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simulation.py
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import pandas as pd
import datetime as dt
from mies.entities.god import God
from mies.entities.bank import Bank
from mies.entities.broker import Broker
from mies.entities.insurer import Insurer
from mies.utilities.queries import query_population, query_customers_by_person_id
pd.set_option('display.max_columns', None)
ahura = God()
ahura.make_population(1000)
pricing_date = dt.date(1, 12, 31)
blargo = Bank(4000000, 'blargo')
rayon = Broker()
company_1 = Insurer(4000000, blargo, pricing_date, 'company_1')
company_2 = Insurer(4000000, blargo, pricing_date, 'company_2')
company_1_formula = 'incurred_loss ~ ' \
'age_class + ' \
'profession + ' \
'health_status + ' \
'education_level'
company_2_formula = 'incurred_loss ~' \
' age_class'
population = query_population()
ids = population['person_id']
blargo.get_customers(ids=ids, customer_type='person')
customer_ids = query_customers_by_person_id(ids, 'blargo')
blargo.assign_accounts(customer_ids=customer_ids, account_type='cash')
ahura.grant_wealth(person_ids=ids, bank=blargo, transaction_date=pricing_date)
policy_count = pd.DataFrame(columns=['year', 'company_1', 'company_2', 'company_1_prem', 'company_2_prem'])
for i in range(50):
rayon.place_business(
pricing_date,
blargo,
company_1,
company_2
)
event_date = pricing_date + dt.timedelta(days=1)
ahura.smite(event_date)
rayon.report_claims(event_date)
company_1.pay_claims(event_date + dt.timedelta(days=1))
company_2.pay_claims(event_date + dt.timedelta(days=1))
company_1.price_book(company_1_formula)
company_2.price_book(company_2_formula)
pricing_date = pricing_date.replace(pricing_date.year + 1)
ahura.send_paychecks(person_ids=ids, bank=blargo, transaction_date=pricing_date)
policy_count = policy_count.append({
'year': pricing_date.year,
'company_1': len(company_1.in_force(pricing_date)),
'company_2': len(company_2.in_force(pricing_date)),
'company_1_prem': company_1.in_force(pricing_date)['premium'].mean(),
'company_2_prem': company_2.in_force(pricing_date)['premium'].mean()
}, ignore_index=True)
policy_count = policy_count.groupby(['year'])[['company_1',
'company_2',
'company_1_prem',
'company_2_prem'
]].mean().reset_index()
import plotly.graph_objects as go
fig = go.Figure()
fig.add_trace(go.Scatter(x=policy_count['year'], y=policy_count['company_1'],
mode='lines',
name='Company 1'))
fig.add_trace(go.Scatter(x=policy_count['year'], y=policy_count['company_2'],
mode='lines',
name='Company 2'))
fig.update_layout(title='Insurer Market Share',
title_x=0.5,
xaxis_title='Underwriting Period',
yaxis_title='Policy Count',
yaxis_range=[0, 1000])
fig['layout'].update({
'title_x': 0.45,
'width': 550,
'height': 400,
'margin': {
'l':10
}
})
fig.write_html('first_figure3.html', auto_open=True)
fig = go.Figure()
fig.add_trace(go.Scatter(x=policy_count['year'], y=policy_count['company_1'],
mode='lines',
name='Company 1'))
fig.add_trace(go.Scatter(x=policy_count['year'], y=policy_count['company_2'],
mode='lines',
name='Company 2'))
fig.update_layout(title='Insurer Market Share',
title_x=0.5,
xaxis_title='Underwriting Period',
yaxis_title='Policy Count',
yaxis_range=[0, 800])
fig.write_html('first_figure6.html', auto_open=True)
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=policy_count['year'], y=policy_count['company_1_prem'],
mode='lines',
name='Company 1'))
fig2.add_trace(go.Scatter(x=policy_count['year'], y=policy_count['company_2_prem'],
mode='lines',
name='Company 2'))
fig2.update_layout(title='Average Premium per Policy',
title_x=0.5,
xaxis_title='Underwriting Period',
yaxis_title='Average Premium')
#yaxis_range=[0, 600])
fig2.write_html('first_figure7.html', auto_open=True)