init repo

test_system.py

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+"""
+系统测试脚本
+"""
+import sys
+import os
+sys.path.append(os.path.dirname(os.path.abspath(__file__)))
+
+from main import StockAnalysisSystem
+import logging
+
+# 设置日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+def test_quick_screening():
+    """测试快速筛选功能"""
+    print("=== 测试快速筛选功能 ===")
+    
+    system = StockAnalysisSystem()
+    
+    # 测试一些知名股票
+    test_symbols = ['AAPL', 'MSFT', 'GOOGL', 'TSLA', 'NVDA']
+    
+    for symbol in test_symbols:
+        print(f"\n正在测试 {symbol}...")
+        try:
+            result = system.quick_screening(symbol)
+            
+            if 'error' in result:
+                print(f"❌ {symbol}: {result['error']}")
+            else:
+                status = "✅ 通过" if result.get('passes_screening') else "❌ 未通过"
+                print(f"{status} {symbol}: {result.get('company_name', 'N/A')}")
+                
+                if result.get('criteria_met'):
+                    print("  符合条件:")
+                    for criteria in result['criteria_met']:
+                        print(f"    ✓ {criteria}")
+                
+                if result.get('criteria_failed'):
+                    print("  不符合条件:")
+                    for criteria in result['criteria_failed']:
+                        print(f"    ✗ {criteria}")
+        except Exception as e:
+            print(f"❌ {symbol}: 测试失败 - {e}")
+
+def test_data_collection():
+    """测试数据收集功能"""
+    print("\n=== 测试数据收集功能 ===")
+    
+    system = StockAnalysisSystem()
+    
+    # 测试AAPL数据收集
+    symbol = 'AAPL'
+    print(f"正在收集 {symbol} 数据...")
+    
+    try:
+        raw_data = system.data_collector.collect_all_data(symbol)
+        
+        if raw_data.get('company_info'):
+            company_info = raw_data['company_info']
+            print(f"✅ 公司信息: {company_info.get('name', 'N/A')}")
+            print(f"   行业: {company_info.get('industry', 'N/A')}")
+            print(f"   市值: ${company_info.get('market_cap', 0):,.0f}")
+        else:
+            print("❌ 无法获取公司信息")
+        
+        if not raw_data['stock_prices'].empty:
+            print(f"✅ 股价数据: {len(raw_data['stock_prices'])} 条记录")
+        else:
+            print("❌ 无法获取股价数据")
+        
+        if raw_data['financial_statements']:
+            print(f"✅ 财务数据: {len(raw_data['financial_statements'])} 个期间")
+        else:
+            print("❌ 无法获取财务数据")
+        
+        key_metrics = raw_data.get('key_metrics', {})
+        if key_metrics:
+            print(f"✅ 关键指标: PE={key_metrics.get('pe_ratio', 0):.2f}, PB={key_metrics.get('pb_ratio', 0):.2f}")
+        else:
+            print("❌ 无法获取关键指标")
+            
+    except Exception as e:
+        print(f"❌ 数据收集失败: {e}")
+
+def test_analysis_engine():
+    """测试分析引擎"""
+    print("\n=== 测试分析引擎 ===")
+    
+    system = StockAnalysisSystem()
+    
+    # 先收集数据
+    symbol = 'AAPL'
+    print(f"正在分析 {symbol}...")
+    
+    try:
+        raw_data = system.data_collector.collect_all_data(symbol)
+        
+        if not raw_data.get('company_info'):
+            print("❌ 无法获取数据进行分析")
+            return
+        
+        # 测试各个分析模块
+        print("  估值分析...")
+        valuation = system.analyzer.calculate_valuation_metrics(raw_data)
+        print(f"    估值评分: {valuation.get('valuation_score', 0):.1f}")
+        
+        print("  财务健康度分析...")
+        health = system.analyzer.calculate_financial_health(raw_data)
+        print(f"    健康度评分: {health.get('health_score', 0):.1f}")
+        
+        print("  成长性分析...")
+        growth = system.analyzer.calculate_growth_metrics(raw_data)
+        print(f"    成长性评分: {growth.get('growth_score', 0):.1f}")
+        
+        print("  风险分析...")
+        risk = system.analyzer.calculate_risk_metrics(raw_data)
+        print(f"    风险评分: {risk.get('risk_score', 0):.1f}")
+        
+        print("  DCF估值...")
+        dcf = system.analyzer.perform_dcf_valuation(raw_data)
+        print(f"    DCF价值: ${dcf.get('dcf_value', 0):,.2f}")
+        
+        print("  生成投资建议...")
+        recommendation = system.analyzer.generate_investment_recommendation({
+            'valuation_score': valuation.get('valuation_score', 0),
+            'financial_health_score': health.get('health_score', 0),
+            'growth_score': growth.get('growth_score', 0),
+            'risk_score': risk.get('risk_score', 0)
+        })
+        
+        print(f"    投资建议: {recommendation.get('recommendation', 'N/A')}")
+        print(f"    综合评分: {recommendation.get('overall_score', 0):.1f}")
+        
+        print("✅ 分析引擎测试完成")
+        
+    except Exception as e:
+        print(f"❌ 分析引擎测试失败: {e}")
+
+def main():
+    """运行所有测试"""
+    print("🚀 开始系统测试...")
+    
+    try:
+        # 测试数据收集
+        test_data_collection()
+        
+        # 测试快速筛选
+        test_quick_screening()
+        
+        # 测试分析引擎
+        test_analysis_engine()
+        
+        print("\n✅ 所有测试完成！")
+        print("\n💡 提示:")
+        print("- 如果看到网络错误，请检查网络连接")
+        print("- 如果看到数据获取失败，可能是API限制或股票代码错误")
+        print("- 系统已准备就绪，可以使用 python main.py <股票代码> 进行分析")
+        
+    except Exception as e:
+        print(f"❌ 测试过程中出现错误: {e}")
+
+if __name__ == "__main__":
+    main()