MCP Stock Details Server

""" Unit tests for ValuationAnalyzer class TDD Red Phase: Write failing tests for valuation analysis engine """ import pytest import asyncio from unittest.mock import Mock, AsyncMock, patch from datetime import datetime, date from typing import Dict, Any, List, Optional # Test imports - initially will fail (TDD Red phase) from src.tools.valuation_tools import ValuationAnalyzer from src.exceptions import MCPStockDetailsError, InsufficientDataError class TestValuationAnalyzer: """Test cases for ValuationAnalyzer class""" @pytest.fixture def valuation_analyzer(self): """Create ValuationAnalyzer instance for testing""" return ValuationAnalyzer() @pytest.fixture def sample_financial_data(self): """Sample financial data for valuation calculations""" return { "company_code": "005930", "current_price": 73000, "shares_outstanding": 5_969_782_550, "market_cap": 435_000_000_000_000, "financial_metrics": { "revenue": 258_774_000_000_000, "net_income": 15_349_000_000_000, "book_value": 319_684_000_000_000, "ebitda": 28_500_000_000_000, "total_debt": 85_000_000_000_000, "cash": 25_000_000_000_000, "free_cash_flow": 29_163_000_000_000, "dividend_per_share": 361, "eps": 2570 # Earnings per share } } @pytest.mark.asyncio async def test_calculate_price_multiples(self, valuation_analyzer, sample_financial_data): """Test price multiples calculation (PER, PBR, PSR)""" multiples = await valuation_analyzer.calculate_price_multiples(sample_financial_data) assert multiples is not None assert isinstance(multiples, dict) # Should contain key price multiples assert "per" in multiples # Price-to-Earnings Ratio assert "pbr" in multiples # Price-to-Book Ratio assert "psr" in multiples # Price-to-Sales Ratio # Values should be reasonable assert multiples["per"] > 0 assert multiples["pbr"] > 0 assert multiples["psr"] > 0 # Should include additional metrics assert "market_cap" in multiples assert "book_value_per_share" in multiples @pytest.mark.asyncio async def test_calculate_enterprise_value_multiples(self, valuation_analyzer, sample_financial_data): """Test enterprise value multiples calculation""" ev_multiples = await valuation_analyzer.calculate_ev_multiples(sample_financial_data) assert ev_multiples is not None assert isinstance(ev_multiples, dict) # Should contain EV multiples assert "ev_ebitda" in ev_multiples assert "ev_sales" in ev_multiples assert "ev_fcf" in ev_multiples # EV/Free Cash Flow # Should include enterprise value calculation assert "enterprise_value" in ev_multiples assert "net_debt" in ev_multiples # Values should be positive assert ev_multiples["enterprise_value"] > 0 assert ev_multiples["ev_ebitda"] > 0 @pytest.mark.asyncio async def test_historical_valuation_bands(self, valuation_analyzer): """Test historical valuation bands analysis""" company_code = "005930" period = "3Y" bands = await valuation_analyzer.calculate_historical_bands( company_code=company_code, period=period, metrics=["per", "pbr"] ) assert bands is not None assert isinstance(bands, dict) # Should contain band analysis for each metric for metric in ["per", "pbr"]: assert metric in bands metric_bands = bands[metric] assert "current" in metric_bands assert "mean" in metric_bands assert "median" in metric_bands assert "std_dev" in metric_bands assert "percentile_25" in metric_bands assert "percentile_75" in metric_bands assert "min" in metric_bands assert "max" in metric_bands @pytest.mark.asyncio async def test_peer_valuation_comparison(self, valuation_analyzer): """Test peer group valuation comparison""" company_code = "005930" industry_code = "26211" # Semiconductor peer_comparison = await valuation_analyzer.compare_with_peers( company_code=company_code, industry_code=industry_code, metrics=["per", "pbr", "psr", "ev_ebitda"] ) assert peer_comparison is not None assert isinstance(peer_comparison, dict) # Should contain comparison data assert "company_metrics" in peer_comparison assert "peer_metrics" in peer_comparison assert "industry_metrics" in peer_comparison # Should include percentile ranking assert "percentile_ranking" in peer_comparison # Should have peer group information