Machine learning in trading: theory, models, practice and algo-trading - page 2076

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you should write a random enumeration of everything with everything and train on it, select what is trainable + genetics to shorten the search, and leave the computer for a couple of months....
I'm about to write an article on random sampling, but in python
to have it )
https://www.mql5.com/ru/articles/8642
I plan to compare it with RNN, CNN, etc. But first make some more modifications on catbust.
I think it is better not to look for a pattern after the pattern, but to look for the rebound price related to this pattern, it is much more difficult to formalize, but it seems to me
It is possible to calculate the percentage of price growth on history at a given pattern (arithmetic mean, root-mean-square), for example.
You can calculate on the history by how many percent the price increase was for a given pattern (arithmetic mean, root mean square) for example.
You can simply normalize the pattern to the range 0-1, for example, and then normalize the space relative to this pattern, and that's it.
see what I mean?you can simply normalize the pattern to the range 0-1, for example, and then normalize the space relative to that pattern, and that's it
see what I mean ?I don't get it.
Here I have the current pattern:
Pattern 'long' = X ,
The 'short' pattern = B,
Past event = L.
I can use history to find when it was the same.
Then normalize the space relative to this pattern - by the type of function in the fix range (or there 25 % below the maximum, etc.)
I don't get it.
Look, it's simpler, I also puzzled for years...
we have the x1 patren
x1 <- rnorm(40)
we have two other patterns x2 and x3 they are the same as x1 but with different volatility
we consider the first 5 points to be patterns, all the following will be considered the space relatively to the pattern
1) bring the patterns into a single range, say 0-1
the usual normalization
all three patterns will be the same
now it is necessary to normalize the space relative to the pattern
this is done by the function r02
where " y " is space and "x" is not a normalized pattern
Then we connect the results of transformations from functions r01 and r02 in one row (vector)
and we obtain a pattern normalized in the range 0-1 and the space is normalized relative to this pattern
similarities should be searched not by correlation but by Euclidean metric
the smaller is the value, the closer the patterns are to each other
I see that it's different.
Your pattern describes the price, and my pattern describes the segments (shorter, longer). If the pattern is equal to the letter B , then the identical ones are only the same B.
But the space around the pattern is naturally different because of volatility, but I don't need it because I care about formality (shorter or longer).
I see that it's different.
Your pattern describes the price, and my pattern describes the segments (shorter, longer). If the pattern is equal to the letter B, then the identical ones are only the same B.
What difference does it make what you're comparing?
Your pattern is 10-20-10 and 20-40-20 is one and the same pattern or not?
What difference does it make what to compare?
Is your pattern size 10-20-10 and 20-40-20 the same pattern or not?
What size? I have several clearly described patterns they are all different as for example 0110 and 1001
What size? I have several clearly described patterns, they are all different, like 0110 and 1001
Well, the sections are yours.
10 candles - 20 candles - 10 candles
Don't slow down)
Well, your segments are
10 candles - 20 candles - 10 candles
don't slow down)
What candles 10-20-10 ?