Average daily journey in points by instrument. - page 21
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It all makes sense. Valery, you can see for yourself - it's about time...
Let's talk about some achievement, some socially useful activity...
It all makes sense. Valery, you can see for yourself - it's about time...
Let's talk about some achievement, some community service...
Good luck with your treatment, I hope it will be productive and I'll leave you to it, but bye-bye.
...then you can go on your own.
By the way, your last post stopped at number 228, I couldn't help but make fun of it.
That's it, I'm gone.
By the way, your last post stopped at number 228, I couldn't help but make fun of it.
That's it, I'm gone.
How can you account for, in a surgical method, for the change in tic flow density?
so, in parallel, i want to look at the standard geometric average calculation of indices, taking into account tick density (change of tick volume).
But how to take it into account in the formula (standard one). maybe for each pair volatility and tick density should be compared first, and then it should be taken into account when calculating the indices.
The true mechanism which determines asset price movements is hardly known for certain. The only thing that can be said with certainty is that there is a random factor in price movements. But the nature of this randomness can vary.
According to one hypothesis, logarithms of price changes follow the normal distribution, but this distribution is non-stationary. That is, both the mathematical expectation and the standard deviation of the distribution can vary over time. As a consequence, when processing an empirical sample using standard statistical methods that assume the entire sample is drawn from a single general population, we obtain a non-Gaussian sample. This can be expressed in the form of heavy tails of an empirical distribution (the kurtosis calculated from a sample exceeds number 3, i.e. the kurtosis of a normal distribution).
Another hypothesis is that logarithms of price changes initially follow a distribution with kurtosis greater than 3. In this situation, even if the distribution itself is stationary, the empirical sample drawn from this distribution can be regarded as non-stationary in time. The point is that the estimation of the mathematical expectation of a random variable x is the arithmetic mean of the sample:
<X> = 1/N * sum(x(i), i =1..N )
The arithmetic mean of random variables is itself a random variable. The standard deviation of the arithmetic mean depends on the standard deviation of a random variable and the sample size:
sigma(<X> ) = sigma(X) / sqrt (N)
Thus, the standard deviation of the mean is less than the standard deviation of the random variable itself by sqrt (N) times, i.e. the accuracy of the mathematical expectation estimate can be improved by increasing the sample size. But this is only true for a random variable with finite mathematical expectation and finite variance. The point is that finite mathematical expectation only exists for those distributions whose probability density in infinity falls as 1 / |x|^(2+delta) or lower, and finite variance only for those distributions whose probability density in infinity falls as 1 / |x|^(3+delta) or higher ( delta - any small positive number). If we model a price chart using as logarithms of the price change a random sample taken from a stationary distribution with infinite variance and/or infinite mathematical expectation, and offer this sample for analysis to an independent observer, he may get an illusion that he deals with a non-stationary process in time.
Finally, we cannot exclude the case when not only the distribution parameters but also the law of distribution of price increments is non-stationary in time, and in the time series of prices there may be sections described by the distribution with infinite variance and/or infinite mathematical expectation.
Polygrafych, this is for you:
middle_period is the average move of a bar on a period timeframe. The move is High - Low (or for example |Close - Open|).
middle_H1 is the average stroke of a bar on TF H1.
In the formula in brackets you should use period in minutes, i.e. H1 = 60.
It works out, for example: middle_H4 ~ middle_H1 * sqrt( H4 / H1 ) = middle_H1 * sqrt( 240 / 60 ) = 2 * middle_H1.
Alexey, please don't beat on me, how useful would it be and is there anything in it, if in this formula we take the count of the period not in minutes (timeline) but in ticks (number of ticks) would this formula be fair? and if so, have you tried to take not say n4 and n1, but (4 ticks and 1 tick)
therefore it is possible to take 1 tick and 0.4 tick - i.e. to get a discreteness value less than 1 tick through this formula, expressed through the existing minimum discreteness equal to 1 tick.
Hardly useful, it seems to me. Why go into 0.4 ticks if they don't exist? Well yes, technically the formula can be applied, but you still have to apply extrapolations beyond economically reasonable values.
Prival talked a lot about the sampling rate and the usefulness of "correct" data. But where are they, this correct data, to be found in a DC? And what is the sense in it, if all the same you will trade only on ticks that your God - brokerage companies give you?
Hardly useful, it seems to me. Why go into 0.4 ticks if they don't exist? Well yes, technically the formula can be applied, but you still have to apply extrapolations beyond economically reasonable values.
Prival talked a lot about the sampling rate and the usefulness of "correct" data. But where are they, this correct data, to be found in a DC? And what is the sense in it, if all the same you will trade only on ticks that your God - brokerage companies give you?
By the way, he told me he got more accuracy than even DT quotes, in points, and he was calculating with fractions of a pip. By the way, maybe he used this mechanism, I don't know, but the intertictic "behavior" of prices may not be so useless.