# ===================================================================
# MODULE 7, LESSON 6: SUSTAINABLE FINANCE AND ESG PRODUCTS
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("SUSTAINABLE FINANCE AND ESG PRODUCTS")
print("="*70)

# ----------------------------------------------------------------
# PART A: ESG PRODUCT PORTFOLIO
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: ESG Product Portfolio")
print("-"*60)

esg_products = pd.DataFrame({
    'Product': [
        'ESG ETF',
        'Green Bond Fund',
        'Clean Energy Fund',
        'Sustainable Savings Account',
        'Green Mortgage',
        'ESG Robo-Advisor',
        'Carbon Offset Card'
    ],
    'Category': [
        'Investment', 'Investment', 'Investment', 'Deposit',
        'Lending', 'Investment', 'Payments'
    ],
    'ESG Focus': [
        'Integrated', 'Environmental', 'Environmental', 'Environmental',
        'Environmental', 'Integrated', 'Environmental'
    ],
    'Assets ($M)': [500, 300, 200, 100, 150, 250, 50],
    'Growth (%)': [25, 30, 35, 20, 15, 40, 45],
    'Fee (%)': [0.30, 0.40, 0.50, 0.10, 1.00, 0.35, 0.05]
})

print("ESG Product Portfolio:")
print(esg_products.to_string(index=False))

# Visualise
fig, axes = plt.subplots(1, 2, figsize=(14, 5))

# Assets by Product
ax = axes[0]
esg_sorted = esg_products.sort_values('Assets ($M)', ascending=True)
ax.barh(esg_sorted['Product'], esg_sorted['Assets ($M)'], color='green', alpha=0.7)
ax.set_xlabel('Assets ($M)')
ax.set_title('ESG Assets by Product')
ax.grid(True, alpha=0.3)

# Growth vs Assets
ax = axes[1]
scatter = ax.scatter(esg_products['Assets ($M)'], esg_products['Growth (%)'], 
                     s=esg_products['Assets ($M)'] * 0.5, alpha=0.7)
for i, row in esg_products.iterrows():
    ax.annotate(row['Product'], (row['Assets ($M)'] + 5, row['Growth (%)'] + 0.5))
ax.set_xlabel('Assets ($M)')
ax.set_ylabel('Growth (%)')
ax.set_title('ESG Product Growth vs Assets')
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('esg_products.png', dpi=300, bbox_inches='tight')
plt.show()
print("ESG product visualisation saved as 'esg_products.png'")

# ----------------------------------------------------------------
# PART B: ESG SCORING FRAMEWORK
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: ESG Scoring Framework")
print("-"*60)

# Define company ESG scores
companies = pd.DataFrame({
    'Company': [
        'Company A', 'Company B', 'Company C', 'Company D', 'Company E'
    ],
    'Sector': [
        'Technology', 'Energy', 'Finance', 'Healthcare', 'Consumer'
    ],
    'E_Score': [85, 45, 65, 70, 55],
    'S_Score': [80, 50, 70, 75, 60],
    'G_Score': [90, 60, 75, 80, 65]
})

# Calculate ESG score (weighted average)
weights = {'E': 0.4, 'S': 0.3, 'G': 0.3}
companies['ESG_Score'] = (
    companies['E_Score'] * weights['E'] +
    companies['S_Score'] * weights['S'] +
    companies['G_Score'] * weights['G']
).round(2)

print("Company ESG Scores:")
print(companies.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(10, 6))
companies.set_index('Company')[['E_Score', 'S_Score', 'G_Score', 'ESG_Score']].plot(kind='bar', ax=ax)
ax.set_ylabel('Score')
ax.set_title('ESG Scores by Company')
ax.legend(loc='best')
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig('esg_scores.png', dpi=300, bbox_inches='tight')
plt.show()
print("ESG scores visualisation saved as 'esg_scores.png'")

# ----------------------------------------------------------------
# PART C: ESG PORTFOLIO CONSTRUCTION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: ESG Portfolio Construction")
print("-"*60)

# Generate synthetic ESG portfolio data
np.random.seed(42)
n_companies = 100

esg_portfolio = pd.DataFrame({
    'company_id': range(1, n_companies + 1),
    'esg_score': np.random.normal(65, 15, n_companies).clip(20, 95),
    'return_expected': np.random.normal(0.08, 0.03, n_companies).clip(0.02, 0.15),
    'volatility': np.random.normal(0.15, 0.05, n_companies).clip(0.05, 0.35),
    'sector': np.random.choice(['Technology', 'Energy', 'Finance', 'Healthcare', 'Consumer', 'Utilities'], n_companies)
})

