tomespel / kairos-volatility-prediction Goto Github PK

Transfer the content of the Jupyter notebooks in the files.

Lay out the main file structure.

We'd need to keep them individually for sakekeeping.

We could also try to find a way to name those predictions to understand which models we are using because we might use the same model but with different parameters or input dataset.

**Dealing with raw data **

The notebooks were implemented with data in its original form, which was easy to manipulate. It might not be necessary to reformat the time stamp format of the different assets given that. It might add more complexity and force us to modify code.

What do you think?

Following #4 . Here are the elements of the class KairosDay.

• __id (int) corresponds to the target id.
• _asset (int) corresponds to the asset id.
• __date (int) corresponds to the date.
• __volatility corresponds to the list of double with the volatility values.
• __returns corresponds to the list of double with the returns values.
• __isClassified (bool) indicates if the asset has received it local volatility classification.
• __localClassification (string) specifies the local classification hlv, mlv or llv. States None otherwise.
• __classificationDetails (dict) specifies the local classification probabilities in the form of a dictionary {'method': double(probability)}.
• __features (dict) specifies the features list in the form of a dictionary {'featureName': list(featureValuesList)}.

Update code to PEP8 and PEP257 conventions.