How to build a predictive model to optimize gain
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:
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:
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:
- 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