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| """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 | |
| 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] | |
| class CompetitorAnalysis: | |
| """Competitor analysis.""" | |
| name: str | |
| market_share: float | |
| strengths: List[str] | |
| weaknesses: List[str] | |
| strategy: str | |
| revenue: Optional[float] | |
| valuation: Optional[float] | |
| 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 | |
| ] | |
| } | |