update

value_stock_ui.py

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+#!/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()