To follow up - page 23
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
1. Exactly the opposite - one algorithm for selecting different contexts. Only I'm talking about highlighting in history. In real time, I think only recognizing the current context is possible.
2. No. The context should not be reduced to a single transaction, there may be several of them within the same context. And above in this post, I've already reminded you of the Senior-Senior ZZ scheme.
3. no I won't, you can't say "yes" or "no" here, you have to agree on concepts long and hard :)
Well, that's fine. I am of the same opinion. Let's consider it agreed again . :-)
The abscissa axis shows the numbers of the intervals in which the values fell. On the axis of ordinates - values of frequencies of hits divided by number of elements of samples.
I see. I think, that you on a correct way. At least on a parallel path. :-)
>> Good luck.
Понятно. Думаю, что вы на правильном пути. Во всяком случае на параллельном. :-)
Успехов.
Are you also studying rebound?
Can I ask you a question?
When trending, how is the shift in the distribution related to the angle of the trend? Linear, for example.
---
It's hard to understand an illustration without knowing the environment that produced it.
Good luck.
Thank you :)
1. Are you also studying rebound?
2. Can I ask you a question?
When trending, does the shift in the distribution have anything to do with the angle of the trend? Linear, for example.
---
It's hard to understand an illustration without knowing the environment that produced it.
1. No.
2. There is certainly a connection. However, I am not the one who posted the illustration and I think it would be more appropriate to ask the author about it.
I just wanted to make the hypothesis that bimodality is just an effect of the distribution of returns from the significant trend line of the mean (if there is one).
But it would be nice to formalise the observations for a "bullshit dog" model as well.
Maybe a measure of non-stationarity could be simplified. ;)
When trending, how is the bias of the distribution related to the angle of the trend? Linear, for example.
I didn't count returns in the usual sense (returns were counted between series transitions from one state to another; that makes sense).
Both distributions are skewed towards positive values.
When price starts directional movement (i.e. returns distribution becomes bimodal), naturally, the sign of LR angle coefficient post factum coincides with the direction of occurred movement.
Я считал returns не совсем в обычном смысле (returns считались между переходами ряда из одного состояния в другое; в этом есть смысл).
Оба распределения скошены в сторону положительных значений.
Когда цена начинает направленное движение (т.е. распределение returns становится бимодальным), естественно, знак углового коэффициента ЛР постфактум совпадает с направлением произошедшего движения.
So what's the point of the illustration then?So what's the point of the illustration then?
In the presence/absence of tails.
the sample volume may have had an effect?
And then there's the "ultimate string tension" state - there's virtually no dispersion.
Maybe you have captured this particular state?
Share it - if it's too much. ;)
What is this data?