Logic is correct.
But I prefer to say experimental because the sample set is narrow. (300 columns)

Let's start:

6 inputs : Volume Change , Bollinger Low Band chg . , Bollinger Mid Band chg ., Bollinger Up Band chg . , RSI change , MACD 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
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A helpful tool for this script :
Whats v in here?

ActivationFunctionTanh(v) => (1 - exp(-2 * v))/( 1 + exp(-2 * v))

Noldo syracusepro
@syracusepro, Hello ! "v" is our Layer Values which we need to take in activation function. For example :

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 :
thanks again for sharing! do we need to make changes to script if i want to use it for other assets like ETHUSD?
+1 Rispondi
Noldo rhanna
@rhanna, Only use with Bitcoin .
@Noldo, alright, thanks a lot
+1 Rispondi
Thanks for sharing your ideas and code
+1 Rispondi
Noldo wroclai
These are not big things as exaggerated, but basic things.
But if I did LSTM, I wouldn't share it :))
@Noldo, Interesting enough to publish? ;) Every nugget of knowledge helps someone. cheers
+1 Rispondi
Noldo wroclai
@wroclai, it's the most advanced in the field, but I haven't made a start right now.
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)
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