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Chart time from right to left, going to zero.
Here is an interesting case, the observation window is 30 minutes. The candle (1) has broken the upper range but the density was 0. The candle (2) has broken the lower range and the density was 0.00557 (taking into account the previous posts about wrong values). So, in the first case price has not returned to the candle minimum (1), and in the second case it returned much higher as you can see.
I don't understand it, it's the same formula, so why the result is different. NormalizeDouble to 5 digits can't have the same effect...
Use library
Use the library
I'm still on four.
In general, we should have done a type conversion and #property strict
K2, there's one question I don't understand
why ticks and not M1?
More details, if you can. I couldn't find any reason for the different results. What were they?
I had the x parameter and variance designated as int , and the density was double.
Corrected it to the following and it all reads correctly
double Density = (1/(MathSqrt((double)Dispersion) * MathSqrt(2 * 3.14159265358979323846)) * MathPow(2.718282845904523536, - ( ((double)X * (double)X)/(2 * (double)Dispersion) ) );
I had the x parameter and variance designated as int , and the density was double.
Corrected to the following and everything started to read correctly
double Density = (1/(MathSqrt((double)Dispersion) * MathSqrt(2 * 3.14159265358979323846)) * MathPow(2.71828182845904523536, - ((double)X * (double)X)/(2 * (double)Dispersion) ) );
Another example of how events unfolded. A probability density graph was added.
K2, there's still one question I don't understand
why ticks and not M1?
I don't know, Rena...
I asked to check on M1, I think Eugene has some results. Now I am waiting for a trade signal from him. I am curious.
Having initially grabbed the ticks, it's hard to give them up now.
But, I repeat, if there are people who show remarkable results on M1, on real, using the sum of increments instead of price (and demonstrate them truthfully, and not hide themselves like Alioshenka from "Machine Learning"), I will immediately switch to tics.