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
These are not big things as exaggerated, but basic things.
But if I did LSTM, I wouldn't share it :))
This command is very good but the more data, the more success it means.
300 columns too low.
If I get comprehensive indicator data, I can make more successful updates.
Then I plan to move to LSTM.
Thank you for feedback ! (y)
i want to ask that this script works only in live market or offline too?