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In this article, I will make additional changes to the CCI affecting the very logic of this indicator. Moreover, we will be able to see it in the main chart window.
In this article, I will consider the possibility of upgrading the CCI indicator. Besides, I will present a modification of the indicator.
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
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
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
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
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
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
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
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