Measurement of vibration amplitude - page 9
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The curve depends on the particular zigzag algorithm.
There is a known zigzag which explicitly states: if the current knee is less than N pips - it is not formed. And such a zigzag will not have knees less than N pips.
The curve depends on the particular zigzag algorithm.
There is a known zigzag which explicitly states: if the current knee is less than N pips - it is not formed. And such a zigzag will not have knees less than N pips.
Golly, Alexey: which curve depends on a particular zigzag algorithm?
The curve depends on the particular zigzag algorithm.
There is a known zigzag which explicitly states: if the current knee is less than N pips - it is not formed. And such a zigzag will not have knees less than N pips.
Well, then don't bother us. Hide your wealth. Good luck in your endeavours and happiness in your personal life.
Alexei, not you.
Which one do you need?
At least two spreads make sense to look at...
;)
For example, the first range is 10-13 pips, which equals 10+30%. I call it the range with 30% deviation. The maximum percentage (on the chart) in the range 42-54.6 points, it means that out of all single fluctuations (say there are 100) in the range 42-54.6 points, fell 26 pieces, or 26%. It means that there is 26% probability that the price having passed 42-54.6 points will reverse and pass the same amount of points in the opposite direction. Naturally, the wider the range, the more probable it is that a single fluctuation will fall into it.
In a short history (a month), we can see minimums and maximums; if we take the history of 3 years, it becomes almost flat, with a fall in the beginning. Thus, the longer is the history, the more even distribution becomes. It shows how the market changes, and amplitudes distribution differs in every separate time period, so TS optimized for one period will fail in the forefront. Therefore, knowing the distribution of amplitudes, we can adjust parameters of TS, like optimizing in real time.
Maybe then it would be more logical to open a new topic or rename this one to something like predicting amplitude distributions.
It's essentially nothing more than a bounce distribution, but it depends on the sample length on which the distribution is based (in bold).
but what is the relationship between changing the sample length and changing the uniformity of the distribution? that would be more interesting to see.