oficcejo
590 lines
21 KiB
Python
590 lines
21 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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低价擒牛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 low_price_bull_selector import LowPriceBullSelector
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from low_price_bull_strategy import LowPriceBullStrategy
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from notification_service import notification_service
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from low_price_bull_monitor import low_price_bull_monitor
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from low_price_bull_service import low_price_bull_service
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def display_low_price_bull():
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"""显示低价擒牛选股界面"""
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# 检查是否显示监控面板
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if st.session_state.get('show_low_price_monitor'):
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from low_price_bull_monitor_ui import display_monitor_panel
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display_monitor_panel()
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# 返回按钮
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if st.button("🔙 返回选股", type="secondary"):
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del st.session_state.show_low_price_monitor
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st.rerun()
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return
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st.markdown("顶部按钮区")
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col_select, col_monitor = st.columns([3, 1])
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with col_select:
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st.markdown("## 🐂 低价擒牛 - 低价高成长股票筛选")
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with col_monitor:
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st.write("") # 占位
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if st.button("📊 策略监控", type="primary", width='content'):
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st.session_state.show_low_price_monitor = True
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st.rerun()
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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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- ✅ 股价 < 10元
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- ✅ 净利润增长率 ≥ 100%（净利润同比增长率）
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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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- ✅ 深圳A股
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- ✅ 按成交额由小至大排名
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**量化交易策略**：
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- 💰 资金量：100万元
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- 📅 持股周期：5天
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- 💼 仓位控制：满仓
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- 📊 个股最大持仓：4成（40%）
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- 🎯 账户最大持股数：4只
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- 🛒 单日最大买入数：2只
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- 📈 买入时机：开盘买入
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- 📉 卖出时机：MA5下穿MA20或持股满5天
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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=3,
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max_value=10,
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value=5,
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step=1,
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help="选择展示的股票数量"
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)
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with col2:
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st.info(f"💡 将筛选成交额最小的前{top_n}只股票")
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st.markdown("---")
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# 开始选股按钮
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if st.button("🚀 开始低价擒牛选股", type="primary", width='content'):
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with st.spinner("正在获取数据，请稍候..."):
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# 创建选股器
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selector = LowPriceBullSelector()
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# 获取股票
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success, stocks_df, message = selector.get_low_price_stocks(top_n=top_n)
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if success and stocks_df is not None:
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# 保存结果
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st.session_state.low_price_bull_stocks = stocks_df
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st.session_state.low_price_bull_selector = selector
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st.success(f"✅ {message}")
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# 发送钉钉通知
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send_dingtalk_notification(stocks_df, top_n)
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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 'low_price_bull_stocks' in st.session_state:
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display_stock_results(
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st.session_state.low_price_bull_stocks,
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st.session_state.get('low_price_bull_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 = st.columns(3)
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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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# 智能计算平均净利增长率（过滤无效值）
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growth_col = stocks_df.get('净利润增长率', stocks_df.get('净利润同比增长率', pd.Series([])))
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valid_growth = growth_col[growth_col.notna() & (growth_col != '') & (growth_col != 'N/A')]
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if len(valid_growth) > 0:
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avg_growth = pd.to_numeric(valid_growth, errors='coerce').mean()
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if not pd.isna(avg_growth):
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st.metric("平均净利增长率", f"{avg_growth:.1f}%")
