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The Kolmogorov-Zhurbenko filter can be considered as a special window function designed to eliminate spectral leakage. This filter is optimal for smoothing stochastic (including financial) time series. The indicator based on this filter contains the following parameters: iLength - the period of the original rectangular window used to build the filter. Valid value is 2 - 255. iDegree - filter order. If iDegree=0, then a simple moving average will be obtained. If iDegree=1, then you get a
The Kolmogorov-Zhurbenko filter can be considered as a special window function designed to eliminate spectral leakage. This filter is optimal for smoothing stochastic (including financial) time series. The indicator based on this filter contains the following parameters: iLength - the period of the original rectangular window used to build the filter. Valid value is 2 - 255. iDegree - filter order. If iDegree=0, then a simple moving average will be obtained. If iDegree=1, then you get a
Various window functions can be used to smooth time series. Window functions can be quite different from each other in their characteristics - the level of smoothing, noise suppression, etc. This indicator allows you to implement the main window functions and evaluate their performance on financial time series. Indicator parameters: iPeriod – indicator period. iPeriod >= 2 iCenter is the index of the reference where the center of the window function will be located. By default
Various window functions can be used to smooth time series. Window functions can be quite different from each other in their characteristics - the level of smoothing, noise suppression, etc. This indicator allows you to implement the main window functions and evaluate their performance on financial time series. Indicator parameters: iPeriod – indicator period. iPeriod >= 2 iCenter is the index of the reference where the center of the window function will be located. By default
This script is designed to evaluate weights in various window functions. An indicator built on these window functions can be downloaded at https://www.mql5.com/ru/market/product/72159 Input parameters: iPeriod – indicator period. iPeriod >= 2 iCenter is the index of the reference where the center of the window function will be located. By default, this parameter is 0 - the center of the window coincides with the center of the indicator. With 1 <= iCenter <= iPeriod, the center
This script is designed to evaluate weights in various window functions. An indicator built on these window functions can be downloaded at https://www.mql5.com/ru/market/product/72160 Input parameters: iPeriod – indicator period. iPeriod >= 2 iCenter is the index of the reference where the center of the window function will be located. By default, this parameter is 0 - the center of the window coincides with the center of the indicator. With 1 <= iCenter <= iPeriod, the center of the
Some traders are guided by trading sessions during trading. Figure 1 shows the average price swing over one week. It can be seen that trading sessions on different days differ in their duration and activity. This indicator is designed to estimate the average price movement at certain intervals within a weekly cycle. It takes into account price movements up and down separately from each other and makes it possible to determine the moments when high volatility is possible in the market. On the
Some traders are guided by trading sessions during trading. Figure 1 shows the average price swing over one week. It can be seen that trading sessions on different days differ in their duration and activity. This indicator is designed to estimate the average price movement at certain intervals within a weekly cycle. It takes into account price movements up and down separately from each other and makes it possible to determine the moments when high volatility is possible in the market. On the
The arithmetic mean or median can be used to determine the measure of the central trend of a time series. Both methods have some disadvantages. The arithmetic mean is calculated by the Simple Moving Average indicator. It is sensitive to emissions and noise. The median behaves more steadily, but there is a loss of information at the boundaries of the interval. In order to reduce these disadvantages, pseudo-median signal filtering can be used. To do this, take the median of a small length and
The arithmetic mean or median can be used to determine the measure of the central trend of a time series. Both methods have some disadvantages. The arithmetic mean is calculated by the Simple Moving Average indicator. It is sensitive to emissions and noise. The median behaves more steadily, but there is a loss of information at the boundaries of the interval. In order to reduce these disadvantages, pseudo-median signal filtering can be used. To do this, take the median of a small length and
The trend allows you to predict the price movement and determine the main directions of the conclusion of transactions. The construction of trend lines is possible using various methods suitable for the trader's trading style. This indicator calculates the parameters of the trend movement based on the von Mises distribution. Using this distribution makes it possible to obtain stable values of the trend equation. In addition to calculating the trend, the levels of possible deviations up and
The trend allows you to predict the price movement and determine the main directions of the conclusion of transactions. The construction of trend lines is possible using various methods suitable for the trader's trading style. This indicator calculates the parameters of the trend movement based on the von Mises distribution. Using this distribution makes it possible to obtain stable values of the trend equation. In addition to calculating the trend, the levels of possible deviations up and
The Cauchy distribution is a classic example of a fat-tailed distribution. Thick tails indicate that the probability of a random variable deviating from the central trend is very high. So, for a normal distribution, the deviation of a random variable from its mathematical expectation by 3 or more standard deviations is extremely rare (the 3 sigma rule), and for the Cauchy distribution, deviations from the center can be arbitrarily large. This property can be used to simulate price changes in
The Cauchy distribution is a classic example of a fat-tailed distribution. Thick tails indicate that the probability of a random variable deviating from the central trend is very high. So, for a normal distribution, the deviation of a random variable from its mathematical expectation by 3 or more standard deviations is extremely rare (the 3 sigma rule), and for the Cauchy distribution, deviations from the center can be arbitrarily large. This property can be used to simulate price changes in
When analyzing financial time series, researchers most often make a preliminary assumption that prices are distributed according to the normal (Gaussian) law. This approach is due to the fact that a large number of real processes can be simulated using the normal distribution. Moreover, the calculation of the parameters of this distribution presents no great difficulties. However, when applied to financial markets, normal distribution does not always work. The returns on financial instruments
When analyzing financial time series, researchers most often make a preliminary assumption that prices are distributed according to the normal (Gaussian) law. This approach is due to the fact that a large number of real processes can be simulated using the normal distribution. Moreover, the calculation of the parameters of this distribution presents no great difficulties. However, when applied to financial markets, normal distribution does not always work. The returns on financial instruments
When making trading decisions, it is useful to rely not only on historical data, but also on the current market situation. In order to make it more convenient to monitor current trends in market movement, you can use the AIS Current Price Filter indicator. This indicator takes into account only the most significant price changes in one direction or another. Thanks to this, it is possible to predict short-term trends in the near future - no matter how the current market situation develops
When making trading decisions, it is useful to rely not only on historical data, but also on the current market situation. In order to make it more convenient to monitor current trends in market movement, you can use the AIS Current Price Filter indicator. This indicator takes into account only the most significant price changes in one direction or another. Thanks to this, it is possible to predict short-term trends in the near future - no matter how the current market situation
Stable distributions can be used to smooth financial series. Since a fairly deep history can be used to calculate the distribution parameters, such smoothing may in some cases be even more effective than other methods. The figure shows an example of the distribution of the opening prices of the currency pair " EUR-USD " on the time frame H1 for ten years (figure 1). Looks fascinating, doesn't it? The main idea behind this indicator is to determine the parameters of a stable distribution based
Stable distributions can be used to smooth financial series. Since a fairly deep history can be used to calculate the distribution parameters, such smoothing may in some cases be even more effective than other methods. The figure shows an example of the distribution of the opening prices of the currency pair " EUR-USD " on the time frame H1 for ten years (figure 1). Looks fascinating, doesn't it? The main idea behind this indicator is to determine the parameters of a stable