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class CapitalAdequacyOptimizer:
    """
    Financial services capital adequacy and regulatory compliance optimization
    """

    def __init__(self, current_capital_ratios: dict, regulatory_minimums: dict):
        self.current_ratios = current_capital_ratios
        self.regulatory_mins = regulatory_minimums

    def optimize_capital_allocation(self, growth_targets: dict, stress_scenarios: list) -> dict:
        """
        Optimize capital allocation for growth while maintaining regulatory compliance
        """
        # Capital requirement calculations
        tier1_requirement = self.calculate_tier1_requirement(growth_targets)
        total_capital_requirement = self.calculate_total_capital_requirement(growth_targets)

        # Stress test validation
        stress_test_results = self.run_stress_scenarios(stress_scenarios, growth_targets)

        # Optimization with regulatory constraints
        optimal_capital_plan = self.optimize_with_constraints(
            tier1_requirement, total_capital_requirement, stress_test_results
        )

        return {
            'optimal_capital_allocation': optimal_capital_plan,
            'projected_capital_ratios': self.calculate_projected_ratios(optimal_capital_plan),
            'stress_test_performance': stress_test_results,
            'regulatory_compliance_score': self.calculate_compliance_score(optimal_capital_plan),
            'growth_feasibility': self.assess_growth_feasibility(optimal_capital_plan, growth_targets)
        }

    def calculate_tier1_requirement(self, growth_targets: dict) -> float:
        """Calculate Tier 1 capital requirement based on growth targets"""
        risk_weighted_assets = growth_targets.get('loan_growth', 0) * 1.0  # Simplified RWA calculation
        return risk_weighted_assets * self.regulatory_mins.get('tier1_ratio', 0.06)

    def run_stress_scenarios(self, scenarios: list, growth_targets: dict) -> dict:
        """Run regulatory stress test scenarios"""
        stress_results = {}

        for scenario in scenarios:
            # Simplified stress test calculation
            credit_losses = scenario.get('credit_loss_rate', 0.02) * growth_targets.get('loan_portfolio', 0)
            market_losses = scenario.get('market_shock', 0.1) * growth_targets.get('trading_assets', 0)
            operational_losses = scenario.get('operational_loss', 0.01) * growth_targets.get('total_assets', 0)

            total_losses = credit_losses + market_losses + operational_losses
            post_stress_capital = self.current_ratios.get('total_capital', 0) - total_losses

            stress_results[scenario['name']] = {
                'total_losses': total_losses,
                'post_stress_capital': post_stress_capital,
                'capital_adequacy_post_stress': post_stress_capital / growth_targets.get('total_assets', 1),
                'passes_stress_test': post_stress_capital > (growth_targets.get('total_assets', 0) * 0.08)
            }

        return stress_results

# Example: Regional bank strategic requirements
bank_requirements = {
    'capital_growth_targets': {
        'tier1_ratio_target': 0.12,  # 12% Tier 1 capital ratio target
        'total_capital_ratio_target': 0.15,  # 15% total capital ratio target
        'leverage_ratio_target': 0.08,  # 8% leverage ratio target
        'loan_growth_target': 0.15  # 15% annual loan growth
    },
    'profitability_targets': {
        'return_on_equity_target': 0.12,  # 12% ROE target
        'return_on_assets_target': 0.011,  # 1.1% ROA target
        'net_interest_margin_target': 0.035,  # 3.5% NIM target
        'efficiency_ratio_target': 0.65  # 65% efficiency ratio target
    },
    'risk_management_requirements': {
        'credit_loss_rate_max': 0.008,  # Maximum 0.8% credit loss rate
        'concentration_limits': {
            'single_borrower': 0.15,  # 15% of capital to single borrower
            'real_estate': 0.80,  # 80% of capital in real estate
            'commercial': 0.90  # 90% of capital in commercial loans
        }
    },
    'regulatory_compliance_targets': {
        'cra_rating_target': 'Satisfactory',  # Community Reinvestment Act rating
        'bsr_rating_target': 2,  # Bank Supervisory Rating (1-5, lower better)
        'consumer_complaint_rate_max': 0.02,  # Maximum 2% consumer complaint rate
        'examination_findings_max': 5  # Maximum regulatory examination findings
    }
}

