Profitability Optimizer

def optimize_yield(asset_portfolio: str, constraints: str = "{}") -> str:
    """
    Optimize asset yield within risk and liquidity constraints.
    
    Args:
        asset_portfolio: Current asset portfolio with yields and amounts
        constraints: Risk and liquidity constraints(JSON)
    
    Returns:
        Yield optimization recommendations
    """
    try:
        import json
        
        if isinstance(asset_portfolio, str):
            try:
                portfolio = json.loads(asset_portfolio)
            except json.JSONDecodeError:
                return "Error: Invalid JSON format for asset portfolio"
        else:
            portfolio = asset_portfolio
        
        if isinstance(constraints, str):
            try:
                const = json.loads(constraints)
            except json.JSONDecodeError:
                const = {}
        else:
            const = constraints
        
        assets = portfolio.get('assets', [])
        total_assets = sum(a.get('amount', 0) for a in assets)
        
        "color: #6b7280;"># Constraints
        min_liquidity_buffer = const.get('min_liquidity_buffer', 0.10)  "color: #6b7280;"># 10% default
        max_concentration = const.get('max_concentration', 0.25)  "color: #6b7280;"># 25% default
        
        "color: #6b7280;"># Calculate current metrics
        current_yield = sum(
            a.get('amount', 0) * a.get('yield', 0.05) for a in assets
        ) / total_assets if total_assets > 0 else 0
        
        liquid_assets = sum(
            a.get('amount', 0) for a in assets if a.get('liquid', False)
        )
        liquidity_ratio = liquid_assets / total_assets if total_assets > 0 else 0
        
        report = "=== YIELD OPTIMIZATION ANALYSIS ===\n\n"
        report += f"Current Portfolio Yield: {current_yield*100:.2f}%\n"
        report += f"Total Assets: {total_assets:,.2f}\n"
        report += f"Liquidity Buffer: {liquidity_ratio*100:.2f}% (Minimum: {min_liquidity_buffer*100:.0f}%)\n\n"
        
        report += "=== OPTIMIZATION RECOMMENDATIONS ===\n\n"
        
        "color: #6b7280;"># Check liquidity buffer
        if liquidity_ratio < min_liquidity_buffer:
            report += f"⚠️ Liquidity buffer below minimum requirement({liquidity_ratio*100:.2f}% < {min_liquidity_buffer*100:.0f}%).\n"
            report += "Recommendation: Increase allocation to liquid assets before pursuing yield optimization.\n\n"
        else:
            excess_liquidity = (liquidity_ratio - min_liquidity_buffer) * total_assets
            report += f"✓ Excess liquidity available: {excess_liquidity:,.2f}\n"
            report += "Recommendation: Consider reallocating excess liquidity to higher-yielding assets:\n"
            report += "  - Corporate bonds(investment grade)\n"
            report += "  - Medium-term government securities\n"
            report += "  - Diversified fixed-income funds\n"
            report += f"  Potential yield improvement: +0.50% to +1.50%\n\n"
        
        "color: #6b7280;"># Check concentration
        for asset in assets:
            asset_concentration = asset.get('amount', 0) / total_assets if total_assets > 0 else 0
            if asset_concentration > max_concentration:
                asset_name = asset.get('name', 'Unknown Asset')
                report += f"⚠️ Concentration risk: {asset_name} represents {asset_concentration*100:.1f}% of total assets(Max: {max_concentration*100:.0f}%).\n"
                report += f"Recommendation: Reduce {asset_name} allocation and diversify.\n\n"
        
        "color: #6b7280;"># Overall strategy
        report += "=== OVERALL STRATEGY ===\n"
        report += "1. Maintain minimum liquidity buffer at all times\n"
        report += "2. Avoid over-concentration(>25% in single asset class)\n"
        report += "3. Prioritize predictable cash flows aligned with liquidity needs\n"
        report += "4. Rebalance portfolio regularly based on market conditions\n"
        
        return report
        
    except Exception as e:
        return f"Error optimizing yield: {str(e)}"

def optimize_funding_cost(liability_structure: str, market_rates: str = "{}") -> str:
    """
    Optimize funding cost through liability structure adjustment.
    
