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AIS Ind Basic Window Functions

Diverses fonctions de fenêtre peuvent être utilisées pour lisser les séries chronologiques. Les fonctions de fenêtre peuvent être très différentes les unes des autres dans leurs caractéristiques - le niveau de lissage, la suppression du bruit, etc. Cet indicateur permet d'implémenter les fonctions de la fenêtre principale et d'évaluer leurs performances sur des séries temporelles financières.
Paramètres de l'indicateur :
  • iPeriod – période de l'indicateur. iPériode >= 2
  • iCenter est l'index de la référence où se situera le centre de la fonction fenêtre. Par défaut, ce paramètre est 0 - le centre de la fenêtre coïncide avec le centre de l'indicateur. Avec 1 <= iCenter <= iPeriod, le centre de la fonction de fenêtre sera décalé, à la suite de quoi certaines caractéristiques de l'indicateur changeront. Dans la figure 1, vous pouvez voir comment le choix du centre affecte la fonction de fenêtre et l'affichage de l'indicateur. Ce paramètre peut être modifié par incréments de 0,5.

Certaines fonctions de fenêtre utilisent des paramètres supplémentaires - ParameterA et ParameterB. Ils affectent les poids des fenêtres. De ce fait, les caractéristiques de l'indicateur changent. Le tableau indique les fonctions de la fenêtre et les limites de modification des paramètres si elles sont utilisées. La distribution des pondérations de la fonction de fenêtre pour des paramètres donnés peut être estimée à l'aide du script https://www.mql5.com/ru/market/product/72156

Window
 Parameter A
 Parameter B
Bartlett - Hann window

Blackman window

Blackman window exact

Blackman – Harris window

Blackman – Harris window approx.

Blackman – Harris window corr.

Blackman - Harris window opt.

Blackman – Harris window ref.

Blackman - Nuttall window

Bohman window

Cauchy window
 0 <= ParameterA

Connes window
 1 < = ParameterA
Cosine gen. window 1st
 0 <= ParameterA <= 100
Cosine gen. window 2nd
 0 <= ParameterA <= 100
Dolph - Chebyshev window
 0 <= ParameterA
Flat Top window

Flat Top window approx.

Gauss window
 1 < = ParameterA <= 2*iPeriod
Gauss window approx.
 1 < = ParameterA <= 2*(iPeriod+1)
Gauss window conf.
 1 < = ParameterA <= 2*(iPeriod+1)
Gauss window gen.
 1 < = ParameterA <= 2*iPeriod
 0 <= ParameterB
Hamming window

Hamming window opt.

Hann window

Hann double window

Hann - Poisson window
 0 <= ParameterA

Hyperbolic tangent window
 0 <= ParameterA
 0 <= ParameterB
Kaiser window
 0 <= ParameterA

Kaiser - Bessel window
 0 <= ParameterA

Kaiser – Bessel window approx.

Karre window

Lanczos window

Lanczos kernel window
 1 <= ParameterA
Log window
 1 <= ParameterA
Logistic window

Modified cosine window

Nuttall's window

Parzen window

Planck - Bessel window
 0 < = ParameterA <= iPeriod/2
 0 <= ParameterB
Plank-taper window
 0 < = ParameterA <= iPeriod/2
Poisson window
 0 <= ParameterA

Rectangular window

Rife – Vincent 3rd window

Rife – Vincent 4th window

Silverman window
 1 <= ParameterA
Sinusoidal windows
 0 <= ParameterA

Smoothed rectangular window
 0 < = ParameterA <= iPeriod/2
Stepped window
 0 <= ParameterA

