Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from datetime import datetime, timedelta

st.title("Actuarial Cashflow Model Analyzer")

# Sidebar inputs
st.sidebar.header("Model Parameters")

# Basic parameters
initial_investment = st.sidebar.number_input("Initial Investment", min_value=0, value=100000)
years = st.sidebar.slider("Projection Years", min_value=1, max_value=50, value=30)
interest_rate = st.sidebar.slider("Annual Interest Rate (%)", min_value=0.0, max_value=15.0, value=5.0) / 100

# Additional parameters
mortality_rate = st.sidebar.slider("Annual Mortality Rate (%)", min_value=0.0, max_value=5.0, value=1.0) / 100
expense_ratio = st.sidebar.slider("Annual Expense Ratio (%)", min_value=0.0, max_value=3.0, value=0.5) / 100
premium_payment = st.sidebar.number_input("Annual Premium Payment", min_value=0, value=10000)

# Calculate cashflows
def calculate_cashflows(initial_investment, years, interest_rate, mortality_rate, expense_ratio, premium_payment):
    timeline = list(range(years + 1))
    fund_value = [initial_investment]
    mortality_outflow = []
    expense_outflow = []
    premium_inflow = [initial_investment]
    net_cashflow = [initial_investment]
    
    for year in range(1, years + 1):
        # Previous year's fund value
        prev_fund = fund_value[-1]
        
        # Calculate flows
        mortality = prev_fund * mortality_rate
        expenses = prev_fund * expense_ratio
        investment_return = prev_fund * interest_rate
        
        # Calculate this year's fund value
        current_fund = (prev_fund 
                       + premium_payment 
                       + investment_return 
                       - mortality 
                       - expenses)
        
        # Store values
        fund_value.append(current_fund)
        mortality_outflow.append(mortality)
        expense_outflow.append(expenses)
        premium_inflow.append(premium_payment)
        net_cashflow.append(premium_payment - mortality - expenses)
    
    return {
        'timeline': timeline,
        'fund_value': fund_value,
        'mortality_outflow': [0] + mortality_outflow,
        'expense_outflow': [0] + expense_outflow,
        'premium_inflow': premium_inflow,
        'net_cashflow': net_cashflow
    }

# Calculate results
results = calculate_cashflows(
    initial_investment,
    years,
    interest_rate,
    mortality_rate,
    expense_ratio,
    premium_payment
)

# Create plots
st.header("Fund Value Projection")
fig_fund = go.Figure()
fig_fund.add_trace(go.Scatter(
    x=results['timeline'],
    y=results['fund_value'],
    mode='lines',
    name='Fund Value'
))
fig_fund.update_layout(
    title='Fund Value Over Time',
    xaxis_title='Year',
    yaxis_title='Fund Value',
    hovermode='x'
)
st.plotly_chart(fig_fund)

st.header("Cashflow Analysis")
fig_cashflow = go.Figure()
fig_cashflow.add_trace(go.Bar(
    x=results['timeline'],
    y=results['premium_inflow'],
    name='Premium Inflow',
    marker_color='green'
))
fig_cashflow.add_trace(go.Bar(
    x=results['timeline'],
    y=[-x for x in results['mortality_outflow']],
    name='Mortality Outflow',
    marker_color='red'
))
fig_cashflow.add_trace(go.Bar(
    x=results['timeline'],
    y=[-x for x in results['expense_outflow']],
    name='Expense Outflow',
    marker_color='orange'
))
fig_cashflow.add_trace(go.Scatter(
    x=results['timeline'],
    y=results['net_cashflow'],
    name='Net Cashflow',
    line=dict(color='blue'),
    mode='lines'
))
fig_cashflow.update_layout(
    title='Annual Cashflow Components',
    xaxis_title='Year',
    yaxis_title='Amount',
    barmode='relative',
    hovermode='x'
)
st.plotly_chart(fig_cashflow)

# Summary statistics
st.header("Summary Statistics")
col1, col2, col3 = st.columns(3)

with col1:
    st.metric(
        "Final Fund Value",
        f"${results['fund_value'][-1]:,.2f}"
    )

with col2:
    total_premiums = sum(results['premium_inflow'])
    st.metric(
        "Total Premiums Paid",
        f"${total_premiums:,.2f}"
    )

with col3:
    total_return = results['fund_value'][-1] - total_premiums
    st.metric(
        "Total Return",
        f"${total_return:,.2f}",
        f"{(total_return/total_premiums)*100:.1f}%"
    )

# Display data table
st.header("Detailed Projections")
df = pd.DataFrame({
    'Year': results['timeline'],
    'Fund Value': results['fund_value'],
    'Premium Inflow': results['premium_inflow'],
    'Mortality Outflow': results['mortality_outflow'],
    'Expense Outflow': results['expense_outflow'],
    'Net Cashflow': results['net_cashflow']
})
st.dataframe(df.style.format("${:,.2f}"))