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The script analyzes the history of quotes and gives recommendations on the minimum deposit. The calculations take into account the variability of prices and the standard deviation. Margin requirements for the instrument are also taken into account. The result of the script is the minimum recommended deposit for trading the given currency pair.
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The script analyzes the history of quotes and gives recommendations on the minimum deposit. The calculations take into account the variability of prices and the standard deviation. Margin requirements for the instrument are also taken into account. The result of the script is the minimum recommended deposit for trading the given currency pair.
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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.
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.
This indicator is designed to determine the time of the greatest trading activity within a day. After this calculation, the indicator builds the most significant trading levels. Comparing these levels with actual price action can provide information about the strength and direction of market trends.
Features of the indicator Timeframe must be below D1. Recommended timeframes: M15, M30 and H1. Timeframes above H1 can give a very rough picture. And the use of timeframes below M15 can lead to sh
This indicator is designed to determine the time of the greatest trading activity within a day. After this calculation, the indicator builds the most significant trading levels. Comparing these levels with actual price action can provide information about the strength and direction of market trends.
Features of the indicator Timeframe must be below D1. Recommended timeframes: M15, M30 and H1. Timeframes above H1 can give a very rough picture. And the use of timeframes below M15 can lead to sh
The script allows selecting the required 'Filter level' value of the AIS-MTF MT5 indicator. Run the script on the required chart and selected timeframe. Once its operation is complete, the HPS.csv file will be created in the Files folder. Open the file. You will see three columns. The 'Filter lvl' column represents the value of the 'Filter level' for the AIS-MTF indicator. Am. dev. - degree and direction of the indicator's deviation from the price level (sorted in ascending order). Negative valu
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The script allows selecting the required 'Filter level' value of the AIS-MTF indicator. Run the script on the required chart and selected timeframe. Once its operation is complete, the HPS.csv file will be created in the Files folder. Open the file. You will see three columns. The 'Filter lvl' column represents the value of the 'Filter level' for the AIS-MTF indicator. Am. dev. - degree and direction of the indicator's deviation from the price level (sorted in ascending order). Negative values i
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This script allows selecting the TakeProfit and StopLoss levels. It analyzes the history data, and then calculates the probability of reaching a given price level.
How the script works Suppose you have a trading strategy and you want to select the TakeProfit and StopLoss levels. Run the script and set the parameter: Number of Bars - the average position holding time in bars. Once the script operation is complete, the AIS-PPL.csv file will be created in the Files folder in the terminal data cat
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This script allows selecting the TakeProfit and StopLoss levels. It analyzes the history data, and then calculates the probability of reaching a given price level.
How the script works Suppose you have a trading strategy and you want to select the TakeProfit and StopLoss levels. Run the script and set the parameter: Number of Bars - the average position holding time in bars. Once the script operation is complete, the AIS-PPL.csv file will be created in the Files folder in the terminal data cat
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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
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
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
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
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, soon
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, so
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 bas
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
The indicator is designed to measure the price volatility. This allows determining the moments for opening or closing trade positions more accurately. High intensity of the market indicates the instability of its movement, but allows for better results. And, conversely, low intensity of the market indicates the stability of its movement.
Parameters Bars to process - the number of bars to measure the price movements. A low value of this parameter allows determining the moments of rapid price mo
The indicator is designed to measure the price volatility. This allows determining the moments for opening or closing trade positions more accurately. High intensity of the market indicates the instability of its movement, but allows for better results. And, conversely, low intensity of the market indicates the stability of its movement.
Parameters Bars to process - the number of bars to measure the price movements. A low value of this parameter allows determining the moments of rapid price mo
This indicator implements the process of simple linear smoothing. One of the disadvantages of exponential smoothing is the rapid attenuation of the signal. This does not allow for a full-fledged monitoring of long-term tendency of a price series. Linear smoothing allows fine-tuning the signal filtering. The indicator is configured by selecting the parameters: LP - period of smoothing. The higher the value, the more long-term tendencies are displayed by the indicator. Valid values are from 0 to 2
This indicator implements the process of simple linear smoothing. One of the disadvantages of exponential smoothing is the rapid attenuation of the signal. This does not allow for a full-fledged monitoring of long-term tendency of a price series. Linear smoothing allows fine-tuning the signal filtering. The indicator is configured by selecting the parameters: LP - period of smoothing. The higher the value, the more long-term tendencies are displayed by the indicator. Valid values are from 0 to 2
This indicator is a sequential hybrid filter based on a combination of median and moving average properties.
