"""Advanced portfolio optimization for venture strategies.""" import logging from typing import Dict, Any, List, Optional, Set, Union, Type, Tuple import json from dataclasses import dataclass, field from enum import Enum from datetime import datetime import numpy as np from collections import defaultdict from .base import ReasoningStrategy @dataclass class VentureMetrics: """Venture performance metrics.""" revenue: float profit: float growth_rate: float risk_score: float resource_usage: Dict[str, float] synergy_score: float @dataclass class ResourceAllocation: """Resource allocation configuration.""" venture_id: str resources: Dict[str, float] constraints: List[str] dependencies: List[str] priority: float class PortfolioOptimizer: """ Advanced portfolio optimization that: 1. Optimizes venture mix 2. Allocates resources 3. Manages risks 4. Maximizes synergies 5. Balances growth """ def __init__(self): self.ventures: Dict[str, VentureMetrics] = {} self.allocations: Dict[str, ResourceAllocation] = {} async def optimize_portfolio(self, ventures: List[str], context: Dict[str, Any]) -> Dict[str, Any]: """Optimize venture portfolio.""" try: # Analyze ventures analysis = await self._analyze_ventures(ventures, context) # Optimize allocation allocation = await self._optimize_allocation(analysis, context) # Risk optimization risk = await self._optimize_risk(allocation, context) # Synergy optimization synergy = await self._optimize_synergies(risk, context) # Performance projections projections = await self._project_performance(synergy, context) return { "success": projections["annual_profit"] >= 1_000_000, "analysis": analysis, "allocation": allocation, "risk": risk, "synergy": synergy, "projections": projections } except Exception as e: logging.error(f"Error in portfolio optimization: {str(e)}") return {"success": False, "error": str(e)} async def _analyze_ventures(self, ventures: List[str], context: Dict[str, Any]) -> Dict[str, Any]: """Analyze venture characteristics.""" prompt = f""" Analyze ventures: Ventures: {json.dumps(ventures)} Context: {json.dumps(context)} Analyze: 1. Performance metrics 2. Resource requirements 3. Risk factors 4. Growth potential 5. Synergy opportunities Format as: [Venture1] Metrics: ... Resources: ... Risks: ... Growth: ... Synergies: ... """ response = await context["groq_api"].predict(prompt) return self._parse_venture_analysis(response["answer"]) async def _optimize_allocation(self, analysis: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Optimize resource allocation.""" prompt = f""" Optimize resource allocation: Analysis: {json.dumps(analysis)} Context: {json.dumps(context)} Optimize for: 1. Resource efficiency 2. Growth potential 3. Risk balance 4. Synergy capture 5. Constraint satisfaction Format as: [Allocation1] Venture: ... Resources: ... Constraints: ... Dependencies: ... Priority: ... """ response = await context["groq_api"].predict(prompt) return self._parse_allocation_optimization(response["answer"]) async def _optimize_risk(self, allocation: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Optimize risk management.""" prompt = f""" Optimize risk management: Allocation: {json.dumps(allocation)} Context: {json.dumps(context)} Optimize for: 1. Risk diversification 2. Exposure limits 3. Correlation management 4. Hedging strategies 5. Contingency planning Format as: [Risk1] Type: ... Exposure: ... Mitigation: ... Contingency: ... Impact: ... """ response = await context["groq_api"].predict(prompt) return self._parse_risk_optimization(response["answer"]) async def _optimize_synergies(self, risk: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Optimize portfolio synergies.""" prompt = f""" Optimize synergies: Risk: {json.dumps(risk)} Context: {json.dumps(context)} Optimize for: 1. Resource sharing 2. Knowledge transfer 3. Market leverage 4. Technology reuse 5. Customer cross-sell Format as: [Synergy1] Type: ... Ventures: ... Potential: ... Requirements: ... Timeline: ... """ response = await context["groq_api"].predict(prompt) return self._parse_synergy_optimization(response["answer"]) async def _project_performance(self, synergy: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Project portfolio performance.""" prompt = f""" Project performance: Synergy: {json.dumps(synergy)} Context: {json.dumps(context)} Project: 1. Revenue growth 2. Profit margins 3. Resource utilization 4. Risk metrics 5. Synergy capture Format as: [Projections] Revenue: ... Profit: ... Resources: ... Risk: ... Synergies: ... """ response = await