From theory to practice - page 202

[Alexander_K2]

Yuriy Asaulenko:

Why should I?

Let's go for the Nobel Prize together. I've already been given the address of the Committee's branch in Moscow, they said to ask Vasya. We should take the sorcerer with us. Just in case. If anything, we'll let him go first, as the most experienced.

[Violetta Novak]

I'm probably here from poking around all sorts of old stuff on the forum pro)), about the sessions:

https://www.mql5.com/ru/articles/1455

Seasonality. Alexander, if you manage to go to milliseconds and build a normal distribution within each session, after all there may just be slumps in volatility at the junctions.

[Alexander_K2]

Novaja:

I'm probably here from poking around all sorts of old stuff on the forum pro)), about the sessions:

https://www.mql5.com/ru/articles/1455

Seasonality. Alexander, if you manage to go to milliseconds and construct a normal distribution within each session, after all there might just be volatility skews at the junctions.

If you find somewhere a probability distribution of time intervals between real ticks and probability distribution of increment rates - please post it.

If you can't find it, it's OK, I'll do it myself this week.

[Violetta Novak]

In a hurry, found something, run through, Alexander

https://www.mql5.com/ru/forum/103289/page4#comment_2982426

Yury Makarov comment 36 and page 5 comment 42

https://www.mql5.com/ru/articles/412

here distributions

https://www.mql5.com/ru/forum/106220 so read

[Violetta Novak]

Econophysics. The laws of leapfrogging?

[Alexander_K2]

Novaja:
Econophysics. The laws of the leap?

Jumping

[Dmitriy Skub]

Hoppers?)

[Violetta Novak]

The time intervals between two consecutive ticks can vary over a wide range. The distribution function of these time intervals decreases with decreasing Δt as (Δt)^4.4.

[Violetta Novak]

Dmitriy Skub:
Hoppers?)

Yes, the ones))))

[Violetta Novak]

The distribution of thenumber of shares traded in one exchange trade (one tick), Q(x), definitely falls into the Levy range, i.e. the asymptotic ("tail") part of the distribution is well described by a law of the form x-ς, where 2>ς>0, if we consider a cumulative distribution function .