update

README.md

@@ -6,9 +6,26 @@
 ## docker部署教程2：https://www.bilibili.com/video/BV1j2FNz4EAi/
 ## 股票知识讲解合集：https://www.bilibili.com/video/BV1Y2FGzzEeS/
 ## 投资认知提升合集：https://www.bilibili.com/video/BV1ugBMBAEbW
+## 价值投资核心逻辑：https://www.bilibili.com/video/BV1eJfxBrEjZ
 
 如果你希望能在股市中长久生存下去，建议你能把上面的合集看完，会对你有很大帮助的！
 
+## ⭐ 2026.2.27更新 - 低估值价值投资策略 💎
+
+**新增选股板块：基于价值投资核心逻辑的优选策略**
+
+基于视频[《头号投资法则》](https://www.bilibili.com/video/BV1eJfxBrEjZ)，通过低估值、高股息、低负债等多维度指标筛选安全边际极高的优质标的。
+
+**核心功能：**
+- 筛选条件：**低PE (≤20) + 低PB (≤1.5) + 高股息 (≥1%) + 低负债 (≤30%)**
+- 排序机制：按流通市值从小到大排序，精准捕捉被错杀的小盘价值股
+- 量化择时：
+    - **买入**：每日扫描，开盘买入，单股限仓30%，最大持股4只。
+    - **卖出**：持股满30天到期卖出，或 **RSI(14) > 70** 超买信号触发卖出。
+- 自动化工具：支持一键模拟买入、实时指标监测及 PDF/Markdown 报告导出。
+
+---
+
 ## ⭐ 2026.2.27更新 - 宏观周期分析 🧭
 
 **全新板块：康波周期 × 美林投资时钟 × 中国政策分析**

app.py

@@ -288,7 +288,7 @@ def main():
         if st.button("🏠 股票分析", width='stretch', key="nav_home", help="返回首页，进行单只股票的深度分析"):
             # 清除所有功能页面标志
             for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
-                       'show_sector_strategy', 'show_longhubang', 'show_portfolio', 'show_low_price_bull', 'show_news_flow', 'show_macro_cycle']:
+                       'show_sector_strategy', 'show_longhubang', 'show_portfolio', 'show_low_price_bull', 'show_news_flow', 'show_macro_cycle', 'show_value_stock']:
                 if key in st.session_state:
                     del st.session_state[key]
 
@@ -322,7 +322,14 @@ def main():
             if st.button("📈 净利增长", width='stretch', key="nav_profit_growth", help="净利润增长稳健股票筛选策略"):
                 st.session_state.show_profit_growth = True
                 for key in ['show_history', 'show_monitor', 'show_config', 'show_sector_strategy',
-                           'show_longhubang', 'show_portfolio', 'show_main_force', 'show_low_price_bull', 'show_small_cap', 'show_news_flow']:
+                           'show_longhubang', 'show_portfolio', 'show_main_force', 'show_low_price_bull', 'show_small_cap', 'show_news_flow', 'show_value_stock']:
+                    if key in st.session_state:
+                        del st.session_state[key]
+
+            if st.button("💎 低估值策略", width='stretch', key="nav_value_stock", help="低PE+低PB+高股息+低负债 价值投资筛选"):
+                st.session_state.show_value_stock = True
+                for key in ['show_history', 'show_monitor', 'show_config', 'show_sector_strategy',
+                           'show_longhubang', 'show_portfolio', 'show_main_force', 'show_low_price_bull', 'show_small_cap', 'show_profit_growth', 'show_news_flow', 'show_macro_cycle']:
                     if key in st.session_state:
                         del st.session_state[key]
 
@@ -519,6 +526,12 @@ def main():
         display_profit_growth()
         return
 
