Machine learning in trading: theory, models, practice and algo-trading - page 1622
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I have.... I draw conclusions from backtests in the tester, what do you think the result you get if your system is trained correctly? Almost 90% outcome of correct inputs. Previously the same backtests did not give such an outcome, from which I conclude that the training in this case was correct.
Try the same in your case.
I can already give you an advisor in exchange for constructive criticism and a bug report. Write in person.
I don't quite get the idea.
I can give you now an Expert Advisor in exchange for constructive criticism and bug report. Send me a personal message.
I think I tried to make it very simple. You don't have a system yet, once you have it, you can run it through history and draw conclusions about how ready (trained) it is.
I think I tried to make it very simple. You don't have a system yet, as soon as you have one, you can run it through history and draw conclusions about how ready (trained) it is.
It's unreal to run it through the history! Every minute the forecast goes out, calculation takes 12-17 seconds (up to 22 seconds), there are 1400 minutes in a day. One day backtest takes hours.
Well... What part of the story do you train the neural network on anyway (5/30 minutes)?
I went to look at their help, but I do not understand it - it's too confusing. I'll try to find this point later in the video, they explain it more clearly.
But I saw that CB added new options for building trees.
The tree growing policy. Defines how to perform greedy tree construction.
Depthwise- A tree is built level by level until the specified depth is reached. On each iteration, all non-terminal leaves from the last tree level are split. Each leaf is split by condition with the best loss improvement.
Lossguide- A tree is built leaf by leaf until the specified maximum number of leaves is reached. On each iteration, the non-terminal leaf with the best loss improvement is split.
12-17 seconds (up to 22 s)
Does it take learning?
The red arrows are already considered stale, but this does not invalidate their performance. As a result, so far not a single minus in transactions, if you have not noticed. Well, who's the ***? :-)
Well... What part of the story do you train the neural network on anyway (5/30 minutes)?
One model (a neural network) does not give the right result. It can learn something, but it is not enough. That is why I make 20-25 models with different chips on input. Now I have 25 models signaling at the same time, and each model's opinion is considered with certain weight in final forecast. Calculation of one model takes about 0.5-0.7 seconds totaling 15-20 seconds + we should prepare input date for 25 models, that is a lot of work for every minute) The answer can be reduced to 1-3 seconds if I use multithreading correctly in python, but I haven't done that yet.
I train models separately, in normal mode, i.e. dataset is collected from history period from one year and then training as usual.
The red arrows are already considered stale, but this does not invalidate their performance. As a result, so far not a single minus in transactions, if you have not noticed. Well, who's the ***? :-)
Do you have an account monitor?
It's not like that.
One model (neuronet) does not give the right result. It may learn something, but it is not enough. Thats why I build 20-25 models with various inputs. Now I have 25 models signaling at the same time, and each model's opinion is considered with certain weight in final forecast. Calculation of one model takes about 0.5-0.7 seconds totaling 15-20 seconds + we should prepare input date for 25 models, that is a lot of work for every minute) The answer can be reduced to 1-3 seconds if I use multithreading correctly in python, but I haven't done that yet.
I train models separately, in normal mode, i.e. dataset is collected with history period from one year and then training as usual.
How serious it is... Now you just have to make sure that you can really trade on it.