context["groq_api"].predict(prompt) return self._parse_performance_projections(response["answer"]) def _calculate_portfolio_metrics(self) -> Dict[str, float]: """Calculate comprehensive portfolio metrics.""" if not self.ventures: return { "total_revenue": 0.0, "total_profit": 0.0, "avg_growth": 0.0, "avg_risk": 0.0, "resource_efficiency": 0.0, "synergy_capture": 0.0 } metrics = { "total_revenue": sum(v.revenue for v in self.ventures.values()), "total_profit": sum(v.profit for v in self.ventures.values()), "avg_growth": np.mean([v.growth_rate for v in self.ventures.values()]), "avg_risk": np.mean([v.risk_score for v in self.ventures.values()]), "resource_efficiency": self._calculate_resource_efficiency(), "synergy_capture": np.mean([v.synergy_score for v in self.ventures.values()]) } return metrics def _calculate_resource_efficiency(self) -> float: """Calculate resource utilization efficiency.""" if not self.ventures or not self.allocations: return 0.0 total_resources = defaultdict(float) used_resources = defaultdict(float) # Sum up total and used resources for venture_id, allocation in self.allocations.items(): for resource, amount in allocation.resources.items(): total_resources[resource] += amount if venture_id in self.ventures: used_resources[resource] += ( amount * self.ventures[venture_id].resource_usage.get(resource, 0) ) # Calculate efficiency for each resource efficiencies = [] for resource in total_resources: if total_resources[resource] > 0: efficiency = used_resources[resource] / total_resources[resource] efficiencies.append(efficiency) return np.mean(efficiencies) if efficiencies else 0.0 def get_portfolio_insights(self) -> Dict[str, Any]: """Get comprehensive portfolio insights.""" metrics = self._calculate_portfolio_metrics() return { "portfolio_metrics": metrics, "venture_metrics": { venture_id: { "revenue": v.revenue, "profit": v.profit, "growth_rate": v.growth_rate, "risk_score": v.risk_score, "synergy_score": v.synergy_score } for venture_id, v in self.ventures.items() }, "resource_allocation": { venture_id: { "resources": a.resources, "priority": a.priority, "constraints": len(a.constraints), "dependencies": len(a.dependencies) } for venture_id, a in self.allocations.items() }, "risk_profile": { "portfolio_risk": metrics["avg_risk"], "risk_concentration": self._calculate_risk_concentration(), "risk_correlation": self._calculate_risk_correlation() }, "optimization_opportunities": self._identify_optimization_opportunities() } def _calculate_risk_concentration(self) -> float: """Calculate risk concentration in portfolio.""" if not self.ventures: return 0.0 risk_weights = [v.risk_score for v in self.ventures.values()] return np.std(risk_weights) if len(risk_weights) > 1 else 0.0 def _calculate_risk_correlation(self) -> float: """Calculate risk correlation between ventures.""" if len(self.ventures) < 2: return 0.0 # Create correlation matrix of risk scores and resource usage venture_metrics = [ [v.risk_score] + list(v.resource_usage.values()) for v in self.ventures.values() ] correlation_matrix = np.corrcoef(venture_metrics) return np.mean(correlation_matrix[np.triu_indices_from(correlation_matrix, k=1)]) def _identify_optimization_opportunities(self) -> List[Dict[str, Any]]: """Identify portfolio optimization opportunities.""" opportunities = [] # Resource optimization opportunities resource_efficiency = self._calculate_resource_efficiency() if resource_efficiency < 0.8: opportunities.append({ "type": "resource_optimization", "potential": 1.0 - resource_efficiency, "description": "Improve resource utilization efficiency" }) # Risk optimization opportunities risk_concentration = self._calculate_risk_concentration() if risk_concentration > 0.2: opportunities.append({ "type": "risk_diversification", "potential": risk_concentration, "description": "Reduce risk concentration" }) # Synergy optimization opportunities avg_synergy = np.mean([v.synergy_score for v in self.ventures.values()]) if self.ventures else 0 if avg_synergy < 0.7: opportunities.append({ "type": "synergy_capture", "potential": 1.0 - avg_synergy, "description": "Increase synergy capture" }) return opportunities class PortfolioOptimizationStrategy(ReasoningStrategy): """ Advanced portfolio optimization strategy that: 1. Analyzes venture metrics 2. Optimizes resource allocation 3. Balances risk-reward 4. Maximizes portfolio synergies 5. Provides actionable recommendations """ def __init__(self, config: Optional[Dict[str, Any]] = None): """Initialize portfolio optimization strategy.""" super().