+    # 检查是否显示低估值策略
+    if 'show_value_stock' in st.session_state and st.session_state.show_value_stock:
+        from value_stock_ui import display_value_stock
+        display_value_stock()
+        return
+
     # 检查是否显示智策板块
     if 'show_sector_strategy' in st.session_state and st.session_state.show_sector_strategy:
         display_sector_strategy()

value_stock_selector.py

@@ -0,0 +1,175 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+低估值选股模块
+使用pywencai获取低估值优质股票
+"""
+
+import pandas as pd
+import pywencai
+from datetime import datetime
+from typing import Tuple, Optional
+import time
+
+
+class ValueStockSelector:
+    """低估值选股类"""
+
+    def __init__(self):
+        self.raw_data = None
+        self.selected_stocks = None
+
+    def get_value_stocks(self, top_n: int = 10) -> Tuple[bool, Optional[pd.DataFrame], str]:
+        """
+        获取低估值优质股票
+
+        选股策略：
+        - 市盈率 ≤ 20
+        - 市净率 ≤ 1.5
+        - 股息率 ≥ 1%
+        - 资产负债率 ≤ 30%
+        - 非ST
+        - 非科创板
+        - 非创业板
+        - 按流通市值由小到大排名
+
+        Args:
+            top_n: 返回前N只股票
+
+        Returns:
+            (success, dataframe, message)
+        """
+        try:
+            print(f"\n{'='*60}")
+            print(f"💎 低估值选股 - 数据获取中")
+            print(f"{'='*60}")
+            print(f"策略: PE≤20 + PB≤1.5 + 股息率≥1% + 资产负债率≤30%")
+            print(f"排除: ST、科创板、创业板")
+            print(f"排序: 按流通市值由小到大")
+            print(f"目标: 筛选前{top_n}只股票")
+
+            # 构建问财查询语句
+            query = (
+                "市盈率小于等于20，"
+                "市净率小于等于1.5，"
+                "股息率大于等于1%，"
+                "资产负债率小于等于30%，"
+                "非st，"
+                "非科创板，"
+                "非创业板，"
+                "按流通市值由小到大排名"
+            )
+
+            print(f"\n查询语句: {query}")
+            print(f"正在调用问财接口...")
+
+            # 调用pywencai
+            result = pywencai.get(query=query, loop=True)
+
+            if result is None:
+                return False, None, "问财接口返回None，请检查网络或稍后重试"
+
+            # 转换为DataFrame
+            df_result = self._convert_to_dataframe(result)
+
+            if df_result is None or df_result.empty:
+                return False, None, "未获取到符合条件的股票数据"
+
+            print(f"✅ 成功获取 {len(df_result)} 只股票")
+
+            # 显示获取到的列名
+            print(f"\n获取到的数据字段:")
+            for col in df_result.columns[:15]:
+                print(f"  - {col}")
+            if len(df_result.columns) > 15:
+                print(f"  ... 还有 {len(df_result.columns) - 15} 个字段")
+
+            # 保存原始数据
+            self.raw_data = df_result
+
+            # 取前N只
+            if len(df_result) > top_n:
+                selected = df_result.head(top_n)
+                print(f"\n从 {len(df_result)} 只股票中选出前 {top_n} 只")
