Machine learning in trading: theory, models, practice and algo-trading - page 2461
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Carrie trading is an old banger....
but all in all, as they say on the internet: keep watching!
p.s.
Vladimir Baskakov , your exclamations in #24597 no longer make you an interested interlocutor, rather a paparazzi, looking for where to shout... shouting?... - the topic of your liquorice is over... you're obviously not interested in working with your brain on the subject, and EA won't work on its own without a coder to implement it...
yep, you've already been answered, (if you couldn't get anything more out of the above topics in your whole life)
p.s.
Vladimir Baskakov , your exclamations in #24597 no longer make you an interested interlocutor, more like a paparazzi looking for a place to yell... shouting?... - the topic of your liquorice is over... you're obviously not interested in working with your brain on the subject, and EA won't work on its own without a coder to implement it...
I like people with such self-confidence, unsupported truth. But they appear here regularly and quickly disappear. Like: quietly waiting for profit...
yep, you have already been answered, (if you couldn't get anything else out of the above topics)
p.s.
I like people with such self-confidence, unsupported by anything true. But such people appear here regularly and quickly disappear. Like: quietly waiting for the profits...
Yeah... because it's about personalities, not the subject matter... you to Einstein with your request to prove something to you with your deposit... You're asking Einstein to prove something to you with his deposit ... (like, I'll lower him too, if he does not surrender all his competitive advantage in this market, I'll persecute and mock and insult and poke him, until he rushes to show pictures, and if "me" (ie, you) like pictures, then pester to death, to stick to his profits or his rotten talk not to give him time to earn until he shares) ... Einstein will understand you...
such dialogues, indeed, testify about you as a person, as a trader, as an analyst, as a programmer ... personally I'm just disgusted to engage in such debates in such talk shows of such shallow "professionals"... so I don't even start the show
Yes... because this is a discussion of personalities, not the subject of the stated topic... you to Einstein with your request to prove something to you with your deposit... (like, I'll put him down too, if he gives up all his competitive advantage in this market, I'll sneer and mock until he rushes to show pictures, and if "me" (ie, you) like pictures, then I'll pester him to death, so that to stick to his profits or his rotten talk not to give him time to earn, until he shares) ... Einstein will understand you...
such dialogues, indeed, testify about you as a person, as a trader, as an analyst, as a programmer ... personally, I'm just disgusted to engage in this kind of debate on such talk shows of such shallow "professionals"... so I don't even start the show
Greetings Brothers!!!
I remember I've already said it more than once, but I'll say it again. Yes the method of training and the architecture of the NS is important, but much more important is the data you use. In many respects with well prepared data will work qualitatively a wide range of network architectures. Naturally each type of NS requires its own specific preprocessing, but if the input data, the information you take to enter the network makes sense for the target then the result will be visible immediately. The point of digging different methods of constructing a system, if you exit only on the unique configuration will not work anyway.
Well, I'm just saying, maybe young people read :-)
From my experience, especially important are the data (pre-processing data) and the target function. In fact, data are "receptors", the output from receptors, you can / must give them the opportunity to "evolve", to be selected. Correctly chosen/formed target function provides robust learning/evolutionary results, solves the problem of potentiation/retraining.
From my experience, data (data preprocessing) and the target function are particularly important. In fact, data are "receptors", output from receptors, you can/should allow them to "evolve", undergo selection. Properly selected/formed target function provides robust learning/evolutionary result, solves the problem of potty-training/re-learning.
I disagree a bit about the target function. Suppose we have an ideal target but learning is bad and getting satisfactory learning results is not possible with the current data, if we start to degrade the target by making it less ideal it will lead to better learning results. It's like we'll be adjusting the target to the input data we have. Yes the quality of training will improve, but it will be of little use. In my opinion it is necessary to build an ideal target and look for such a set of data in which we get the best possible learning results. That is, you need to search in the input data, not in the target data.
When we talk about data, we mean exactly the information fed to the input. As for preprocessing, it is standard to me and applies to any data that we use. This is at least centering and scaling.
I disagree a bit about the target. Suppose we have an ideal target, but learning is bad and getting satisfactory learning results is not possible with the current data, if we start to degrade the target, making it less than ideal, it will lead to better learning results. It's like we'll be adjusting the target to the input data we have. Yes the quality of training will improve, but it will be of little use. In my opinion it is necessary to build an ideal target and look for such a set of data in which we get the best possible learning results. That is, you need to search in the input data, not in the target data.
When we talk about data, we mean the information fed to the input. As for preprocessing, it is standard and applies to any data that we use. This is at least centering and scaling.
You have interpreted my opinion to the exact "opposite", in nature, the target is the most practical - the most "necessary" survives and multiplies. And it is necessary to train the most "practical" target without changing it in any way.
About data, yes, information given as input, but ideally we should have "eyes", "ears", "nose" etc.
You have interpreted my opinion exactly to the contrary, in nature the target is the most practical - the most "necessary" survives and reproduces. And it is necessary to train the most "practical" target without changing it in any way.
About the data, yes, the information fed to the input, but ideally, we should form/receive - "eyes", "ears", "nose", etc.
Yes... Because this is a discussion of personalities, not the subject of the stated topic...