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class CreditRiskOptimizer:
    """
    Credit risk and loan portfolio optimization for financial institutions
    """

    def __init__(self, current_portfolio: dict, market_conditions: dict):
        self.portfolio = current_portfolio
        self.market_conditions = market_conditions

    def optimize_loan_portfolio(self, risk_appetite: dict, growth_targets: dict) -> dict:
        """
        Optimize loan portfolio allocation across segments and risk categories
        """
        # Portfolio segments
        segments = ['commercial_real_estate', 'commercial_industrial', 'consumer', 'residential_mortgage']

        # Risk-return optimization
        expected_returns = self.calculate_segment_returns(segments)
        risk_matrix = self.calculate_risk_correlations(segments)

        # Optimization with regulatory constraints
        optimal_allocation = self.solve_portfolio_optimization(
            expected_returns, risk_matrix, risk_appetite, growth_targets
        )

        return {
            'optimal_portfolio_allocation': optimal_allocation,
            'expected_portfolio_return': self.calculate_portfolio_return(optimal_allocation, expected_returns),
            'portfolio_risk_metrics': self.calculate_risk_metrics(optimal_allocation, risk_matrix),
            'regulatory_compliance': self.validate_regulatory_limits(optimal_allocation),
            'stress_test_performance': self.stress_test_portfolio(optimal_allocation)
        }

    def calculate_segment_returns(self, segments: list) -> dict:
        """Calculate expected returns by loan segment"""
        base_rates = {
            'commercial_real_estate': 0.055,
            'commercial_industrial': 0.065,
            'consumer': 0.085,
            'residential_mortgage': 0.045
        }

        # Adjust for market conditions
        market_adjustment = self.market_conditions.get('rate_environment', 1.0)

        return {segment: base_rate * market_adjustment
                for segment, base_rate in base_rates.items()}

    def stress_test_portfolio(self, allocation: dict) -> dict:
        """Run stress tests on optimized portfolio"""
        stress_scenarios = [
            {'name': 'recession', 'loss_multiplier': 3.0, 'probability': 0.15},
            {'name': 'real_estate_decline', 'loss_multiplier': 2.5, 'probability': 0.20},
            {'name': 'interest_rate_shock', 'loss_multiplier': 1.8, 'probability': 0.25}
        ]

        stress_results = {}
        for scenario in stress_scenarios:
            total_losses = 0
            for segment, allocation_amount in allocation.items():
                base_loss_rate = self.portfolio.get(segment, {}).get('historical_loss_rate', 0.01)
                stressed_loss_rate = base_loss_rate * scenario['loss_multiplier']
                segment_loss = allocation_amount * stressed_loss_rate
                total_losses += segment_loss

            stress_results[scenario['name']] = {
                'total_losses': total_losses,
                'loss_rate': total_losses / sum(allocation.values()),
                'passes_stress_test': total_losses < (sum(allocation.values()) * 0.05),  # 5% loss tolerance
                'probability': scenario['probability']
            }

        return stress_results

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class InvestmentManagementRequirements:
    """
    Investment management strategic requirements with fiduciary focus
    """

    def __init__(self, aum_targets: dict, client_segments: dict):
        self.aum_targets = aum_targets
        self.client_segments = client_segments

    def define_performance_requirements(self, benchmark_universe: dict) -> dict:
        """
        Define performance requirements by client segment and investment strategy
        """
        performance_targets = {}

        for segment, segment_data in self.client_segments.items():
            risk_tolerance = segment_data['risk_tolerance']
            time_horizon = segment_data['time_horizon']

            # Performance targets based on risk tolerance and time horizon
            if risk_tolerance == 'conservative':
                target_return = benchmark_universe['fixed_income'] + 0.01  # +100bp over fixed income
                target_volatility = 0.06  # 6% maximum volatility
                max_drawdown = 0.05  # 5% maximum drawdown
            elif risk_tolerance == 'moderate':
                target_return = benchmark_universe['balanced'] + 0.015  # +150bp over balanced
                target_volatility = 0.10  # 10% maximum volatility
                max_drawdown = 0.08  # 8% maximum drawdown
            elif risk_tolerance == 'aggressive':
                target_return = benchmark_universe['equity'] + 0.02  # +200bp over equity
                target_volatility = 0.15  # 15% maximum volatility
                max_drawdown = 0.12  # 12% maximum drawdown

