Ceo Advisor

#!/usr/bin/env python3
"""
Financial Scenario Analyzer - Model different business scenarios and their financial impact
"""

import json
from typing import Dict, List, Tuple
import math

class FinancialScenarioAnalyzer:
    def __init__(self):
        self.key_metrics = [
            'revenue', 'gross_margin', 'operating_expenses', 
            'ebitda', 'cash_flow', 'runway', 'valuation'
        ]
        
        self.growth_models = {
            'linear': lambda base, rate, period: base * (1 + rate * period),
            'exponential': lambda base, rate, period: base * math.pow(1 + rate, period),
            'logarithmic': lambda base, rate, period: base * (1 + rate * math.log(period + 1)),
            's_curve': lambda base, rate, period: base * (2 / (1 + math.exp(-rate * period)))
        }
    
    def analyze_scenarios(self, base_case: Dict, scenarios: List[Dict]) -> Dict:
        """Analyze multiple financial scenarios"""
        results = {
            'base_case_summary': self._summarize_financials(base_case),
            'scenario_analysis': [],
            'sensitivity_analysis': {},
            'recommendation': {},
            'risk_adjusted_view': {}
        }
        
        # Analyze each scenario
        for scenario in scenarios:
            scenario_result = self._analyze_scenario(base_case, scenario)
            results['scenario_analysis'].append(scenario_result)
        
        # Sensitivity analysis
        results['sensitivity_analysis'] = self._perform_sensitivity_analysis(
            base_case, 
            scenarios
        )
        
        # Risk-adjusted view
        results['risk_adjusted_view'] = self._calculate_risk_adjusted_returns(
            results['scenario_analysis']
        )
        
        # Generate recommendation
        results['recommendation'] = self._generate_recommendation(
            results['scenario_analysis'],
            results['risk_adjusted_view']
        )
        
        return results
    
    def _summarize_financials(self, financials: Dict) -> Dict:
        """Summarize key financial metrics"""
        revenue = financials.get('revenue', 0)
        cogs = financials.get('cogs', 0)
        opex = financials.get('operating_expenses', 0)
        
        gross_profit = revenue - cogs
        gross_margin = (gross_profit / revenue * 100) if revenue > 0 else 0
        ebitda = gross_profit - opex
        ebitda_margin = (ebitda / revenue * 100) if revenue > 0 else 0
        
        return {
            'revenue': revenue,
            'gross_profit': gross_profit,
            'gross_margin': gross_margin,
            'operating_expenses': opex,
            'ebitda': ebitda,
            'ebitda_margin': ebitda_margin,
            'cash': financials.get('cash', 0),
            'burn_rate': financials.get('burn_rate', 0),
            'runway_months': self._calculate_runway(
                financials.get('cash', 0),
                financials.get('burn_rate', 0)
            )
        }
    
    def _calculate_runway(self, cash: float, burn_rate: float) -> float:
        """Calculate months of runway"""
        if burn_rate <= 0:
            return float('inf')
        return cash / burn_rate
    
    def _analyze_scenario(self, base_case: Dict, scenario: Dict) -> Dict:
        """Analyze a single scenario"""
        name = scenario.get('name', 'Unnamed Scenario')
        probability = scenario.get('probability', 0.5)
        
        # Apply scenario changes
        projected_financials = self._apply_scenario_changes(base_case, scenario)
        
        # Calculate metrics for each year
        projections = []
        current_state = projected_financials.copy()
        
        for year in range(1, 4):  # 3-year projection
            year_projection = self._project_year(
                current_state,
                scenario,
                year
            )
            projections.append(year_projection)
            current_state = year_projection
        
        # Calculate NPV and IRR
        cash_flows = [p['free_cash_flow'] for p in projections]
        npv = self._calculate_npv(cash_flows, scenario.get('discount_rate', 0.1))
        irr = self._calculate_irr(cash_flows, base_case.get('initial_investment', 0))
        
        return {
            'name': name,
            'probability': probability,
            'projections': projections,
            'npv': npv,
            'irr': irr,
            'break_even_month': self._find_break_even(projections),
            'total_return': self._calculate_total_return(projections, base_case),
            'key_assumptions': scenario.get('assumptions', [])
        }
    
    def _apply_scenario_changes(self, base_case: Dict, scenario: Dict) -> Dict:
        """Apply scenario changes to base case"""
        result = base_case.copy()
        changes = scenario.get('changes', {})
        
        for key, change in changes.items():
            if key in result:
                if isinstance(change, dict):
                    # Relative change
                    if 'multiply' in change:
                        result[key] *= change['multiply']
                    elif 'add' in change:
                        result[key] += change['add']
                else:
                    # Absolute change
                    result[key] = change
        
        return result
    
    def _project_year(self, current_state: Dict, scenario: Dict, year: int) -> Dict:
        """Project financials for a specific year"""
        growth_model = scenario.get('growth_model', 'exponential')
        growth_rate = scenario.get('growth_rate', 0.3)
        