peer_metrics = peer_comparison["peer_metrics"] assert "mean" in peer_metrics assert "median" in peer_metrics assert "peer_count" in peer_metrics @pytest.mark.asyncio async def test_dividend_analysis(self, valuation_analyzer, sample_financial_data): """Test dividend yield and payout analysis""" dividend_analysis = await valuation_analyzer.analyze_dividend_metrics(sample_financial_data) assert dividend_analysis is not None assert isinstance(dividend_analysis, dict) # Should contain dividend metrics assert "dividend_yield" in dividend_analysis assert "payout_ratio" in dividend_analysis assert "dividend_per_share" in dividend_analysis # Should include dividend sustainability metrics assert "dividend_coverage" in dividend_analysis assert "free_cash_flow_payout" in dividend_analysis # Values should be reasonable assert 0 <= dividend_analysis["dividend_yield"] <= 20 # 0-20% assert 0 <= dividend_analysis["payout_ratio"] <= 200 # 0-200% @pytest.mark.asyncio async def test_fair_value_estimation(self, valuation_analyzer, sample_financial_data): """Test fair value estimation using multiple methods""" fair_value = await valuation_analyzer.estimate_fair_value( financial_data=sample_financial_data, methods=["dcf", "multiple", "dividend_discount"] ) assert fair_value is not None assert isinstance(fair_value, dict) # Should contain different valuation methods assert "dcf_value" in fair_value assert "multiple_value" in fair_value assert "dividend_discount_value" in fair_value # Should include weighted average assert "weighted_fair_value" in fair_value assert "valuation_range" in fair_value # Should provide investment recommendation assert "recommendation" in fair_value assert "upside_downside" in fair_value # Values should be positive for method in ["dcf_value", "multiple_value", "weighted_fair_value"]: if fair_value[method] is not None: assert fair_value[method] > 0 @pytest.mark.asyncio async def test_valuation_summary_generation(self, valuation_analyzer, sample_financial_data): """Test comprehensive valuation summary generation""" summary = await valuation_analyzer.generate_valuation_summary( financial_data=sample_financial_data, include_peer_comparison=True, include_historical_analysis=True ) assert summary is not None assert isinstance(summary, dict) # Should contain all major sections assert "price_multiples" in summary assert "ev_multiples" in summary assert "dividend_analysis" in summary assert "fair_value_analysis" in summary # Should include overall assessment assert "valuation_assessment" in summary assert "key_insights" in summary assert "risk_factors" in summary @pytest.mark.asyncio async def test_error_handling_insufficient_data(self, valuation_analyzer): """Test error handling for insufficient data scenarios""" # Test with incomplete financial data incomplete_data = { "company_code": "999999", "current_price": 1000 # Missing other required fields } with pytest.raises(InsufficientDataError): await valuation_analyzer.calculate_price_multiples(incomplete_data) @pytest.mark.asyncio async def test_error_handling_invalid_inputs(self, valuation_analyzer): """Test error handling for invalid inputs""" # Test with negative values invalid_data = { "company_code": "005930", "current_price": -1000, # Invalid negative price "shares_outstanding": 0, # Invalid zero shares "financial_metrics": { "revenue": -100000 # Invalid negative revenue } } with pytest.raises(MCPStockDetailsError): await valuation_analyzer.calculate_price_multiples(invalid_data) @pytest.mark.asyncio async def test_performance_optimization(self, valuation_analyzer, sample_financial_data): """Test performance optimization for valuation calculations""" import time start_time = time.time() # Run comprehensive valuation analysis summary = await valuation_analyzer.generate_valuation_summary( financial_data=sample_financial_data, include_peer_comparison=True, include_historical_analysis=True ) execution_time = time.time() - start_time # Should complete within 2 seconds assert execution_time < 2.0 assert summary is not None # Should contain substantial analysis assert len(summary) >= 5 # Multiple analysis sections