__init__() self.config = config or {} self.optimizer = PortfolioOptimizer() async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """ Generate portfolio optimization strategy based on query and context. Args: query: The portfolio optimization query context: Additional context and parameters Returns: Dict containing optimization strategy and confidence scores """ try: # Extract portfolio parameters params = self._extract_parameters(query, context) # Optimize portfolio optimization_result = self.optimizer.optimize_portfolio( ventures=params.get('ventures', []), constraints=params.get('constraints', []), objectives=params.get('objectives', []) ) # Get metrics metrics = self.optimizer.get_portfolio_metrics() # Generate recommendations recommendations = self._generate_recommendations( optimization_result, metrics ) return { 'answer': self._format_strategy(optimization_result, metrics, recommendations), 'confidence': self._calculate_confidence(optimization_result), 'optimization': optimization_result, 'metrics': metrics, 'recommendations': recommendations } except Exception as e: logging.error(f"Portfolio optimization failed: {str(e)}") return { 'error': f"Portfolio optimization failed: {str(e)}", 'confidence': 0.0 } def _extract_parameters(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """Extract optimization parameters from query and context.""" params = {} # Extract ventures if 'ventures' in context: params['ventures'] = context['ventures'] else: # Default empty portfolio params['ventures'] = [] # Extract constraints if 'constraints' in context: params['constraints'] = context['constraints'] else: # Default constraints params['constraints'] = [ 'budget_limit', 'risk_tolerance', 'resource_capacity' ] # Extract objectives if 'objectives' in context: params['objectives'] = context['objectives'] else: # Default objectives params['objectives'] = [ 'maximize_returns', 'minimize_risk', 'maximize_synergies' ] return params def _generate_recommendations( self, optimization_result: Dict[str, Any], metrics: Dict[str, Any] ) -> List[str]: """Generate actionable recommendations.""" recommendations = [] # Portfolio composition recommendations if 'allocation' in optimization_result: allocation = optimization_result['allocation'] recommendations.extend([ f"Allocate {alloc['percentage']:.1f}% to {alloc['venture']}" for alloc in allocation ]) # Risk management recommendations if 'risk_analysis' in metrics: risk = metrics['risk_analysis'] if risk.get('total_risk', 0) > 0.7: recommendations.append( "Consider reducing exposure to high-risk ventures" ) if risk.get('correlation', 0) > 0.8: recommendations.append( "Increase portfolio diversification to reduce correlation" ) # Performance optimization recommendations if 'performance' in metrics: perf = metrics['performance'] if perf.get('sharpe_ratio', 0) < 1.0: recommendations.append( "Optimize risk-adjusted returns through better venture selection" ) if perf.get('efficiency', 0) < 0.8: recommendations.append( "Improve resource allocation efficiency across ventures" ) return recommendations def _calculate_confidence(self, optimization_result: Dict[str, Any]) -> float: """Calculate confidence score based on optimization quality.""" # Base confidence confidence = 0.5 # Adjust based on optimization completeness if optimization_result.get('allocation'): confidence += 0.1 if optimization_result.get('risk_analysis'): confidence += 0.1 if optimization_result.get('performance_metrics'): confidence += 0.1 # Adjust based on solution quality if optimization_result.get('convergence_status') == 'optimal': confidence += 0.2 elif optimization_result.get('convergence_status') == 'suboptimal': confidence += 0.1 return min(confidence, 1.0) def _format_strategy( self, optimization_result: Dict[str, Any], metrics: Dict[str, Any], recommendations: List[str] ) -> str: """Format optimization strategy into readable text.""" sections = [] # Portfolio allocation if 'allocation' in optimization_result: allocation = optimization_result['allocation'] sections.append("Portfolio Allocation:") for alloc in allocation: sections.append( f"- {alloc['venture']}: {alloc['percentage']:.1f}%" ) # Key metrics if metrics: sections.append("\nKey Metrics:") for key, value in metrics.items(): if isinstance(value, (int, float)): sections.append(f"- {key.replace('_', ' ').title()}: {value:.2f}") else: sections.append(f"- {key.replace('_', ' ').title()}: {value}") # Recommendations if recommendations: sections.append("\nKey Recommendations:") for rec in recommendations: sections.append(f"- {rec}") return "\n".join(sections)