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A framework for actuarial modelling.

Project description

Predictable

License: MIT pytest Documentation Status

What is it?

A framework for actuarial modelling.

Installation

pip install predictable

Quick start example

A model.py file will be used to house the modelling logic which will be applied to each modelpoint.

# import the library
from predictable import CashFlow, DiscountFactors, Model, StaticFlow

# Create new model instance
model = Model()

# Add a premium component
model.add_component(
    CashFlow(
        input_array=[100], formula=lambda prev: prev * 1.05, label="premium"
    )
)

# Add a sum assured component
model.add_component(CashFlow(label="cover", input_array=[1_000_000]))

# Add an expense component
model.add_component(
    StaticFlow(
        input_array=[10, 10, 10, 10, 10],
        label="expense",
    )
)

# Add discounting component
model.add_component(DiscountFactors(interest_rate=0.05, label="V"))

# Project cashflows over term
# Results return a pandas df object
df = model.project(term=10)

# Perform linear combination style manipulations
# Discounting the components
components = ["premium", "cover", "expense"]
for component in components:
    df[f"V_{component}"] = df[component] * df["V"]


# Define reserving relationship
df["Reserve"] = df["V_cover"] + df["V_expense"] - df["V_premium"]

# Results get returned as a pandas dataframe
print(df)

License

MIT

Documentation

This project is documented using sphinx and the full documentation can be found at predictable.readthedocs.io.

Development & Contibutions

The following steps can be followed to set up a development environment.

  1. Clone the project:
git clone https://github.com/RatulMaharaj/predictable.git
cd predictable
  1. Create a virtual environment and activate it using:
python -m venv venv
source venv/bin/activate # mac
venv\Scripts\activate # windows
  1. Install the project dependencies:
pip install -r requirements-dev.txt

In development mode, the package can be installed by running:

pip install -e .
  1. Install the pre-commit hooks
pre-commit install

Testing

The tests for this project can be found in the predictable/tests directory. Tests will run after every commit (locally) and on every push (using github actions) but can also be run manually using:

pytest

Project details


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