I'm trying to train the neural network to predict the movement of a particular security on the market.
I teach on historical data collected for the year. At the entrance of the neural network candlesticks are served: close price and value
Before submitting, these data are normalized separately for each dataset. This happens with the z Score algorithm. Then the question immediately arises... output can not be obtained in the limit [0;1] or [-1;1] and can reach up to 10 or more (in both directions). Is that okay?
All sets are first shuffled, and then divided into two parts:: train and test (80:20) The output is a class: the price will go up or down.
For train, the error is reduced to zero and the accuracy to 100%. For test, the error reaches a minimum of 0.28 and the accuracy reaches 90%
I tried to test this neural network to predict the next two months, which were not used in this neural network. They went through exactly the same normalization. However, the accuracy of this forecast is 0.5289256204750912. And the error - 1.8223290671986982
I know I'm doing something wrong, but I don't know what... I hope someone can help me figure this out. PS: ZScore was used To the usual min, max. However, this particular success was not observed.