+            else:
+                selected = df_result
+                print(f"\n共 {len(df_result)} 只符合条件的股票")
+
+            self.selected_stocks = selected
+
+            # 显示选中的股票
+            print(f"\n✅ 选中的股票:")
+            for idx, row in selected.iterrows():
+                code = row.get('股票代码', 'N/A')
+                name = row.get('股票简称', 'N/A')
+                pe = row.get('市盈率', row.get('市盈率(动态)', 'N/A'))
+                pb = row.get('市净率', 'N/A')
+                div_rate = row.get('股息率', 'N/A')
+                debt_ratio = row.get('资产负债率', 'N/A')
+                cap = row.get('流通市值', 'N/A')
+                print(f"  {idx+1}. {code} {name} - PE:{pe} PB:{pb} 股息率:{div_rate}% 负债率:{debt_ratio}% 流通市值:{cap}")
+
+            print(f"{'='*60}\n")
+
+            return True, selected, f"成功筛选出{len(selected)}只低估值优质股票"
+
+        except Exception as e:
+            error_msg = f"获取数据失败: {str(e)}"
+            print(f"❌ {error_msg}")
+            import traceback
+            traceback.print_exc()
+            return False, None, error_msg
+
+    def _convert_to_dataframe(self, result) -> Optional[pd.DataFrame]:
+        """将pywencai返回结果转换为DataFrame"""
+        try:
+            if isinstance(result, pd.DataFrame):
+                return result
+            elif isinstance(result, dict):
+                if 'data' in result:
+                    return pd.DataFrame(result['data'])
+                elif 'result' in result:
+                    return pd.DataFrame(result['result'])
+                else:
+                    return pd.DataFrame(result)
+            elif isinstance(result, list):
+                return pd.DataFrame(result)
+            else:
+                print(f"⚠️ 未知的数据格式: {type(result)}")
+                return None
+        except Exception as e:
+            print(f"转换DataFrame失败: {e}")
+            return None
+
+    def get_stock_codes(self) -> list:
+        """
+        获取选中股票的代码列表（去掉市场后缀）
+
+        Returns:
+            股票代码列表
+        """
+        if self.selected_stocks is None or self.selected_stocks.empty:
+            return []
+
+        codes = []
+        for code in self.selected_stocks['股票代码'].tolist():
+            if isinstance(code, str):
+                clean_code = code.split('.')[0] if '.' in code else code
+                codes.append(clean_code)
+            else:
+                codes.append(str(code))
+
+        return codes
+
+
+# 测试
+if __name__ == "__main__":
+    print("=" * 60)
+    print("测试低估值选股模块")
+    print("=" * 60)
+
+    selector = ValueStockSelector()
+    success, df, msg = selector.get_value_stocks(top_n=10)
+    print(f"\n结果: {msg}")
+    if success and df is not None:
+        print(f"共 {len(df)} 只股票")

