Re: Ehlers based indicator(s) - cTrader

2
MAMA


MAMA is the mother of all Adaptive Moving Averages.
Actualy the name is an acronym for MESA Adaptive Moving Average.

Developed by John Ehlers, the MESA Adaptive Moving Average is a technical trend-following indicator which, according to its creator, adapts to price movement “based on the rate change of phase as measured by the Hilbert Transform Discriminator”. This method of adaptation features a fast and a slow moving average so that the composite moving average swiftly responds to price changes and holds the average value until the next bar’s close.

The nonlinear action of this filter is produced by the flyback of phase every half cycle. When combined with FAMA, a Following Adaptive Moving Average, the crossovers form excellent entry and exit signals that are relative free of whipsaws.

Basically the indicator looks like two Moving Averages (MA), but instead of curving around the price action, the MESA Adaptive MA moves in a staircase manner as the price ratchets. It produces two outputs, MAMA and FAMA. FAMA (Following Adaptive Moving Average) is a result of MAMA being applied to the first MAMA line. The FAMA is synchronized in time with MAMA, but its vertical movement comes with a lag. Thus, the two don’t cross unless a major change in market direction occurs, resulting in a moving average crossover system which is “virtually free of whipsaw trades”, according to Ehlers.
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Re: Ehlers based indicator(s) - cTrader

3
FRAMA


FRAMA (FRactal Adaptive Moving Average). A nonlinear moving average is derived using the Hurst exponent.

The Fractal Adaptive Moving Average aka FRAMA is a particularly clever indicator. It uses the Fractal Dimension of stock prices to dynamically adjust its smoothing period. In this post we will reveal how the FRAMA performs and if it is worthy of being included in your trading arsenal.

The FRAMA indicator averages the distinctions of the Highest Highs and Lowest Lows over fluctuating segments of the Length of the Time Frame.

The figures obtained are mathematically maneuvered utilizing the Boolean enquiries, Euler’s figure, familiar logarithms, and, forth with some pointer from the prior Fractal Adaptive Moving Average, the most current Fractal Adaptive Moving Average is carved.

The time frame length of the Fractal Adaptive Moving Average as well as the cost type can be adjusted to show the trader’s desire. The major concept behind the FRAMA is to take into suggestion just very important price adjustments. If price moves in a particular direction extremely, the FRAMA indicator is able to pursue price very firmly.

If price encounters itself range destined and does not makes any important move, the FRAMA stays level. The FRAMA fundamental divides the activity chart into smaller groups and then examine in contrast these groups one after the other. These demonstrate the activity chart is a collection of bunch of squares i.e. smaller and even larger ones.

The FRAMA is astoundingly effective as both a Fast and a Slow moving average and will outperform any SMA or EMA.
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Re: Ehlers based indicator(s) - cTrader

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Laguerre Filter


The Laguerre Filter (LF) is a Smoothing filter based on Laguerre polynomials.
Its first term is an EMA, which is then further smoothened with a damping factor.

The damping factor may take any value between 0 and 1.
When the damping factor is set to 0, the Laguerre Filter becomes a finite impulse response (FIR) filter.
When the damping factor is set to a value close to 1, the filter becomes dramatically smoother, but will have a significant lag.

The smoothing is controlled by an alpha factor which is the alpha for the EMA and also damps the further terms.
Alpha can range from 1 to follow prices almost exactly, down to 0 for a very slow response.

The Laguerre filter (same length) has virtually the same crossing signals but is much smoother due to the introduction of the dampening factor (gamma).
If you set gamma to 0 Laguerre will be identical to the FIR filter. This filter can be used to dampen other indicators with similar results.
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Re: Ehlers based indicator(s) - cTrader

5
GaussianFilterAl


The Gaussian Filter (GF) is a filter whose impulse response is a Gaussian function.
The Gaussian filter is designed not to give in excess to the function level while minimizing the rise and descent.

The Gaussian filter changes the incoming signal by a convolution with a Gaussian function, this transformation is also called Weierstrass transformation.

Gaussian filter, a.k.a. GaussianMovingAverage is an indicator that attempts to eliminate lag while preserving responsiveness.