Using the median allows filtering out abnormal outliers and random impulses in the values of the price series. At the same time, the median filter does not act on the trend in price movement, leaving it unchanged. Since the median filter is non-linear, averaging using a simple moving average is used to smooth its values. This approach allows us to more accurately identify not only the
Этот индикатор представляет гибридный фильтр основанный на совместном использовании медианы и скользящей средней. Использование медианы позволяет отфильтровывать аномальные выбросы и случайные импульсы в значениях ценового ряда. При этом на трендовую составляющую медианный фильтр не действует, оставляя ее без изменений. Так как медианный фильтр является нелинейным, то для сглаживания его значений используется скользящая средняя. Такой подход позволяет более точно выделять не только тренд, но и п
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
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
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 rec
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 rec
The AIS Advanced Grade Feasibility indicator is designed for forecasting levels that a price can reach in future. Its operation is based on the analysis of the last three bars and creating the forecast. The indicator can be used on any timeframe and any currency pair. Using settings, you can achieve the desired quality of the forecast. Depth of forecast - sets the desired depth of forecast in bars. The recommended values for these parameters are 18-31. You can try beyond this range. But in this
The AIS Advanced Grade Feasibility indicator is designed for forecasting levels that a price can reach in future. Its operation is based on the analysis of the last three bars and creating the forecast. The indicator can be used on any timeframe and any currency pair. Using settings, you can achieve the desired quality of the forecast. Depth of forecast - sets the desired depth of forecast in bars. The recommended values for these parameters are 18-31. You can try beyond this range. But in this
The AIS Weighted Moving Average indicator calculates weighted moving average, allowing to determine the beginning of market trend. Weights are calculated taking into account the specific characteristics of each bar. This allows filtering random market movements. The main signal, confirming the beginning of a trend, is change in the direction of indicator line movement and the intersection of the indicator lines with the indicator price. WH (blue line) - the weighted average High price. WL (red l
The AIS Weighted Moving Average indicator calculates weighted moving average, allowing to determine the beginning of market trend. Weights are calculated taking into account the specific characteristics of each bar. This allows filtering random market movements. The main signal, confirming the beginning of a trend, is change in the direction of indicator line movement and the intersection of the indicator lines with the indicator price. WH (blue line) - the weighted average High price. WL (red l
AIS Correct Averages indicator allows you to determine the beginning of the trend movement in the market. Another important quality of the indicator is a clear signal of the end of the trend. This indicator is not repainted and not recalculated.
Displayed values h_AE - the upper limit of the AE channel l_AE - the lower boundary of the AE channel h_EC - High value predicted for the current bar l_EC - Low value predicted for the current bar
Signals for the indicator The main channel signal is
AIS Correct Averages indicator allows you to determine the beginning of the trend movement in the market. Another important quality of the indicator is a clear signal of the end of the trend. The indicator is not redrawn or recalculated.
Displayed values h_AE - the upper limit of the AE channel
l_AE - the lower boundary of the AE channel
h_EC - High value projected for the current bar l_EC - Low predicted value for the current bar
Signals for the indicator The main channel signal is the in
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 of the window f
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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 window fun
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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 paramet
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 paramet
One of the number sequences is called "Forest Fire Sequence". It has been recognized as one of the most beautiful new sequences. Its main feature is that this sequence avoids linear trends, even the shortest ones. It is this property that formed the basis of this indicator. When analyzing a financial time series, this indicator tries to reject all possible trend options. And only if he fails, then he recognizes the presence of a trend and gives the appropriate signal. This approach allows one t
One of the number sequences is called "Forest Fire Sequence". It has been recognized as one of the most beautiful new sequences. Its main feature is that this sequence avoids linear trends, even the shortest ones. It is this property that formed the basis of this indicator. When analyzing a financial time series, this indicator tries to reject all possible trend options. And only if he fails, then he recognizes the presence of a trend and gives the appropriate signal. This approach allows one t
This indicator is based on ogive forms. Such shapes are used in aerodynamics and space technology. Even bullets have some sort of ogival shape. The use of such forms in a technical indicator makes it possible to achieve a compromise between the sensitivity of the indicator and its stability. That gives additional possibilities at its application. Indicator parameters: iType - the type of the ogive form. iPeriod - indicator period. iFactor is an additional parameter used in parabolic and e
This indicator is based on ogive forms. Such shapes are used in aerodynamics and space technology. Even bullets have some sort of ogival shape. The use of such forms in a technical indicator makes it possible to achieve a compromise between the sensitivity of the indicator and its stability. That gives additional possibilities at its application. Indicator parameters: iType - the type of the ogive form. iPeriod - indicator period. iFactor is an additional parameter used in parabolic and exponen
This indicator uses the so-called "evil" numbers as weights. Their opposite is "odious" numbers, which are also presented in this indicator. The division of numbers into these two classes is associated with the Hamming weight, which is determined by the number of units in the binary notation of a particular number. Using these numbers as weighting factors results in a trend-following indicator. Moreover, odious numbers give a more sensitive indicator, and evil numbers give a conservative one. T
This indicator uses the so-called "evil" numbers as weights. Their opposite is "odious" numbers, which are also presented in this indicator. The division of numbers into these two classes is associated with the Hamming weight, which is determined by the number of units in the binary notation of a particular number. Using these numbers as weighting factors results in a trend-following indicator. Moreover, odious numbers give a more sensitive indicator, and evil numbers give a conservative one. T
The main purpose of this trading expert is to perform the functions of a trailing stop. It does not open or close positions, but only sets and moves take profit and stop loss levels. To calculate take profit and stop loss, the statistics of price movement and the moral expectation of D. Bernoulli are used. Due to this, the new levels set by the expert provide the best (of the possible) option in terms of risk/reward ratio. Let's look at the parameters of the trading robot.
