This indicator adds onto Vitali Apirine's work by including three different types of momentum used to calculate RSEMA as well as fixed and adaptive cycle calculations to be used as dynamic inputs to calculate momentum. The purpose of these additional calculation methods is to attempt to filter out noice and track trends by using different methods and inputs to calculation momentum.
-Momentum component of Jurik's RSX
Cycle calculation methods
-Vertical horizontal filter
-Ehlers' Autocorrelation Dominant Cycle
What is Wilder relative strength?
The ( ), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The oscillates between zero and 100. Traditionally the is considered overbought when above 70 and oversold when below 30.
What is Chande momentum?
Chande Momentum was designed specifically to track the movement and momentum of a security. It calculates the difference between the sum of both recent gains and recent losses, then dividing the result by the sum of all price movement over the same period.
What is the momentum component of Jurik's RSX RSI?
is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of , but with one important exception: the noise is gone with no added lag. For our purposes here, we derive momentum minus the lag.
Vertical horizontal filter?
(VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to in the System. Trend indicators can then be employed in trending markets and momentum indicators in ranging markets.
What is autocorrelation?
Autocorrelation is used in the calculation of dominant cycle length to be injected into standard tools to improve TA accuracy. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
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