436 lines
17 KiB
Python
436 lines
17 KiB
Python
"""
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天下体系 - 儒门天下观资本生态分析Tab
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基于"天命树"结构模型分析全球资本市场权力结构
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重构版本：
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- 移除硬编码API密钥
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- 使用统一配置管理
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- 改进数据结构
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- 增强错误处理
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"""
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import streamlit as st
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import pandas as pd
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import plotly.express as px
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from datetime import datetime
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import time
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import random
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from typing import Dict, List, Any, Optional
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from dataclasses import dataclass
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# 导入配置管理
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try:
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from config.settings import get_rapidapi_key
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except ImportError:
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# 如果配置模块不可用，使用环境变量
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import os
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def get_rapidapi_key():
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return os.getenv('RAPIDAPI_KEY', '')
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@dataclass
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class StockEntity:
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"""股票实体数据类"""
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symbol: str
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name: str
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role: str
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dependency: Optional[str] = None
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serves: Optional[str] = None
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type: Optional[str] = None
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@dataclass
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class EcosystemData:
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"""生态系统数据类"""
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tianzi: Dict[str, str]
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dafu: List[StockEntity]
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shi: List[StockEntity]
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jiajie: List[StockEntity]
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class TianxiaAnalyzer:
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"""天下体系分析器 - 天命树结构分析"""
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def __init__(self):
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"""初始化分析器"""
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try:
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self.rapidapi_key = get_rapidapi_key()
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except Exception:
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self.rapidapi_key = ""
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st.warning("⚠️ 未配置RapidAPI密钥，将使用模拟数据")
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# 定义三大天命树生态系统
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self.ecosystems = self._initialize_ecosystems()
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def _initialize_ecosystems(self) -> Dict[str, EcosystemData]:
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"""初始化生态系统数据"""
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return {
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'AI': EcosystemData(
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tianzi={'symbol': 'NVDA', 'name': 'NVIDIA', 'tianming': 'CUDA + GPU硬件，定义AI计算范式'},
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dafu=[
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StockEntity('TSM', 'TSMC', '芯片代工', '高端芯片唯一代工厂'),
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StockEntity('000660.SZ', 'SK Hynix', 'HBM内存', 'GPU性能关键'),
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StockEntity('MU', 'Micron', 'HBM内存', 'GPU性能关键'),
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StockEntity('SMCI', 'Supermicro', '服务器集成', 'GPU转化为计算能力')
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],
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shi=[
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StockEntity('ASML', 'ASML', '光刻设备', serves='TSMC'),
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StockEntity('AMAT', 'Applied Materials', '半导体设备', serves='TSMC')
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],
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jiajie=[
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StockEntity('AMD', 'AMD', '竞争对手', type='竞争天子'),
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StockEntity('GOOGL', 'Google', '云计算', type='云计算天子'),
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StockEntity('AMZN', 'Amazon', '云计算', type='云计算天子')
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]
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),
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'EV': EcosystemData(
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tianzi={'symbol': 'TSLA', 'name': 'Tesla', 'tianming': '软件定义汽车 + 超级充电网络'},
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dafu=[
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StockEntity('300750.SZ', 'CATL', '动力电池', '动力系统基石'),
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StockEntity('6752.T', 'Panasonic', '动力电池', '动力系统基石'),
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StockEntity('ALB', 'Albemarle', '锂矿', '源头命脉'),
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StockEntity('002460.SZ', 'Ganfeng Lithium', '锂矿', '源头命脉')
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],
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shi=[
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StockEntity('002497.SZ', 'Yahua Industrial', '氢氧化锂', serves='CATL'),
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StockEntity('002850.SZ', 'Kedali', '精密结构件', serves='CATL')
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],
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jiajie=[
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StockEntity('002594.SZ', 'BYD', '电动车', type='诸侯'),
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StockEntity('VWAGY', 'Volkswagen', '传统车企', type='诸侯'),
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StockEntity('F', 'Ford', '传统车企', type='诸侯')
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]
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),
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'Consumer_Electronics': EcosystemData(
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tianzi={'symbol': 'AAPL', 'name': 'Apple', 'tianming': 'iOS + App Store生态系统'},
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dafu=[