        # Apply growth model
        model_func = self.growth_models.get(growth_model, self.growth_models['linear'])
        
        revenue = model_func(
            current_state.get('revenue', 0),
            growth_rate,
            year
        )
        
        # Scale other metrics
        cogs = revenue * scenario.get('cogs_ratio', 0.3)
        opex = current_state.get('operating_expenses', 0) * (1 + scenario.get('opex_growth', 0.15))
        
        gross_profit = revenue - cogs
        ebitda = gross_profit - opex
        
        # Calculate free cash flow (simplified)
        capex = revenue * scenario.get('capex_ratio', 0.05)
        working_capital_change = (revenue - current_state.get('revenue', 0)) * 0.1
        free_cash_flow = ebitda - capex - working_capital_change
        
        return {
            'year': year,
            'revenue': revenue,
            'gross_profit': gross_profit,
            'gross_margin': (gross_profit / revenue * 100) if revenue > 0 else 0,
            'operating_expenses': opex,
            'ebitda': ebitda,
            'ebitda_margin': (ebitda / revenue * 100) if revenue > 0 else 0,
            'free_cash_flow': free_cash_flow,
            'cumulative_cash_flow': current_state.get('cumulative_cash_flow', 0) + free_cash_flow
        }
    
    def _calculate_npv(self, cash_flows: List[float], discount_rate: float) -> float:
        """Calculate Net Present Value"""
        npv = 0
        for i, cf in enumerate(cash_flows):
            npv += cf / math.pow(1 + discount_rate, i + 1)
        return npv
    
    def _calculate_irr(self, cash_flows: List[float], initial_investment: float) -> float:
        """Calculate Internal Rate of Return (simplified)"""
        if not cash_flows or initial_investment == 0:
            return 0
        
        # Simple IRR approximation
        total_return = sum(cash_flows)
        years = len(cash_flows)
        
        if initial_investment > 0:
            return math.pow(total_return / initial_investment, 1/years) - 1
        return 0
    
    def _find_break_even(self, projections: List[Dict]) -> int:
        """Find break-even month"""
        months = 0
        for projection in projections:
            months += 12
            if projection.get('ebitda', 0) > 0:
                # Interpolate to find exact month
                if months == 12:
                    return months
                prev_ebitda = projections[projection['year']-2].get('ebitda', 0) if projection['year'] > 1 else 0
                monthly_improvement = (projection['ebitda'] - prev_ebitda) / 12
                if monthly_improvement > 0:
                    months_to_breakeven = abs(prev_ebitda) / monthly_improvement
                    return int(months - 12 + months_to_breakeven)
        return -1  # Not reached
    
    def _calculate_total_return(self, projections: List[Dict], base_case: Dict) -> float:
        """Calculate total return multiple"""
        initial = base_case.get('valuation', 1000000)
        
        # Simple valuation at end (10x revenue multiple for SaaS)
        final_revenue = projections[-1]['revenue'] if projections else 0
        final_valuation = final_revenue * 10
        
        return (final_valuation / initial) if initial > 0 else 0
    
    def _perform_sensitivity_analysis(self, base_case: Dict, scenarios: List[Dict]) -> Dict:
        """Perform sensitivity analysis on key variables"""
        sensitivity = {}
        
        key_variables = ['growth_rate', 'gross_margin', 'customer_acquisition_cost']
        
        for variable in key_variables:
            sensitivity[variable] = {
                'low': self._calculate_variable_impact(base_case, variable, -0.2),
                'base': self._calculate_variable_impact(base_case, variable, 0),
                'high': self._calculate_variable_impact(base_case, variable, 0.2)
            }
        
        return sensitivity
    
    def _calculate_variable_impact(self, base_case: Dict, variable: str, change: float) -> float:
        """Calculate impact of variable change on valuation"""
        # Simplified impact calculation
        impacts = {
            'growth_rate': 2.5,  # 2.5x multiplier on valuation
            'gross_margin': 1.8,  # 1.8x multiplier
            'customer_acquisition_cost': -1.2  # Negative impact
        }
        
        base_value = 10000000  # Base valuation
        impact_multiplier = impacts.get(variable, 1.0)
        
        return base_value * (1 + change * impact_multiplier)
    
    def _calculate_risk_adjusted_returns(self, scenarios: List[Dict]) -> Dict:
        """Calculate risk-adjusted returns"""
        expected_value = 0
        best_case = None
        worst_case = None
        
        for scenario in scenarios:
            probability = scenario['probability']
            npv = scenario['npv']
            
            expected_value += probability * npv
            
            if best_case is None or npv > best_case['npv']:
                best_case = scenario
            
            if worst_case is None or npv < worst_case['npv']:
                worst_case = scenario
        