    Args:
        liability_structure: Current liability mix with costs and maturities
        market_rates: Current market rates for different funding instruments
    
    Returns:
        Funding cost optimization recommendations
    """
    try:
        import json
        
        if isinstance(liability_structure, str):
            try:
                liabilities = json.loads(liability_structure)
            except json.JSONDecodeError:
                return "Error: Invalid JSON format for liability structure"
        else:
            liabilities = liability_structure
        
        if isinstance(market_rates, str):
            try:
                rates = json.loads(market_rates)
            except json.JSONDecodeError:
                rates = {}
        else:
            rates = market_rates
        
        liabs = liabilities.get('liabilities', [])
        total_liabilities = sum(l.get('amount', 0) for l in liabs)
        
        "color: #6b7280;"># Calculate weighted average funding cost
        current_funding_cost = sum(
            l.get('amount', 0) * l.get('rate', 0.03) for l in liabs
        ) / total_liabilities if total_liabilities > 0 else 0
        
        "color: #6b7280;"># Calculate structure mix
        short_term = sum(l.get('amount', 0) for l in liabs if l.get('maturity', '1Y+') in ['0-1M', '1-3M', '3-6M'])
        short_term_pct = short_term / total_liabilities * 100 if total_liabilities > 0 else 0
        
        report = "=== FUNDING COST OPTIMIZATION ===\n\n"
        report += f"Current Weighted Average Funding Cost: {current_funding_cost*100:.2f}%\n"
        report += f"Total Liabilities: {total_liabilities:,.2f}\n"
        report += f"Short-term Funding(<6M): {short_term_pct:.1f}% (Recommended: <15%)\n\n"
        
        report += "Funding Structure:\n"
        report += f"{'Type':<25} {'Amount':>15} {'Rate':>10} {'% of Total':>12}\n"
        report += "-" * 62 + "\n"
        
        funding_types = {}
        for liab in liabs:
            liab_type = liab.get('type', 'Other')
            if liab_type not in funding_types:
                funding_types[liab_type] = {'amount': 0, 'cost': 0}
            funding_types[liab_type]['amount'] += liab.get('amount', 0)
            funding_types[liab_type]['cost'] += liab.get('amount', 0) * liab.get('rate', 0.03)
        
        for liab_type, data in funding_types.items():
            avg_rate = data['cost'] / data['amount'] if data['amount'] > 0 else 0
            pct_total = data['amount'] / total_liabilities * 100 if total_liabilities > 0 else 0
            report += f"{liab_type:<25} {data['amount']:>15,.2f} {avg_rate*100:>9.2f}% {pct_total:>11.1f}%\n"
        
        report += "\n=== OPTIMIZATION RECOMMENDATIONS ===\n\n"
        
        "color: #6b7280;"># Check short-term concentration
        if short_term_pct > 15:
            report += f"⚠️ High short-term funding concentration({short_term_pct:.1f}%).\n"
            report += "Recommendation: Extend funding maturities to reduce refinancing risk.\n"
            report += "  - Replace short-term wholesale with term deposits\n"
            report += "  - Issue longer-term funding instruments\n\n"
        
        "color: #6b7280;"># Funding mix recommendations
        retail_deposits = funding_types.get('retail_deposits', {}).get('amount', 0)
        retail_pct = retail_deposits / total_liabilities * 100 if total_liabilities > 0 else 0
        
        if retail_pct < 40:
            report += "💡 Opportunity: Increase retail deposit base(currently {retail_pct:.1f}%).\n"
            report += "Recommendation: Retail deposits typically offer lower cost and more stable funding.\n"
            report += "  - Launch competitive deposit products\n"
            report += "  - Enhance digital banking offerings\n\n"
        
        "color: #6b7280;"># Cost reduction potential
        wholesale_pct = funding_types.get('wholesale', {}).get('amount', 0) / total_liabilities * 100 if total_liabilities > 0 else 0
        if wholesale_pct > 30:
            report += "💡 Opportunity: Reduce wholesale funding dependence.\n"
            report += "Recommendation: Diversify funding sources to reduce costs and concentration risk.\n\n"
        
        report += "=== EXPECTED IMPACT ===\n"
        report += "Potential funding cost reduction: 10-30 basis points\n"
        report += "Implementation: Gradual rebalancing over 6-12 months\n"
        report += "Monitor: Ensure changes maintain liquidity ratios and diversification\n"
        
        return report
        
    except Exception as e:
        return f"Error optimizing funding cost: {str(e)}"