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StockEntity('2317.TW', 'Foxconn', '代工制造', '物理执行者'),
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StockEntity('TSM', 'TSMC', '芯片代工', '性能优势保障'),
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StockEntity('005930.KS', 'Samsung Display', '屏幕供应', '显示技术'),
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StockEntity('QCOM', 'Qualcomm', '基带芯片', '通信命脉')
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],
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shi=[
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StockEntity('002475.SZ', 'Luxshare', '精密制造', serves='Foxconn'),
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StockEntity('002241.SZ', 'Goertek', '声学器件', serves='Foxconn')
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],
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jiajie=[
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StockEntity('005930.KS', 'Samsung', '手机制造', type='亦敌亦友天子'),
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StockEntity('1810.HK', 'Xiaomi', '手机制造', type='诸侯'),
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StockEntity('NVDA', 'NVIDIA', 'AI芯片', type='跨生态天子')
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]
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)
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}
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def get_stock_data(self, symbol: str) -> Dict[str, Any]:
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"""
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获取股票数据
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Args:
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symbol: 股票代码
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Returns:
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股票数据字典
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"""
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# TODO: 实现真实API调用
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# 目前使用模拟数据
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try:
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return {
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'price': round(random.uniform(50, 500), 2),
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'change_pct': round(random.uniform(-5, 5), 2),
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'market_cap': f"{random.randint(100, 3000)}B",
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'volume': random.randint(1000000, 100000000)
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}
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except Exception:
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return {
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'price': 'N/A',
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'change_pct': 0,
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'market_cap': 'N/A',
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'volume': 'N/A'
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}
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def create_tianming_card(self, ecosystem_name: str, ecosystem_data: EcosystemData) -> None:
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"""
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创建天命卡片
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Args:
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ecosystem_name: 生态系统名称
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ecosystem_data: 生态系统数据
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"""
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tianzi = ecosystem_data.tianzi
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stock_data = self.get_stock_data(tianzi['symbol'])
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st.markdown(f"### 👑 {ecosystem_name} 天命树")
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# 天子信息
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col1, col2, col3 = st.columns([1, 2, 1])
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with col1:
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st.markdown("#### 🌟 天子")
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st.markdown(f"**{tianzi['name']}** ({tianzi['symbol']})")
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with col2:
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st.markdown("#### 📜 天命")
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st.info(tianzi['tianming'])
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with col3:
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st.metric(
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label="股价",
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value=f"${stock_data['price']}",
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delta=f"{stock_data['change_pct']:+.2f}%"
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)
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# 大夫层级
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if ecosystem_data.dafu:
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st.markdown("#### 🏛️ 大夫 (核心依赖)")
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dafu_cols = st.columns(min(len(ecosystem_data.dafu), 4))
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for i, dafu in enumerate(ecosystem_data.dafu):
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col_index = i % 4
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with dafu_cols[col_index]:
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data = self.get_stock_data(dafu.symbol)
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st.metric(
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label=f"{dafu.name}",
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value=f"${data['price']}",
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delta=f"{data['change_pct']:+.2f}%"
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)
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st.caption(f"**{dafu.role}**: {dafu.dependency}")
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# 士层级
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if ecosystem_data.shi:
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st.markdown("#### ⚔️ 士 (专业供应商)")
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shi_cols = st.columns(min(len(ecosystem_data.shi), 3))
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for i, shi in enumerate(ecosystem_data.shi):
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col_index = i % 3
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with shi_cols[col_index]:
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data = self.get_stock_data(shi.symbol)
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st.metric(
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label=f"{shi.name}",
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value=f"${data['price']}",
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delta=f"{data['change_pct']:+.2f}%"
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)
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st.caption(f"**{shi.role}** → 服务于{shi.serves}")
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# 嫁接关系
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if ecosystem_data.jiajie:
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st.markdown("#### 🔗 嫁接关系 (跨生态链接)")