        # Calculate standard deviation (simplified)
        variance = sum([
            scenario['probability'] * math.pow(scenario['npv'] - expected_value, 2)
            for scenario in scenarios
        ])
        std_dev = math.sqrt(variance)
        
        return {
            'expected_value': expected_value,
            'best_case': best_case['name'] if best_case else 'None',
            'best_case_npv': best_case['npv'] if best_case else 0,
            'worst_case': worst_case['name'] if worst_case else 'None',
            'worst_case_npv': worst_case['npv'] if worst_case else 0,
            'standard_deviation': std_dev,
            'sharpe_ratio': (expected_value / std_dev) if std_dev > 0 else 0
        }
    
    def _generate_recommendation(self, scenarios: List[Dict], risk_adjusted: Dict) -> Dict:
        """Generate recommendation based on analysis"""
        recommendation = {
            'recommended_scenario': '',
            'rationale': [],
            'key_actions': [],
            'risk_mitigation': []
        }
        
        # Find optimal scenario
        best_risk_adjusted = max(scenarios, key=lambda s: s['npv'] * s['probability'])
        recommendation['recommended_scenario'] = best_risk_adjusted['name']
        
        # Generate rationale
        if best_risk_adjusted['npv'] > 0:
            recommendation['rationale'].append(f"Positive NPV of ${best_risk_adjusted['npv']:,.0f}")
        
        if best_risk_adjusted['irr'] > 0.15:
            recommendation['rationale'].append(f"Strong IRR of {best_risk_adjusted['irr']:.1%}")
        
        if best_risk_adjusted['break_even_month'] > 0 and best_risk_adjusted['break_even_month'] < 24:
            recommendation['rationale'].append(f"Quick path to profitability ({best_risk_adjusted['break_even_month']} months)")
        
        # Key actions
        recommendation['key_actions'] = [
            'Secure funding for growth initiatives',
            'Build scalable operational infrastructure',
            'Invest in customer acquisition channels',
            'Strengthen unit economics',
            'Establish financial controls'
        ]
        
        # Risk mitigation
        if risk_adjusted['standard_deviation'] > risk_adjusted['expected_value'] * 0.5:
            recommendation['risk_mitigation'].append('High variability - consider hedging strategies')
        
        recommendation['risk_mitigation'].extend([
            'Maintain 12+ months runway',
            'Diversify revenue streams',
            'Build contingency plans for downside scenarios'
        ])
        
        return recommendation

def analyze_financial_scenarios(base_case: Dict, scenarios: List[Dict]) -> str:
    """Main function to analyze financial scenarios"""
    analyzer = FinancialScenarioAnalyzer()
    results = analyzer.analyze_scenarios(base_case, scenarios)
    
    # Format output
    output = [
        "=== Financial Scenario Analysis ===",
        "",
        "Base Case Summary:",
        f"  Revenue: ${results['base_case_summary']['revenue']:,.0f}",
        f"  Gross Margin: {results['base_case_summary']['gross_margin']:.1f}%",
        f"  EBITDA: ${results['base_case_summary']['ebitda']:,.0f}",
        f"  Runway: {results['base_case_summary']['runway_months']:.1f} months",
        "",
        "Scenario Analysis:"
    ]
    
    for scenario in results['scenario_analysis']:
        output.append(f"\n{scenario['name']} (Probability: {scenario['probability']:.0%})")
        output.append(f"  NPV: ${scenario['npv']:,.0f}")
        output.append(f"  IRR: {scenario['irr']:.1%}")
        output.append(f"  Break-even: {scenario['break_even_month']} months")
        output.append(f"  Return Multiple: {scenario['total_return']:.1f}x")
        
