Machine learning in trading: theory, models, practice and algo-trading - page 3527
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All I fancy is my method - where predictor ranges are essentially zeroed out if a probability bias is detected for the null class, thus avoiding inconsistencies.
What is the point of your method?
If in your terminology, 0 and 1 are spread across different predictor ranges.
If in your terminology, 0 and 1 are spread across different predictor ranges.
Did I understand correctly that conditional cleaning is happening - replacing 1 with 0 after randomisation?
Or sort of picking predictors to do this?
But, how do you relate this to the stability of the probability bias, because you can do anything on training, but what happens afterwards?
Did I understand correctly that conditional cleanup is happening - replacing 1 with 0 after random markup?
Or sort of picking predictors for that?
But, how do you relate this to the stability of the probability bias, because you can do anything on training, but what happens afterwards?
I'm removing examples that are on the conditional class separation boundary. It's all according to the canons of MoD. The improvement of stability is due to simpler boundaries.
Gradually reduce the number of attributes still, there are models that work well on 2 mashas. So it is possible to reduce the number of features to 2.
Bousting is trained at most 100 iterations later. It must be something from Active Learning.Obviously a price reversal timetable)
Too bad it turned out not to exist)
First you have to answer the question of what a price reversal is.
and then look for the timetable.
First, we need to answer the question of what a price reversal is.
and then look for a timetable.
Naturally, we failed to see any "turnaround according to the schedule".
But what can be seen on the charts that can be taken as a "schedule"?
Obviously, these are effects related to daily volatility fluctuations.
Suppose we have two realisations of the SB, which differ only in the dispersion of increments. No one in their right mind would say that one is more predictable than the other. In fact, they differ only in that the more volatile realisation makes time seem to speed up. BUT, if these realisations are sliced into pieces, mixed and combined into one, the eye can start to see some patterns. For example, it is obvious that in a faster implementation the zigzag tops will appear more often, which of course does not make them more predictable.
Conclusion: you can start writing a fanfic based on Taleb's "Fooled by Randomness")
But what is it then that is visible in the graphs that can be taken as a "schedule"?
Obviously, these are effects related to daily volatility fluctuations.
Suppose we have two realisations of the SB, which differ only in the variance of increments. No one in their right mind would say that one is more predictable than the other. In fact, they differ only in that the more volatile realisation makes time seem to speed up. BUT, if these realisations are sliced into pieces, mixed and combined into one, the eye can start to see some patterns. For example, it is obvious that in a faster realisation the zigzag tops will appear more often, which of course does not make them more predictable.
Conclusion: you can start writing a fanfic based on Taleb's "Fooled by Randomness")
Well, that's if you take the SB. But it is possible to cut into pieces and then make tests for differences from the SB.
It also seems obvious that there is some kind of temporal structure related to the sessionality of the market. Another thing is that it can hardly be described by simple methods like "scheduled reversals".
Imho, there should be some dependence on the size of price movement. Large movements (from the daily range and above) are hardly determined by daily rhythms.
Here's a squeeze on cluster analysis from gpt-omni
It also seems obvious that there is some time structure related to market sessionality. Another thing is that it is hardly described by simple methods like "scheduled reversals".
Imho, there should be some dependence on the size of price movement. Large movements (from the daily range and above) are hardly determined by daily rhythms.
Well, yes, situationally.
Imho, the development of TS is more like shamanism than some scientific work :)