Triangular window
 0 < = ParameterA

Tukey window
 0 < = ParameterA <= iPeriod/2
Welch window
 1 <= ParameterA




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This indicator uses the Fibonacci p-numbers to smooth a price series. This allows combining the advantages of the simple and exponential moving averages. The smoothing coefficients depend on the level of the p-number, which is set in the indicator parameters. The higher the level, the greater the influence of the simple moving average and the less significant the exponential moving average. Parameters Fibonacci Numbers Order - order of the Fibonacci p-number, specified by trader. Valid values
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The indicator is based on the analysis of interaction of two filters. The first filter is the popular Moving Average. It helps to identify linear price movements and to smooth minor price fluctuations. The second filter is the Sliding Median. It is a non-linear filter. It allows to filter out noise and single spikes in the price movement. A predictive filter implemented in this indicator is based on the difference between these filters. The indicator is trained during operation and is therefore
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This indicator studies the price action as a combination of micro-trends. All micro-trends are analyzed and averaged. Price movement is filtered based on this averaging. IP_High and IP_Low (blue and red dashed lines) show the instantaneous price movement. They display the forecast only for the current price values, taking into account only the number of bars defined by the 'Filter level' parameter. SP_High and SP_Low (blue and red solid lines) smooth the price movements with respect to history.
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Cet indicateur met en œuvre un processus de lissage linéaire simple. L'un des inconvénients du lissage exponentiel est la décroissance rapide du signal. Il est donc impossible de suivre pleinement les tendances à long terme de la fourchette de prix. Le lissage linéaire vous permet de régler plus précisément et plus finement le filtrage du signal. L'indicateur est configuré en sélectionnant les paramètres : LP - ce paramètre vous permet de sélectionner la période de lissage. Plus sa valeur
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This indicator is a combination of two modified Lanczos filters. The first filter serves to extrapolate the price. Based on past values, he predicts a possible price movement within the current bar. That is, it shows what the price would be if the past trends remained unchanged. The second filter for smoothing and averaging prices within the window, determined by the level of the filter. Thanks to the selection of weights, this filter is most actively responding to the periodic component that is
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Indicateurs
Let's look at the nature of price changes in the Forex market, not paying attention to the reasons why these changes occur. This approach will allow us to identify the main factors affecting the price movement. For example, let's take the opening prices of bars on the EUR-USD currency pair and the H1 timeframe. For these prices, we construct the Lameray diagram (Figure 1). In this diagram, it can be seen that the price movement basically occurs according to a linear equation. To determine the pa
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Aleksej Poljakov
Indicateurs
This indicator is more informative. His work is based on the assumption that the price movement in the market can be represented as noise of a particular color, which depends on the parameters of the distribution of price values. Thanks to this, it is possible to analyze the price change from different angles, and considering the price movement as noise of a particular color, one can get information about the current state of affairs in the market and make a forecast about the price behavior. W
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Aleksej Poljakov
Indicateurs
Despite some drawbacks of the “ AIS Color Noise Filter ” indicator, the idea to use it to smooth the price series and forecast prices looks quite attractive. This is due to several reasons: first, taking into account several noise components allows building a forecast on factors independent of each other, which can improve the quality of forecasting; secondly, the noise characteristics of the price series behave quite stably throughout the entire history, which makes it possible to obtain stabl
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Indicateurs
Very often, in the study of financial series apply their smoothing. Using smoothing, you can remove high-frequency components - it is believed that they are caused by random factors and therefore irrelevant. Smoothing always includes some way of averaging the data, in which random changes in the time series mutually absorb each other. Most often, for this purpose, simple or weighted moving average methods are used, as well as exponential smoothing. Each of these methods has its advantages and d
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Indicateurs
In order to isolate long-term and non-random components, it is necessary to know not only how much the price has changed, but also how these changes occurred. In other words - we are interested not only in the values ​​of price levels, but also in the order in which these levels replace each other. Through this approach, one can find long-term and stable factors that influence (or may influence) the price change at a given point in time. And knowledge of these factors allows you to make a more
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Indicateurs
One of the powerful methods of analysis is the modeling of financial series using Levy processes. The main advantage of these processes is that they can be used to model a huge number of phenomena - from the simplest to the most complex. Suffice it to say that the idea of ​​the fractal price movement in the market is only a special case of Levy processes. On the other hand, with proper selection of parameters, any Levy process can be represented as a simple moving average. Figure 1 shows an exa
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Aleksej Poljakov
Indicateurs
Very often, the trader is faced with the task of determining the extent to which the price may change in the near future. For this purpose, you can use the Johnson distribution type SB. The main advantage of this distribution is that it can be used even with a small amount of accumulated data. The empirical approach used in determining the parameters of this distribution, allows you to accurately determine the maximum and minimum levels of the price channel. These values ​​can be used in differ
AIS Probable High Low
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Indicateurs
This indicator allows you to determine the likelihood that the price will reach one or another level. Its algorithm is quite simple and is based on the use of statistical data on the price levels of a particular currency pair. Thanks to the collected historical data, it is possible to determine the extent to which the price will change during the current bar. Despite its simplicity, this indicator can provide invaluable assistance in trading. So, with its help it is possible to determine TakePr
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Version 4.0 2022.09.06
Ajout des fenêtres Fibonacci et Carré - Rouye
Version 3.0 2022.08.11
Amélioration de certaines fenêtres
Version 2.0 2022.06.01
Ajout de nouvelles fonctions de fenêtre, modifications apportées aux calculs.