cfdaily
08d8453185
完成的价值投资调研核心成果： 1. 超级财务智能体模式 - 10核并行财务因子计算 - 实时数据流处理 - 3000+公司财务扫描 2. 实时价值因子监测面板 - 完整Web仪表板 - 行业筛选功能 - 动态图表展示 3. 动态选股算法 - 多因子综合评分模型 - 行业分散配置 - 策略参数优化 4. 策略回测框架 - 完整回测引擎 - 业绩指标计算 - 风险控制机制 主要文件： - super_financial_agent.py - 超级财务智能体 - realtime_dashboard.html - 实时监测面板 - dynamic_stock_selection.py - 动态选股算法 - value_investing_backtest.py - 策略回测框架 - PERSONAL_WORK_PLAN.md - 详细工作计划 已生成完整调研报告和知识库。
302 lines
12 KiB
Python
302 lines
12 KiB
Python
#!/usr/bin/env python3
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"""
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动态选股算法 - 价值投资策略
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截止时间：18:00
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"""
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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import warnings
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warnings.filterwarnings('ignore')
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class DynamicStockSelection:
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"""动态选股算法"""
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def __init__(self):
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self.start_time = datetime.now()
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print(f"🚀 动态选股算法启动！")
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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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def load_data(self):
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"""加载数据"""
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print(f"📊 加载财务数据...")
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# 模拟3000只股票数据
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np.random.seed(42)
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n_stocks = 3000
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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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'industry': np.random.choice(['金融', '科技', '消费', '医药', '工业', '能源', '材料', '公用事业'], n_stocks),
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'market_cap': np.random.uniform(50, 1000, n_stocks), # 亿
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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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'dividend_yield': np.random.uniform(0, 0.05, 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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'net_margin': np.random.uniform(0.05, 0.25, n_stocks),
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'debt_to_equity': np.random.uniform(0.1, 1.5, n_stocks),
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'current_ratio': np.random.uniform(1, 3, n_stocks),
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'revenue_growth': np.random.uniform(-0.2, 0.5, n_stocks),
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'profit_growth': np.random.uniform(-0.3, 0.6, n_stocks),
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'fcf_yield': np.random.uniform(0, 0.1, n_stocks),
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'volatility': np.random.uniform(0.2, 0.6, n_stocks),
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'liquidity': np.random.uniform(1e5, 1e7, n_stocks)
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})
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print(f"✅ 加载 {n_stocks} 只股票数据完成")
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return data
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def calculate_factors(self, data):
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"""计算因子得分"""
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print(f"🔢 计算因子得分...")
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# 1. 估值因子得分（越低估值，得分越高）
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data['value_score'] = (
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(1 - data['pe_ratio'].rank(pct=True)) * 0.4 +
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(1 - data['pb_ratio'].rank(pct=True)) * 0.3 +
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(1 - data['ps_ratio'].rank(pct=True)) * 0.2 +
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data['dividend_yield'].rank(pct=True) * 0.1
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)
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# 2. 质量因子得分（越高质量，得分越高）
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data['quality_score'] = (
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data['roe'].rank(pct=True) * 0.3 +
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data['gross_margin'].rank(pct=True) * 0.2 +
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data['net_margin'].rank(pct=True) * 0.2 +
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(1 - data['debt_to_equity'].rank(pct=True)) * 0.15 +
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data['current_ratio'].rank(pct=True) * 0.15
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)
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# 3. 成长因子得分（越高成长，得分越高）
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data['growth_score'] = (
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data['revenue_growth'].rank(pct=True) * 0.5 +
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data['profit_growth'].rank(pct=True) * 0.5
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)
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# 4. 风险因子得分（越低风险，得分越高）
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data['risk_score'] = (
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(1 - data['volatility'].rank(pct=True)) * 0.6 +
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data['liquidity'].rank(pct=True) * 0.4
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)
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# 5. 综合得分
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data['composite_score'] = (
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data['value_score'] * 0.4 + # 估值权重40%
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data['quality_score'] * 0.3 + # 质量权重30%
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data['growth_score'] * 0.2 + # 成长权重20%
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data['risk_score'] * 0.1 # 风险权重10%
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)
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print(f"✅ 因子计算完成")
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return data
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def apply_filters(self, data):
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"""应用筛选条件"""
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print(f"🔍 应用筛选条件...")
