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
from dateutil.relativedelta import relativedelta

def simulate_portfolio(total_balance, avg_lifetime_months, apr, cost_of_risk, 
                      cost_of_funds, opex_ratio, acquisition_cost_ratio):
    # Monthly rates
    monthly_apr = apr / 12
    monthly_cor = cost_of_risk / 12
    monthly_cof = cost_of_funds / 12
    
    # Create monthly schedule
    months = range(int(avg_lifetime_months * 1.5))  # Extend beyond avg lifetime
    
    # Initialize arrays
    balance = []
    interest_income = []
    credit_losses = []
    funding_costs = []
    operating_costs = []
    
    # Initial balance and acquisition cost
    current_balance = total_balance
    acquisition_cost = total_balance * acquisition_cost_ratio
    
    for month in months:
        # Calculate monthly changes
        repayment = current_balance / (avg_lifetime_months - month) if month < avg_lifetime_months else current_balance
        interest = current_balance * monthly_apr
        losses = current_balance * monthly_cor
        funding = current_balance * monthly_cof
        opex = current_balance * (opex_ratio / 12)
        
        # Store results
        balance.append(current_balance)
        interest_income.append(interest)
        credit_losses.append(losses)
        funding_costs.append(funding)
        operating_costs.append(opex)
        
        # Update balance
        current_balance = max(0, current_balance - repayment)
        
        if current_balance == 0:
            break
    
    # Create results DataFrame
    results = pd.DataFrame({
        'Month': range(len(balance)),
        'Balance': balance,
        'Interest Income': interest_income,
        'Credit Losses': credit_losses,
        'Funding Costs': funding_costs,
        'Operating Costs': operating_costs
    })
    
    # Calculate cumulative P&L
    results['Monthly P&L'] = (results['Interest Income'] - 
                             results['Credit Losses'] - 
                             results['Funding Costs'] - 
                             results['Operating Costs'])
    
    results['Cumulative P&L'] = results['Monthly P&L'].cumsum() - acquisition_cost
    
    return results

st.title('Loan Portfolio Simulator')

st.sidebar.header('Portfolio Parameters')

total_balance = st.sidebar.number_input('Total Portfolio Balance', 
                                      min_value=1000000, 
                                      value=10000000,
                                      step=1000000,
                                      format='%d')

avg_lifetime = st.sidebar.number_input('Average Lifetime (months)',
                                     min_value=1,
                                     value=24,
                                     step=1)

apr = st.sidebar.number_input('Annual Percentage Rate (APR)',
                            min_value=0.0,
                            value=0.15,
                            format='%.3f')

cost_of_risk = st.sidebar.number_input('Annual Cost of Risk',
                                     min_value=0.0,
                                     value=0.03,
                                     format='%.3f')

cost_of_funds = st.sidebar.number_input('Annual Cost of Funds',
                                      min_value=0.0,
                                      value=0.04,
                                      format='%.3f')

opex_ratio = st.sidebar.number_input('Annual Operating Expense Ratio',
                                   min_value=0.0,
                                   value=0.02,
                                   format='%.3f')

acquisition_cost_ratio = st.sidebar.number_input('Acquisition Cost Ratio',
                                               min_value=0.0,
                                               value=0.01,
                                               format='%.3f')

# Run simulation
results = simulate_portfolio(
    total_balance=total_balance,
    avg_lifetime_months=avg_lifetime,
    apr=apr,
    cost_of_risk=cost_of_risk,
    cost_of_funds=cost_of_funds,
    opex_ratio=opex_ratio,
    acquisition_cost_ratio=acquisition_cost_ratio
)

# Display key metrics
st.header('Key Portfolio Metrics')
col1, col2, col3 = st.columns(3)

with col1:
    st.metric('Total Interest Income', 
              f'${results["Interest Income"].sum():,.0f}')

with col2:
    st.metric('Final Cumulative P&L', 
              f'${results["Cumulative P&L"].iloc[-1]:,.0f}')

with col3:
    roi = (results["Cumulative P&L"].iloc[-1] / total_balance) * 100
    st.metric('Return on Investment', 
              f'{roi:.1f}%')

# Create charts
st.header('Portfolio Performance Charts')

# Balance Evolution
fig_balance = go.Figure()
fig_balance.add_trace(go.Scatter(x=results['Month'], 
                                y=results['Balance'],
                                name='Portfolio Balance',
                                fill='tozeroy'))
fig_balance.update_layout(title='Portfolio Balance Evolution',
                         xaxis_title='Month',
                         yaxis_title='Balance ($)')
st.plotly_chart(fig_balance, use_container_width=True)

# P&L Components
fig_pl = go.Figure()
fig_pl.add_trace(go.Scatter(x=results['Month'], 
                           y=results['Interest Income'],
                           name='Interest Income',
                           stackgroup='positive'))
fig_pl.add_trace(go.Scatter(x=results['Month'], 
                           y=-results['Credit Losses'],
                           name='Credit Losses',
                           stackgroup='negative'))
fig_pl.add_trace(go.Scatter(x=results['Month'], 
                           y=-results['Funding Costs'],
                           name='Funding Costs',
                           stackgroup='negative'))
fig_pl.add_trace(go.Scatter(x=results['Month'], 
                           y=-results['Operating Costs'],
                           name='Operating Costs',
                           stackgroup='negative'))
fig_pl.update_layout(title='Monthly P&L Components',
                    xaxis_title='Month',
                    yaxis_title='Amount ($)')
st.plotly_chart(fig_pl, use_container_width=True)

# Cumulative P&L
fig_cumulative = go.Figure()
fig_cumulative.add_trace(go.Scatter(x=results['Month'], 
                                   y=results['Cumulative P&L'],
                                   name='Cumulative P&L',
                                   fill='tozeroy'))
fig_cumulative.update_layout(title='Cumulative P&L',
                            xaxis_title='Month',
                            yaxis_title='Cumulative P&L ($)')
st.plotly_chart(fig_cumulative, use_container_width=True)

# Display detailed results
st.header('Detailed Results')
st.dataframe(results.style.format({
    'Balance': '${:,.0f}',
    'Interest Income': '${:,.0f}',
    'Credit Losses': '${:,.0f}',
    'Funding Costs': '${:,.0f}',
    'Operating Costs': '${:,.0f}',
    'Monthly P&L': '${:,.0f}',
    'Cumulative P&L': '${:,.0f}'
}))