Market etiquette or good manners in a minefield - page 46
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Why do I need timeframes? Whoever invented them, let them trade on them.
And what is the increment for you then? A certain amount of time? Volume? Takeprofit?
What then is incremental for you? In a certain amount of time? >> Takeprofit?
Quotient increment is not related to time in any way. There is a quotient increment by N points, for example upwards - this is one symbol
A quotient increment is not related to time in any way. There is an increase of quote by N points, for example upwards - this is one symbol
And how long to wait for this increase and what the drawdown will be - is a tenth matter... All right, you are the boss...
And how long to wait for this increase and what the drawdown will be is a tenth matter... Alright, that's it, you're the boss...
You don't have to wait, it's not necessary, but the drawdown will be anyway - it's business.
Here is an example of how the weights converge to a global minimum during grid training:
Here are two cases with different starting points for the weights. You can see that by the end of training the oscillations smoothly fade for each of the weights and they converge to approximately the same values. The example is given for a single neuron with three inputs.
Amazing, pictures! Share your treatment :-)
By the way, I live in Korolev
No problem.
This picture illustrates learning on the next countdown of the quotient:
You can see that the optimal values of the weights are stable from experiment to experiment, but they are significantly shifted relative to the previous results obtained on the count backwards. This indicates a weak stationarity of the BP of the market type and the necessity to retrain the girl at each countdown.
No problem.
This picture illustrates learning on the next countdown of the quotient:
You can see that the optimal values of the weights are stable from experiment to experiment, but they are significantly biased relative to the previous results obtained on the count backwards. This indicates a weak stationarity of the BP of the market type and the necessity to retrain the girl at each counting.
This may be due to rationing as well.
It could be because of rationing as well.
But the thing, agree, is beautiful -:)
But the thing is, agree, beautiful -:)
Decrease learning rate - coefficient before 1-L/Epox bracket (stability will improve) and have compressing function on weights after each epoch, otherwise they take too big amplitudes in learning process. It's all the same, but sometimes weight goes to saturation and becomes lost for learning (typical for nonlinear bilayer).