            performance_targets[segment] = {
                'annual_return_target': target_return,
                'volatility_limit': target_volatility,
                'max_drawdown_limit': max_drawdown,
                'sharpe_ratio_target': target_return / target_volatility,
                'tracking_error_limit': 0.04,  # 4% tracking error limit
                'information_ratio_target': 0.5  # 0.5 information ratio target
            }

        return performance_targets

    def define_client_satisfaction_requirements(self) -> dict:
        """
        Define client satisfaction and retention requirements
        """
        return {
            'client_retention_targets': {
                'high_net_worth': 0.95,  # 95% annual retention for HNW clients
                'institutional': 0.90,   # 90% annual retention for institutional clients
                'retail': 0.85           # 85% annual retention for retail clients
            },
            'satisfaction_score_targets': {
                'overall_satisfaction': 4.2,     # 4.2/5.0 overall satisfaction
                'performance_satisfaction': 4.0,  # 4.0/5.0 performance satisfaction
                'service_satisfaction': 4.3,     # 4.3/5.0 service satisfaction
                'communication_satisfaction': 4.1 # 4.1/5.0 communication satisfaction
            },
            'business_development_targets': {
                'new_client_acquisition_rate': 0.20,    # 20% new client acquisition annually
                'aum_growth_rate': 0.15,                # 15% AUM growth annually
                'revenue_per_client_growth': 0.08,      # 8% revenue per client growth
                'client_referral_rate': 0.25            # 25% of new clients from referrals
            }
        }

# Example: Asset management firm requirements
asset_manager_requirements = {
    'aum_growth_targets': {
        'total_aum_target': 5000000000,  # $5B AUM target
        'organic_growth_rate': 0.15,    # 15% organic growth
        'acquisition_growth_rate': 0.05, # 5% growth through acquisitions
        'client_retention_rate': 0.92   # 92% client retention rate
    },
    'performance_requirements': {
        'benchmark_outperformance': {
            'equity_strategies': 0.02,   # +200bp vs benchmark
            'fixed_income_strategies': 0.01, # +100bp vs benchmark
            'alternative_strategies': 0.03   # +300bp vs benchmark
        },
        'risk_adjusted_performance': {
            'sharpe_ratio_target': 1.2,      # 1.2 Sharpe ratio
            'sortino_ratio_target': 1.5,     # 1.5 Sortino ratio
            'maximum_drawdown': 0.08         # 8% maximum drawdown
        }
    },
    'operational_excellence': {
        'cost_ratio_targets': {
            'total_expense_ratio_max': 0.015, # 1.5% maximum expense ratio
            'operational_cost_ratio': 0.008,  # 0.8% operational costs
            'technology_investment': 0.003    # 0.3% technology investment
        },
        'service_quality_targets': {
            'trade_execution_quality': 0.95,  # 95% trades executed at best price
            'reporting_accuracy': 0.998,      # 99.8% reporting accuracy
            'client_service_response_time': 2  # 2-hour response time
        }
    }
}

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class ClinicalQualityOptimizer:
    """
    Healthcare clinical quality and patient outcome optimization
    """

    def __init__(self, quality_baseline: dict, patient_population: dict):
        self.baseline_metrics = quality_baseline
        self.patient_population = patient_population

    def optimize_quality_initiatives(self, improvement_targets: dict, resource_constraints: dict) -> dict:
        """
        Optimize clinical quality improvement initiatives for maximum patient benefit
        """
        # Quality improvement opportunities
        initiatives = [
            {'name': 'infection_prevention', 'cost': 500000, 'impact_factor': 0.8, 'patient_benefit': 0.95},
            {'name': 'medication_safety', 'cost': 300000, 'impact_factor': 0.7, 'patient_benefit': 0.85},
            {'name': 'patient_experience', 'cost': 200000, 'impact_factor': 0.6, 'patient_benefit': 0.75},
            {'name': 'care_coordination', 'cost': 400000, 'impact_factor': 0.75, 'patient_benefit': 0.80}
        ]

