增加选股策略
This commit is contained in:
oficcejo
@@ -93,3 +93,32 @@ WEBHOOK_KEYWORD=
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# ========== 时区配置 ==========
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# 系统时区设置（可选）
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TZ=Asia/Shanghai
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# ========== TDX数据源API配置（策略监控必须）==========
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# TDX API服务地址
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# 说明：
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# - 用于获取股票K线数据和技术指标
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# - 默认本地服务地址：http://127.0.0.1:5000
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# - 如果使用远程服务，请修改为实际地址
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# API接口：
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# - /api/kline?code=000001&type=day # 获取日K线数据
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# - /api/quote?code=000001 # 获取实时行情
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# - /api/health # 健康检查
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TDX_BASE_URL=http://127.0.0.1:5000
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# ========== 低价擒牛策略监控配置 ==========
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# 扫描间隔（秒）
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# 说明：
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# - 监控服务扫描股票的时间间隔
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# - 建议范围：30-300秒
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# - 默认值：60秒（每分钟扫描1次）
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LOW_PRICE_BULL_SCAN_INTERVAL=60
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# 持股天数限制
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# 说明：
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# - 持股达到该天数后自动提醒卖出
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# - 按自然日计算，不区分交易日
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# - 默认值：5天
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LOW_PRICE_BULL_HOLDING_DAYS=5
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@@ -294,7 +294,7 @@ def main():
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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"):
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# 清除所有功能页面标志
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for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
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'show_sector_strategy', 'show_longhubang', 'show_portfolio']:
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'show_sector_strategy', 'show_longhubang', 'show_portfolio', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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@@ -307,7 +307,14 @@ def main():
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if st.button("💰 主力选股", width='stretch', key="nav_main_force", help="基于主力资金流向的选股策略"):
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st.session_state.show_main_force = True
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for key in ['show_history', 'show_monitor', 'show_config', 'show_sector_strategy',
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'show_longhubang', 'show_portfolio']:
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'show_longhubang', 'show_portfolio', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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if st.button("🐂 低价擒牛", width='stretch', key="nav_low_price_bull", help="低价高成长股票筛选策略"):
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st.session_state.show_low_price_bull = True
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for key in ['show_history', 'show_monitor', 'show_config', 'show_sector_strategy',
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'show_longhubang', 'show_portfolio', 'show_main_force']:
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if key in st.session_state:
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del st.session_state[key]
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@@ -318,14 +325,14 @@ def main():
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if st.button("🎯 智策板块", width='stretch', key="nav_sector_strategy", help="AI板块策略分析"):
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st.session_state.show_sector_strategy = True
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for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
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'show_longhubang', 'show_portfolio', 'show_smart_monitor']:
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'show_longhubang', 'show_portfolio', 'show_smart_monitor', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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if st.button("🐉 智瞰龙虎", width='stretch', key="nav_longhubang", help="龙虎榜深度分析"):
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st.session_state.show_longhubang = True
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for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
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'show_sector_strategy', 'show_portfolio', 'show_smart_monitor']:
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'show_sector_strategy', 'show_portfolio', 'show_smart_monitor', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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@@ -336,21 +343,21 @@ def main():
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if st.button("📊 持仓分析", width='stretch', key="nav_portfolio", help="投资组合分析与定时跟踪"):
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st.session_state.show_portfolio = True
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for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
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'show_sector_strategy', 'show_longhubang', 'show_smart_monitor']:
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'show_sector_strategy', 'show_longhubang', 'show_smart_monitor', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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if st.button("🤖 AI盯盘", width='stretch', key="nav_smart_monitor", help="DeepSeek AI自动盯盘决策交易（支持A股T+1）"):
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st.session_state.show_smart_monitor = True
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for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force',
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'show_sector_strategy', 'show_longhubang', 'show_portfolio']:
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'show_sector_strategy', 'show_longhubang', 'show_portfolio', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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if st.button("📡 实时监测", width='stretch', key="nav_monitor", help="价格监控与预警提醒"):
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st.session_state.show_monitor = True
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for key in ['show_history', 'show_main_force', 'show_longhubang', 'show_portfolio',
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'show_config', 'show_sector_strategy', 'show_smart_monitor']:
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'show_config', 'show_sector_strategy', 'show_smart_monitor', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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@@ -360,7 +367,7 @@ def main():
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if st.button("📖 历史记录", width='stretch', key="nav_history", help="查看历史分析记录"):
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st.session_state.show_history = True
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for key in ['show_monitor', 'show_longhubang', 'show_portfolio', 'show_config',
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'show_main_force', 'show_sector_strategy']:
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'show_main_force', 'show_sector_strategy', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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@@ -368,7 +375,7 @@ def main():
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if st.button("⚙️ 环境配置", width='stretch', key="nav_config", help="系统设置与API配置"):
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st.session_state.show_config = True
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for key in ['show_history', 'show_monitor', 'show_main_force', 'show_sector_strategy',
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'show_longhubang', 'show_portfolio']:
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'show_longhubang', 'show_portfolio', 'show_low_price_bull']:
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if key in st.session_state:
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del st.session_state[key]
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@@ -460,6 +467,12 @@ def main():
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if 'show_main_force' in st.session_state and st.session_state.show_main_force:
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display_main_force_selector()
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return