value_stock_strategy.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+低估值量化交易策略
+实现基于持股周期和RSI超买的买卖择时策略
+"""
+
+import pandas as pd
+import akshare as ak
+from datetime import datetime, timedelta
+from typing import Dict, List, Optional
+import logging
+
+
+class ValueStockStrategy:
+    """低估值量化交易策略"""
+
+    def __init__(self, initial_capital: float = 1000000.0):
+        """
+        初始化策略
+
+        Args:
+            initial_capital: 初始资金（默认100万）
+        """
+        self.logger = logging.getLogger(__name__)
+
+        # 策略参数
+        self.initial_capital = initial_capital
+        self.available_cash = initial_capital
+        self.max_stocks = 4              # 账户最大持股数
+        self.max_position_per_stock = 0.3  # 个股最大仓位30%
+        self.max_daily_buy = 2           # 单日最大买入数
+        self.holding_period = 30         # 持股周期（天）
+        self.rsi_period = 14             # RSI计算周期
+        self.rsi_overbought = 70         # RSI超买阈值
+
+        # 持仓信息
+        self.positions: Dict[str, Dict] = {}  # {股票代码: {买入价, 数量, 买入日期, 持有天数}}
+        self.trade_history: List[Dict] = []
+
+        # 当日交易计数
+        self.daily_buy_count = 0
+        self.current_date = None
+
+    def reset_daily_counter(self, date):
+        """重置当日计数器"""
+        if self.current_date != date:
+            self.current_date = date
+            self.daily_buy_count = 0
+
+    def can_buy(self, stock_code: str) -> tuple:
+        """
+        检查是否可以买入
+
+        Returns:
+            (是否可买, 原因)
+        """
+        if stock_code in self.positions:
+            return False, "已持有该股票"
+
+        if len(self.positions) >= self.max_stocks:
+            return False, f"已达最大持股数限制({self.max_stocks}只)"
+
+        if self.daily_buy_count >= self.max_daily_buy:
+            return False, f"今日已达最大买入数限制({self.max_daily_buy}只)"
+
+        if self.available_cash <= 0:
+            return False, "可用资金不足"
+
+        return True, "可以买入"
+
+    def calculate_buy_amount(self, stock_price: float) -> tuple:
+        """
+        计算买入数量
+
+        Args:
+            stock_price: 股票价格
+
+        Returns:
+            (买入股数, 买入金额)
+        """
+        max_amount = self.available_cash
+        max_per_stock = self.initial_capital * self.max_position_per_stock
+        target_amount = min(max_amount, max_per_stock)
+
+        # A股100股为1手
+        shares = int(target_amount / stock_price / 100) * 100
+
+        if shares < 100:
+            return 0, 0
+
+        actual_amount = shares * stock_price
+        return shares, actual_amount
+
+    def buy(self, stock_code: str, stock_name: str, price: float, date: str) -> tuple:
+        """
+        执行买入操作
+
+        Returns:
+            (是否成功, 消息, 交易详情)
+        """
+        can, reason = self.can_buy(stock_code)
+        if not can:
+            return False, reason, None
+
+        shares, amount = self.calculate_buy_amount(price)
+        if shares == 0:
+            return False, "资金不足以买入1手", None
+
+        # 更新持仓
+        self.positions[stock_code] = {
+            'name': stock_name,
+            'buy_price': price,
+            'shares': shares,
+            'amount': amount,
+            'buy_date': date,
+            'holding_days': 0
+        }
+
+        self.available_cash -= amount
+        self.daily_buy_count += 1
+
+        trade = {
+            'action': '买入',
+            'code': stock_code,
+            'name': stock_name,
+            'price': price,
+            'shares': shares,
+            'amount': amount,
+            'date': date,
+            'reason': '开盘买入信号'
+        }
+        self.trade_history.append(trade)
+
+        msg = f"买入 {stock_code} {stock_name} {shares}股 @ {price}元, 金额: {amount:.2f}元"
+        return True, msg, trade
+
+    def calculate_rsi(self, stock_code: str) -> Optional[float]:
+        """
+        计算股票的RSI指标
+
+        Args:
+            stock_code: 股票代码
+
+        Returns:
+            RSI值 或 None
+        """
+        try:
+            # 获取近60天日线数据
+            df = ak.stock_zh_a_hist(
+                symbol=stock_code,
+                period="daily",
+                start_date=(datetime.now() - timedelta(days=90)).strftime("%Y%m%d"),
+                end_date=datetime.now().strftime("%Y%m%d"),
+                adjust="qfq"
+            )
+
+            if df is None or len(df) < self.rsi_period + 1:
+                return None
+
+            # 计算RSI
+            close = df['收盘'].astype(float)
+            delta = close.diff()
+            gain = delta.where(delta > 0, 0)
+            loss = (-delta).where(delta < 0, 0)