        # Optimization with budget constraints
        optimal_portfolio = self.solve_quality_optimization(
            initiatives, improvement_targets, resource_constraints
        )

        return {
            'optimal_initiative_portfolio': optimal_portfolio,
            'projected_quality_improvements': self.calculate_quality_impact(optimal_portfolio),
            'patient_outcome_projections': self.project_patient_outcomes(optimal_portfolio),
            'cost_effectiveness_analysis': self.calculate_cost_effectiveness(optimal_portfolio),
            'regulatory_compliance_impact': self.assess_regulatory_impact(optimal_portfolio)
        }

    def calculate_quality_impact(self, initiative_portfolio: list) -> dict:
        """Calculate quality metric improvements from initiative portfolio"""
        quality_improvements = {}

        base_metrics = {
            'hospital_acquired_infection_rate': 0.045,  # 4.5% baseline
            'medication_error_rate': 0.025,            # 2.5% baseline
            'patient_satisfaction_score': 3.8,         # 3.8/5.0 baseline
            'readmission_rate': 0.12,                  # 12% baseline
            'mortality_rate': 0.018                    # 1.8% baseline
        }

        for initiative in initiative_portfolio:
            if initiative['selected']:
                impact_factor = initiative['impact_factor']

                # Apply improvements based on initiative type
                if initiative['name'] == 'infection_prevention':
                    base_metrics['hospital_acquired_infection_rate'] *= (1 - impact_factor * 0.4)
                elif initiative['name'] == 'medication_safety':
                    base_metrics['medication_error_rate'] *= (1 - impact_factor * 0.5)
                elif initiative['name'] == 'patient_experience':
                    base_metrics['patient_satisfaction_score'] += impact_factor * 0.8
                elif initiative['name'] == 'care_coordination':
                    base_metrics['readmission_rate'] *= (1 - impact_factor * 0.25)

        return {
            'improved_metrics': base_metrics,
            'improvement_percentages': {
                metric: ((self.baseline_metrics.get(metric, 0) - improved_value) /
                        self.baseline_metrics.get(metric, 1)) * 100
                for metric, improved_value in base_metrics.items()
            }
        }

    def project_patient_outcomes(self, initiative_portfolio: list) -> dict:
        """Project patient outcome improvements"""
        patient_population_size = self.patient_population.get('annual_admissions', 10000)

        # Calculate lives impacted
        lives_saved = 0
        complications_prevented = 0
        satisfaction_improvements = 0

        for initiative in initiative_portfolio:
            if initiative['selected']:
                patient_benefit = initiative['patient_benefit']

                if initiative['name'] == 'infection_prevention':
                    infections_prevented = patient_population_size * 0.045 * patient_benefit * 0.4
                    lives_saved += infections_prevented * 0.05  # 5% of infections are life-threatening
                    complications_prevented += infections_prevented * 0.8

                elif initiative['name'] == 'medication_safety':
                    errors_prevented = patient_population_size * 0.025 * patient_benefit * 0.5
                    lives_saved += errors_prevented * 0.02  # 2% of medication errors are life-threatening
                    complications_prevented += errors_prevented * 0.3

                elif initiative['name'] == 'patient_experience':
                    satisfaction_improvements += patient_population_size * patient_benefit * 0.2

                elif initiative['name'] == 'care_coordination':
                    readmissions_prevented = patient_population_size * 0.12 * patient_benefit * 0.25
                    complications_prevented += readmissions_prevented * 0.6

        return {
            'estimated_lives_saved': lives_saved,
            'complications_prevented': complications_prevented,
            'patients_with_improved_experience': satisfaction_improvements,
            'total_patient_benefit_score': lives_saved * 10 + complications_prevented * 2 + satisfaction_improvements * 1
        }