# ESG screening: include companies with ESG score > 60
esg_portfolio['pass_esg'] = esg_portfolio['esg_score'] > 60

# Portfolio options
normal_portfolio = esg_portfolio.sample(frac=0.3, random_state=42)
esg_portfolio_selected = esg_portfolio[esg_portfolio['pass_esg']].sample(frac=0.3, random_state=42)

print("ESG Portfolio Summary:")
print(f"Total Companies: {len(esg_portfolio)}")
print(f"ESG Pass Rate: {esg_portfolio['pass_esg'].mean():.2%}")
print(f"Normal Portfolio: {len(normal_portfolio)} companies")
print(f"ESG Portfolio: {len(esg_portfolio_selected)} companies")

# Compare portfolios
normal_return = normal_portfolio['return_expected'].mean()
normal_volatility = normal_portfolio['volatility'].mean()
esg_return = esg_portfolio_selected['return_expected'].mean()
esg_volatility = esg_portfolio_selected['volatility'].mean()

comparison = pd.DataFrame({
    'Portfolio': ['Normal', 'ESG'],
    'Expected Return (%)': [normal_return * 100, esg_return * 100],
    'Volatility (%)': [normal_volatility * 100, esg_volatility * 100],
    'Avg ESG Score': [
        normal_portfolio['esg_score'].mean(),
        esg_portfolio_selected['esg_score'].mean()
    ]
})

print("\nPortfolio Comparison:")
print(comparison.to_string(index=False))

# ----------------------------------------------------------------
# PART D: GREEN LOAN PRODUCT SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Green Loan Product Simulation")
print("-"*60)

class GreenLoanProduct:
    """Simulate a green loan product."""
    
    def __init__(self):
        self.loans = []
        self.green_projects = []
    
    def create_loan(self, customer_id, amount, term, purpose):
        """Create a green loan."""
        loan = {
            'loan_id': len(self.loans) + 1,
            'customer_id': customer_id,
            'amount': amount,
            'term': term,
            'purpose': purpose,
            'interest_rate': self.calculate_rate(purpose),
            'green': self.is_green(purpose),
            'created_at': datetime.now().isoformat(),
            'status': 'Active'
        }
        self.loans.append(loan)
        return loan
    
    def calculate_rate(self, purpose):
        """Calculate interest rate based on purpose."""
        base_rate = 0.05
        if purpose in ['Solar Panels', 'EV', 'Energy Efficiency', 'Green Building']:
            return base_rate - 0.01  # Green discount
        return base_rate
    
    def is_green(self, purpose):
        """Check if the loan is green."""
        green_purposes = ['Solar Panels', 'EV', 'Energy Efficiency', 'Green Building', 
                         'Sustainable Agriculture', 'Water Conservation']
        return purpose in green_purposes
    
    def get_portfolio_stats(self):
        """Get green loan portfolio statistics."""
        if not self.loans:
            return {'total_loans': 0}
        
        green_loans = [l for l in self.loans if l['green']]
        total_amount = sum(l['amount'] for l in self.loans)
        green_amount = sum(l['amount'] for l in green_loans)
        
        return {
            'total_loans': len(self.loans),
            'green_loans': len(green_loans),
            'total_amount': total_amount,
            'green_amount': green_amount,
            'green_ratio': green_amount / total_amount if total_amount > 0 else 0
        }

# Test green loan product
green_loan = GreenLoanProduct()

# Create loans
green_loan.create_loan('CUST001', 25000, 60, 'Solar Panels')
green_loan.create_loan('CUST002', 15000, 48, 'EV')
green_loan.create_loan('CUST003', 30000, 72, 'Home Renovation')
green_loan.create_loan('CUST004', 50000, 84, 'Energy Efficiency')
green_loan.create_loan('CUST005', 10000, 36, 'Car Purchase')

stats = green_loan.get_portfolio_stats()
print("Green Loan Portfolio:")
print(f"  Total Loans: {stats['total_loans']}")
print(f"  Green Loans: {stats['green_loans']}")
print(f"  Green Ratio: {stats['green_ratio']:.2%}")