        # Show Year 3 projection
        if scenario['projections']:
            year3 = scenario['projections'][-1]
            output.append(f"  Year 3 Revenue: ${year3['revenue']:,.0f}")
            output.append(f"  Year 3 EBITDA Margin: {year3['ebitda_margin']:.1f}%")
    
    output.extend([
        "",
        "Risk-Adjusted Analysis:",
        f"  Expected Value: ${results['risk_adjusted_view']['expected_value']:,.0f}",
        f"  Best Case: {results['risk_adjusted_view']['best_case']} (${results['risk_adjusted_view']['best_case_npv']:,.0f})",
        f"  Worst Case: {results['risk_adjusted_view']['worst_case']} (${results['risk_adjusted_view']['worst_case_npv']:,.0f})",
        f"  Risk (Std Dev): ${results['risk_adjusted_view']['standard_deviation']:,.0f}",
        f"  Sharpe Ratio: {results['risk_adjusted_view']['sharpe_ratio']:.2f}",
        "",
        f"RECOMMENDATION: {results['recommendation']['recommended_scenario']}",
        "",
        "Rationale:"
    ])
    
    for reason in results['recommendation']['rationale']:
        output.append(f"  • {reason}")
    
    output.extend([
        "",
        "Key Actions:"
    ])
    
    for action in results['recommendation']['key_actions'][:3]:
        output.append(f"  • {action}")
    
    return '\n'.join(output)

if __name__ == "__main__":
    # Example usage
    example_base_case = {
        'revenue': 5000000,
        'cogs': 1500000,
        'operating_expenses': 3000000,
        'cash': 2000000,
        'burn_rate': 200000,
        'valuation': 20000000,
        'initial_investment': 5000000
    }
    
    example_scenarios = [
        {
            'name': 'Aggressive Growth',
            'probability': 0.3,
            'growth_model': 'exponential',
            'growth_rate': 0.5,
            'changes': {
                'operating_expenses': {'multiply': 1.3}
            },
            'assumptions': ['Market expansion successful', 'Product-market fit achieved'],
            'cogs_ratio': 0.25,
            'opex_growth': 0.3,
            'capex_ratio': 0.08,
            'discount_rate': 0.12
        },
        {
            'name': 'Moderate Growth',
            'probability': 0.5,
            'growth_model': 'exponential',
            'growth_rate': 0.3,
            'changes': {},
            'assumptions': ['Steady market growth', 'Competition remains stable'],
            'cogs_ratio': 0.3,
            'opex_growth': 0.15,
            'capex_ratio': 0.05,
            'discount_rate': 0.10
        },
        {
            'name': 'Conservative',
            'probability': 0.2,
            'growth_model': 'linear',
            'growth_rate': 0.15,
            'changes': {
                'operating_expenses': {'multiply': 0.9}
            },
            'assumptions': ['Market headwinds', 'Focus on profitability'],
            'cogs_ratio': 0.35,
            'opex_growth': 0.05,
            'capex_ratio': 0.03,
            'discount_rate': 0.08
        }
    ]
    
    print(analyze_financial_scenarios(example_base_case, example_scenarios))

#!/usr/bin/env python3
"""
Strategic Planning Analyzer - Comprehensive business strategy assessment tool
"""

import json
from typing import Dict, List, Tuple
from datetime import datetime, timedelta
import math

class StrategyAnalyzer:
    def __init__(self):
        self.strategic_pillars = {
            'market_position': {
                'weight': 0.25,
                'factors': ['market_share', 'brand_strength', 'competitive_advantage', 'customer_loyalty']
            },
            'financial_health': {
                'weight': 0.25,
                'factors': ['revenue_growth', 'profitability', 'cash_flow', 'unit_economics']
            },
            'operational_excellence': {
                'weight': 0.20,
                'factors': ['efficiency', 'quality', 'scalability', 'innovation']
            },
            'organizational_capability': {
                'weight': 0.20,
                'factors': ['talent', 'culture', 'leadership', 'agility']
            },
            'growth_potential': {
                'weight': 0.10,
                'factors': ['market_size', 'expansion_opportunities', 'product_pipeline', 'partnerships']
            }
        }
        
        self.strategic_frameworks = {
            'porter_five_forces': [
                'competitive_rivalry',
                'supplier_power', 
                'buyer_power',
                'threat_of_substitution',
                'threat_of_new_entry'
            ],
            'swot': ['strengths', 'weaknesses', 'opportunities', 'threats'],
            'bcg_matrix': ['stars', 'cash_cows', 'question_marks', 'dogs'],
            'ansoff_matrix': ['market_penetration', 'market_development', 'product_development', 'diversification']
        }
    
    def analyze_strategic_position(self, company_data: Dict) -> Dict:
        """Comprehensive strategic analysis"""
        results = {
            'timestamp': datetime.now().isoformat(),
            'company': company_data.get('name', 'Company'),
            'strategic_health_score': 0,
            'pillar_analysis': {},
            'framework_analysis': {},
            'strategic_options': [],
            'risk_assessment': {},
            'recommendations': [],
            'roadmap': {}
        }
        