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else:
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st.metric("平均净利增长率", "-")
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else:
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st.metric("平均净利增长率", "-")
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with col3:
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# 智能计算平均股价（过滤无效值）
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price_col = stocks_df.get('股价', stocks_df.get('最新价', pd.Series([])))
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valid_price = price_col[price_col.notna() & (price_col != '') & (price_col != 'N/A')]
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if len(valid_price) > 0:
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avg_price = pd.to_numeric(valid_price, errors='coerce').mean()
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if not pd.isna(avg_price):
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st.metric("平均股价", f"{avg_price:.2f} 元")
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else:
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st.metric("平均股价", "-")
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else:
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st.metric("平均股价", "-")
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st.markdown("---")
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# 显示股票列表
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st.markdown("### 📋 精选股票列表")
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for idx, row in stocks_df.iterrows():
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# 获取股票代码和简称
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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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# 获取价格信息作为标题补充
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price = row.get('股价', row.get('最新价', None))
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price_str = ''
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if price is not None and not pd.isna(price):
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try:
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price_float = float(price)
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price_str = f" | 价格: {price_float:.2f}元"
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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}{price_str}",
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expanded=(idx < 3)
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):
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display_stock_detail(row)
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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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# 选择关键列显示
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display_cols = ['股票代码', '股票简称']
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# 智能匹配列名
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for pattern in ['股价', '最新价']:
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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 ['净利润增长率', '净利润同比增长率']:
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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 ['成交额']:
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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 col_name in ['总市值', '市盈率', '市净率', '所属行业']:
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matching = [col for col in stocks_df.columns if col_name in col]
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if matching:
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display_cols.append(matching[0])
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# 选择存在的列
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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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# 下载按钮
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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"low_price_bull_{datetime.now().strftime('%Y%m%d')}.csv",
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mime="text/csv"
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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):
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"""显示单个股票详情"""
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def is_valid_value(value):
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"""判断值是否有效（非None、非NaN、非空字符串、非'N/A'）"""
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if value is None:
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return False
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if pd.isna(value):
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return False
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if str(value).strip() in ['', 'N/A', 'nan', 'None']:
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return False
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return True
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def format_value(value, suffix=''):
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"""格式化显示值"""
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if isinstance(value, float):
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if abs(value) >= 100000000: # 亿
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return f"{value/100000000:.2f}亿{suffix}"
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elif abs(value) >= 10000: # 万
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return f"{value/10000:.2f}万{suffix}"
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else:
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return f"{value:.2f}{suffix}"
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return f"{value}{suffix}"
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# 先检查是否有任何财务数据
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has_any_data = False
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financial_fields = [
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('所属行业', row.get('所属行业', row.get('所属同花顺行业', None))),
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('总市值', row.get('总市值', row.get('总市值[20241211]', None))),
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('市盈率', row.get('市盈率', row.get('市盈率pe', None))),
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('市净率', row.get('市净率', row.get('市净率pb', None))),
