restore value-investing from before-directory-restructure-20240325 to pangtong-value/research

2026-03-25 21:03:31 +08:00
parent 2b569d5a98
commit 99631e3edc
18 changed files with 6801 additions and 0 deletions
@@ -0,0 +1,194 @@
+#!/usr/bin/env python3
+"""
+超级财务智能体 - 并行财务因子计算引擎
+启动时间：2026-03-21 17:45
+截止时间：18:00
+"""
+
+import sys
+import os
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import warnings
+warnings.filterwarnings('ignore')
+
+class SuperFinancialAgent:
+    """超级财务智能体"""
+    
+    def __init__(self):
+        self.start_time = datetime.now()
+        self.cpu_cores = min(10, mp.cpu_count())
+        print(f"⚡ 超级财务智能体启动！")
+        print(f"🕐 启动时间: {self.start_time.strftime('%H:%M:%S')}")
+        print(f"🎯 目标时间: 18:00")
+        print(f"⏰ 剩余时间: {15}分钟")
+        print(f"💻 CPU核心: {self.cpu_cores}核并行")
+        
+    def calculate_valuation_factors(self, stock_data):
+        """计算估值因子（核心1-3）"""
+        factors = {}
+        
+        # 1. 市盈率因子
+        if 'pe_ratio' in stock_data.columns:
+            factors['pe_rank'] = stock_data['pe_ratio'].rank(pct=True)
+            factors['pe_zscore'] = (stock_data['pe_ratio'] - stock_data['pe_ratio'].mean()) / stock_data['pe_ratio'].std()
+        
+        # 2. 市净率因子
+        if 'pb_ratio' in stock_data.columns:
+            factors['pb_rank'] = stock_data['pb_ratio'].rank(pct=True)
+            factors['pb_zscore'] = (stock_data['pb_ratio'] - stock_data['pb_ratio'].mean()) / stock_data['pb_ratio'].std()
+        
+        # 3. 市销率因子
+        if 'ps_ratio' in stock_data.columns:
+            factors['ps_rank'] = stock_data['ps_ratio'].rank(pct=True)
+            factors['ps_zscore'] = (stock_data['ps_ratio'] - stock_data['ps_ratio'].mean()) / stock_data['ps_ratio'].std()
+        
+        return factors
+    
+    def calculate_quality_factors(self, stock_data):
+        """计算质量因子（核心4-6）"""
+        factors = {}
+        
+        # 4. ROE因子
+        if 'roe' in stock_data.columns:
+            factors['roe_rank'] = stock_data['roe'].rank(pct=True)
+            factors['roe_stability'] = stock_data['roe'].rolling(5).std()
+        
+        # 5. 毛利率因子
+        if 'gross_margin' in stock_data.columns:
+            factors['gross_margin_rank'] = stock_data['gross_margin'].rank(pct=True)
+            factors['margin_stability'] = stock_data['gross_margin'].rolling(5).std()
+        
+        # 6. 现金流因子
+        if 'free_cash_flow' in stock_data.columns:
+            factors['fcf_rank'] = stock_data['free_cash_flow'].rank(pct=True)
+            factors['fcf_yield'] = stock_data['free_cash_flow'] / stock_data['market_cap']
+        
+        return factors
+    
+    def calculate_growth_factors(self, stock_data):
+        """计算成长因子（核心7-8）"""
+        factors = {}
+        
+        # 7. 营收增长因子
+        if 'revenue' in stock_data.columns:
+            revenue_growth = stock_data['revenue'].pct_change(periods=4)
+            factors['revenue_growth_rank'] = revenue_growth.rank(pct=True)
+        
+        # 8. 盈利增长因子
+        if 'net_profit' in stock_data.columns:
+            profit_growth = stock_data['net_profit'].pct_change(periods=4)
+            factors['profit_growth_rank'] = profit_growth.rank(pct=True)
+        
+        return factors
+    
+    def calculate_risk_factors(self, stock_data):
+        """计算风险因子（核心9-10）"""
+        factors = {}
+        
+        # 9. 波动率因子
+        if 'close' in stock_data.columns:
+            volatility = stock_data['close'].rolling(20).std()
+            factors['volatility_rank'] = volatility.rank(pct=True)
+        
+        # 10. 流动性因子
+        if 'volume' in stock_data.columns:
+            avg_volume = stock_data['volume'].rolling(20).mean()
+            factors['liquidity_rank'] = avg_volume.rank(pct=True)
+        
+        return factors
+    
+    def parallel_factor_calculation(self, stock_data):
+        """并行计算所有因子"""
+        print(f"🔢 开始并行因子计算...")
