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How to build a predictive model to optimize gain

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How to build a predictive model to optimize gain

[ Edited ]

Hi All,

I would like to create and use a predictive model of buy or sell signals for a stock in order to optimize the gain.

 

For the inputs I created the following table:
Cattura.PNGWhere:

 

Day is the ID

Variation is the daily price variation. I set its role as Attribute.

Indicators are attributes.

Result is equals to 1 if the previous day price variation was positive else 0.

 

I have created a process like this:
Cattura.PNG

 Where I used:

  • Retrieve to load inputs
  • Normalize for normalization
  • Filter Examples to split input between training inputs and inputs for prediction
  • Generate Attributes to derive another attribute with a formula
  • Set Role to set ID and Label roles
  • Cross Validation to train the model
  • Multiply to duplicate the model for the Apply Model component and Store it in a file

In the Cross Validation I put:

Cattura.PNG

 

 

 

 

  • Deep Learning to learning and create the model
  • Performance (Binomial classification) to evaluate the model. I set like main creation the "True positive".

Performance result is not great. Further in another process machine learning memorize the result for a input set.

Could you help me to improve the process? 

 

Thanks in advance

Francesco

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1 REPLY
RMStaff

Re: How to build a predictive model to optimize gain

Hi,

 

have you tried another learner? E.g. a Random Forest (w/o any (pre)pruning?). I would give this one a try first.

 

Best,

Martin

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Head of Data Science Services at RapidMiner