"""Advanced market analysis tools 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 MarketSegment: """Market segment analysis.""" size: float growth_rate: float cagr: float competition: List[Dict[str, Any]] barriers: List[str] opportunities: List[str] risks: List[str] @dataclass class CompetitorAnalysis: """Competitor analysis.""" name: str market_share: float strengths: List[str] weaknesses: List[str] strategy: str revenue: Optional[float] valuation: Optional[float] @dataclass class MarketTrend: """Market trend analysis.""" name: str impact: float timeline: str adoption_rate: float market_potential: float risk_level: float class MarketAnalyzer: """ Advanced market analysis toolkit that: 1. Analyzes market segments 2. Tracks competitors 3. Identifies trends 4. Predicts opportunities 5. Assesses risks """ def __init__(self): self.segments: Dict[str, MarketSegment] = {} self.competitors: Dict[str, CompetitorAnalysis] = {} self.trends: List[MarketTrend] = [] async def analyze_market(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: """Perform comprehensive market analysis.""" try: # Segment analysis segment_analysis = await self._analyze_segment(segment, context) # Competitor analysis competitor_analysis = await self._analyze_competitors(segment, context) # Trend analysis trend_analysis = await self._analyze_trends(segment, context) # Opportunity analysis opportunity_analysis = await self._analyze_opportunities( segment_analysis, competitor_analysis, trend_analysis, context) # Risk analysis risk_analysis = await self._analyze_risks( segment_analysis, competitor_analysis, trend_analysis, context) return { "success": True, "segment_analysis": segment_analysis, "competitor_analysis": competitor_analysis, "trend_analysis": trend_analysis, "opportunity_analysis": opportunity_analysis, "risk_analysis": risk_analysis, "metrics": { "market_score": self._calculate_market_score(segment_analysis), "opportunity_score": self._calculate_opportunity_score(opportunity_analysis), "risk_score": self._calculate_risk_score(risk_analysis) } } except Exception as e: logging.error(f"Error in market analysis: {str(e)}") return {"success": False, "error": str(e)} async def _analyze_segment(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: """Analyze market segment.""" prompt = f""" Analyze market segment: Segment: {segment} Context: {json.dumps(context)} Analyze: 1. Market size and growth 2. Customer segments 3. Value chain 4. Entry barriers 5. Competitive dynamics Format as: [Analysis] Size: ... Growth: ... Segments: ... Value_Chain: ... Barriers: ... """ response = await context["groq_api"].predict(prompt) return self._parse_segment_analysis(response["answer"]) async def _analyze_competitors(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: """Analyze competitors in segment.""" prompt = f""" Analyze competitors: Segment: {segment} Context: {json.dumps(context)} For each competitor analyze: 1. Market share 2. Business model 3. Strengths/weaknesses 4. Strategy 5. Performance metrics Format as: [Competitor1] Share: ... Model: ... Strengths: ... Weaknesses: ... Strategy: ... Metrics: ... """ response = await context["groq_api"].predict(prompt) return self._parse_competitor_analysis(response["answer"]) async def _analyze_trends(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: """Analyze market trends.""" prompt = f""" Analyze market trends: Segment: {segment} Context: {json.dumps(context)} Analyze trends in: 1. Technology 2. Customer behavior 3. Business models 4. Regulation 5. Market dynamics Format as: [Trend1] Type: ... Impact: ... Timeline: ... Adoption: ... Potential: ... """ response = await context["groq_api"].predict(prompt) return self._parse_trend_analysis(response["answer"]) async def _analyze_opportunities(self, segment_analysis: Dict[str, Any], competitor_analysis: Dict[str, Any], trend_analysis: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Analyze market opportunities.""" prompt = f""" Analyze market opportunities: Segment: {json.dumps(segment_analysis)} Competitors: {json.dumps(competitor_analysis)} Trends: {json.dumps(trend_analysis)} Context: {json.dumps(context)} Identify opportunities in: 1. Unmet needs 2. Market gaps 3. Innovation potential 4. Scaling potential 5. Value creation Format as: [Opportunity1] Type: ... Description: ... Potential: ... Requirements: ... Timeline: ... """ response = await context["groq_api"].predict(prompt) return self._parse_opportunity_analysis(response["answer"]) async def _analyze_risks(self, segment_analysis: Dict[str, Any], competitor_analysis: Dict[str, Any], trend_analysis: Dict[str, Any], context: Dict[str, Any]) -> Dict[str, Any]: """Analyze market risks.""" prompt = f""" Analyze market risks: Segment: {json.dumps(segment_analysis)} Competitors: {json.dumps(competitor_analysis)} Trends: {json.dumps(trend_analysis)} Context: {json.dumps(context)} Analyze risks in: 1. Market dynamics 2. Competition 3. Technology 4. Regulation 5. Execution Format as: [Risk1] Type: ... Description: ... Impact: ... Probability: ... Mitigation: ... """ response = await context["groq_api"].predict(prompt) return self._parse_risk_analysis(response["answer"]) def _calculate_market_score(self, analysis: Dict[str, Any]) -> float: """Calculate market attractiveness score.""" weights = { "size": 0.3, "growth": 