It imitates the Gaussian distribution found in many systems. The filter Poles number is used to adjust the Lag.
This indicator is very responsive and can also be used as a Price proxy in the same way as the Hull Moving Average (HMA)
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Re: Ehlers based indicator(s) - cTrader

6
X-Pole Super Smother


A Super Smoother filter from John Ehlers' book "Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology.

A Pole is a single-frequency point of pushing gain up, and a zero is a single-frequency point of pulling the gain down; with a single pole, you are not going to get complex response curves such as bandpass, peak, and shelving filters that you can get with the two poles and zeros of a biquad. That leave us with lowpass and highpass, which have a single point of change.

However, a One-pole makes a poor highpass filter for cases in which we might be likely to use it—in particular, a DC blocker.
That’s because it makes a highpass by pushing response up at high frequencies—we really need a zero to pull the response down at very low frequencies.
So, we’ll only implement coefficient calculation for lowpass response here. However, we can still make a highpass filter suitable for a DC blocker by subtracting the output of a one-pole lowpass filter, set to a low frequency, from the direct signal.

The 6 dB per octave slope of the one-pole filter—a halving of amplitude for every doubling of frequency—is gentle and natural. It’s extremely cheap, computationally, so it’s the perfect choice when you need just a bit of smoothing. And it doesn’t “ring” (overshoot), so it’s an excellent choice for filtering knob changes. Run each of your user interface sliders and knobs through a one-pole lowpass set to a low (sub-audio) frequency, and your glitchy, zippered control changes turn into smooth parameter changes.
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Re: Ehlers based indicator(s) - cTrader

7
ZLEMA


This is a Zero lag EMA as described by John Ehlers ( without Lag and Overshoot )

Exponential averages belong to the calculation methods with shorter lags. In simple terms, for each time period in Market Data, a simple calculation is carried out that first determines the deviation of the current price from the previous average value. This value – described as an error – is multiplied with a weighing factor and then added to the previous average value. This results in the current average value.

The Zero Lag Exponential Moving Average is a Variation on the Exponential Moving Average.
The Zero-Lag keeps the benefit of the heavier weighting of recent values, but attempts to remove lag by subtracting older data to minimize the cumulative effect.

The ZLEMA indicator or Zero Lag Exponential Moving Average is a Technical Analysis indicator originally created by Ric Way and John Ehlers.
The goal of the indicator is to try to eliminate the inherent Lag of all trend following indicators which Average prices over time such as the Simple or Exponential Moving Averages.

Instead of applying the Exponential Moving Average on the regular data (usually the Close price), the indicator applies it to a de-Lagged data, which is the original data removed from the "Lag" days.
Usually, a Stock is considered Bullish when the Zero Lag EMA is above the original EMA and Bearish when the Zero Lag EMA is below the original EMA.

This Paper show and interactive way to eliminate as much Lag as desired from smoothing filters. Of course, reduced lag comes at the price of decreased filter smoothness. The filter exhibits no transient overshoot commonly found in higher order Filters.
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Re: Ehlers based indicator(s) - cTrader

8
EhlersFilterAl


Ehlers Filter (EF) was authored, not surprisingly, by John Ehlers.
The EF uses Current Prices, prior prices (determined by Momentum length) and their difference over a time period to calculate its value.

Ehlers Filter is a powerful trend-following VertexFX indicator, which provides precise BUY and SELL signal.
This indicator has a minimal lag, of approximately 4 bars, which is very useful in detecting start of trends.
At first we calculate the smoothing coefficient, which is the absolute difference between the Current price and price MOM_PERIOD bars ago.
Next we smooth the price using this smoothing coefficient over the LENGTH bars period. This is called the Ehlers filter.

The current Ehler filter is based on Acceleration and Speed.
The filter uses the close and volume data along with three inputs/parameters to Smooth the price series.

The parameters that should be provided to the Ehler filter function are :
- Length of the FIR (Finite Impulse Response). This is one type of filter, the other one is the Infinite Impulse Response (IIR). For example, the EMA is an IIR filter.
- Exponential weight of passed acceleration and speed
- Weighting factor between acceleration and speed


The Ehler filter is interpreted like any Moving Average (MA) where usually a close price Above the Ehler filter is considered a Bullish sign and a value Below the filter is considered a Bearish sign.