Tracked Symbols -
The main purpose of this trading expert is to perform the functions of a trailing stop. It does not open or close positions, but only sets and moves take profit and stop loss levels. To calculate take profit and stop loss, the statistics of price movement and the moral expectation of D. Bernoulli are used. Due to this, the new levels set by the expert provide the best (of the possible) option in terms of risk/reward ratio. Let's look at the parameters of the trading robot.
Tracked Symbols - c
This indicator uses local highs and lows of the price series. After highlighting the extrema, their values are smoothed. Thanks to this, two channels are built - external and internal. The inner channel shows the limits if the price movement strictly follows a linear trend. The outer channel shows the boundaries for the price movement with a logarithmic trend. After calculating the channels, the indicator analyzes the real price movement and offers recommendations for opening and closing posi
This indicator uses local highs and lows of the price series. After highlighting the extrema, their values are smoothed. Thanks to this, two channels are built - external and internal. The inner channel shows the limits if the price movement strictly follows a linear trend. The outer channel shows the boundaries for the price movement with a logarithmic trend. After calculating the channels, the indicator analyzes the real price movement and offers recommendations for opening and closing posi
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 movin
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 movin
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. Th
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. Th
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 high-freque
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 high-freque
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 h
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 harmo
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 triangu
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 triangu
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 dow
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 do
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 of
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 of
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 th
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 th
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 ch
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 ch
The script is based on the simulation of trade transactions using a random number generator. This allows you to get completely different results, even with the same input parameters. When you run the script, a dialog box opens in which you can set the desired values for external variables. In the Trading options block, the basic parameters that are necessary for trading modeling are defined. Start Balance - sets the initial size of the trade balance. Number Trade - sets the number of trad
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The script is based on the simulation of trade transactions using a random number generator. This allows you to get completely different results, even with the same input parameters. When you run the script, a dialog box opens in which you can set the desired values for external variables. In the Trading options block, the basic parameters that are necessary for trading modeling are defined. Start Balance - sets the initial size of the trade balance. Number Trade - sets the number of trade tr
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Choosing the levels of StopLoss and TakeProfit can have a very strong impact on the overall performance of trading. In addition to the obvious parameters of a trade transaction - the size of a possible win or probable loss - the levels of StopLoss and TakeProfit also affect the expected duration of the transaction, and the profitability of trading in general. If you have already determined the optimal transaction duration using the “ AIS-ODT ” script, then you can begin to determine the paramete
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Choosing the levels of StopLoss and TakeProfit can have a very strong impact on the overall performance of trading. In addition to the obvious parameters of a trade transaction - the size of a possible win or probable loss - the levels of StopLoss and TakeProfit also affect the expected duration of the transaction, and the profitability of trading in general. If you have already determined the optimal transaction duration using the “ AIS-ODT ” script, then you can begin to determine the paramete
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This script is designed so that the trader can determine the average duration of trade transactions, at which the ratio of possible profits and losses will be optimal. First, let's look at the general approach to determining the optimal duration of trade transactions. We introduce the following variables: R - the result of the transaction; T - the time during which the transaction was open; W - the time between the closing of the previous transaction and the opening of the next one. Every tr
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This script is designed so that the trader can determine the average duration of trade transactions, at which the ratio of possible profits and losses will be optimal. First, let's look at the general approach to determining the optimal duration of trade transactions. We introduce the following variables: R - the result of the transaction; T - the time during which the transaction was open; W - the time between the closing of the previous transaction and the opening of the next one. Every t
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