Machine learning in trading: theory, models, practice and algo-trading - page 2353
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no way in forex)
Then if you go to other topics of the book, it becomes even moreMaybe to count separately for bids and asks, and then combine somehow? Most likely, it will turn out to be absurd.
It sounds logical, because it's already pretty clogged.)
Maybe instead of a hullabaloo we should do something more meaningful.) For example,take something from Prado apart. I found his idea of imbalance bars interesting but I have not understood how it can be adapted to forex.
Is there a Russian translation of Prado?
Is there a Russian translation of the Prado?
There is, but it is better in English - the narrative is brief and not simple, you will have to get the details in the articles, which no one will translate into Russian.
What is the point of his book then?
;))
there's useful stuff about resampling and random forest training, and in general it's a good material for getting acquainted with different methods
Maybe to count separately for bids and asks, and then somehow combine? Most likely, it would not make sense.
Sounds logical, because it's already quite polluted).
I don't know what kind of dream he had about such transformations but they only make sense when they actually have some sense) otherwise the same Renko
I don't know what dream he had about such transformations, but they only make sense when they really make sense ) otherwise the same Rencos
I don't know) But someone who wants to be like Prado, needs to think like Prado)
Yes, it looks like a Renko, but there are also some associations with CUSUM.
How we can improve the predictability of time series
Using zigzag classification as an example.
Normalization by volatility
0) create an empty vector
1) follow the price in the sliding window of size n
2) normalize prices in the sliding window to the range 0-1
3) write the difference of the last normalized value with the previous one into the empty vector
4) make cumulative sum over vector
the code on P , at once with iterpolation of NA-boxes if they are present
auxiliary normalization function
This is what we get, the red row is the price, the blue row is normalized according to volatility
As we can see the series retains all the properties of the price but is more stable according to its characteristics
Let's try to compare the quality of the classification of the phase slope
the target - the declination of RG
signs - a dozen of standard indicators
AMO - forrest , with the same parameters and sids
trace 10k , test 10k
forecast at standard price
forecast at changed price
I urge you to test!!!!!
Are you hinting that it's time to leave your home nest of retail Forex?
There is a sense to stay if there is a small machine which can increase the deposit in a month, in all other cases it is easier to work in any other place.
How you can improve the predictability of time series
Using zigzag classification as an example.
Normalization by volatility
0) create an empty vector
1) follow the price in the sliding window of size n
2) normalize prices in the sliding window to the range 0-1
3) write the difference of the last normalized value with the previous one into the empty vector
4) make cumulative sum over vector
the code on P , at once with iterpolation of NA-boxes if they are present
auxiliary normalization function
This is what we get, the red row is the price, the blue row is normalized according to volatility
As we can see the series retains all the properties of the price but is more stable according to its characteristics
Let's try to compare the quality of the classification of the phase slope
the target - the declination of RG
signs - a dozen of standard indicators
AMO - forrest , with the same parameters and sids
trace 10k , test 10k
forecast at standard price
forecast at changed price
I urge you to test!!!!!
How we can improve the predictability of time series
Using zigzag classification as an example.
Normalization by volatility