The generalized Ehlers filter can be adapted to any Statistic of your choice. In this case it uses a Triangular Moving Average (TMA) for Smoothing.
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Re: Ehlers based indicator(s) - cTrader

9
CyclePeriod


This article written by Oscar Cuevas was originally published in the December 2014 issue of Traders' Magazine.

Oscar Cuevas is a computer engineer and provides online webinars on Expert Advisor programming. He has also been a content developer and trading strategies programmer in Visual Chart for more than five years


Cycle Indicator. An Analysis Tool Based on the Phenomenon of Cyclical Movements

A fundamental aspect of trading strategies research is the detection of specific behaviours or patterns within the data stream studied. The purpose of this process is nothing other than trying to estimate, with varying reliability, the possible future movements based on those guidelines. Because financial data follow a temporal structure, time cycle based tools provide information of considerable interest. In this article, we will discuss one of these cases.


The Cyclical Movement Phenomenon

The evolution of prices of an asset usually follows a temporal sequence. When we talk about timing, we are not necessarily referring to each of the cycles having a period of similar length, rather we only refer to the fact that there is a relationship between a set of data for a period of time. Once this period of time is finished, the behaviour of the following data will probably show a different distribution.

Therefore, the key is to locate the beginning phases of each cycle, so you can take advantage of the information obtained from the study of the distributions. One of the existing methods to locate those phases would be the study of the price ROC (Rate of Change). Next, we will describe this tool and also the way to interpret it. Study of the Rate of Change in Prices The ROC, as the name suggests, shows the difference between the current price and the price given over a period of time, oscillating around zero depending on the direction taken by the price of the asset.

The interesting thing about this relies on the fact that, when a relevant change in the price movement occurs, the value of the exchange rate soars automatically. From that time, we consider that a new time cycle has started, since most variation between prices reflects consolidated forces favourable to the direction of thrust attack.

Note the speed with which this oscillator operates, since as soon as a bull or bear attack begins, the ROC is altered. This differs from other oscillators such as MACD or TRIX, which suffer from the lagging moving averages on which its calculations are based. The ROC follows a wave sequence, since the variation between prices stabilises sooner or later and marks the end of the open cycle. However, the fact that the ROC remains stable does not necessarily mean that the extreme point of the movement has been reached, simply that the change in prices remains constant. This means that once the new cycle is closed, the ROC loses its prediction value. To illustrate this we use the Visual Chart platform. Within this platform, we can find a tool that computes the function of the exchange rate, specifically the ROC Prices indicator.

In Figure 1, you can see a chart this indicator has been applied to. Here, we see an example of the above: The end of the cycle itself is not the point of exhaustion of the impulse but the location of a point of equilibrium where future events can be expected. On the basis of a tool to calculate the method we can evaluate the results obtained with the indicator and act accordingly.

As for the calculation process, the period of time that is often used to analyse the variation between prices depends on the type of study you want to do: When working in the short term the period is usually set on twelve bars while when working in the medium term the period used is at 25 bars. Since it is necessary to establish a specific time period, this can be a problem if the phases of accumulation last longer than expected. When this happens, it is normal that the exchange rate generates smaller cycles due to a lack of directionality. This problem is common with most analysis tools detecting trends. In order to filter bracketing movements we are going to include the volatility calculation in the study of the ROC.



John Ehlers says about the Cycles :

“The dictionary definition of a cycle is that it is ‘an interval or space of time in which is completed one round of events or phenomena that recur regularly and in the same sequence.’ In the market, we consider a classic cycle exists when the price starts low, rises smoothly to a high over a length of time, and then smoothly falls back to the original price over the same length of time. The time required to complete the cycle is called the period of the cycle, or the cycle length. Cycles certainly exist in the market. Many times they are justified on the basis of fundamental considerations. The clearest is the seasonal change for agricultural prices (lowest at harvest)…”
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Re: Ehlers based indicator(s) - cTrader

10
Fisher Center of Gravity


The Fisher CG Oscillator is unique because it is an oscillator that is both Smoothed and has Zero Lag.
It finds the Center of Gravity (CG) of the price values in an FIR filter.

The CG automatically has the smoothing of the FIR filter (similar to a Simple Moving Average) with the position of the CG being exactly in phase with the Price movement.
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