MCP Stock Details Server

""" Unit tests for BusinessSegmentAnalyzer class TDD Red Phase: Write failing tests for business segment analysis engine """ import pytest import asyncio from unittest.mock import Mock, AsyncMock, patch from datetime import datetime, date, timedelta from typing import Dict, Any, List, Optional # Test imports - initially will fail (TDD Red phase) from src.tools.business_segment_tools import BusinessSegmentAnalyzer from src.exceptions import MCPStockDetailsError, InsufficientDataError class TestBusinessSegmentAnalyzer: """Test cases for BusinessSegmentAnalyzer class""" @pytest.fixture def segment_analyzer(self): """Create BusinessSegmentAnalyzer instance for testing""" return BusinessSegmentAnalyzer() @pytest.fixture def sample_segment_data(self): """Sample business segment data for analysis""" return { "company_code": "005930", "segments": [ { "name": "반도체", "revenue_2023": 63747000000000, "revenue_2022": 76956000000000, "revenue_2021": 83018000000000, "operating_profit_2023": 3369000000000, "operating_profit_2022": 18325000000000, "operating_profit_2021": 26009000000000, "assets": 145632000000000, "employees": 85000, "capex": 24500000000000 }, { "name": "디스플레이패널", "revenue_2023": 50148000000000, "revenue_2022": 47543000000000, "revenue_2021": 45897000000000, "operating_profit_2023": 1561000000000, "operating_profit_2022": 1204000000000, "operating_profit_2021": 895000000000, "assets": 82451000000000, "employees": 45000, "capex": 8900000000000 }, { "name": "IM(모바일)", "revenue_2023": 124871000000000, "revenue_2022": 118345000000000, "revenue_2021": 109385000000000, "operating_profit_2023": 8952000000000, "operating_profit_2022": 7825000000000, "operating_profit_2021": 6851000000000, "assets": 67234000000000, "employees": 92000, "capex": 12300000000000 } ], "total_revenue_2023": 247674000000000, "total_operating_profit_2023": 14303000000000 } @pytest.fixture def sample_geographic_data(self): """Sample geographic segment data for testing""" return { "geographic_segments": [ {"region": "한국", "revenue": 75234000000000, "operating_profit": 4521000000000, "assets": 145623000000000}, {"region": "중국", "revenue": 58967000000000, "operating_profit": 3214000000000, "assets": 89421000000000}, {"region": "미국", "revenue": 68453000000000, "operating_profit": 4123000000000, "assets": 67234000000000}, {"region": "유럽", "revenue": 25129000000000, "operating_profit": 1524000000000, "assets": 32145000000000}, {"region": "기타", "revenue": 19091000000000, "operating_profit": 921000000000, "assets": 18234000000000} ] } @pytest.mark.asyncio async def test_analyze_segment_performance(self, segment_analyzer, sample_segment_data): """Test segment performance analysis""" performance = await segment_analyzer.analyze_segment_performance( segment_data=sample_segment_data, analysis_years=3 ) assert performance is not None assert isinstance(performance, dict) # Should contain performance metrics for each segment assert "segment_performance" in performance assert "performance_ranking" in performance assert "key_insights" in performance segment_performance = performance["segment_performance"] assert len(segment_performance) == 3 # 3 segments # Each segment should have performance metrics for segment in segment_performance: assert "name" in segment assert "revenue_growth_rate" in segment assert "operating_margin" in segment assert "profit_growth_rate" in segment assert "performance_score" in segment @pytest.mark.asyncio async def test_calculate_segment_growth_rates(self, segment_analyzer, sample_segment_data): """Test segment growth rate calculation""" growth_analysis = await segment_analyzer.calculate_segment_growth_rates( segment_data=sample_segment_data, growth_period="3Y" ) assert growth_analysis is not None assert isinstance(growth_analysis, dict) # Should contain growth metrics assert "segment_growth" in growth_analysis assert "average_growth_rate" in growth_analysis assert "growth_stability" in growth_analysis assert "fastest_growing_segment" in growth_analysis # Should calculate growth rates correctly segment_growth = growth_analysis["segment_growth"] semiconductor_growth = next(s for s in segment_growth if s["name"] == "반도체") # Check semiconductor segment decline (negative growth) assert