#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ 低价擒牛UI模块 """ import streamlit as st import pandas as pd from datetime import datetime from low_price_bull_selector import LowPriceBullSelector from low_price_bull_strategy import LowPriceBullStrategy from notification_service import notification_service from low_price_bull_monitor import low_price_bull_monitor from low_price_bull_service import low_price_bull_service def display_low_price_bull(): """显示低价擒牛选股界面""" # 检查是否显示监控面板 if st.session_state.get('show_low_price_monitor'): from low_price_bull_monitor_ui import display_monitor_panel display_monitor_panel() # 返回按钮 if st.button("🔙 返回选股", type="secondary"): del st.session_state.show_low_price_monitor st.rerun() return st.markdown("顶部按钮区") col_select, col_monitor = st.columns([3, 1]) with col_select: st.markdown("## 🐂 低价擒牛 - 低价高成长股票筛选") with col_monitor: st.write("") # 占位 if st.button("📊 策略监控", type="primary", width='content'): st.session_state.show_low_price_monitor = True st.rerun() st.markdown("---") st.markdown(""" ### 📋 选股策略说明 **筛选条件**： - ✅ 股价 < 10元 - ✅ 净利润增长率 ≥ 100%（净利润同比增长率） - ✅ 非ST股票 - ✅ 非科创板 - ✅ 非创业板 - ✅ 沪深A股 - ✅ 按成交额由小至大排名 **量化交易策略**： - 💰 资金量：100万元 - 📅 持股周期：5天 - 💼 仓位控制：满仓 - 📊 个股最大持仓：4成（40%） - 🎯 账户最大持股数：4只 - 🛒 单日最大买入数：2只 - 📈 买入时机：开盘买入 - 📉 卖出时机：MA5下穿MA20或持股满5天 """) st.markdown("---") # 参数设置 col1, col2 = st.columns([2, 1]) with col1: top_n = st.slider( "筛选数量", min_value=3, max_value=10, value=5, step=1, help="选择展示的股票数量" ) with col2: st.info(f"💡 将筛选成交额最小的前{top_n}只股票") st.markdown("---") # 开始选股按钮 if st.button("🚀 开始低价擒牛选股", type="primary", width='content'): with st.spinner("正在获取数据，请稍候..."): # 创建选股器 selector = LowPriceBullSelector() # 获取股票 success, stocks_df, message = selector.get_low_price_stocks(top_n=top_n) if success and stocks_df is not None: # 保存结果 st.session_state.low_price_bull_stocks = stocks_df st.session_state.low_price_bull_selector = selector st.success(f"✅ {message}") # 发送钉钉通知 send_dingtalk_notification(stocks_df, top_n) st.rerun() else: st.error(f"❌ {message}") # 显示选股结果 if 'low_price_bull_stocks' in st.session_state: display_stock_results( st.session_state.low_price_bull_stocks, st.session_state.get('low_price_bull_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 = st.columns(3) with col1: st.metric("筛选数量", f"{len(stocks_df)} 只") with col2: # 智能计算平均净利增长率（过滤无效值） growth_col = stocks_df.get('净利润增长率', stocks_df.get('净利润同比增长率', pd.Series([]))) valid_growth = growth_col[growth_col.notna() & (growth_col != '') & (growth_col != 'N/A')] if len(valid_growth) > 0: avg_growth = pd.to_numeric(valid_growth, errors='coerce').mean() if not pd.isna(avg_growth): st.metric("平均净利增长率", f"{avg_growth:.1f}%") else: st.metric("平均净利增长率", "-") else: st.metric("平均净利增长率", "-") with col3: # 智能计算平均股价（过滤无效值） price_col = stocks_df.get('股价', stocks_df.get('最新价', pd.Series([]))) valid_price = price_col[price_col.notna() & (price_col != '') & (price_col != 'N/A')] if len(valid_price) > 0: avg_price = pd.to_numeric(valid_price, errors='coerce').mean() if not pd.isna(avg_price): st.metric("平均股价", f"{avg_price:.2f} 元") else: st.metric("平均股价", "-") else: st.metric("平均股价", "-") st.markdown("---") # 显示股票列表 st.markdown("### 📋 精选股票列表") for idx, row in stocks_df.iterrows(): # 获取股票代码和简称 code = row.get('股票代码', 'N/A') name = row.get('股票简称', 'N/A') # 获取价格信息作为标题补充 price = row.get('股价', row.get('最新价', None)) price_str = '' if price is not None and not pd.isna(price): try: price_float = float(price) price_str = f" | 价格: {price_float:.2f}元" except: pass with st.expander( f"【第{idx+1}名】{code} - {name}{price_str}", expanded=(idx < 3) ): display_stock_detail(row) # 完整数据表格 st.markdown("---") st.markdown("### 📊 完整数据表格") # 选择关键列显示 display_cols = ['股票代码', '股票简称'] # 智能匹配列名 for pattern in ['股价', '最新价']: matching = [col for col in stocks_df.columns if pattern in col] if matching: display_cols.append(matching[0]) break for pattern in ['净利润增长率', '净利润同比增长率']: matching = [col for col in stocks_df.columns if pattern in col] if matching: display_cols.append(matching[0]) break for pattern in ['成交额']: matching = [col for col in stocks_df.columns if pattern in col] if matching: display_cols.append(matching[0]) break for col_name in ['总市值', '市盈率', '市净率', '所属行业']: matching = [col for col in stocks_df.columns if col_name in col] if matching: display_cols.append(matching[0]) # 选择存在的列 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) # 下载按钮 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"low_price_bull_{datetime.now().strftime('%Y%m%d')}.csv", mime="text/csv" ) # 量化交易模拟 st.markdown("---") display_strategy_simulation(stocks_df, selector) def display_stock_detail(row: pd.Series): """显示单个股票详情""" def