+
+            avg_gain = gain.rolling(window=self.rsi_period).mean()
+            avg_loss = loss.rolling(window=self.rsi_period).mean()
+
+            rs = avg_gain / avg_loss
+            rsi = 100 - (100 / (1 + rs))
+
+            latest_rsi = rsi.iloc[-1]
+            return round(float(latest_rsi), 2) if pd.notna(latest_rsi) else None
+
+        except Exception as e:
+            self.logger.warning(f"RSI计算失败 {stock_code}: {e}")
+            return None
+
+    def should_sell(self, stock_code: str, current_date: str = None) -> tuple:
+        """
+        判断是否应该卖出
+
+        策略：
+        1. 持股满30天强制卖出
+        2. RSI超买（>70）卖出
+
+        Returns:
+            (是否卖出, 原因, RSI值)
+        """
+        if stock_code not in self.positions:
+            return False, "未持有该股票", None
+
+        position = self.positions[stock_code]
+        position['holding_days'] += 1
+
+        # 条件1：持股满30天
+        if position['holding_days'] >= self.holding_period:
+            return True, f"持股满{self.holding_period}天，到期卖出", None
+
+        # 条件2：RSI超买
+        rsi = self.calculate_rsi(stock_code)
+        if rsi is not None and rsi > self.rsi_overbought:
+            return True, f"RSI={rsi} 超买（>{self.rsi_overbought}），卖出离场", rsi
+
+        return False, f"继续持有 (已持{position['holding_days']}天, RSI={rsi})", rsi
+
+    def sell(self, stock_code: str, price: float, date: str, reason: str = "") -> tuple:
+        """
+        执行卖出操作
+
+        Returns:
+            (是否成功, 消息, 交易详情)
+        """
+        if stock_code not in self.positions:
+            return False, "未持有该股票", None
+
+        position = self.positions[stock_code]
+        amount = position['shares'] * price
+        profit = amount - position['amount']
+        profit_pct = (price - position['buy_price']) / position['buy_price'] * 100
+
+        trade = {
+            'action': '卖出',
+            'code': stock_code,
+            'name': position['name'],
+            'price': price,
+            'shares': position['shares'],
+            'amount': amount,
+            'date': date,
+            'buy_price': position['buy_price'],
+            'profit': profit,
+            'profit_pct': round(profit_pct, 2),
+            'holding_days': position['holding_days'],
+            'reason': reason
+        }
+        self.trade_history.append(trade)
+
+        self.available_cash += amount
+        del self.positions[stock_code]
+
+        emoji = "🟢" if profit >= 0 else "🔴"
+        msg = f"{emoji} 卖出 {stock_code} {position['name']} {position['shares']}股 @ {price}元, 盈亏: {profit:.2f}元 ({profit_pct:+.2f}%), 原因: {reason}"
+        return True, msg, trade
+
+    def get_portfolio_summary(self) -> Dict:
+        """获取投资组合摘要"""
+        total_position_value = sum(
+            pos['shares'] * pos['buy_price'] for pos in self.positions.values()
+        )
+        total_assets = self.available_cash + total_position_value
+
+        # 统计交易
+        sells = [t for t in self.trade_history if t['action'] == '卖出']
+        total_profit = sum(t.get('profit', 0) for t in sells)
+        win_trades = sum(1 for t in sells if t.get('profit', 0) > 0)
+        total_trades = len(sells)
+        win_rate = (win_trades / total_trades * 100) if total_trades > 0 else 0
+
+        return {
+            'initial_capital': self.initial_capital,
+            'available_cash': round(self.available_cash, 2),
+            'position_value': round(total_position_value, 2),
+            'total_assets': round(total_assets, 2),
+            'total_return': round((total_assets - self.initial_capital) / self.initial_capital * 100, 2),
+            'total_profit': round(total_profit, 2),
+            'holding_count': len(self.positions),
+            'max_stocks': self.max_stocks,
+            'total_trades': total_trades,
+            'win_trades': win_trades,
+            'win_rate': round(win_rate, 2)
+        }
+
+    def get_positions(self) -> List[Dict]:
+        """获取当前持仓列表"""
+        positions = []
+        for code, pos in self.positions.items():
+            positions.append({
+                'code': code,
+                'name': pos['name'],
+                'buy_price': pos['buy_price'],
+                'shares': pos['shares'],
+                'amount': pos['amount'],
+                'buy_date': pos['buy_date'],
+                'holding_days': pos['holding_days']
+            })
+        return positions
+
+    def get_trade_history(self) -> List[Dict]:
+        """获取交易历史"""
+        return self.trade_history