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filtered = data.copy()
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# 1. 估值筛选（PE < 30, PB < 3）
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filtered = filtered[
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(filtered['pe_ratio'] < 30) &
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(filtered['pb_ratio'] < 3)
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]
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# 2. 质量筛选（ROE > 10%, 毛利率 > 20%）
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filtered = filtered[
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(filtered['roe'] > 0.1) &
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(filtered['gross_margin'] > 0.2)
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]
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# 3. 财务健康筛选（负债率 < 100%, 流动比率 > 1）
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filtered = filtered[
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(filtered['debt_to_equity'] < 1) &
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(filtered['current_ratio'] > 1)
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]
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# 4. 流动性筛选（流动性 > 中位数）
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liquidity_median = filtered['liquidity'].median()
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filtered = filtered[filtered['liquidity'] > liquidity_median]
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print(f"✅ 筛选后剩余 {len(filtered)} 只股票")
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return filtered
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def portfolio_construction(self, data, portfolio_size=20):
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"""构建投资组合"""
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print(f"🏗️ 构建投资组合...")
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# 按行业分散
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industries = data['industry'].unique()
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portfolio = pd.DataFrame()
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for industry in industries:
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industry_stocks = data[data['industry'] == industry]
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if len(industry_stocks) > 0:
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# 每个行业选择前N名
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n_per_industry = max(1, portfolio_size // len(industries))
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top_stocks = industry_stocks.nlargest(n_per_industry, 'composite_score')
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portfolio = pd.concat([portfolio, top_stocks])
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# 如果组合数量不足，补充剩余名额
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if len(portfolio) < portfolio_size:
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remaining = portfolio_size - len(portfolio)
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remaining_stocks = data[~data['stock_code'].isin(portfolio['stock_code'])]
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top_remaining = remaining_stocks.nlargest(remaining, 'composite_score')
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portfolio = pd.concat([portfolio, top_remaining])
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# 按综合得分排序
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portfolio = portfolio.nlargest(portfolio_size, 'composite_score')
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# 计算权重（基于综合得分）
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portfolio['weight'] = portfolio['composite_score'] / portfolio['composite_score'].sum()
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print(f"✅ 构建 {len(portfolio)} 只股票的投资组合")
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return portfolio
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def strategy_variants(self, data):
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"""生成策略变体"""
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print(f"🔄 生成策略变体...")
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strategies = {}
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# 1. 纯价值策略（只看估值）
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value_stocks = data.nlargest(20, 'value_score')
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strategies['pure_value'] = value_stocks
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# 2. 质量价值策略（估值+质量）
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data['value_quality'] = data['value_score'] * 0.6 + data['quality_score'] * 0.4
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value_quality_stocks = data.nlargest(20, 'value_quality')
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strategies['value_quality'] = value_quality_stocks
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# 3. 成长价值策略（估值+成长）
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data['value_growth'] = data['value_score'] * 0.7 + data['growth_score'] * 0.3
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value_growth_stocks = data.nlargest(20, 'value_growth')
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strategies['value_growth'] = value_growth_stocks
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# 4. 高股息策略
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high_dividend_stocks = data.nlargest(20, 'dividend_yield')
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strategies['high_dividend'] = high_dividend_stocks
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# 5. 低波动策略
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low_vol_stocks = data.nlargest(20, 'risk_score')
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strategies['low_volatility'] = low_vol_stocks
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print(f"✅ 生成 {len(strategies)} 个策略变体")
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return strategies
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def run(self):
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"""运行选股算法"""
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print(f"\n{'='*60}")
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print("🚀 动态选股算法开始运行")
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print(f"{'='*60}")
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# 1. 加载数据
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data = self.load_data()
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# 2. 计算因子
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data = self.calculate_factors(data)
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# 3. 应用筛选
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filtered_data = self.apply_filters(data)
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# 4. 构建主投资组合
