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 +1,391 @@
+#!/usr/bin/env python3
+"""
+实时价值因子监测面板
+更新时间：每5分钟
+"""
+
+import dash
+from dash import dcc, html, Input, Output
+import plotly.graph_objs as go
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+import threading
+import time
+
+class RealTimeValueDashboard:
+    """实时价值因子监测面板"""
+    
+    def __init__(self):
+        self.app = dash.Dash(__name__)
+        self.data = None
+        self.update_interval = 300  # 5分钟更新
+        self.last_update = datetime.now()
+        
+        # 初始化布局
+        self.setup_layout()
+        
+    def setup_layout(self):
+        """设置仪表板布局"""
+        self.app.layout = html.Div([
+            # 标题栏
+            html.Div([
+                html.H1("📊 实时价值因子监测面板", style={'textAlign': 'center', 'color': '#2E86C1'}),
+                html.Div([
+                    html.Span("🕐 最后更新: ", style={'fontWeight': 'bold'}),
+                    html.Span(id='last-update-time'),
+                    html.Span(" | ", style={'margin': '0 10px'}),
+                    html.Span("📈 监控股票数: ", style={'fontWeight': 'bold'}),
+                    html.Span(id='stock-count'),
+                    html.Span(" | ", style={'margin': '0 10px'}),
+                    html.Span("⚡ 更新频率: ", style={'fontWeight': 'bold'}),
+                    html.Span("5分钟")
+                ], style={'textAlign': 'center', 'marginBottom': '20px'})
+            ]),
+            
+            # 第一行：关键指标
+            html.Div([
+                html.Div([
+                    html.H3("🏆 价值投资Top 10", style={'textAlign': 'center'}),
+                    dcc.Graph(id='top-10-chart', style={'height': '400px'})
+                ], className='six columns'),
+                
+                html.Div([
+                    html.H3("📊 因子分布", style={'textAlign': 'center'}),
+                    dcc.Graph(id='factor-distribution', style={'height': '400px'})
+                ], className='six columns')
+            ], className='row'),
+            
+            # 第二行：详细分析
+            html.Div([
+                html.Div([
+                    html.H3("💰 估值因子热力图", style={'textAlign': 'center'}),
+                    dcc.Graph(id='valuation-heatmap', style={'height': '400px'})
+                ], className='six columns'),
+                
+                html.Div([
+                    html.H3("📈 质量因子趋势", style={'textAlign': 'center'}),
+                    dcc.Graph(id='quality-trend', style={'height': '400px'})
+                ], className='six columns')
+            ], className='row'),
+            
+            # 第三行：控制面板
+            html.Div([
+                html.Div([
+                    html.H3("⚙️ 控制面板", style={'textAlign': 'center'}),
+                    html.Div([
+                        html.Label("选择行业:"),
+                        dcc.Dropdown(
+                            id='industry-selector',
+                            options=[
+                                {'label': '全部行业', 'value': 'all'},
+                                {'label': '金融', 'value': 'financial'},
+                                {'label': '科技', 'value': 'tech'},
+                                {'label': '消费', 'value': 'consumer'},
+                                {'label': '医药', 'value': 'medical'},
+                                {'label': '工业', 'value': 'industrial'}
+                            ],
+                            value='all',
+                            style={'marginBottom': '20px'}
+                        ),
+                        
+                        html.Label("选择市值范围:"),
+                        dcc.RangeSlider(
+                            id='market-cap-slider',
+                            min=0,
+                            max=1000,
+                            step=50,
+                            value=[100, 500],
+                            marks={i: f'{i}亿' for i in range(0, 1001, 100)},
+                            style={'marginBottom': '20px'}
+                        ),
+                        
+                        html.Button('🔄 立即更新数据', id='update-button', n_clicks=0,
+                                   style={'width': '100%', 'padding': '10px', 'backgroundColor': '#2E86C1', 'color': 'white'})
+                    ])
+                ], className='six columns'),
+                
+                html.Div([
+                    html.H3("📋 实时数据表", style={'textAlign': 'center'}),
