|
9+ years
experience
|
92
products
|
7
demo versions
|
|
6
jobs
|
0
signals
|
0
subscribers
|
The hypergeometric series is used to calculate the weight coefficients of this filter. This approach allows you to get a rather interesting smoothing of the time series. The hypergeometric filter weights do not decay as fast as exponential and linear weighted moving averages, but faster than smoothed moving averages. Due to this, the behavior of this filter is in many ways similar to the behavior of moving averages. However, it has several advantages. Its lag is much less than that of the
The hypergeometric series is used to calculate the weight coefficients of this filter. This approach allows you to get a rather interesting smoothing of the time series. The hypergeometric filter weights do not decay as fast as exponential and linear weighted moving averages, but faster than smoothed moving averages. Due to this, the behavior of this filter is in many ways similar to the behavior of moving averages. However, it has several advantages. Its lag is much less than that of the
This indicator displays optimal take profit and stop loss levels. These levels are calculated based on historical data. At the first start, the indicator is trained on history. After that, he evaluates the probability that the price will overcome this or that level in the future and selects the most optimal options for placing stop orders. For example, take profit values are selected so that the profit is maximum and the probability that the price reaches its level is the highest possible
This indicator displays optimal take profit and stop loss levels. These levels are calculated based on historical data. At the first start, the indicator is trained on history. After that, he evaluates the probability that the price will overcome this or that level in the future and selects the most optimal options for placing stop orders. For example, take profit values are selected so that the profit is maximum and the probability that the price reaches its level is the highest possible
This filter is based on Bessel polynomials. Its main advantage is a small time delay. Another feature of this filter is its high sensitivity to the latest values of the financial time series. Because of this, the indicator highlights active price movements, while smoothing out noise deviations. In addition to the classic variant, the logarithms of the Bessel coefficients have been added to the indicator as a weighting function. In this case, the indicator turns out to be smoother, but at the
This filter is based on Bessel polynomials. Its main advantage is a small time delay. Another feature of this filter is its high sensitivity to the latest values of the financial time series. Because of this, the indicator highlights active price movements, while smoothing out noise deviations. In addition to the classic variant, the logarithms of the Bessel coefficients have been added to the indicator as a weighting function. In this case, the indicator turns out to be smoother, but at the
This indicator is based on the discrete Hartley transform. Using this transformation allows you to apply different approaches when processing financial time series. A distinctive feature of this indicator is that its readings refer not to one point on the chart, but to all points of the indicator period. When processing a time series, the indicator allows you to select various elements of the time series. The first possibility of filtering is built on this approach - all unnecessary
This indicator is based on the discrete Hartley transform. Using this transformation allows you to apply different approaches when processing financial time series. A distinctive feature of this indicator is that its readings refer not to one point on the chart, but to all points of the indicator period. When processing a time series, the indicator allows you to select various elements of the time series. The first possibility of filtering is built on this approach - all unnecessary
The Lehmer mean can be considered as a window function, the weight coefficients of which depend on the values of the variables used in the calculation. This average is non-linear because exponentiation is used in its calculation. The characteristics of the indicator depend on two parameters: iPeriod - indicator period, valid value is greater than or equal to 2; iPower - exponent, which is used when calculating indicator values. Valid range is -32768 to 32767 With iPower = 0 we
The Lehmer mean can be considered as a window function, the weight coefficients of which depend on the values of the variables used in the calculation. This average is non-linear because exponentiation is used in its calculation. The characteristics of the indicator depend on two parameters: iPeriod - indicator period, valid value is greater than or equal to 2; iPower - exponent, which is used when calculating indicator values. Valid range is -32768 to 32767 With iPower = 0 we get the
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