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