+                    html.Div(id='real-time-table', style={'height': '400px', 'overflowY': 'scroll'})
+                ], className='six columns')
+            ], className='row'),
+            
+            # 定时器
+            dcc.Interval(
+                id='interval-component',
+                interval=5*60*1000,  # 5分钟
+                n_intervals=0
+            )
+        ])
+        
+        # 设置回调
+        self.setup_callbacks()
+    
+    def setup_callbacks(self):
+        """设置回调函数"""
+        
+        @self.app.callback(
+            [Output('last-update-time', 'children'),
+             Output('stock-count', 'children'),
+             Output('top-10-chart', 'figure'),
+             Output('factor-distribution', 'figure'),
+             Output('valuation-heatmap', 'figure'),
+             Output('quality-trend', 'figure'),
+             Output('real-time-table', 'children')],
+            [Input('interval-component', 'n_intervals'),
+             Input('update-button', 'n_clicks'),
+             Input('industry-selector', 'value'),
+             Input('market-cap-slider', 'value')]
+        )
+        def update_dashboard(n_intervals, n_clicks, industry, market_cap_range):
+            """更新仪表板"""
+            # 更新数据
+            self.update_data()
+            
+            # 过滤数据
+            filtered_data = self.filter_data(industry, market_cap_range)
+            
+            # 更新时间
+            current_time = datetime.now().strftime('%H:%M:%S')
+            
+            # 1. Top 10图表
+            top_10_fig = self.create_top_10_chart(filtered_data)
+            
+            # 2. 因子分布图
+            factor_fig = self.create_factor_distribution(filtered_data)
+            
+            # 3. 估值热力图
+            heatmap_fig = self.create_valuation_heatmap(filtered_data)
+            
+            # 4. 质量趋势图
+            trend_fig = self.create_quality_trend(filtered_data)
+            
+            # 5. 实时数据表
+            table = self.create_real_time_table(filtered_data)
+            
+            return [
+                current_time,
+                len(filtered_data),
+                top_10_fig,
+                factor_fig,
+                heatmap_fig,
+                trend_fig,
+                table
+            ]
+    
+    def update_data(self):
+        """更新数据"""
+        # 这里应该从数据源获取实时数据
+        # 暂时使用模拟数据
+        np.random.seed(int(time.time()))
+        n_stocks = 3000
+        
+        self.data = pd.DataFrame({
+            'stock_code': [f'{i:06d}.XSHE' for i in range(1, n_stocks + 1)],
+            'industry': np.random.choice(['financial', 'tech', 'consumer', 'medical', 'industrial'], n_stocks),
+            'market_cap': np.random.uniform(50, 1000, n_stocks),
+            'pe_ratio': np.random.uniform(5, 50, n_stocks),
+            'pb_ratio': np.random.uniform(0.5, 5, n_stocks),
+            'roe': np.random.uniform(0.05, 0.3, n_stocks),
+            'gross_margin': np.random.uniform(0.2, 0.6, n_stocks),
+            'value_score': np.random.uniform(0, 1, n_stocks),
+            'quality_score': np.random.uniform(0, 1, n_stocks),
+            'composite_score': np.random.uniform(0, 1, n_stocks)
+        })
+        
+        self.last_update = datetime.now()
+    
+    def filter_data(self, industry, market_cap_range):
+        """过滤数据"""
+        if self.data is None:
+            return pd.DataFrame()
+        
+        filtered = self.data.copy()
+        
+        # 按行业过滤
+        if industry != 'all':
+            filtered = filtered[filtered['industry'] == industry]
+        
+        # 按市值过滤
+        filtered = filtered[(filtered['market_cap'] >= market_cap_range[0]) & 
+                           (filtered['market_cap'] <= market_cap_range[1])]
+        
+        return filtered
+    
+    def create_top_10_chart(self, data):
+        """创建Top 10图表"""
+        if len(data) == 0:
+            return {}
+        
+        top_10 = data.nlargest(10, 'composite_score')
+        
+        fig = go.Figure(data=[
+            go.Bar(
+                x=top_10['stock_code'],
+                y=top_10['composite_score'],
+                text=top_10['composite_score'].round(3),
+                textposition='auto',
+                marker_color='#2E86C1'
+            )
+        ])
+        
+        fig.update_layout(
+            title='价值投资综合得分Top 10',
+            xaxis_title='股票代码',
+            yaxis_title='综合得分',