is_valid_value(value): """判断值是否有效（非None、非NaN、非空字符串、非'N/A'）""" if value is None: return False if pd.isna(value): return False if str(value).strip() in ['', 'N/A', 'nan', 'None']: return False return True def format_value(value, suffix=''): """格式化显示值""" if isinstance(value, float): if abs(value) >= 100000000: # 亿 return f"{value/100000000:.2f}亿{suffix}" elif abs(value) >= 10000: # 万 return f"{value/10000:.2f}万{suffix}" else: return f"{value:.2f}{suffix}" return f"{value}{suffix}" # 先检查是否有任何财务数据 has_any_data = False financial_fields = [ ('所属行业', row.get('所属行业', row.get('所属同花顺行业', None))), ('总市值', row.get('总市值', row.get('总市值[20241211]', None))), ('市盈率', row.get('市盈率', row.get('市盈率pe', None))), ('市净率', row.get('市净率', row.get('市净率pb', None))), ('流通市值', row.get('流通市值', row.get('流通市值[20241211]', None))), ('换手率', row.get('换手率', row.get('换手率[%]', None))) ] for _, value in financial_fields: if is_valid_value(value): has_any_data = True break # 只有当存在有效数据时才显示两列布局 if has_any_data: col1, col2 = st.columns(2) else: col1 = st.container() col2 = None with col1: st.markdown("#### 📊 基本信息") # 股票代码（必显示） code = row.get('股票代码', '') if is_valid_value(code): st.markdown(f"**股票代码**: {code}") # 股票简称（必显示） name = row.get('股票简称', '') if is_valid_value(name): st.markdown(f"**股票简称**: {name}") # 当前价格 price = row.get('股价', row.get('最新价', None)) if is_valid_value(price): st.markdown(f"**当前价格**: {format_value(price, '元')}") # 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 is_valid_value(growth): st.markdown(f"**净利润增长率**: {format_value(growth, '%')}") # 成交额 turnover = row.get('成交额', None) if is_valid_value(turnover): st.markdown(f"**成交额**: {format_value(turnover, '元')}") # 涨跌幅 change_pct = row.get('涨跌幅', row.get('涨跌幅:前复权[%]', None)) if is_valid_value(change_pct): st.markdown(f"**涨跌幅**: {format_value(change_pct, '%')}") # 只有当有财务数据时才显示财务指标栏目 if col2 is not None: with col2: st.markdown("#### 💼 财务指标") # 所属行业 industry = row.get('所属行业', row.get('所属同花顺行业', None)) if is_valid_value(industry): st.markdown(f"**所属行业**: {industry}") # 总市值 market_cap = row.get('总市值', row.get('总市值[20241211]', None)) if is_valid_value(market_cap): st.markdown(f"**总市值**: {format_value(market_cap, '元')}") # 市盈率 pe = row.get('市盈率', row.get('市盈率pe', None)) if is_valid_value(pe): st.markdown(f"**市盈率**: {format_value(pe, '')}") # 市净率 pb = row.get('市净率', row.get('市净率pb', None)) if is_valid_value(pb): st.markdown(f"**市净率**: {format_value(pb, '')}") # 流通市值 float_cap = row.get('流通市值', row.get('流通市值[20241211]', None)) if is_valid_value(float_cap): st.markdown(f"**流通市值**: {format_value(float_cap, '元')}") # 换手率 turnover_rate = row.get('换手率', row.get('换手率[%]', None)) if is_valid_value(turnover_rate): st.markdown(f"**换手率**: {format_value(turnover_rate, '%')}") # 添加监控按钮 st.markdown("---") st.markdown("#### 📊 策略监控") from low_price_bull_monitor_ui import add_stock_to_monitor_button stock_code = row.get('股票代码', '') stock_name = row.get('股票简称', '') price = row.get('股价', row.get('最新价', None)) # 去掉代码后缀 if isinstance(stock_code, str) and '.' in stock_code: stock_code = stock_code.split('.')[0] # 转换价格 try: price_float = float(price) if price and not pd.isna(price) else None except: price_float = None if stock_code and stock_name: add_stock_to_monitor_button(stock_code, stock_name, price_float) def display_strategy_simulation(stocks_df: pd.DataFrame, selector): """显示量化交易策略模拟""" st.markdown("## 🎯 策略监控与模拟") st.info(""" **监控说明**： - 在上方股票列表中点击"➕ 加入策略监控"按钮即可加入 - 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 - 扫描频率：每分钟扫描1次（可在监控面板配置） - 提醒卖出后自动移出监控列表 - 点击右上角"📊 策略监控"按钮查看监控面板 """) col1, col2 = st.columns(2) with col1: if st.button("🎮 开始策略模拟", type="primary", width='content'): st.session_state.show_strategy_simulation = True with col2: if st.button("🔗 连接MiniQMT实盘", type="secondary", width='content'): st.warning("⚠️ MiniQMT实盘交易功能需要先配置环境变量，详见系统配置") # 显示模拟结果 if st.session_state.get('show_strategy_simulation'): 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") # 创建策略实例 strategy = LowPriceBullStrategy(initial_capital=1000000.0) # 模拟买入（按成交额排序，优先买入成交额小的） 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') price = float(row.get('股价', row.get('最新价', 0))) if price > 0: success, message, trade = strategy.buy(code, name, price, current_date) buy_results.append({ 'success': success, 'message': message, 'trade': trade }) # 显示买入结果 for result in buy_results: if result['success']: st.success(result['message']) else: st.warning(f"⚠️ {result['message']}") # 显示持仓 st.markdown("---") st.markdown("#### 2️⃣ 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("#### 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% + 沪深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)}")