+        
+        # 准备任务
+        tasks = [
+            (self.calculate_valuation_factors, stock_data),
+            (self.calculate_quality_factors, stock_data),
+            (self.calculate_growth_factors, stock_data),
+            (self.calculate_risk_factors, stock_data)
+        ]
+        
+        # 并行计算
+        all_factors = {}
+        with ProcessPoolExecutor(max_workers=self.cpu_cores) as executor:
+            future_to_task = {executor.submit(func, data): (func.__name__, data) for func, data in tasks}
+            
+            for future in as_completed(future_to_task):
+                task_name, _ = future_to_task[future]
+                try:
+                    factors = future.result()
+                    all_factors.update(factors)
+                    print(f"✅ {task_name} 计算完成")
+                except Exception as e:
+                    print(f"❌ {task_name} 计算失败: {e}")
+        
+        return all_factors
+
+def main():
+    """主函数"""
+    agent = SuperFinancialAgent()
+    
+    # 1. 生成模拟数据（实际项目中从数据源获取）
+    print(f"\n📊 生成模拟财务数据...")
+    np.random.seed(42)
+    n_stocks = 3000
+    
+    stock_data = pd.DataFrame({
+        'stock_code': [f'{i:06d}.XSHE' for i in range(1, n_stocks + 1)],
+        'pe_ratio': np.random.uniform(5, 50, n_stocks),
+        'pb_ratio': np.random.uniform(0.5, 5, n_stocks),
+        'ps_ratio': np.random.uniform(0.5, 10, n_stocks),
+        'roe': np.random.uniform(0.05, 0.3, n_stocks),
+        'gross_margin': np.random.uniform(0.2, 0.6, n_stocks),
+        'free_cash_flow': np.random.uniform(1e6, 1e9, n_stocks),
+        'market_cap': np.random.uniform(1e8, 1e11, n_stocks),
+        'revenue': np.random.uniform(1e7, 1e10, n_stocks),
+        'net_profit': np.random.uniform(1e6, 1e9, n_stocks),
+        'close': np.random.uniform(10, 100, n_stocks),
+        'volume': np.random.uniform(1e5, 1e7, n_stocks)
+    })
+    
+    print(f"✅ 生成 {n_stocks} 只股票财务数据")
+    
+    # 2. 并行计算因子
+    factors = agent.parallel_factor_calculation(stock_data)
+    
+    # 3. 合并因子数据
+    for factor_name, factor_values in factors.items():
+        stock_data[factor_name] = factor_values
+    
+    # 4. 计算综合价值得分
+    print(f"\n🎯 计算综合价值得分...")
+    
+    # 价值因子（越低越好）
+    value_factors = ['pe_rank', 'pb_rank', 'ps_rank']
+    value_score = stock_data[value_factors].mean(axis=1)
+    
+    # 质量因子（越高越好）
+    quality_factors = ['roe_rank', 'gross_margin_rank', 'fcf_rank']
+    quality_score = stock_data[quality_factors].mean(axis=1)
+    
+    # 综合得分：价值得分（高） + 质量得分（高）
+    stock_data['value_quality_score'] = (1 - value_score) * 0.6 + quality_score * 0.4
+    stock_data['value_quality_rank'] = stock_data['value_quality_score'].rank(ascending=False, pct=True)
+    
+    # 5. 输出结果
+    print(f"\n📈 计算完成！")
+    print(f"⏰ 耗时: {(datetime.now() - agent.start_time).total_seconds():.2f}秒")
+    print(f"📊 总因子数: {len(factors)}个")
+    print(f"🏆 综合价值得分计算完成")
+    
+    # 显示前10名
+    top_stocks = stock_data.nlargest(10, 'value_quality_score')[['stock_code', 'value_quality_score', 'value_quality_rank']]
+    print(f"\n🏅 价值投资前10名:")
+    print(top_stocks.to_string(index=False))
+    
+    return stock_data
+
+if __name__ == "__main__":
+    stock_data = main()