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jiajie_cols = st.columns(min(len(ecosystem_data.jiajie), 4))
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for i, jiajie in enumerate(ecosystem_data.jiajie):
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col_index = i % 4
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with jiajie_cols[col_index]:
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data = self.get_stock_data(jiajie.symbol)
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st.metric(
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label=f"{jiajie.name}",
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value=f"${data['price']}",
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delta=f"{data['change_pct']:+.2f}%"
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)
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st.caption(f"**{jiajie.type}**")
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st.markdown("---")
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def create_tianming_tree_table(self) -> pd.DataFrame:
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"""
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创建天命树完整表格 - 用于投资组合去相关性分析
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Returns:
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包含所有股票信息的DataFrame
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"""
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st.markdown("### 📋 天命树完整表格 - 投资组合去相关性分析")
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st.markdown("**核心理念**: 投资组合的本质是去相关性 - 从不同root下的不同spine下的不同leaf进行配置")
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all_stocks = []
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for eco_name, eco_data in self.ecosystems.items():
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# 天子
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tianzi = eco_data.tianzi
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stock_data = self.get_stock_data(tianzi['symbol'])
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all_stocks.append({
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'Root': eco_name,
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'Level': '👑 天子',
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'Symbol': tianzi['symbol'],
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'Company': tianzi['name'],
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'Role': '定义范式',
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'Dependency_Path': f"{eco_name}",
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'Price': stock_data['price'],
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'Change%': stock_data['change_pct'],
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'Market_Cap': stock_data['market_cap'],
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'Correlation_Risk': '极高 - 生态核心'
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})
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# 大夫
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for dafu in eco_data.dafu:
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stock_data = self.get_stock_data(dafu.symbol)
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all_stocks.append({
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'Root': eco_name,
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'Level': '🏛️ 大夫',
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'Symbol': dafu.symbol,
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'Company': dafu.name,
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'Role': dafu.role,
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'Dependency_Path': f"{eco_name} → {tianzi['name']} → {dafu.name}",
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'Price': stock_data['price'],
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'Change%': stock_data['change_pct'],
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'Market_Cap': stock_data['market_cap'],
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'Correlation_Risk': '高 - 深度绑定天子'
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})
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# 士
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for shi in eco_data.shi:
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stock_data = self.get_stock_data(shi.symbol)
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all_stocks.append({
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'Root': eco_name,
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'Level': '⚔️ 士',
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'Symbol': shi.symbol,
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'Company': shi.name,
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'Role': shi.role,
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'Dependency_Path': f"{eco_name} → {shi.serves} → {shi.name}",
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'Price': stock_data['price'],
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'Change%': stock_data['change_pct'],
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'Market_Cap': stock_data['market_cap'],
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'Correlation_Risk': '中 - 专业供应商'
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})
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# 嫁接
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for jiajie in eco_data.jiajie:
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stock_data = self.get_stock_data(jiajie.symbol)
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all_stocks.append({
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'Root': '🔗 跨生态',
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'Level': '🔗 嫁接',
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'Symbol': jiajie.symbol,
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'Company': jiajie.name,
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'Role': jiajie.type or jiajie.role,
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'Dependency_Path': f"多生态嫁接 → {jiajie.name}",
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'Price': stock_data['price'],
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'Change%': stock_data['change_pct'],
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'Market_Cap': stock_data['market_cap'],
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'Correlation_Risk': '低 - 多元化依赖'
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})
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df = pd.DataFrame(all_stocks)
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# 显示表格
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st.dataframe(
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df,
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use_container_width=True,
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column_config={
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"Root": st.column_config.TextColumn("生态根节点", width="small"),
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"Level": st.column_config.TextColumn("层级", width="small"),
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"Symbol": st.column_config.TextColumn("代码", width="small"),
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"Company": st.column_config.TextColumn("公司", width="medium"),