        # Analyze strategic pillars
        total_score = 0
        for pillar, config in self.strategic_pillars.items():
            pillar_score = self._analyze_pillar(
                company_data.get(pillar, {}),
                config['factors']
            )
            weighted_score = pillar_score * config['weight']
            results['pillar_analysis'][pillar] = {
                'score': pillar_score,
                'weighted_score': weighted_score,
                'level': self._get_level(pillar_score),
                'factors': self._get_pillar_details(company_data.get(pillar, {}), config['factors'])
            }
            total_score += weighted_score
        
        results['strategic_health_score'] = round(total_score, 1)
        
        # Framework analysis
        results['framework_analysis'] = self._apply_frameworks(company_data)
        
        # Generate strategic options
        results['strategic_options'] = self._generate_strategic_options(
            results['pillar_analysis'],
            company_data.get('context', {})
        )
        
        # Risk assessment
        results['risk_assessment'] = self._assess_strategic_risks(
            company_data,
            results['strategic_options']
        )
        
        # Generate roadmap
        results['roadmap'] = self._create_strategic_roadmap(
            results['strategic_options'],
            company_data.get('timeline', 12)
        )
        
        # Generate recommendations
        results['recommendations'] = self._generate_recommendations(results)
        
        return results
    
    def _analyze_pillar(self, pillar_data: Dict, factors: List) -> float:
        """Analyze a strategic pillar"""
        if not pillar_data:
            return 50.0
        
        total_score = 0
        count = 0
        
        for factor in factors:
            if factor in pillar_data:
                score = pillar_data[factor]
                total_score += score
                count += 1
        
        return (total_score / count) if count > 0 else 50.0
    
    def _get_pillar_details(self, pillar_data: Dict, factors: List) -> List[Dict]:
        """Get detailed factor analysis"""
        details = []
        
        for factor in factors:
            score = pillar_data.get(factor, 50)
            details.append({
                'factor': factor.replace('_', ' ').title(),
                'score': score,
                'status': 'Strong' if score >= 70 else 'Adequate' if score >= 40 else 'Weak'
            })
        
        return details
    
    def _get_level(self, score: float) -> str:
        """Convert score to level"""
        if score >= 80:
            return 'Excellent'
        elif score >= 70:
            return 'Strong'
        elif score >= 50:
            return 'Adequate'
        elif score >= 30:
            return 'Weak'
        else:
            return 'Critical'
    
    def _apply_frameworks(self, company_data: Dict) -> Dict:
        """Apply strategic frameworks"""
        frameworks = {}
        
        # SWOT Analysis
        swot_data = company_data.get('swot', {})
        frameworks['swot'] = {
            'strengths': swot_data.get('strengths', [
                'Strong brand recognition',
                'Experienced leadership team',
                'Robust technology platform'
            ]),
            'weaknesses': swot_data.get('weaknesses', [
                'Limited geographic presence',
                'High customer acquisition cost',
                'Technical debt'
            ]),
            'opportunities': swot_data.get('opportunities', [
                'Growing market demand',
                'M&A opportunities',
                'New product categories'
            ]),
            'threats': swot_data.get('threats', [
                'Increasing competition',
                'Regulatory changes',
                'Economic uncertainty'
            ])
        }
        
        # Porter's Five Forces
        forces = company_data.get('competitive_forces', {})
        frameworks['porter_analysis'] = {
            'competitive_rivalry': forces.get('rivalry', 70),
            'supplier_power': forces.get('suppliers', 40),
            'buyer_power': forces.get('buyers', 60),
            'threat_of_substitutes': forces.get('substitutes', 50),
            'threat_of_new_entrants': forces.get('new_entrants', 45),
            'overall_attractiveness': self._calculate_industry_attractiveness(forces)
        }
        
