A lightweight actuarial modelling framework for Python
- single script
- installation optional: package with your models.
- only depends on
pandas
andnumpy
Model:
- projection controller
- class to subclass with your proprietary models
BeforeRun
andAfterRun
methods- get all values as a list with
values
attribute - get the sum of all values with
sum()
method
Table:
- simple long format table object
- type information encoded via
|int
,|int_bound
,|band
,|str
header suffixes
Create your model as a subclass of heavylight.Model
. Each model variable is defined as a method:
import heavylight
class Annuity(heavylight.Model):
def t(self, t):
return t
def expected_claim(self, t):
return self.number_alive(t) * self.data["annuity_per_period"]
def number_alive(self, t):
if t == 0:
return self.data["initial_policies"]
else:
return self.number_alive(t - 1) - self.deaths(t - 1)
def deaths(self, t):
return self.number_alive(t) * self.mortality_rate(t)
def mortality_rate(self, t):
return 0.02
def v(self, t):
"""discount factor from time t to time 0"""
if t == 0:
return 1
else:
return self.v(t - 1) / (1 + self.forward_rate(t))
def forward_rate(self, t):
return 0.04
def pv_expected_claim(self, t):
return self.expected_claim(t) * self.v(t)
Define input data as a dictionary
policy_data = {
"initial_policies": 10,
"annuity_per_period": 55,
}
Call the model, passing in the data dictionary, with a projection length of 20.
model = Annuity(data = policy_data,
do_run = True,
proj_len = 20,
)
Get the sum of pv_expected_claim
:
print(model.pv_expected_claim.sum())
Display result as a pandas table
model_cashflows = model.ToDataFrame()
-
This package is designed for projecting actuarial variables, and calculates t=0, 1... in order.
-
Actuarial models are generally highly recursive.
-
If you create a method which refers to future t value (such as an NPV function) you may hit the python stack limit.
-
The recommended solution is to project forward first, and then calculate T0 metrics based on the result, for example using an `npv()`` function