semiconductor_growth["cagr"] < 0 # Declining revenue assert isinstance(semiconductor_growth["revenue_volatility"], float) @pytest.mark.asyncio async def test_analyze_segment_profitability(self, segment_analyzer, sample_segment_data): """Test segment profitability analysis""" profitability = await segment_analyzer.analyze_segment_profitability( segment_data=sample_segment_data, benchmark_margins={"반도체": 25.5, "디스플레이패널": 8.2, "IM(모바일)": 12.1} ) assert profitability is not None assert isinstance(profitability, dict) # Should contain profitability metrics assert "segment_margins" in profitability assert "profitability_ranking" in profitability assert "margin_trends" in profitability assert "benchmark_comparison" in profitability # Should calculate margins correctly segment_margins = profitability["segment_margins"] mobile_segment = next(s for s in segment_margins if s["name"] == "IM(모바일)") # Mobile segment should have positive margin assert mobile_segment["operating_margin"] > 0 assert mobile_segment["roa"] >= 0 # Return on Assets @pytest.mark.asyncio async def test_calculate_segment_concentration(self, segment_analyzer, sample_segment_data): """Test segment concentration analysis""" concentration = await segment_analyzer.calculate_segment_concentration( segment_data=sample_segment_data, concentration_metric="revenue" ) assert concentration is not None assert isinstance(concentration, dict) # Should contain concentration metrics assert "hhi_index" in concentration assert "top_segment_share" in concentration assert "concentration_level" in concentration assert "diversification_score" in concentration # HHI should be calculated correctly hhi = concentration["hhi_index"] assert isinstance(hhi, float) assert 0 <= hhi <= 10000 # Top segment share should match mobile segment (~50%) top_share = concentration["top_segment_share"] assert 45 <= top_share <= 55 # Mobile segment dominance # Concentration level should be categorical assert concentration["concentration_level"] in ["low", "moderate", "high", "very_high"] @pytest.mark.asyncio async def test_analyze_geographic_segments(self, segment_analyzer, sample_geographic_data): """Test geographic segment analysis""" geographic = await segment_analyzer.analyze_geographic_segments( geographic_data=sample_geographic_data, risk_assessment=True ) assert geographic is not None assert isinstance(geographic, dict) # Should contain geographic analysis assert "regional_performance" in geographic assert "geographic_concentration" in geographic assert "market_risk_assessment" in geographic assert "currency_exposure" in geographic # Should have regional performance data regional_performance = geographic["regional_performance"] assert len(regional_performance) == 5 # 5 regions # Each region should have metrics for region in regional_performance: assert "region" in region assert "revenue_share" in region assert "operating_margin" in region assert "market_risk_score" in region @pytest.mark.asyncio async def test_evaluate_competitive_positioning(self, segment_analyzer, sample_segment_data): """Test competitive positioning evaluation""" competitive = await segment_analyzer.evaluate_competitive_positioning( segment_data=sample_segment_data, market_data={ "반도체": {"market_size": 574000000000000, "growth_rate": 3.2}, "디스플레이패널": {"market_size": 165000000000000, "growth_rate": 1.8}, "IM(모바일)": {"market_size": 512000000000000, "growth_rate": 4.5} } ) assert competitive is not None assert isinstance(competitive, dict) # Should contain competitive analysis assert "market_position" in competitive assert "competitive_advantages" in competitive assert "market_share_estimates" in competitive assert "strategic_recommendations" in competitive # Should analyze market positioning market_position = competitive["market_position"] assert len(market_position) == 3 # 3 segments for position in market_position: assert "segment" in position assert "estimated_market_share" in position assert "competitive_strength" in position @pytest.mark.asyncio async def test_assess_segment_risks(self, segment_analyzer, sample_segment_data): """Test segment risk assessment""" risk_assessment = await segment_analyzer.assess_segment_risks( segment_data=sample_segment_data, external_factors={ "economic_indicators": {"gdp_growth": 2.1, "inflation": 3.5}, "industry_trends": {"semiconductor": "cyclical_downturn", "mobile": "market_saturation"} } ) assert risk_assessment is not None assert isinstance(risk_assessment, dict) # Should contain risk assessment assert "segment_risks" in risk_assessment assert "risk_concentration" in risk_assessment assert "overall_risk_score" in risk_assessment assert "risk_mitigation" in risk_assessment # Should assess risks for each segment segment_risks = risk_assessment["segment_risks"] assert len(segment_risks) == 3 for risk in segment_risks: assert "segment" in risk assert "risk_level" in risk assert "key_risks" in risk assert "risk_score" in risk @pytest.mark.asyncio async def test_generate_segment_insights(self, segment_analyzer, sample_segment_data): """Test segment insights generation""" insights = await segment_analyzer.generate_segment_insights( segment_data=sample_segment_data, analysis_context={"industry": "technology", "market_cycle": "mature", "competitive_intensity": "high"} ) assert insights is not None assert isinstance(insights, dict) # Should contain strategic insights assert "key_findings" in insights assert "growth_opportunities" in insights assert "performance_concerns" in insights assert "strategic_recommendations" in insights assert "outlook_assessment" in insights # Each insight should have content for category in insights.values(): assert isinstance(category, (str, list, dict)) if isinstance(category, list): assert len(category) > 0 @pytest.mark.asyncio async def test_comprehensive_segment_analysis(self, segment_analyzer, sample_segment_data): """Test comprehensive segment analysis""" comprehensive = await segment_analyzer.comprehensive_segment_analysis( company_code="005930", include_all_metrics=True ) assert comprehensive is not None assert isinstance(comprehensive, dict) # Should contain all major analysis components assert "segment_overview" in comprehensive assert "performance_analysis" in comprehensive assert "growth_analysis" in comprehensive assert "profitability_analysis" in comprehensive assert "risk_assessment" in comprehensive assert "strategic_insights" in comprehensive # Should have executive summary if "executive_summary" in comprehensive: summary = comprehensive["executive_summary"] assert "key_segments" in summary assert "performance_highlights" in summary assert "strategic_priorities" in summary @pytest.mark.asyncio async def test_calculate_segment_efficiency_metrics(self, segment_analyzer, sample_segment_data): """Test segment efficiency metrics calculation""" efficiency = await segment_analyzer.calculate_segment_efficiency_metrics( segment_data=sample_segment_data, efficiency_benchmarks={"asset_turnover": 1.2, "employee_productivity": 2.8} ) assert efficiency is not None assert isinstance(efficiency, dict) # Should contain efficiency metrics assert "segment_efficiency" in efficiency assert "efficiency_ranking" in efficiency assert "improvement_opportunities" in efficiency # Should calculate efficiency for each segment segment_efficiency = efficiency["segment_efficiency"] assert len(segment_efficiency) == 3 for segment in segment_efficiency: assert "name" in segment assert "asset_turnover" in segment assert "employee_productivity" in segment assert "efficiency_score" in segment @pytest.mark.asyncio async def test_error_handling_insufficient_data(self, segment_analyzer): """Test error handling for insufficient data""" # Test with insufficient segment data insufficient_data = {"company_code": "005930", "segments": []} with pytest.raises(InsufficientDataError): await segment_analyzer.analyze_segment_performance(insufficient_data) @pytest.mark.asyncio async def test_error_handling_invalid_parameters(self, segment_analyzer, sample_segment_data): """Test error handling for invalid parameters""" # Test with invalid analysis years with pytest.raises(MCPStockDetailsError): await segment_analyzer.analyze_segment_performance( segment_data=sample_segment_data, analysis_years=0 # Invalid zero years ) # Test with invalid growth period with pytest.raises(MCPStockDetailsError): await segment_analyzer.calculate_segment_growth_rates( segment_data=sample_segment_data, growth_period="invalid_period" # Invalid period format )