        # BCG Matrix for product portfolio
        products = company_data.get('products', [])
        frameworks['portfolio_analysis'] = self._analyze_portfolio(products)
        
        return frameworks
    
    def _calculate_industry_attractiveness(self, forces: Dict) -> float:
        """Calculate industry attractiveness from Porter's forces"""
        # Lower forces = more attractive industry
        rivalry = 100 - forces.get('rivalry', 50)
        supplier = 100 - forces.get('suppliers', 50)
        buyer = 100 - forces.get('buyers', 50)
        substitutes = 100 - forces.get('substitutes', 50)
        new_entrants = 100 - forces.get('new_entrants', 50)
        
        avg = (rivalry + supplier + buyer + substitutes + new_entrants) / 5
        return round(avg, 1)
    
    def _analyze_portfolio(self, products: List) -> Dict:
        """Analyze product portfolio using BCG matrix"""
        portfolio = {
            'stars': [],
            'cash_cows': [],
            'question_marks': [],
            'dogs': []
        }
        
        for product in products:
            growth = product.get('market_growth', 0)
            share = product.get('market_share', 0)
            
            if growth > 10 and share > 50:
                portfolio['stars'].append(product.get('name', 'Product'))
            elif growth <= 10 and share > 50:
                portfolio['cash_cows'].append(product.get('name', 'Product'))
            elif growth > 10 and share <= 50:
                portfolio['question_marks'].append(product.get('name', 'Product'))
            else:
                portfolio['dogs'].append(product.get('name', 'Product'))
        
        return portfolio
    
    def _generate_strategic_options(self, pillar_analysis: Dict, context: Dict) -> List[Dict]:
        """Generate strategic options based on analysis"""
        options = []
        
        # Check market position
        market_score = pillar_analysis['market_position']['score']
        if market_score < 60:
            options.append({
                'name': 'Market Leadership Initiative',
                'type': 'market_penetration',
                'description': 'Aggressive market share capture through competitive pricing and marketing',
                'investment': 'High',
                'timeframe': '12-18 months',
                'expected_impact': 'Increase market share by 10-15%',
                'priority': 9
            })
        
        # Check financial health
        financial_score = pillar_analysis['financial_health']['score']
        if financial_score < 50:
            options.append({
                'name': 'Profitability Turnaround',
                'type': 'operational_excellence',
                'description': 'Cost reduction and revenue optimization program',
                'investment': 'Medium',
                'timeframe': '6-9 months',
                'expected_impact': 'Improve margins by 5-8%',
                'priority': 10
            })
        
        # Check growth potential
        growth_score = pillar_analysis['growth_potential']['score']
        if growth_score > 70:
            options.append({
                'name': 'Expansion Strategy',
                'type': 'market_development',
                'description': 'Enter new geographic markets or customer segments',
                'investment': 'High',
                'timeframe': '18-24 months',
                'expected_impact': 'Revenue growth of 30-40%',
                'priority': 8
            })
        
        # Innovation opportunities
        if context.get('industry_disruption', False):
            options.append({
                'name': 'Digital Transformation',
                'type': 'innovation',
                'description': 'Comprehensive digitalization of business processes and customer experience',
                'investment': 'Very High',
                'timeframe': '24-36 months',
                'expected_impact': 'Future-proof business model',
                'priority': 9
            })
        
        # M&A opportunities
        if context.get('cash_available', 0) > 100000000:
            options.append({
                'name': 'Strategic Acquisition',
                'type': 'acquisition',
                'description': 'Acquire complementary businesses or competitors',
                'investment': 'Very High',
                'timeframe': '6-12 months',
                'expected_impact': 'Instant scale and capability',
                'priority': 7
            })
        
        # Sort by priority
        options.sort(key=lambda x: x['priority'], reverse=True)
        
        return options[:5]  # Top 5 strategic options
    
    def _assess_strategic_risks(self, company_data: Dict, strategic_options: List) -> Dict:
        """Assess strategic risks"""
        risks = {
            'execution_risk': self._calculate_execution_risk(company_data),
            'market_risk': self._calculate_market_risk(company_data),
            'financial_risk': self._calculate_financial_risk(company_data),
            'competitive_risk': self._calculate_competitive_risk(company_data),
            'regulatory_risk': company_data.get('regulatory_risk', 30),
            'overall_risk': 0,
            'mitigation_strategies': []
        }
        
        # Calculate overall risk
        risk_values = [
            risks['execution_risk'],
            risks['market_risk'],
            risks['financial_risk'],
            risks['competitive_risk'],
            risks['regulatory_risk']
        ]
        risks['overall_risk'] = sum(risk_values) / len(risk_values)
        
