Efficient way to Run Model For Multiple Policies

[mbici]

I have created a model that works well for individual policy reserve calculations, I now want to run the model acrosss multiple policies. How do I go about , I need to store all the cashflows at each time e.g premiums, benefits for each of the policies

[fumitoh]

A common approach would involve the following steps:

• Create a cells that reads in the entire model point table as a pandas DataFrame indexed with policy ID.
• Parameterize your space by policy ID.
• Make policy attributes and assumptions read values from the table by looking up the parameter.
• Individual policy results are created as parameterized spaces, such as Projection[1] for Policy ID 1, Projection[2] for Policy 2, and so on.

Here are simplified examples for the steps above:

@mx.defcells
def model_point_table():
    # Assuming model_point_table.csv is in the same directory as the model.
    return pd.read_csv(_model.path.parent / "model_point_table.csv")   

space.parameters = ("policy_id",)    # Assuming your space is assigned to 'space'.

@mx.defcells
def issue_age():
    return model_point_table().loc[policy_id, "issue_age"]

print(space[3].premiums(10))    # Get the premiums of Policy 3 at time 10 and print it out.

I think examining simple models in lifelib, such as BasicTerm_S in the basiclife library is helpful.