# ----------------------------------------------------------------
# PART E: ESG PRODUCT METRICS
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART E: ESG Product Metrics Dashboard")
print("-"*60)

esg_metrics = pd.DataFrame({
    'Metric': [
        'ESG Assets Under Management',
        'Green Loan Portfolio',
        'ESG Fund Performance',
        'Carbon Reduction Impact',
        'ESG Client Adoption',
        'SFDR Article 8/9 Funds',
        'ESG NPS',
        'Regulatory Compliance'
    ],
    'Current Value': [
        '$1.2B',
        '$250M',
        '8.2%',
        '15,000 tonnes',
        '35%',
        '12',
        '58',
        '92%'
    ],
    'Target Value': [
        '$5.0B',
        '$1.0B',
        '> 10%',
        '50,000 tonnes',
        '> 60%',
        '25+',
        '> 70',
        '100%'
    ],
    'Status': ['🟡', '🟡', '🟡', '🟡', '🔴', '🟡', '🟡', '🟡']
})

print("ESG Product Metrics Dashboard:")
print(esg_metrics.to_string(index=False))

# ----------------------------------------------------------------
# PART F: ESG PRODUCT ROADMAP
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART F: ESG Product Roadmap")
print("-"*60)

roadmap = {
    "Phase 1 (0-6 months) – Foundation": {
        "Focus": "Build ESG product foundation.",
        "Activities": [
            "Launch ESG ETF and green bond funds.",
            "Develop ESG scoring framework.",
            "Implement green loan product.",
            "Comply with SFDR Article 8 requirements."
        ],
        "Success Metrics": ["ESG AUM > $500M", "Green loan portfolio > $100M"]
    },
    "Phase 2 (6-12 months) – Scale": {
        "Focus": "Scale ESG products.",
        "Activities": [
            "Launch ESG robo-advisory.",
            "Add sustainable savings account.",
            "Implement green mortgage product.",
            "Achieve SFDR Article 9 for select funds."
        ],
        "Success Metrics": ["ESG AUM > $1.5B", "ESG client adoption > 40%"]
    },
    "Phase 3 (12-24 months) – Expansion": {
        "Focus": "Expand ESG product range.",
        "Activities": [
            "Launch impact investing products.",
            "Implement carbon offset products.",
            "Build ESG analytics platform.",
            "Achieve industry-leading ESG credentials."
        ],
        "Success Metrics": ["ESG AUM > $3B", "Carbon reduction > 30,000 tonnes"]
    },
    "Phase 4 (24+ months) – Leadership": {
        "Focus": "Industry-leading ESG products.",
        "Activities": [
            "Launch full ESG banking suite.",
            "Build global ESG capabilities.",
            "Achieve sustainability leadership.",
            "Continuous innovation."
        ],
        "Success Metrics": ["Industry-leading ESG products", "Continuous improvement"]
    }
}

for phase, details in roadmap.items():
    print(f"\n{phase}:")
    print(f"  Focus: {details['Focus']}")
    print("  Activities:")
    for activity in details['Activities']:
        print(f"    • {activity}")
    print("  Success Metrics:")
    for metric in details['Success Metrics']:
        print(f"    • {metric}")

# ----------------------------------------------------------------
# PART G: SUMMARY AND RECOMMENDATIONS
# ----------------------------------------------------------------

print("\n" + "="*70)
print("PART G: Summary and Recommendations")
print("="*70)

print("""
Sustainable Finance and ESG Products – Key Takeaways:

1. Sustainable finance integrates ESG criteria into financial services.
2. ESG pillars: Environmental, Social, Governance.
3. Key frameworks: GRI, SASB, TCFD, ISSB, EU Taxonomy, SFDR.
4. ESG products: green loans, ESG funds, green bonds, impact investing.
5. Investment strategies: negative screening, positive screening, ESG integration, impact investing.
6. Regulatory landscape: SFDR, EU Taxonomy, CSRD, TCFD.
7. Key metrics: ESG AUM, green loan portfolio, ESG performance, carbon reduction.

Recommendations:
  - Launch ESG ETF and green bond funds.
  - Implement green loan products.
  - Develop ESG scoring and analytics.
  - Comply with SFDR and EU Taxonomy.
  - Build ESG robo-advisory capabilities.
  - Continuously innovate and improve ESG products.
""")

print("="*70)
print("END OF LESSON 6 – MODULE 7")
print("="*70)