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"Role": st.column_config.TextColumn("角色", width="medium"),
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"Dependency_Path": st.column_config.TextColumn("依赖路径", width="large"),
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"Price": st.column_config.NumberColumn("股价", format="$%.2f"),
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"Change%": st.column_config.NumberColumn("涨跌幅", format="%.2f%%"),
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"Market_Cap": st.column_config.TextColumn("市值", width="small"),
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"Correlation_Risk": st.column_config.TextColumn("相关性风险", width="medium")
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}
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)
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return df
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def render_tianxia_tab() -> None:
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"""渲染天下体系Tab"""
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# 页面标题
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st.markdown("### 🏛️ 天下体系 - 儒门天下观资本生态分析")
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st.markdown("**基于'天命树'结构模型，穿透市场表象，绘制全球资本市场真实的权力结构**")
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st.markdown("---")
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# 初始化分析器
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analyzer = TianxiaAnalyzer()
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# 控制面板
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col1, col2, col3 = st.columns([1, 1, 2])
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with col1:
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auto_refresh = st.checkbox("🔄 自动刷新", value=False, key="tianxia_auto_refresh")
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with col2:
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if st.button("🏛️ 扫描天下", type="primary", key="tianxia_scan_btn"):
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st.session_state.trigger_tianxia_scan = True
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with col3:
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st.markdown("*正在分析全球资本生态权力结构...*")
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# 理论介绍
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with st.expander("📚 天命树理论基础"):
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st.markdown("""
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### 🏛️ 儒门天下观核心思想
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**两大哲学基石：**
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1. **结构非平权**: 资本宇宙本质是不平权的、层级森严的树状结构
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2. **天命与脉络**: 每个生态都有唯一的"根节点"(天子)，拥有定义整个生态的"天命"
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**四层架构：**
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- **👑 天子**: 定义范式的平台型公司 (如Apple, NVIDIA, Tesla)
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- **🏛️ 大夫**: 深度绑定天子的核心供应商 (如TSMC, CATL)
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- **⚔️ 士**: 专业供应商和服务商 (如ASML, Luxshare)
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- **🔗 嫁接**: 跨生态的策略性链接关系
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""")
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# 自动刷新逻辑
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if auto_refresh:
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time.sleep(60)
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st.rerun()
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# 触发扫描或显示数据
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if st.session_state.get('trigger_tianxia_scan', False) or 'tianxia_scan_time' not in st.session_state:
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with st.spinner("🏛️ 正在扫描天下体系..."):
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st.session_state.tianxia_scan_time = datetime.now()
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st.session_state.trigger_tianxia_scan = False
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# 显示扫描时间
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if 'tianxia_scan_time' in st.session_state:
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st.info(f"📅 最后扫描时间: {st.session_state.tianxia_scan_time.strftime('%Y-%m-%d %H:%M:%S')}")
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# 显示三大生态系统
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st.markdown("## 🌍 三大天命树生态系统")
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# 分析模式选择
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analysis_mode = st.selectbox(
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"选择分析模式",
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["生态系统分析", "投资组合去相关性分析"],
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key="tianxia_analysis_mode"
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)
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if analysis_mode == "生态系统分析":
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# 生态系统选择
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selected_ecosystem = st.selectbox(
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"选择要分析的生态系统",
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["全部", "AI", "EV", "Consumer_Electronics"],
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format_func=lambda x: {
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"全部": "🌍 全部生态系统",
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"AI": "🤖 AI人工智能生态",
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"EV": "⚡ 电动汽车生态",
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"Consumer_Electronics": "📱 消费电子生态"
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}[x],
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key="tianxia_ecosystem_select"
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)
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if selected_ecosystem == "全部":
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# 显示所有生态系统
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for eco_name, eco_data in analyzer.ecosystems.items():
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analyzer.create_tianming_card(eco_name, eco_data)
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else:
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# 显示选定的生态系统
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analyzer.create_tianming_card(selected_ecosystem, analyzer.ecosystems[selected_ecosystem])
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else: # 投资组合去相关性分析
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st.markdown("## 🎯 投资组合去相关性分析")
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st.info("**核心理念**: 真正的分散投资是从不同的root（天子）下的不同spine（大夫）下的不同leaf（士）进行配置")
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# 创建完整天命树表格
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df = analyzer.create_tianming_tree_table()
|
||
|
||
# 页面底部说明
|
||
st.markdown("---")
|
||
st.markdown("""
|
||
### 🎯 天下体系核心洞察
|
||
|
||
**权力结构分析**：
|
||
- **AI生态**: NVIDIA通过CUDA平台统治AI计算，TSMC是关键"嫁接"节点
|
||
- **电动车生态**: Tesla定义软件汽车范式，CATL掌握电池命脉
|
||
- **消费电子生态**: Apple建立iOS护城河，供应链高度集中化
|
||
|
||
**投资策略启示**：
|
||
1. **投资天子**: 寻找定义范式的平台型公司
|
||
2. **关注大夫**: 深度绑定天子的核心供应商往往被低估
|
||
3. **警惕嫁接**: 被多个天子"嫁接"的公司风险与机会并存
|
||
4. **避开士层**: 缺乏议价能力的专业供应商投资价值有限
|
||
|
||
⚠️ **免责声明**: 天下体系分析仅供参考，投资有风险，决策需谨慎！
|
||
""") |