethal the bite.
Here comes a tactic, or technical trend, called double hooves, that is, PDI and MDI split, ADX and ADXR upward to produce golden forks, PDI and MDI are like the double front hooves of a horse, ADX and ADXR The golden fork is like the rear hooves of a steed ready to take off, and this trend of the four lines is like the four legs of a steed that is about to run.
If you think it is too complicated to look at DMI like this, then I can tell you the easiest way to judge, that is, just look at the PDI line. When the PDI line falls below 10, boldly buy the dip, because it is a dip, so you need to calculate the rebound At this time, combined with the golden section theory I often talk about, you can easily find the selling point by making the golden section of the downward trend for the previous trend.
This kind of bottom-hunting method uses the golden section theory, and basically there will be no losses. Remember that one thing is not to be greedy and strictly enforce discipline. This is bottom-hunting, and advancing with both hooves is chasing up. The two styles are different, and the operation styles are different. You also need to explore more in actual combat. Any kind of trick, if you practice it proficiently, it is a unique trick.
Remark
Hanning Window Enhanced DMI
Free and Open Source Indicator
windowing_taAll Signals Are the Sum of Sines. When looking at real-world signals, you usually view them as a price changing over time. This is referred to as the time domain. Fourier’s theorem states that any waveform in the time domain can be represented by the weighted sum of sines and cosines. For example, take two sine waves, where one is three times as fast as the other–or the frequency is 1/3 the first signal. When you add them, you can see you get a different signal.
Although performing an FFT on a signal can provide great insight, it is important to know the limitations of the FFT and how to improve the signal clarity using windowing. When you use the FFT to measure the frequency component of a signal, you are basing the analysis on a finite set of data. The actual FFT transform assumes that it is a finite data set, a continuous spectrum that is one period of a periodic signal. For the FFT, both the time domain and the frequency domain are circular topologies, so the two endpoints of the time waveform are interpreted as though they were connected together. When the measured signal is periodic and an integer number of periods fill the acquisition time interval, the FFT turns out fine as it matches this assumption. However, many times, the measured signal isn’t an integer number of periods. Therefore, the finiteness of the measured signal may result in a truncated waveform with different characteristics from the original continuous-time signal, and the finiteness can introduce sharp transition changes into the measured signal. The sharp transitions are discontinuities.
When the number of periods in the acquisition is not an integer, the endpoints are discontinuous. These artificial discontinuities show up in the FFT as high-frequency components not present in the original signal. These frequencies can be much higher than the Nyquist frequency and are aliased between 0 and half of your sampling rate. The spectrum you get by using a FFT, therefore, is not the actual spectrum of the original signal, but a smeared version. It appears as if energy at one frequency leaks into other frequencies. This phenomenon is known as spectral leakage, which causes the fine spectral lines to spread into wider signals.
You can minimize the effects of performing an FFT over a noninteger number of cycles by using a technique called windowing. Windowing reduces the amplitude of the discontinuities at the boundaries of each finite sequence acquired by the digitizer. Windowing consists of multiplying the time record by a finite-length window with an amplitude that varies smoothly and gradually toward zero at the edges. This makes the endpoints of the waveform meet and, therefore, results in a continuous waveform without sharp transitions. This technique is also referred to as applying a window.
Here is a windowing_ta library with J.F Ehlers Windowing functions proposed on Sep, 2021.
Library "windowing_ta"
hann()
hamm()
fir_sma()
fir_triangle()
Ehlers Hann Relative Strength Index [CC]The Hann Relative Strength Index was created by John Ehlers (Stocks and Commodities Jan 2022 pgs 26-28) and this indicator builds upon his Hann Window Indicator to create an unique rsi indicator that doesn't rely on overbought or oversold levels to determine a reversal point and also provides a very superior smoothing without any of the lag associated with traditional smoothing. A much more useful RSI than the standard version in my honest opinion. Short term you buy when the line turns green and sell when it turns red. Medium to long term you buy when the indicator rises above the 0 line and sell when it falls below the 0 line. I have included strong buy and sell signals in addition to normal ones so strong signals are darker in color and normal signals are lighter in color.
Let me know if there are any other indicators or scripts you would like to see me publish!
TASC 2022.01 Improved RSI w/Hann█ OVERVIEW
TASC's January 2022 edition Traders' Tips includes the "(Yet Another Improved) RSI Enhanced With Hann Windowing" article authored by John Ehlers. Once again John Ehlers revolutionizes the RSI indicator. This is TradingView's Pine Script code for the indicator.
█ CONCEPTS
By employing a Hann windowed finite impulse response filter ( FIR ), John Ehlers has enhanced the "Relative Strength Indicator" ( RSI ) to provide an improved oscillator with exceptional smoothness.