value_stock_ui.py

@@ -0,0 +1,420 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+低估值策略UI模块
+"""
+
+import streamlit as st
+import pandas as pd
+from datetime import datetime
+from value_stock_selector import ValueStockSelector
+from value_stock_strategy import ValueStockStrategy
+
+
+def display_value_stock():
+    """显示低估值选股界面"""
+
+    st.markdown("""
+    <div style="background: linear-gradient(135deg, #1a5276 0%, #2e86c1 50%, #1a5276 100%); 
+                padding: 2rem; border-radius: 15px; margin-bottom: 1.5rem;
+                box-shadow: 0 8px 32px rgba(0,0,0,0.3);">
+        <h1 style="color: #fff; margin: 0; font-size: 2rem;">
+            💎 低估值策略 - 价值投资选股
+        </h1>
+        <p style="color: rgba(255,255,255,0.7); margin: 0.5rem 0 0 0; font-size: 0.9rem;">
+            基于视频 <a href="https://www.bilibili.com/video/BV1eJfxBrEjZ" target="_blank" style="color: #7ec8e3; text-decoration: underline;">头号投资法则</a>
+        </p>
+        <p style="color: rgba(255,255,255,0.8); margin: 0.3rem 0 0 0; font-size: 1.1rem;">
+            低PE + 低PB + 高股息 + 低负债 — 寻找被市场低估的优质标的
+        </p>
+    </div>
+    """, unsafe_allow_html=True)
+
+    st.markdown("---")
+
+    st.markdown("""
+    ### 📋 选股策略说明
+
+    **筛选条件**：
+    - ✅ 市盈率（PE）≤ 20
+    - ✅ 市净率（PB）≤ 1.5
+    - ✅ 股息率 ≥ 1%
+    - ✅ 资产负债率 ≤ 30%
+    - ✅ 非ST股票
+    - ✅ 非科创板
+    - ✅ 非创业板
+    - ✅ 按流通市值由小到大排名
+
+    **量化交易策略**：
+    - 💰 资金量：100万元
+    - 📈 买入时机：开盘买入
+    - 💼 单股最大仓位：30%
+    - 🎯 最大持股数：4只
+    - 🛒 每日最多买入：2只
+    - 📉 卖出条件①：持股满30天到期卖出
+    - 📉 卖出条件②：RSI超买（>70）卖出
+    """)
+
+    st.markdown("---")
+
+    # 参数设置
+    col1, col2 = st.columns([2, 1])
+
+    with col1:
+        top_n = st.slider(
+            "筛选数量",
+            min_value=5,
+            max_value=20,
+            value=10,
+            step=1,
+            help="选择展示的股票数量",
+            key="value_stock_top_n"
+        )
+
+    with col2:
+        st.info(f"💡 将筛选流通市值最小的前{top_n}只低估值股票")
+
+    st.markdown("---")
+
+    # 开始选股按钮
+    if st.button("🚀 开始低估值选股", type="primary", width='content', key="value_stock_start"):
+
+        with st.spinner("正在获取数据，请稍候..."):
+            selector = ValueStockSelector()
+            success, stocks_df, message = selector.get_value_stocks(top_n=top_n)
+
+            if success and stocks_df is not None:
+                st.session_state.value_stocks = stocks_df
+                st.session_state.value_stock_selector = selector
+                st.success(f"✅ {message}")
+                st.rerun()
+            else:
+                st.error(f"❌ {message}")
+
+    # 显示选股结果
+    if 'value_stocks' in st.session_state:
+        display_stock_results(
+            st.session_state.value_stocks,
+            st.session_state.get('value_stock_selector')
+        )
+
+
+def display_stock_results(stocks_df: pd.DataFrame, selector):
+    """显示选股结果"""
+
+    st.markdown("---")
+    st.markdown("## 📊 选股结果")
+
+    # 统计信息
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.metric("筛选数量", f"{len(stocks_df)} 只")
+
+    with col2:
+        pe_col = None
+        for pattern in ['市盈率', '市盈率(动态)']:
+            matching = [col for col in stocks_df.columns if pattern in col]
+            if matching:
+                pe_col = matching[0]
+                break
+        if pe_col:
+            valid = pd.to_numeric(stocks_df[pe_col], errors='coerce').dropna()
+            if len(valid) > 0:
+                st.metric("平均PE", f"{valid.mean():.1f}")
+            else:
+                st.metric("平均PE", "-")
+        else:
+            st.metric("平均PE", "-")
+
+    with col3:
+        pb_col = None
+        matching = [col for col in stocks_df.columns if '市净率' in col]
+        if matching:
+            pb_col = matching[0]
+            valid = pd.to_numeric(stocks_df[pb_col], errors='coerce').dropna()
+            if len(valid) > 0:
+                st.metric("平均PB", f"{valid.mean():.2f}")
+            else:
+                st.metric("平均PB", "-")
+        else:
+            st.metric("平均PB", "-")
+
+    with col4:
+        div_col = None
