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and the version hasn't changed :o(
I'll finish the article today and send it in for checking.
I'll finish the article today and send it in for checking.
Great, we'll follow your progress :o)
the forecast is more or less the same (the one with the maximum entropy) :o) A small refinement, the following trajectories remain, with the one that is "channeled" being the most likely.
The file would have to be dated (at least "verbally") ;-).
A word of advice. Do not increase the array by 1 element in the loop. Keep the number of used (filled) elements in an additional variable and increase the array by a dozen or so elements (estimate for your own task) when you run out of space. This way you can get a significant performance gain.
I assert, speed gain is significant (I checked it personally).
Example-illustration of use:
ps: sorry for the off-top )
to komposter, lea, Yurixx
Ok, it's silly to argue with MQL-masters. Although my estimations gave me a monopenial result, but I must have made a mistake somewhere, or the car is just strong, or I've used a smaller pitch. I think it would be better.
to marketeer
Файличик бы, с датой (хотя б "устно") ;-)
My bad, I forgot. Here's a new forecast with 300 samples (3 days since the post was published), M15, 17 base realizations in total:
Entropies calculated for them:
In the file is a matrix, each column is a realization, the column numbers correspond to the identifiers in the figure above. ie are the "winners", the realizations with maximum entropy:
PS: Ose, holiday, flippers, snorkel for 3 weeks. Good luck to all! You'll tell me later if the prediction came true... :о)
The calculated entropies are for them:
Could you tell us how you calculate entropy? Do you use any price transformations to calculate entropy or do you calculate directly for price series (forecast)?
I tried to count by box-counting - to calculate frequencies I divided the vertical axis into intervals, counted frequencies of price hits (not transformed), and then determined the entropy using the formula entropy=sum(i=1...n) -probability[i]*log2(probability[i]). But for some reason I didn't get such values of entropy, they were much smaller.
Could you tell us how you calculate entropy? Do you use any price transformations to calculate entropy or do you count directly for price series (forecast)?
I tried to count by box-counting - to calculate frequencies I divided the vertical axis into intervals, counted frequencies of price hits (not transformed), and then determined the entropy using the formula entropy=sum(i=1...n) -probability[i]*log2(probability[i]). But for some reason I didn't get such values of entropy, they were much smaller.
while waiting for the plane ... :о) The formula is of course the same, but using specific price range conversions invented. Hence these values
while waiting for the plane ... :о) The formula is of course the same, but using specific price range conversions invented. Hence these values.
>> I see, thank you :)
Here, everything seems to be in line with the prediction theme:
A and B - optimization on different periods
There are templates in the archive.
Here's the new forecast still the same, for 300 counts (3 days since the post was published), M15, Total - 17 base realizations:
PS: That's it, holiday, flippers, snorkel for 3 weeks. Good luck everyone! You'll tell me later if the forecast came true... :о)
Some kind of a discrepancy turns out: forecast for 3 days and a holiday for 3 weeks ;-).