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