ZigZags Shepherds - page 15
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Swings... Eh-ma... :)))
Okay. I won't go deep into the terminology. No time.
We have the purest exponent on anything.
The sum of these components would be a negative binomial distribution (Erlang distribution for continuous NE) again, I stress, with known dispersion. In the limit - the normal distribution you are looking for.
it's hard to guess what you're researching now, you can't see any formulas or... can't see anything, but I would venture to guess that the pattern found is NOT a pattern because there is no statistical analysis, has to do with "Double Relation":
https://ru.wikipedia.org/wiki/%D0%94%D0%B2%D0%BE%D0%B9%D0%BD%D0%BE%D0%B5_%D0%BE%D1%82%D0%BD%D0%BE%D1%88%D0%B5%D0%BD%D0%B8%D0%B5
The properties are quite interesting, very similar to the graphical analysis of what is drawn on price charts
I don't know...
The feature is interesting, but how do you take advantage of it? It's hard to suggest anything, considering I'm no good at either Kagi or Renko from the word "at all".
But, I'll give it a shot.
1. One has to give up the desire to achieve a normal distribution and search for centuries to find it. Working with the patterns already found is already a very, very big deal.
2.https://en.wikipedia.org/wiki/Generalized_normal_distribution
3. Simply put, xy-squared with k=1 is the sum of squares of normally distributed NAs, xy-squared with k=2 is the sum of NAs with a Laplace distribution
4. I was interested in the case with k=2, because studies show that the market is dominated by the Laplace distribution (double geometric to be exact)
5. It is not clear here - in these Renko's what counts? The sum of differences (High-Low)?
6. If so - then difference (High-Low) in Renko is SV belonging to Laplace distribution - it should be confirmed experimentally.
7. Then sum of differences (High-Low) in sliding window (for certain sample volume) forms xy-square with k=2 with known quantile function
https://keisan.casio.com/exec/system/1180573197
8. We wait for the exit (High-Low) in the moving window beyond the limits of a confidence interval for a certain quantile and enter the trade.
Well, this is just a rough draft of the algorithm, just to develop the subject and nothing more :)))
ha, draft....
not a badly worked out question!
I even wanted to see the resultAnd which ZZ and with what settings is involved in the study?
1. We have to give up the desire to achieve a normal distribution and spend centuries looking for it. Working with the patterns already found is a very, very big deal.
8. We wait for the exit (High-Low) in the sliding window beyond the boundaries of the confidence interval for a certain quantile and enter the trade.
Blind betting on return to the average distribution without any additional filters for the trend does not take into account that this return may occur with a large drawdown, that is, it will trigger SL, which will eat all the profits, especially given that entries go against the trend.
In general, so far everything is very illiterately planned from a common sense point of view, not to mention the lack of a scientific approach.
Blind betting on a return to the average distribution without any additional trend filters does not take into account that this return can occur with a large drawdown, i.e. SL will be triggered which will eat up all profits, especially considering that entries go against the trend.
That's where I completely agree. Finally, you are making some good sense, not childish babble.
Here I completely agree. You are finally making some good sense, not childish babble.
It's not us who are talking sense, it's you who are sobering up, because it's been explained to you a hundred times already. ))
I don't know...
The feature is interesting, but how do you take advantage of it? It's hard to suggest anything, considering I'm no good at either Kagi or Renko from the word "at all".
But, I'll give it a shot.
1. One has to give up the desire to achieve a normal distribution and search for centuries to find it. Working with the patterns already found is already a very, very big deal.
2.https://en.wikipedia.org/wiki/Generalized_normal_distribution
3. Simply put, xy-squared with k=1 is the sum of squares of normally distributed NAs, xy-squared with k=2 is the sum of NAs with a Laplace distribution
4. I was interested in the case with k=2, because studies show that the market is dominated by the Laplace distribution (double geometric to be precise)
5. It is not clear here - in these Renko's what counts? The sum of differences (High-Low)?
6. If so - then difference (High-Low) in Renko is SV belonging to Laplace distribution - it should be confirmed experimentally.
7. Then sum of differences (High-Low) in sliding window (for certain sample volume) forms xy-square with k=2 with known quantile function
https://keisan.casio.com/exec/system/1180573197
8. We wait for the exit (High-Low) in the moving window beyond the limits of a confidence interval for a certain quantile and enter the trade.
Well, this is just a rough draft of the algorithm just to develop the subject at most :)))
I decided to add an interesting article, which may suggest transformation of Laplace distribution into a normal distribution
http://www.mathprofi.ru/normalnoe_raspredelenie_veroyatnostei.html
Illiteracy also lies in the fact that knowledge of distributions says nothing about the direction of price movement, it is useful only in volatility trading. But they will sober up on this topic in a year)
What has price direction got to do with it at all, with the ratio of ZZ sigments (those in irreversibility) to one (constant, e.g. 3p.) the sign is always positive, the distribution of such ratios (frequency of occurrence of 2 sigments, 3,4, etc.) is about it.
What does the price direction have to do with the ratio of ZZ sigments (those that are in a breakaway) to one (constant, e.g. 3 pips)? The sign is always positive, the distribution of such ratios (frequency of occurrence of 2 sigments, 3, 4, etc.), this is what we are talking about.
A little distraction from the scientific fog, and look at this problem from the perspective of an ordinary Trader - his needs and aspirations ...
What does a trader need? He needs Profit! To make a profit, a trader needs information... and above all the PRICE (current price and direction)... This information is enough for the trader to solve HIS TROUBLE...
If your abstruse science will help trader make a profit, consider that your efforts have not been wasted...