# Example: Hospital system strategic requirements
hospital_requirements = {
    'clinical_quality_targets': {
        'patient_safety_targets': {
            'hospital_acquired_infection_rate_max': 0.020,  # <2% HAI rate
            'medication_error_rate_max': 0.015,            # <1.5% medication error rate
            'falls_with_injury_rate_max': 0.008,           # <0.8% falls with injury
            'pressure_ulcer_rate_max': 0.025               # <2.5% pressure ulcer rate
        },
        'clinical_outcome_targets': {
            'mortality_rate_max': 0.015,                   # <1.5% mortality rate
            'readmission_rate_max': 0.10,                  # <10% readmission rate
            'average_length_of_stay_max': 4.2,             # <4.2 days average LOS
            'emergency_department_wait_time_max': 30       # <30 minutes ED wait time
        },
        'patient_experience_targets': {
            'overall_satisfaction_min': 4.2,               # >4.2/5.0 overall satisfaction
            'likelihood_to_recommend_min': 4.0,            # >4.0/5.0 likelihood to recommend
            'communication_score_min': 4.1,                # >4.1/5.0 communication score
            'pain_management_score_min': 3.9               # >3.9/5.0 pain management score
        }
    },
    'operational_efficiency_targets': {
        'financial_performance': {
            'operating_margin_target': 0.04,               # 4% operating margin
            'cost_per_patient_day_max': 2500,              # <$2,500 cost per patient day
            'revenue_per_patient_target': 12000,           # $12,000 revenue per patient
            'bad_debt_rate_max': 0.03                      # <3% bad debt rate
        },
        'resource_utilization': {
            'bed_occupancy_rate_target': 0.85,             # 85% bed occupancy rate
            'or_utilization_rate_target': 0.80,            # 80% OR utilization rate
            'staff_productivity_index_target': 1.1,        # 110% staff productivity index
            'equipment_utilization_rate_target': 0.75      # 75% equipment utilization rate
        }
    },
    'regulatory_compliance_targets': {
        'accreditation_scores': {
            'joint_commission_score_min': 90,              # >90% Joint Commission score
            'cms_star_rating_target': 4,                   # 4-star CMS rating
            'state_inspection_score_min': 95,              # >95% state inspection score
            'safety_grade_target': 'A'                     # 'A' safety grade
        },
        'compliance_metrics': {
            'hipaa_violation_rate_max': 0.001,             # <0.1% HIPAA violation rate
            'regulatory_citation_rate_max': 0.02,          # <2% regulatory citation rate
            'staff_compliance_training_rate_min': 0.98     # >98% staff compliance training rate
        }
    }
}

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class TechnologyPlatformOptimizer:
    """
    Technology platform and innovation optimization for technology companies
    """

    def __init__(self, current_capabilities: dict, market_dynamics: dict):
        self.capabilities = current_capabilities
        self.market_dynamics = market_dynamics

    def optimize_platform_investment(self, innovation_targets: dict, resource_constraints: dict) -> dict:
        """
        Optimize platform development investment for maximum competitive advantage
        """
        # Platform development opportunities
        platform_initiatives = [
            {
                'name': 'ai_ml_platform',
                'investment': 5000000,
                'development_time': 18,
                'competitive_advantage': 0.8,
                'revenue_potential': 15000000,
                'market_timing_factor': 0.9
            },
            {
                'name': 'api_ecosystem',
                'investment': 2000000,
                'development_time': 12,
                'competitive_advantage': 0.6,
                'revenue_potential': 8000000,
                'market_timing_factor': 0.95
            },
            {
                'name': 'security_platform',
                'investment': 3000000,
                'development_time': 15,
                'competitive_advantage': 0.7,
                'revenue_potential': 10000000,
                'market_timing_factor': 0.85
            },
            {
                'name': 'analytics_platform',
                'investment': 4000000,
                'development_time': 20,
                'competitive_advantage': 0.75,
                'revenue_potential': 12000000,
                'market_timing_factor': 0.8
            }
        ]

        # Optimization with resource and time constraints
        optimal_portfolio = self.solve_platform_optimization(
            platform_initiatives, innovation_targets, resource_constraints
        )

        return {
            'optimal_platform_portfolio': optimal_portfolio,
            'projected_competitive_advantage': self.calculate_competitive_impact(optimal_portfolio),
            'revenue_projections': self.project_platform_revenues(optimal_portfolio),
            'time_to_market_analysis': self.analyze_time_to_market(optimal_portfolio),
            'resource_utilization_efficiency': self.calculate_resource_efficiency(optimal_portfolio)
        }

    def calculate_competitive_impact(self, platform_portfolio: list) -> dict:
        """Calculate competitive advantage impact of platform portfolio"""
        total_advantage = 0
        platform_synergies = 0

        selected_platforms = [p for p in platform_portfolio if p['selected']]

        for platform in selected_platforms:
            advantage_contribution = platform['competitive_advantage'] * platform['market_timing_factor']
            total_advantage += advantage_contribution