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('流通市值', row.get('流通市值', row.get('流通市值[20241211]', None))),
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('换手率', row.get('换手率', row.get('换手率[%]', None)))
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]
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for _, value in financial_fields:
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if is_valid_value(value):
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has_any_data = True
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break
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# 只有当存在有效数据时才显示两列布局
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if has_any_data:
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col1, col2 = st.columns(2)
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else:
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col1 = st.container()
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col2 = None
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with col1:
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st.markdown("#### 📊 基本信息")
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# 股票代码（必显示）
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code = row.get('股票代码', '')
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if is_valid_value(code):
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st.markdown(f"**股票代码**: {code}")
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# 股票简称（必显示）
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name = row.get('股票简称', '')
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if is_valid_value(name):
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st.markdown(f"**股票简称**: {name}")
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# 当前价格
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price = row.get('股价', row.get('最新价', None))
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if is_valid_value(price):
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st.markdown(f"**当前价格**: {format_value(price, '元')}")
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# 净利润增长率
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growth = row.get('净利润增长率', row.get('净利润同比增长率', None))
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if is_valid_value(growth):
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st.markdown(f"**净利润增长率**: {format_value(growth, '%')}")
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# 成交额
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turnover = row.get('成交额', None)
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if is_valid_value(turnover):
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st.markdown(f"**成交额**: {format_value(turnover, '元')}")
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# 涨跌幅
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change_pct = row.get('涨跌幅', row.get('涨跌幅:前复权[%]', None))
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if is_valid_value(change_pct):
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st.markdown(f"**涨跌幅**: {format_value(change_pct, '%')}")
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# 只有当有财务数据时才显示财务指标栏目
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if col2 is not None:
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with col2:
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st.markdown("#### 💼 财务指标")
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# 所属行业
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industry = row.get('所属行业', row.get('所属同花顺行业', None))
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if is_valid_value(industry):
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st.markdown(f"**所属行业**: {industry}")
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# 总市值
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market_cap = row.get('总市值', row.get('总市值[20241211]', None))
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if is_valid_value(market_cap):
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st.markdown(f"**总市值**: {format_value(market_cap, '元')}")
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# 市盈率
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pe = row.get('市盈率', row.get('市盈率pe', None))
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if is_valid_value(pe):
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st.markdown(f"**市盈率**: {format_value(pe, '')}")
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# 市净率
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pb = row.get('市净率', row.get('市净率pb', None))
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if is_valid_value(pb):
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st.markdown(f"**市净率**: {format_value(pb, '')}")
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# 流通市值
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float_cap = row.get('流通市值', row.get('流通市值[20241211]', None))
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if is_valid_value(float_cap):
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st.markdown(f"**流通市值**: {format_value(float_cap, '元')}")
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# 换手率
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turnover_rate = row.get('换手率', row.get('换手率[%]', None))
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if is_valid_value(turnover_rate):
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st.markdown(f"**换手率**: {format_value(turnover_rate, '%')}")
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# 添加监控按钮
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st.markdown("---")
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st.markdown("#### 📊 策略监控")
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from low_price_bull_monitor_ui import add_stock_to_monitor_button
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stock_code = row.get('股票代码', '')
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stock_name = row.get('股票简称', '')
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price = row.get('股价', row.get('最新价', None))
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# 去掉代码后缀
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if isinstance(stock_code, str) and '.' in stock_code:
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stock_code = stock_code.split('.')[0]
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# 转换价格
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try:
