Logic is correct.
But I prefer to say experimental because the sample set is narrow. (300 columns)
6 inputs : Change , Low Band chg . , Mid Band chg ., Up Band chg . , change , histogram change.
1 output : Future bar change (Historical)
Training timeframe : 15 mins (Analysis TF > 4 hours (My opinion))
Learning cycles : 337
Training error: 0.009999
Input columns: 6
Output columns: 1
Excluded columns: 0
Training example rows: 301
Validating example rows: 0
Querying example rows: 0
Excluded example rows: 0
Duplicated example rows: 0
Input nodes connected: 6
Hidden layer 1 nodes: 8
Hidden layer 2 nodes: 0
Hidden layer 3 nodes: 0
Output nodes: 1
Learning rate : 0.6 Momentum : 0.8
More info :
EDIT : This code is open source under the MIT License. If you have any improvements or corrections to suggest, please send me a pull request via the github repository https://github.com/user-Noldo
i want to ask that this script works only in live market or offline too?
It works in every market, provided you wait for the closing.
This is the combined script of 25 instruments (including Bitcoin , Ethereum , Ripple and most popular instruments :
On Line 188 : v = (-2.580743 * n_0 + -1.883627 * n_1 + -3.512462 * n_2 + -0.891063 * n_3 + -0.767728 * n_4 + -0.542699 * n_5 + 0.221093)
So we can create connection between layers and output . I suggest you to read article for more information : https://hackernoon.com/everything-you-need-to-know-about-neural-networks-8988c3ee4491
These are not big things as exaggerated, but basic things.
But if I did LSTM, I wouldn't share it :))