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