█ NOTES
Calculations
The method of calculations using "closes up" and "closes down" from Welles Wilder's RSI described in his 1978 book is still inherent to Ehlers enhanced formula. However, a finite impulse response (FIR) Hann windowing technique is employed following the closes up/down calculations instead of the original Wilder infinite impulse response averaging filter. The resulting oscillator waveform is confined between +/-1.0 with a 0.0 centerline regardless of chart interval, as opposed to Wilder's original formulation, which was confined between 0 and 100 with a centerline of 50. On any given trading timeframe, the value of Ehlers' enhanced RSI found above the centerline typically represents an overvalued region, while undervalued regions are typically found below the centerline.
Background
The original RSI indicator was designed by J. Welles Wilder and presented in his "New Concepts in Technical Trading Systems" book published in 1978.
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Ehlers Moving Average Difference Hann Indicator [CC]The Moving Average Difference Hann Indicator was created by John Ehlers (Stocks and Commodities Nov 2021) and this is an improved variation of his Moving Average Difference Indicator that uses smoothing from his Hann Windowing Indicator to provide smoother buy and sell signals. As for how this indicator works it is an improved version of the classic MACD indicator which of course takes a difference between two exponential moving averages. I have included strong buy and signals in addition to normal ones so lighter colors are normal signals and darker colors are strong signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like to see me publish!
Ehlers Simple Window Indicator [CC]The Simple Window Indicator was created by John Ehlers (Stocks and Commodities Sep 2021) and this is the last of the 4 new indicators that he published in the latest issue of Stocks & Commodities. Since these are all part of a series, the idea behind each indicator is the exact same. The only difference is of course the calculation for each indicator. This script is different mostly because it is extremely noisy in comparison so I had to smooth it twice to provide clear buy and sell signals. Window functions are used in digital signal processing to filter out noise and the end result is an oscillator that centers around the 0 line. The easy way to understand these indicators that I will be publishing and those are that when they are above 0, it usually means an uptrend and below 0 then a downtrend. For more immediate signals, I have included both normal and strong buy and sell signals so darker colors for strong signals and lighter colors for normal signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like me to publish!
Ehlers Triangle Window Indicator [CC]The Triangle Window Indicator was created by John Ehlers (Stocks and Commodities Sep 2021) and this is one of 4 new indicators that he published in the latest issue of Stocks & Commodities and I will be publishing the last one tomorrow. Since these are all part of a series, the idea behind each indicator is the exact same. The only difference is of course the calculation for each indicator. Window functions are used in digital signal processing to filter out noise and the end result is an oscillator that centers around the 0 line. The easy way to understand these indicators that I will be publishing and those are that when they are above 0, it usually means an uptrend and below 0 then a downtrend. For more immediate signals, I have included both normal and strong buy and sell signals so darker colors for strong signals and lighter colors for normal signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like me to publish!
Ehlers Hamming Window Indicator [CC]The Hamming Window Indicator was created by John Ehlers (Stocks and Commodities Sep 2021) and this is one of 4 new indicators that he published in the latest issue of Stocks & Commodities and I will be publishing the other 2 in the next few days. Since these are all part of a series, the idea behind each indicator is the exact same. The only difference is of course the calculation for each indicator. Window functions are used in digital signal processing to filter out noise and the end result is an oscillator that centers around the 0 line. The easy way to understand these indicators that I will be publishing and those are that when they are above 0, it usually means an uptrend and below 0 then a downtrend. For more immediate signals, I have included both normal and strong buy and sell signals so darker colors for strong signals and lighter colors for normal signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like me to publish!
Ehlers Hann Window Indicator [CC]The Hann Window Indicator was created by John Ehlers (Stocks & Commodities Sep 2021) and this is one of 4 new indicators that he published in the latest issue of Stocks & Commodities and I will be publishing the other 3 in the next few days. Since these are all part of a series, the idea behind each indicator is the exact same. The only difference is of course the calculation for each indicator. Window functions are used in digital signal processing to filter out noise and the end result is an oscillator that centers around the 0 line. The easy way to understand these indicators that I will be publishing and those are that when they are above 0, it usually means an uptrend and below 0 then a downtrend. For more immediate signals, I have included both normal and strong buy and sell signals so darker colors for strong signals and lighter colors for normal signals. Buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you would like me to publish!
FIR Hann Window Indicator (Ehlers)From Ehlers' Windowing article:
"A still-smoother weighting function that is easy to program is called the Hann window. The Hann window is often described as a “sine squared” distribution, although it is easier to program as a cosine subtracted from unity. The shape of the coefficient outline looks like a sinewave whose valleys are at the ends of the array and whose peak is at the center of the array. This configuration offers a smooth window transition from the smallest coefficient amplitude to the largest coefficient amplitude."
Ported from: { TASC SEP 2021 FIR Hann Window Indicator } (C) 2021 John F. Ehlers
Stocks & Commodities V. 39:09 (8–14, 23): Windowing by John F. Ehlers
Original code found here: traders.com
FIR Chart: traders.com
ROC Chart: traders.com
Ehlers style implementation mostly maintained for easy verification.
Added optional ROC display.
Style and efficiency updates + Hann windowing as a function coming soon.
Indicator added twice to chart show both FIR and ROC.
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 !
