From theory to practice - page 579
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I'm actually looking at the sum of the increments.
i.e. on the price chart....
You're not telling the truth
;)
I'm actually looking at the sum of the increments.
If you think of drift as a shift in the starting point, you can also use just the price in the sliding window.
Like this:
If you think of drift as a shift in the starting point, you can also use just the price in the sliding window.
Like this:
ok
formulas?
i will use MQL to create an indict on the MQL, so let's put it here
I'm really tired of chewing on the same thing.
I wrote the final realization in my PM earlier
And the indica, as practice has shown, is not everything
so post it, do not be afraid
I'm interested in red, blue and black lines from the bottom window
three formulas
interested in red, blue and black lines
three formulas
He's written them a thousand times already.
I tried to count the std deviation as SUM(ABS(returns))/DEVEL(N,0.3333333) or even SUM(ABS(returns))/DEVEL(N,0.4) instead of SUM(ABS(returns))/DEVEL(N,0.5).
Maybe these 0.3333 , 0.4 , 0.5 must be dynamic? I was thinking somehow, if we calculate the number of real quotes, then we must consider the number of pseudo-quotes.
For example: 992 real quotes, 448 pseudo-quotes = 1440, or 31% of pseudo-quotes or 0,31111 for the formula above, or maybe we should put Hirst's index there, i don't know....
i.e. on the price chart....
Clueless
;)
The sum of the increments over the observation window.
Maybe these 0.3333 , 0.4 , 0.5 should be dynamic? I was thinking, if we count the number of real quotes, then we should count the number of pseudo-quotes somewhere.
For example: 992 real quotes, 448 pseudo-quotes = 1440, or 31% of pseudo-quotes or 0,31111 for the formula above, or maybe we put Hirst index there, i dont know....
All quotes that come in are real.
the deviations are what you're catching.
ok
formulas?
Let's make an indication in MQL and put it here
I'm really tired of chewing on the same thing.
I'll give you the final implementation in person.
and the indica, as practice shows, is not everything
So go ahead, don't worry.
i am interested in the red, blue and black lines
three formulas
OK. Let's put it down. I don't mind - I just want to line my own pockets, and I don't care about other people's.
1. I work with ticks in a sliding second time window.
2. for example, take a window = 14400 seconds, and create 3 (three) FIFO(14400) buffers.
3. With frequency = 1 sec. count the difference between current and previous price value (increment). Everything in a row, no matter whether it was a real tick or not, is written into buffer #1. We calculate the sum of all values in it. It is the price. Black line.
4. Count increment modules - we write them into buffer #2. Count the sum. Divide by 14400. This is the average rate of change in price. Let's call it C.
5. Now it is a little bit more difficult. We need to count the number of real ticks in this window. At every step, we look whether the increment itself or the time of value arrival has changed. If it has, we write a unit (1) into the buffer №3, if not - 0. Count the sum of the units. For example, we get 12345. This is the real number of incoming ticks in 14400 seconds. The sum of increment units from buffer #2 is divided by 12345. This is the average value of Lambda increments.
6. Calculate diffusion coefficient by formula: D^2=C*Lambda*T. Standard deviation Sigma=sqrt(C*Lambda*T).
7. Now make the assumption that all increments in BP are weakly dependent. The sum of such values gives a number belonging to a normal distribution.
6. From zero we plot support/resistance lines = +-2.5758*Sigma, where 2.5758 is the 99th quantile of the normal distribution. These are red and blue lines.
7. For the price it is the same, only +-2.5758*Sigma is not taken from 0, but from the initial reference point, i.e. the first element in the FIFO(14400) buffer.
That's it. This is the maximum we can squeeze out of standard (not abnormal!) diffusion.
OK.
Oh, come on.