+        matching = [col for col in stocks_df.columns if '股息率' in col]
+        if matching:
+            div_col = matching[0]
+            valid = pd.to_numeric(stocks_df[div_col], errors='coerce').dropna()
+            if len(valid) > 0:
+                st.metric("平均股息率", f"{valid.mean():.2f}%")
+            else:
+                st.metric("平均股息率", "-")
+        else:
+            st.metric("平均股息率", "-")
+
+    st.markdown("---")
+
+    # 显示股票列表
+    st.markdown("### 📋 精选低估值股票")
+
+    for idx, row in stocks_df.iterrows():
+        code = row.get('股票代码', 'N/A')
+        name = row.get('股票简称', 'N/A')
+
+        # 获取关键指标用于标题
+        pe_val = ''
+        for pattern in ['市盈率', '市盈率(动态)']:
+            matching = [col for col in stocks_df.columns if pattern in col]
+            if matching:
+                v = row.get(matching[0])
+                if v is not None and not pd.isna(v):
+                    try:
+                        pe_val = f" PE:{float(v):.1f}"
+                    except:
+                        pass
+                break
+
+        pb_val = ''
+        matching = [col for col in stocks_df.columns if '市净率' in col]
+        if matching:
+            v = row.get(matching[0])
+            if v is not None and not pd.isna(v):
+                try:
+                    pb_val = f" PB:{float(v):.2f}"
+                except:
+                    pass
+
+        with st.expander(
+            f"【第{idx+1}名】{code} - {name}{pe_val}{pb_val}",
+            expanded=(idx < 3)
+        ):
+            display_stock_detail(row, stocks_df)
+
+    # 完整数据表格
+    st.markdown("---")
+    st.markdown("### 📊 完整数据表格")
+
+    # 选择关键列
+    display_cols = ['股票代码', '股票简称']
+    for pattern in ['最新价', '股价']:
+        matching = [col for col in stocks_df.columns if pattern in col]
+        if matching:
+            display_cols.append(matching[0])
+            break
+    for pattern in ['市盈率', '市净率', '股息率', '资产负债率', '流通市值', '所属行业']:
+        matching = [col for col in stocks_df.columns if pattern in col]
+        if matching:
+            display_cols.append(matching[0])
+
+    final_cols = [col for col in display_cols if col in stocks_df.columns]
+
+    if final_cols:
+        st.dataframe(stocks_df[final_cols], width='content', height=400)
+
+        csv = stocks_df[final_cols].to_csv(index=False, encoding='utf-8-sig')
+        st.download_button(
+            label="📥 下载股票列表CSV",
+            data=csv,
+            file_name=f"value_stock_{datetime.now().strftime('%Y%m%d')}.csv",
+            mime="text/csv",
+            key="value_csv_download"
+        )
+
+    # 量化交易模拟
+    st.markdown("---")
+    display_strategy_simulation(stocks_df, selector)
+
+
+def display_stock_detail(row: pd.Series, df: pd.DataFrame):
+    """显示单个股票详情"""
+
+    def is_valid(value):
+        if value is None:
+            return False
+        if isinstance(value, float) and pd.isna(value):
+            return False
+        if isinstance(value, str) and value.strip() in ('', 'N/A', 'nan', 'None'):
+            return False
+        return True
+
+    def fmt(value, suffix=''):
+        if not is_valid(value):
+            return "-"
+        try:
+            return f"{float(value):.2f}{suffix}"
+        except:
+            return str(value) + suffix
+
+    # 基本估值数据
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        for p in ['市盈率', '市盈率(动态)']:
+            m = [c for c in df.columns if p in c]
+            if m:
+                st.metric("📊 市盈率(PE)", fmt(row.get(m[0])))
+                break
+
+    with col2:
+        m = [c for c in df.columns if '市净率' in c]
+        if m:
+            st.metric("📊 市净率(PB)", fmt(row.get(m[0])))
+
+    with col3:
+        m = [c for c in df.columns if '股息率' in c]
+        if m:
+            st.metric("💰 股息率", fmt(row.get(m[0]), '%'))
+
+    with col4:
+        m = [c for c in df.columns if '资产负债率' in c]
+        if m:
+            st.metric("📉 资产负债率", fmt(row.get(m[0]), '%'))
+
+    # 补充信息
+    st.markdown("**其他指标**：")
+    info_parts = []