        # Generate mitigation strategies
        if risks['execution_risk'] > 60:
            risks['mitigation_strategies'].append({
                'risk': 'Execution',
                'strategy': 'Strengthen PMO, hire experienced executives, implement OKRs'
            })
        
        if risks['market_risk'] > 60:
            risks['mitigation_strategies'].append({
                'risk': 'Market',
                'strategy': 'Diversify revenue streams, build strategic partnerships'
            })
        
        if risks['financial_risk'] > 60:
            risks['mitigation_strategies'].append({
                'risk': 'Financial',
                'strategy': 'Improve cash management, secure credit facilities, optimize working capital'
            })
        
        return risks
    
    def _calculate_execution_risk(self, data: Dict) -> float:
        """Calculate execution risk"""
        org_capability = data.get('organizational_capability', {})
        
        factors = [
            100 - org_capability.get('leadership', 50),
            100 - org_capability.get('talent', 50),
            100 - org_capability.get('agility', 50),
            data.get('complexity_score', 50)
        ]
        
        return sum(factors) / len(factors)
    
    def _calculate_market_risk(self, data: Dict) -> float:
        """Calculate market risk"""
        market = data.get('market_position', {})
        
        factors = [
            100 - market.get('market_share', 50),
            data.get('market_volatility', 50),
            data.get('customer_concentration', 50)
        ]
        
        return sum(factors) / len(factors)
    
    def _calculate_financial_risk(self, data: Dict) -> float:
        """Calculate financial risk"""
        financial = data.get('financial_health', {})
        
        factors = [
            100 - financial.get('cash_flow', 50),
            100 - financial.get('profitability', 50),
            data.get('debt_ratio', 50),
            data.get('burn_rate', 50) if 'burn_rate' in data else 30
        ]
        
        return sum(factors) / len(factors)
    
    def _calculate_competitive_risk(self, data: Dict) -> float:
        """Calculate competitive risk"""
        forces = data.get('competitive_forces', {})
        
        return (forces.get('rivalry', 50) + forces.get('new_entrants', 50)) / 2
    
    def _create_strategic_roadmap(self, options: List, timeline_months: int) -> Dict:
        """Create implementation roadmap"""
        roadmap = {
            'phases': [],
            'milestones': [],
            'resource_requirements': {},
            'success_metrics': []
        }
        
        # Define phases
        phases = [
            {
                'phase': 'Foundation',
                'months': '0-3',
                'focus': 'Build capabilities and quick wins',
                'initiatives': []
            },
            {
                'phase': 'Acceleration',
                'months': '3-9',
                'focus': 'Execute core strategies',
                'initiatives': []
            },
            {
                'phase': 'Scale',
                'months': '9-18',
                'focus': 'Expand and optimize',
                'initiatives': []
            },
            {
                'phase': 'Transform',
                'months': '18+',
                'focus': 'Long-term transformation',
                'initiatives': []
            }
        ]
        
        # Assign initiatives to phases
        for i, option in enumerate(options[:4]):
            if i == 0:
                phases[0]['initiatives'].append(option['name'])
            elif i == 1:
                phases[1]['initiatives'].append(option['name'])
            elif i == 2:
                phases[2]['initiatives'].append(option['name'])
            else:
                phases[3]['initiatives'].append(option['name'])
        
        roadmap['phases'] = phases
        
        # Define key milestones
        roadmap['milestones'] = [
            {'month': 3, 'milestone': 'Complete foundation phase', 'success_criteria': 'Core team hired, processes defined'},
            {'month': 6, 'milestone': 'First major initiative launch', 'success_criteria': 'KPIs showing positive trend'},
            {'month': 12, 'milestone': 'Strategic review', 'success_criteria': 'ROI demonstrated, strategy validated'},
            {'month': 18, 'milestone': 'Scale achievement', 'success_criteria': 'Market position improved, financial targets met'}
        ]
        
        # Resource requirements
        roadmap['resource_requirements'] = {
            'leadership': 'C-suite alignment and commitment',
            'financial': '$X million investment over 18 months',
            'human': 'Additional 20-30 FTEs across functions',
            'technology': 'Platform upgrades and new tools',
            'external': 'Consultants and advisors as needed'
        }
        
        # Success metrics
        roadmap['success_metrics'] = [
            'Revenue growth: 25% YoY',
            'Market share: +5 percentage points',
            'EBITDA margin: +8 percentage points',
            'Customer NPS: >70',
            'Employee engagement: >80%'
        ]
        
        return roadmap
    
    def _generate_recommendations(self, results: Dict) -> List[str]:
        """Generate strategic recommendations"""
        recommendations = []
        