0.3, "competition": 0.2, "barriers": 0.1, "dynamics": 0.1 } scores = { "size": min(analysis.get("size", 0) / 1e9, 1.0), # Normalize to 1B "growth": min(analysis.get("growth", 0) / 30, 1.0), # Normalize to 30% "competition": 1.0 - min(len(analysis.get("competitors", [])) / 10, 1.0), "barriers": 1.0 - min(len(analysis.get("barriers", [])) / 5, 1.0), "dynamics": analysis.get("dynamics_score", 0.5) } return sum(weights[k] * scores[k] for k in weights) def _calculate_opportunity_score(self, analysis: Dict[str, Any]) -> float: """Calculate opportunity attractiveness score.""" weights = { "market_potential": 0.3, "innovation_potential": 0.2, "execution_feasibility": 0.2, "competitive_advantage": 0.2, "timing": 0.1 } scores = { "market_potential": analysis.get("market_potential", 0.5), "innovation_potential": analysis.get("innovation_potential", 0.5), "execution_feasibility": analysis.get("execution_feasibility", 0.5), "competitive_advantage": analysis.get("competitive_advantage", 0.5), "timing": analysis.get("timing_score", 0.5) } return sum(weights[k] * scores[k] for k in weights) def _calculate_risk_score(self, analysis: Dict[str, Any]) -> float: """Calculate risk level score.""" weights = { "market_risk": 0.2, "competition_risk": 0.2, "technology_risk": 0.2, "regulatory_risk": 0.2, "execution_risk": 0.2 } scores = { "market_risk": analysis.get("market_risk", 0.5), "competition_risk": analysis.get("competition_risk", 0.5), "technology_risk": analysis.get("technology_risk", 0.5), "regulatory_risk": analysis.get("regulatory_risk", 0.5), "execution_risk": analysis.get("execution_risk", 0.5) } return sum(weights[k] * scores[k] for k in weights) def get_market_insights(self) -> Dict[str, Any]: """Get comprehensive market insights.""" return { "segment_insights": { segment: { "size": s.size, "growth_rate": s.growth_rate, "cagr": s.cagr, "opportunity_score": self._calculate_market_score({ "size": s.size, "growth": s.growth_rate, "competitors": s.competition, "barriers": s.barriers }) } for segment, s in self.segments.items() }, "competitor_insights": { competitor: { "market_share": c.market_share, "strength_score": len(c.strengths) / (len(c.strengths) + len(c.weaknesses)), "revenue": c.revenue, "valuation": c.valuation } for competitor, c in self.competitors.items() }, "trend_insights": [ { "name": t.name, "impact": t.impact, "potential": t.market_potential, "risk": t.risk_level } for t in self.trends ] } class MarketAnalysisStrategy(ReasoningStrategy): """ Advanced market analysis strategy that combines multiple analytical tools to provide comprehensive market insights. """ def __init__(self, config: Optional[Dict[str, Any]] = None): """Initialize market analysis strategy.""" super().__init__() self.config = config or {} self.analyzer = MarketAnalyzer() async def reason(self, query: str, context: Dict[str, Any]) -> Dict[str, Any]: """ Perform market analysis based on query and context. Args: query: The market analysis query context: Additional context and parameters Returns: Dict containing market analysis results and confidence scores """ try: # Extract market segment from query/context segment = self._extract_segment(query, context) # Perform market analysis analysis = await self._analyze_market(segment, context) # Get insights insights = self.analyzer.get_market_insights() # Calculate confidence based on data quality and completeness confidence = self._calculate_confidence(analysis, insights) return { 'answer': self._format_insights(insights), 'confidence': confidence, 'analysis': analysis, 'insights': insights, 'segment': segment } except Exception as e: logging.error(f"Market analysis failed: {str(e)}") return { 'error': f"Market analysis failed: {str(e)}", 'confidence': 0.0 } def _extract_segment(self, query: str, context: Dict[str, Any]) -> str: """Extract market segment from query and context.""" # Use context if available if 'segment' in context: return context['segment'] # Default to general market return 'general' async def _analyze_market(self, segment: str, context: Dict[str, Any]) -> Dict[str, Any]: """Perform comprehensive market analysis.""" return await self.analyzer.analyze_market(segment, context) def _calculate_confidence(self, analysis: Dict[str, Any], insights: Dict[str, Any]) -> float: """Calculate confidence score based on analysis quality.""" # Base confidence confidence = 0.5 # Adjust based on data completeness if analysis.get('segment_analysis'): confidence += 0.1 if analysis.get('competitor_analysis'): confidence += 0.1 if analysis.get('trend_analysis'): confidence += 0.1 # Adjust based on insight quality if insights.get('opportunities'): confidence += 0.1 if insights.get('risks'): confidence += 0.1 return min(confidence, 1.0) def _format_insights(self, insights: Dict[str, Any]) -> str: """Format market insights into readable text.""" sections = [] if 'market_overview' in insights: sections.append(f"Market Overview: {insights['market_overview']}") if 'opportunities' in insights: opps = insights['opportunities'] sections.append("Key Opportunities:\n- " + "\n- ".join(opps)) if 'risks' in insights: risks = insights['risks'] sections.append("Key Risks:\n- " + "\n- ".join(risks)) if 'recommendations' in insights: recs = insights['recommendations'] sections.append("Recommendations:\n- " + "\n- ".join(recs)) return "\n\n".join(sections)