The network is trained on past price data since 2014 and is looking at the prices of the past 100 days to estimate the price of tomorrow.
Internally it is a black-box and I have no idea how it is coming up with the numbers. But here is a useful plot
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These are normalised relative price changes in the training sample in blue and the network prediction in orange. It is immediately clear that they differ a lot and the network does not perfectly predict the price at all. But there is still a clear correlation between the two curves so that the network does predict a small hint.
Okay, I see now what is the black-box and network. How does the training looks like? The network gets a training sample and reacts according to which principal? Is there a scalation?
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The network is training on past data trying to minimise the post-diction error.
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Okay. I guess to see now the intuition in it.
Thanks for the post✌️
Greets
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