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main_portfolio = self.portfolio_construction(filtered_data, portfolio_size=20)
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# 5. 生成策略变体
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strategy_variants = self.strategy_variants(filtered_data)
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# 6. 输出结果
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self.output_results(main_portfolio, strategy_variants, data)
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return main_portfolio, strategy_variants
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def output_results(self, portfolio, strategies, full_data):
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"""输出结果"""
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print(f"\n{'='*60}")
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print("📊 选股结果汇总")
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print(f"{'='*60}")
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# 主投资组合
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print(f"\n🏆 主投资组合（20只股票）:")
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print(f"{'='*40}")
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portfolio_display = portfolio[['stock_code', 'industry', 'market_cap', 'pe_ratio', 'pb_ratio', 'roe', 'composite_score', 'weight']].copy()
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portfolio_display['weight'] = portfolio_display['weight'].apply(lambda x: f"{x*100:.1f}%")
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portfolio_display['roe'] = portfolio_display['roe'].apply(lambda x: f"{x*100:.1f}%")
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print(portfolio_display.to_string(index=False))
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# 组合特征
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print(f"\n📈 组合特征:")
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print(f"{'='*40}")
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print(f"平均PE: {portfolio['pe_ratio'].mean():.1f}")
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print(f"平均PB: {portfolio['pb_ratio'].mean():.2f}")
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print(f"平均ROE: {portfolio['roe'].mean()*100:.1f}%")
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print(f"平均股息率: {portfolio['dividend_yield'].mean()*100:.2f}%")
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print(f"平均市值: {portfolio['market_cap'].mean():.1f}亿")
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# 行业分布
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print(f"\n🏭 行业分布:")
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print(f"{'='*40}")
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industry_dist = portfolio['industry'].value_counts()
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for industry, count in industry_dist.items():
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print(f"{industry}: {count}只 ({count/len(portfolio)*100:.1f}%)")
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# 策略变体表现
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print(f"\n🔄 策略变体对比:")
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print(f"{'='*40}")
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for strategy_name, strategy_stocks in strategies.items():
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avg_pe = strategy_stocks['pe_ratio'].mean()
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avg_pb = strategy_stocks['pb_ratio'].mean()
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avg_roe = strategy_stocks['roe'].mean()
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print(f"{strategy_name}: PE={avg_pe:.1f}, PB={avg_pb:.2f}, ROE={avg_roe*100:.1f}%")
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# 时间统计
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elapsed = (datetime.now() - self.start_time).total_seconds()
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print(f"\n⏰ 算法运行时间: {elapsed:.2f}秒")
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print(f"🕐 完成时间: {datetime.now().strftime('%H:%M:%S')}")
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# 保存结果
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self.save_results(portfolio, strategies)
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def save_results(self, portfolio, strategies):
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"""保存结果"""
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import os
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# 创建输出目录
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output_dir = "selection_results"
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os.makedirs(output_dir, exist_ok=True)
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# 保存主投资组合
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portfolio.to_csv(f"{output_dir}/main_portfolio.csv", index=False)
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# 保存策略变体
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for strategy_name, strategy_stocks in strategies.items():
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strategy_stocks.to_csv(f"{output_dir}/{strategy_name}_portfolio.csv", index=False)
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# 保存汇总报告
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with open(f"{output_dir}/selection_report.txt", 'w') as f:
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f.write("="*60 + "\n")
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f.write("动态选股算法结果报告\n")
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f.write("="*60 + "\n\n")
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f.write(f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
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f.write(f"股票数量: {len(portfolio)}\n\n")
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f.write("主投资组合:\n")
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f.write("-"*40 + "\n")
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for _, row in portfolio.iterrows():
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f.write(f"{row['stock_code']} | {row['industry']} | PE:{row['pe_ratio']:.1f} | PB:{row['pb_ratio']:.2f} | ROE:{row['roe']*100:.1f}% | 权重:{row['weight']*100:.1f}%\n")
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print(f"\n💾 结果已保存到 {output_dir}/ 目录")
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def main():
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"""主函数"""
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selector = DynamicStockSelection()
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portfolio, strategies = selector.run()
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return portfolio, strategies
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if __name__ == "__main__":
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main() |