+    for pattern in ['最新价', '股价', '流通市值', '总市值', '所属行业', '涨跌幅']:
+        m = [c for c in df.columns if pattern in c]
+        if m:
+            val = row.get(m[0])
+            if is_valid(val):
+                info_parts.append(f"**{pattern}**: {val}")
+    if info_parts:
+        st.markdown(" | ".join(info_parts))
+
+
+def display_strategy_simulation(stocks_df: pd.DataFrame, selector):
+    """显示量化交易策略模拟"""
+
+    st.markdown("## 🎯 策略模拟")
+
+    st.info("""
+    **策略规则**：
+    - 📈 **买入**：开盘价买入，单股最大仓位30%，每日最多买2只
+    - 📉 **卖出条件①**：持股满30天，到期自动卖出
+    - 📉 **卖出条件②**：RSI(14) > 70 超买，触发卖出
+    - 🎯 **最大持股**：4只
+    - 💰 **初始资金**：100万元
+    """)
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        if st.button("🎮 开始策略模拟", type="primary", width='content', key="value_sim_start"):
+            st.session_state.show_value_strategy_sim = True
+
+    with col2:
+        pass
+
+    if st.session_state.get('show_value_strategy_sim'):
+        run_strategy_simulation(stocks_df)
+
+
+def run_strategy_simulation(stocks_df: pd.DataFrame):
+    """运行策略模拟"""
+
+    st.markdown("---")
+    st.markdown("### 📈 策略模拟执行")
+
+    strategy = ValueStockStrategy(initial_capital=1000000.0)
+
+    # 模拟买入
+    st.markdown("#### 1️⃣ 模拟买入信号")
+
+    buy_results = []
+    current_date = datetime.now().strftime("%Y-%m-%d")
+
+    for idx, row in stocks_df.head(strategy.max_daily_buy).iterrows():
+        code = str(row.get('股票代码', '')).split('.')[0]
+        name = row.get('股票简称', 'N/A')
+
+        # 尝试获取价格
+        price = 0
+        for p in ['最新价', '股价']:
+            m = [c for c in stocks_df.columns if p in c]
+            if m:
+                try:
+                    price = float(row.get(m[0], 0))
+                except:
+                    pass
+                if price > 0:
+                    break
+
+        if price > 0:
+            success, message, trade = strategy.buy(code, name, price, current_date)
+            buy_results.append({
+                'success': success,
+                'message': message,
+                'trade': trade
+            })
+
+    for result in buy_results:
+        if result['success']:
+            st.success(result['message'])
+        else:
+            st.warning(f"⚠️ {result['message']}")
+
+    # RSI检查
+    st.markdown("---")
+    st.markdown("#### 2️⃣ RSI卖出信号检测")
+
+    with st.spinner("正在计算RSI指标..."):
+        for code, pos in list(strategy.positions.items()):
+            rsi = strategy.calculate_rsi(code)
+            if rsi is not None:
+                if rsi > strategy.rsi_overbought:
+                    st.warning(f"⚠️ {code} {pos['name']} RSI={rsi} > {strategy.rsi_overbought}，触发超买卖出信号！")
+                else:
+                    st.info(f"ℹ️ {code} {pos['name']} RSI={rsi}，正常范围")
+            else:
+                st.info(f"ℹ️ {code} {pos['name']} RSI计算中...")
+
+    # 显示持仓
+    st.markdown("---")
+    st.markdown("#### 3️⃣ 当前持仓")
+
+    positions = strategy.get_positions()
+    if positions:
+        positions_df = pd.DataFrame(positions)
+        st.dataframe(positions_df, width='content')
+    else:
+        st.info("暂无持仓")
+
+    # 显示账户摘要
+    st.markdown("---")
+    st.markdown("#### 4️⃣ 账户摘要")
+
+    summary = strategy.get_portfolio_summary()
+
+    col1, col2, col3, col4 = st.columns(4)
+
+    with col1:
+        st.metric("初始资金", f"{summary['initial_capital']:,.0f} 元")
+    with col2:
+        st.metric("可用资金", f"{summary['available_cash']:,.0f} 元")
+    with col3:
+        st.metric("持仓市值", f"{summary['position_value']:,.0f} 元")
+    with col4:
+        st.metric("总资产", f"{summary['total_assets']:,.0f} 元")
+
+    st.markdown("---")
+    st.markdown("#### 📝 策略说明")
+    st.markdown("""
+    **后续操作**：
+    1. **持有期管理**：系统跟踪每只股票的持有天数（30天到期）
+    2. **RSI监测**：每日收盘后计算RSI(14)
+       - RSI > 70：超买信号，提示卖出
+       - RSI < 30：超卖信号（可作为加仓参考）
+    3. **轮动买入**：卖出后释放资金，继续买入新的低估值股票
+
+    **风险提示**：
+    - ⚠️ 本策略为模拟演示，实际交易存在滑点、手续费等成本
+    - ⚠️ 低估值不代表没有风险，价值陷阱需警惕
+    - ⚠️ 请谨慎评估风险，理性投资
+    """)
+
+
+# 主入口
+if __name__ == "__main__":
+    display_value_stock()