        # Based on overall score
        score = results['strategic_health_score']
        if score < 40:
            recommendations.append('🚨 URGENT: Immediate turnaround required - consider bringing in crisis management team')
            recommendations.append('Focus on cash preservation and core business stabilization')
        elif score < 60:
            recommendations.append('⚠️ Strategic repositioning needed - prioritize 2-3 key initiatives')
            recommendations.append('Strengthen weak pillars before pursuing growth')
        elif score < 80:
            recommendations.append('✓ Solid position - focus on selective improvements and growth')
            recommendations.append('Invest in innovation and market expansion')
        else:
            recommendations.append('⭐ Excellent position - maintain momentum and explore bold moves')
            recommendations.append('Consider industry disruption or category creation')
        
        # Based on specific weaknesses
        for pillar, analysis in results['pillar_analysis'].items():
            if analysis['score'] < 50:
                if pillar == 'market_position':
                    recommendations.append(f'Strengthen {pillar}: Launch competitive differentiation program')
                elif pillar == 'financial_health':
                    recommendations.append(f'Improve {pillar}: Implement profitability improvement plan')
                elif pillar == 'organizational_capability':
                    recommendations.append(f'Build {pillar}: Invest in talent and culture transformation')
        
        # Based on opportunities
        if results['framework_analysis']['porter_analysis']['overall_attractiveness'] > 70:
            recommendations.append('Industry is attractive - consider aggressive expansion')
        
        # Risk-based recommendations
        if results['risk_assessment']['overall_risk'] > 60:
            recommendations.append('High risk profile - implement comprehensive risk management')
        
        return recommendations

def analyze_strategy(company_data: Dict) -> str:
    """Main function to analyze strategy"""
    analyzer = StrategyAnalyzer()
    results = analyzer.analyze_strategic_position(company_data)
    
    # Format output
    output = [
        f"=== Strategic Analysis Report ===",
        f"Company: {results['company']}",
        f"Date: {results['timestamp'][:10]}",
        f"",
        f"STRATEGIC HEALTH SCORE: {results['strategic_health_score']}/100",
        f"",
        "Strategic Pillars:"
    ]
    
    for pillar, analysis in results['pillar_analysis'].items():
        output.append(f"  {pillar.replace('_', ' ').title()}: {analysis['score']:.1f} ({analysis['level']})")
        for factor in analysis['factors'][:2]:  # Show top 2 factors
            output.append(f"    • {factor['factor']}: {factor['status']}")
    
    output.extend([
        f"",
        "Strategic Options:"
    ])
    
    for i, option in enumerate(results['strategic_options'][:3], 1):
        output.append(f"\n{i}. {option['name']} (Priority: {option['priority']}/10)")
        output.append(f"   Type: {option['type']}")
        output.append(f"   Investment: {option['investment']}")
        output.append(f"   Timeframe: {option['timeframe']}")
        output.append(f"   Impact: {option['expected_impact']}")
    
    output.extend([
        f"",
        f"Risk Assessment:",
        f"  Overall Risk: {results['risk_assessment']['overall_risk']:.1f}%",
        f"  Execution Risk: {results['risk_assessment']['execution_risk']:.1f}%",
        f"  Market Risk: {results['risk_assessment']['market_risk']:.1f}%",
        f"  Financial Risk: {results['risk_assessment']['financial_risk']:.1f}%",
        f"",
        "Strategic Roadmap:"
    ])
    
    for phase in results['roadmap']['phases'][:3]:
        output.append(f"  {phase['phase']} ({phase['months']}): {phase['focus']}")
        for initiative in phase['initiatives']:
            output.append(f"    • {initiative}")
    
    output.extend([
        f"",
        "Key Recommendations:"
    ])
    
    for rec in results['recommendations'][:5]:
        output.append(f"  • {rec}")
    
    return '\n'.join(output)

if __name__ == "__main__":
    # Example usage
    example_company = {
        'name': 'TechCorp Inc.',
        'market_position': {
            'market_share': 35,
            'brand_strength': 65,
            'competitive_advantage': 70,
            'customer_loyalty': 60
        },
        'financial_health': {
            'revenue_growth': 45,
            'profitability': 40,
            'cash_flow': 55,
            'unit_economics': 60
        },
        'organizational_capability': {
            'talent': 70,
            'culture': 65,
            'leadership': 75,
            'agility': 60
        },
        'growth_potential': {
            'market_size': 80,
            'expansion_opportunities': 70,
            'product_pipeline': 60,
            'partnerships': 55
        },
        'competitive_forces': {
            'rivalry': 70,
            'suppliers': 40,
            'buyers': 60,
            'substitutes': 50,
            'new_entrants': 45
        },
        'context': {
            'industry_disruption': True,
            'cash_available': 150000000
        },
        'timeline': 18
    }
    
    print(analyze_strategy(example_company))