acturtle

pip install cashflower

acturtle

main = ModelPointSet(data=pd.DataFrame({
    "id": [1, 2, 3],
    "sum_assured": [100_000, 50_000, 200_000],
    "remaining_term": [36, 24, 60],
}))

fund = ModelPointSet(data=pd.DataFrame({
    "id": [1, 1, 3],
    "fund_code": ["AX", "BP", "FY"],
    "value": [10_000, 5_000, 12_000]

acturtle

import sqlite3
conn = sqlite3.connect("model_point")
sql_query = pd.read_sql_query("SELECT * FROM policyholder", conn)
main = ModelPointSet(data=pd.DataFrame(sql_query))

acturtle

main = ModelPointSet(data=pd.read_csv("data.csv"))

acturtle

main = ModelPointSet(data=pd.DataFrame({
    "id": [1, 2, 3],
    "sum_assured": [100_000, 50_000, 200_000],
    "remaining_term": [36, 24, 60],
}))

acturtle

 t survival_rate expected_benefit net_single_premium
 0      1.000000             0.00            9375.89
 1      0.997000           300.00            9422.77
 2      0.994009           299.10            9168.39
 3      0.991027           298.20            8913.63
...
34      0.902891           271.68             808.58
35      0.900182           270.87             539.58
36      0.897482           270.05             270.05

acturtle

@variable()
def net_single_premium(t):
    if t == settings["T_MAX_CALCULATION"]:
        return expected_benefit(t)
    return expected_benefit(t) + net_single_premium(t+1) * 1/(1+assumption["INTEREST_RATE"])

acturtle

@variable()
def expected_benefit(t):
    if t == 0:
        return 0
    if t > main.get("remaining_term"):
        return 0
    return survival_rate(t-1) * assumption["DEATH_PROB"] * main.get("sum_assured")

acturtle

from cashflower import variable

from input import assumption, main
from settings import settings


@variable()
def survival_rate(t):
    if t == 0:
        return 1

acturtle

import pandas as pd
from cashflower import ModelPointSet


main = ModelPointSet(data=pd.DataFrame({
    "id": [1],
    "sum_assured": [100_000],
    "remaining_term": [36],
}))