        # Calculate platform synergies
        if len(selected_platforms) >= 2:
            ai_and_analytics = any(p['name'] in ['ai_ml_platform', 'analytics_platform'] for p in selected_platforms)
            api_ecosystem = any(p['name'] == 'api_ecosystem' for p in selected_platforms)

            if ai_and_analytics and api_ecosystem:
                platform_synergies += 0.15  # 15% synergy bonus

        return {
            'total_competitive_advantage_score': total_advantage + platform_synergies,
            'platform_synergy_bonus': platform_synergies,
            'market_leadership_probability': min((total_advantage + platform_synergies) / 2.0, 0.95),
            'competitive_moat_strength': self.assess_moat_strength(selected_platforms)
        }

    def assess_moat_strength(self, selected_platforms: list) -> dict:
        """Assess competitive moat strength from platform portfolio"""
        moat_factors = {
            'network_effects': 0,
            'switching_costs': 0,
            'data_advantages': 0,
            'technology_barriers': 0
        }

        for platform in selected_platforms:
            if platform['name'] == 'ai_ml_platform':
                moat_factors['data_advantages'] += 0.4
                moat_factors['technology_barriers'] += 0.3

            elif platform['name'] == 'api_ecosystem':
                moat_factors['network_effects'] += 0.5
                moat_factors['switching_costs'] += 0.3

            elif platform['name'] == 'security_platform':
                moat_factors['switching_costs'] += 0.4
                moat_factors['technology_barriers'] += 0.2

            elif platform['name'] == 'analytics_platform':
                moat_factors['data_advantages'] += 0.3
                moat_factors['technology_barriers'] += 0.2

        # Calculate overall moat strength
        moat_strength = sum(moat_factors.values()) / 4.0

        return {
            'moat_factors': moat_factors,
            'overall_moat_strength': moat_strength,
            'moat_sustainability': moat_strength * 0.8,  # Sustainability factor
            'competitive_protection_score': min(moat_strength * 1.2, 1.0)
        }

# Example: SaaS technology company requirements
saas_company_requirements = {
    'growth_and_scale_targets': {
        'revenue_growth_targets': {
            'annual_recurring_revenue_target': 100000000,  # $100M ARR target
            'growth_rate_target': 0.50,                    # 50% YoY growth
            'new_customer_acquisition_target': 1000,       # 1,000 new customers annually
            'expansion_revenue_rate_target': 0.25          # 25% expansion revenue rate
        },
        'platform_scale_targets': {
            'monthly_active_users_target': 1000000,        # 1M MAU target
            'platform_uptime_target': 0.9995,             # 99.95% uptime
            'api_calls_per_day_target': 10000000,         # 10M API calls daily
            'data_processing_capacity_target': 1000000000 # 1B data points processed daily
        }
    },
    'innovation_and_technology_targets': {
        'product_development_metrics': {
            'feature_release_velocity': 52,                # 52 feature releases annually (weekly)
            'customer_feature_adoption_rate': 0.60,        # 60% feature adoption rate
            'time_to_market_target': 90,                   # 90 days average time to market
            'innovation_pipeline_value': 50000000          # $50M innovation pipeline value
        },
        'technology_excellence_metrics': {
            'code_quality_score_target': 0.90,             # 90% code quality score
            'automated_test_coverage_target': 0.85,        # 85% automated test coverage
            'security_vulnerability_rate_max': 0.001,      # <0.1% security vulnerability rate
            'technical_debt_ratio_max': 0.15              # <15% technical debt ratio
        }
    },
    'customer_success_and_retention': {
        'customer_satisfaction_metrics': {
            'net_promoter_score_target': 60,               # NPS score of 60
            'customer_satisfaction_score_target': 4.3,     # 4.3/5.0 satisfaction score
            'customer_success_score_target': 0.85,         # 85% customer success score
            'support_response_time_target': 2              # 2-hour support response time
        },
        'retention_and_expansion_metrics': {
            'gross_revenue_retention_target': 0.95,        # 95% gross revenue retention
            'net_revenue_retention_target': 1.15,          # 115% net revenue retention
            'customer_lifetime_value_target': 50000,       # $50K average customer LTV
            'churn_rate_max': 0.05                        # <5% annual churn rate
        }
    }
}