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price_float = float(price) if price and not pd.isna(price) else None
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except:
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price_float = None
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if stock_code and stock_name:
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add_stock_to_monitor_button(stock_code, stock_name, price_float)
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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("## 🎯 策略监控与模拟")
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st.info("""
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**监控说明**：
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- 在上方股票列表中点击"➕ 加入策略监控"按钮即可加入
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- 监控条件：① 持股满5天第6天开盘提醒卖出 ② MA5下穿MA20提醒卖出
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- 扫描频率：每分钟扫描1次（可在监控面板配置）
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- 提醒卖出后自动移出监控列表
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- 点击右上角"📊 策略监控"按钮查看监控面板
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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'):
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st.session_state.show_strategy_simulation = True
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with col2:
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if st.button("🔗 连接MiniQMT实盘", type="secondary", width='content'):
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st.warning("⚠️ MiniQMT实盘交易功能需要先配置环境变量，详见系统配置")
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# 显示模拟结果
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if st.session_state.get('show_strategy_simulation'):
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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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# 创建策略实例
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strategy = LowPriceBullStrategy(initial_capital=1000000.0)
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# 模拟买入（按成交额排序，优先买入成交额小的）
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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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price = float(row.get('股价', row.get('最新价', 0)))
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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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# 显示买入结果
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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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# 显示持仓
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st.markdown("---")
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st.markdown("#### 2️⃣ 当前持仓")
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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("#### 3️⃣ 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_value']:,.0f} 元")
|
||
|
||
st.markdown("---")
|
||
|
||
# 策略说明
|
||
st.markdown("#### 📝 策略执行说明")
|
||
st.markdown("""
|
||
**后续操作**：
|
||
1. **持有期管理**：系统会自动跟踪每只股票的持有天数
|
||
2. **卖出信号监测**：
|
||
- 每日收盘后计算MA5和MA20
|
||
- 如果MA5下穿MA20，触发卖出信号
|
||
- 如果持股满5天，强制卖出
|
||
3. **轮动买入**：卖出后释放资金，继续买入新的符合条件的股票
|
||
|
||
**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
|
||
- ⚠️ 历史业绩不代表未来收益
|
||
- ⚠️ 请谨慎评估风险，理性投资
|
||
""")
|
||
|
||
|
||
def send_dingtalk_notification(stocks_df: pd.DataFrame, top_n: int):
|
||
"""发送钉钉通知"""
|
||
|
||
try:
|
||
# 检查webhook配置
|
||
webhook_config = notification_service.get_webhook_config_status()
|
||
|
||
if not webhook_config['enabled'] or not webhook_config['configured']:
|
||
st.info("💡 未配置Webhook通知，如需接收钉钉消息请在环境配置中设置")
|
||
return
|
||
|
||
# 构建消息内容
|
||
keyword = notification_service.config.get('webhook_keyword', 'aiagents通知')
|
||
|
||
message_text = f"### {keyword} - 低价擒牛选股完成\n\n"
|
||
message_text += f"**筛选策略**: 股价<10元 + 净利润增长率≥100% + 深圳A股\n\n"
|
||
message_text += f"**筛选数量**: {len(stocks_df)} 只\n\n"
|
||
message_text += f"**精选股票**:\n\n"
|
||
|
||
for idx, row in stocks_df.head(top_n).iterrows():
|
||
code = row.get('股票代码', '')
|
||
name = row.get('股票简称', '')
|
||
|
||
# 只显示有效的信息
|
||
message_text += f"{idx+1}. **{code} {name}**\n"
|
||
|
||
# 股价
|
||
price = row.get('股价', row.get('最新价', None))
|
||
if price is not None and not pd.isna(price) and str(price).strip() not in ['', 'N/A']:
|
||
try:
|
||
price_float = float(price)
|
||
message_text += f" - 股价: {price_float:.2f}元\n"
|
||
except:
|
||
pass
|
||
|
||
# 净利润增长率
|
||
growth = row.get('净利润增长率', row.get('净利润同比增长率', None))
|
||
if growth is not None and not pd.isna(growth) and str(growth).strip() not in ['', 'N/A']:
|
||
try:
|
||
growth_float = float(growth)
|
||
message_text += f" - 净利增长: {growth_float:.2f}%\n"
|
||
except:
|
||
pass
|
||
|
||
# 成交额
|
||
turnover = row.get('成交额', None)
|
||
if turnover is not None and not pd.isna(turnover) and str(turnover).strip() not in ['', 'N/A']:
|
||
try:
|
||
turnover_float = float(turnover)
|
||
if turnover_float >= 100000000: # 亿
|
||
message_text += f" - 成交额: {turnover_float/100000000:.2f}亿元\n"
|
||
elif turnover_float >= 10000: # 万
|
||
message_text += f" - 成交额: {turnover_float/10000:.2f}万元\n"
|
||
else:
|
||
message_text += f" - 成交额: {turnover_float:.2f}元\n"
|
||
except:
|
||
pass
|
||
|
||
# 所属行业
|
||
industry = row.get('所属行业', row.get('所属同花顺行业', None))
|
||
if industry is not None and not pd.isna(industry) and str(industry).strip() not in ['', 'N/A']:
|
||
message_text += f" - 所属行业: {industry}\n"
|
||
|
||
message_text += "\n"
|
||
|
||
message_text += f"**生成时间**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n"
|
||
message_text += "_此消息由AI股票分析系统自动发送_"
|
||
|
||
# 直接发送钉钉Webhook（不使用notification_service的默认格式）
|
||
if notification_service.config['webhook_type'] == 'dingtalk':
|
||
import requests
|
||
|
||
data = {
|
||
"msgtype": "markdown",
|
||
"markdown": {
|
||
"title": f"{keyword}",
|
||
"text": message_text
|
||
}
|
||
}
|
||
|
||
try:
|
||
response = requests.post(
|
||
notification_service.config['webhook_url'],
|
||
json=data,
|
||
headers={'Content-Type': 'application/json'},
|
||
timeout=10
|
||
)
|
||
|
||
if response.status_code == 200:
|
||
result = response.json()
|
||
if result.get('errcode') == 0:
|
||
st.success("✅ 已发送钉钉通知")
|
||
else:
|
||
st.warning(f"⚠️ 钉钉通知发送失败: {result.get('errmsg')}")
|
||
else:
|
||
st.warning(f"⚠️ 钉钉通知请求失败: HTTP {response.status_code}")
|
||
except Exception as e:
|
||
st.warning(f"⚠️ 发送钉钉通知失败: {str(e)}")
|
||
|
||
except Exception as e:
|
||
st.warning(f"⚠️ 发送通知时出错: {str(e)}")
|