Machine learning in trading: theory, models, practice and algo-trading - page 928
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The main thing is that Akello won't miss again next week.
Thought my basic strategies sucked. Can someone give me some basic strategies, which I will try to improve with my agents?
Take a counter-trend. I'm telling you as a surgeon. It's the most appropriate approach. There's no better time for analysis than the beginning of a pullback....
Take a countertrend. I'm telling you as a surgeon. It's the most appropriate approach. There's no better time for analysis than the beginning of a pullback....
https://www.mql5.com/en/code/19598
I'll take this one for now, it's an ancient strategy, but it's good for optics
mnogovhodov_02 2016 arr_Buy turned out like this:
The girl says you're right, but we need more champagne.
She's cool, tell her I said hi!!!!
Cool, tell her I said hi!!!!
Alternative. The result immediately in classes, without probabilities. That seems worse to me.
OK, you too from her :)
Maybe it will teach you some sober thoughts about TC. How you can and can't do.....
Maybe it will teach you some sober thoughts about TC. How you can and can't do.....
She says I'm smart and just have to take her to Australia, she has a friend there
We need a fake marriage for that
I stopped counting errors by these tables as standard.
My reasoning is, I take class 1 right away, it is more obvious: Initial class "0" gave class "1" prediction = 86118, and class "1" gave class "1" prediction = 12256. This means that when trading, we will get false class predictions = 86118, while correct predictions = 12256, i.e. error = 86116/(86116+12256) = 87.5%9(!!), if class "1" = entry/position, then it is a disaster. But the position of class "0" is very decent - the erroneous zeros in decision making will be only 5.3%.
I don't use such tables when comparing models, either. I added it here just for clarity. You can see right away that there are a lot of zeros predicted where there should be "1", and you can see that with one tree the forecast is bad.