+            yaxis_range=[0, 1]
+        )
+        
+        return fig
+    
+    def create_factor_distribution(self, data):
+        """创建因子分布图"""
+        if len(data) == 0:
+            return {}
+        
+        fig = go.Figure()
+        
+        # 估值因子分布
+        fig.add_trace(go.Histogram(
+            x=data['pe_ratio'],
+            name='市盈率分布',
+            opacity=0.7,
+            marker_color='#E74C3C'
+        ))
+        
+        # 质量因子分布
+        fig.add_trace(go.Histogram(
+            x=data['roe'],
+            name='ROE分布',
+            opacity=0.7,
+            marker_color='#2ECC71'
+        ))
+        
+        fig.update_layout(
+            title='因子分布图',
+            xaxis_title='因子值',
+            yaxis_title='频数',
+            barmode='overlay'
+        )
+        
+        return fig
+    
+    def create_valuation_heatmap(self, data):
+        """创建估值热力图"""
+        if len(data) == 0:
+            return {}
+        
+        # 创建热力图数据
+        heatmap_data = data.pivot_table(
+            values='composite_score',
+            index=pd.cut(data['pe_ratio'], bins=10),
+            columns=pd.cut(data['pb_ratio'], bins=10),
+            aggfunc='mean'
+        )
+        
+        fig = go.Figure(data=go.Heatmap(
+            z=heatmap_data.values,
+            x=[f'{col.left:.1f}-{col.right:.1f}' for col in heatmap_data.columns],
+            y=[f'{idx.left:.1f}-{idx.right:.1f}' for idx in heatmap_data.index],
+            colorscale='Viridis'
+        ))
+        
+        fig.update_layout(
+            title='估值因子热力图 (PE vs PB)',
+            xaxis_title='市净率(PB)区间',
+            yaxis_title='市盈率(PE)区间'
+        )
+        
+        return fig
+    
+    def create_quality_trend(self, data):
+        """创建质量趋势图"""
+        if len(data) == 0:
+            return {}
+        
+        # 按行业分组计算平均质量得分
+        industry_quality = data.groupby('industry').agg({
+            'roe': 'mean',
+            'gross_margin': 'mean',
+            'quality_score': 'mean'
+        }).reset_index()
+        
+        fig = go.Figure()
+        
+        fig.add_trace(go.Scatter(
+            x=industry_quality['industry'],
+            y=industry_quality['roe'],
+            mode='lines+markers',
+            name='ROE',
+            line=dict(color='#2ECC71', width=3)
+        ))
+        
+        fig.add_trace(go.Scatter(
+            x=industry_quality['industry'],
+            y=industry_quality['gross_margin'],
+            mode='lines+markers',
+            name='毛利率',
+            line=dict(color='#E74C3C', width=3)
+        ))
+        
+        fig.update_layout(
+            title='各行业质量因子趋势',
+            xaxis_title='行业',
+            yaxis_title='因子值',
+            yaxis_range=[0, 1]
+        )
+        
+        return fig
+    
+    def create_real_time_table(self, data):
+        """创建实时数据表"""
+        if len(data) == 0:
+            return "暂无数据"
+        
+        top_20 = data.nlargest(20, 'composite_score')
+        
+        table = html.Table([
+            html.Thead(
+                html.Tr([
+                    html.Th('股票代码'),
+                    html.Th('行业'),
+                    html.Th('市值(亿)'),
+                    html.Th('PE'),
+                    html.Th('PB'),
+                    html.Th('ROE'),
+                    html.Th('综合得分')
+                ])
+            ),
+            html.Tbody([
+                html.Tr([
+                    html.Td(row['stock_code']),
+                    html.Td(row['industry']),
+                    html.Td(f"{row['market_cap']:.1f}"),
+                    html.Td(f"{row['pe_ratio']:.1f}"),
+                    html.Td(f"{row['pb_ratio']:.2f}"),
+                    html.Td(f"{row['roe']:.2%}"),
+                    html.Td(f"{row['composite_score']:.3f}")
+                ]) for _, row in top_20.iterrows()
+            ])
+        ], style={'width': '100%', 'borderCollapse': 'collapse'})
+        
+        return table
+    
+    def run(self, debug=False):
+        """运行仪表板"""
+        print(f"🚀 启动实时价值因子监测面板...")
+        print(f"📊 监控股票数: 3000+")
+        print(f"⚡ 更新频率: 每5分钟")
+        print(f"🌐 访问地址: http://127.0.0.1:8050")
+        print(f"🕐 启动时间: {datetime.now().strftime('%H:%M:%S')}")
+        
+        self.app.run_server(debug=debug)
+
+def main():
+    """主函数"""
+    dashboard = RealTimeValueDashboard()
+    dashboard.run(debug=False)
+
+if __name__ == '__main__':
+    main()