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# 检查是否显示低价擒牛
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if 'show_low_price_bull' in st.session_state and st.session_state.show_low_price_bull:
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from low_price_bull_ui import display_low_price_bull
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display_low_price_bull()
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return
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# 检查是否显示智策板块
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if 'show_sector_strategy' in st.session_state and st.session_state.show_sector_strategy:
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@@ -0,0 +1,246 @@
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# TDX API配置快速指南
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## 问题说明
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如果您看到以下警告：
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```
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WARNING:low_price_bull_service:TDX数据源未配置，无法获取股票数据
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```
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说明系统无法连接到TDX API服务，需要进行配置。
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## 解决方案
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### 步骤1：配置TDX API地址
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在 `.env` 文件中添加以下配置：
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```bash
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# TDX数据源API配置
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TDX_BASE_URL=http://127.0.0.1:5000
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```
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### 步骤2：确保TDX API服务已启动
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TDX API服务需要单独启动，请检查：
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1. **检查服务是否运行**
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```bash
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# 在浏览器中访问
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http://127.0.0.1:5000/api/health
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# 或使用curl命令
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curl http://127.0.0.1:5000/api/health
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```
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2. **如果服务未启动**
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- 请启动您的TDX API服务
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- 默认端口：5000
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- 如使用其他端口，请修改.env中的URL
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### 步骤3：测试配置
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运行测试脚本验证配置：
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```bash
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python test_tdx_api.py
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```
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**期望输出**：
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```
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============================================================
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TDX API配置测试
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============================================================
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1. TDX API地址: http://127.0.0.1:5000
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2. 测试健康检查接口...
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✅ 健康检查成功
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响应: OK
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3. 测试K线数据接口...
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测试股票: 000001
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✅ K线数据获取成功
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数据条数: 250
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最新K线数据:
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- 日期: 2024-12-12
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- 开盘: 10.50
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- 收盘: 10.60
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- 最高: 10.80
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- 最低: 10.30
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- 成交量: 1000000
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✅ 数据量充足，可以计算MA20（需要至少20条）
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4. 测试均线计算...
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✅ 均线计算成功
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- 收盘价: 10.60
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- MA5: 10.55
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- MA20: 10.45
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- 趋势: 🟢 MA5 > MA20 (多头)
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============================================================
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✅ 所有测试通过！TDX API配置正常
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============================================================
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```
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## TDX API接口说明
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### 核心接口
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| 接口 | 说明 | 示例 |
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|------|------|------|
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| /api/health | 健康检查 | - |
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| /api/kline | K线数据 | ?code=000001&type=day |
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| /api/quote | 五档行情 | ?code=000001 |
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| /api/stock-info | 综合信息 | ?code=000001 |
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### K线数据接口详情
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**URL**: `/api/kline`
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**参数**:
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- `code`: 股票代码（必填）
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- 支持格式：
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- 纯数字：`000001`、`600000`
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- 市场前缀：`SZ000001`、`SH600000`
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- 示例：000001（平安银行）
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- 示例：600000（浦发银行）
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- `type`: K线类型（必填）
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- `day`: 日K线
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- `week`: 周K线
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- `month`: 月K线
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**代码格式说明**：
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- 系统会自动处理不同格式的股票代码
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- 如果使用`SZ`/`SH`前缀失败，会自动重试纯数字代码
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- 支持带后缀的代码（如`002259.SZ`），系统会自动去除后缀
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**返回格式**:
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```json
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[
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{
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"date": "2024-12-12",
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"open": 10.50,
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"high": 10.80,
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"low": 10.30,
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"close": 10.60,
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"volume": 1000000
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},
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...
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]
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```
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**使用示例**:
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```bash
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# 获取平安银行日K线
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curl "http://127.0.0.1:5000/api/kline?code=000001&type=day"
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```
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## 常见问题
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### Q1: 连接失败，显示连接超时？
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**原因**：
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- TDX API服务未启动
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- 防火墙阻止了连接
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- 端口被占用
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**解决方案**：
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1. 检查TDX API服务是否启动
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2. 检查端口5000是否被占用
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3. 尝试使用其他端口并修改.env配置
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### Q2: 健康检查失败，HTTP 404？
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**原因**：
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- TDX API服务版本不支持`/api/health`接口
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- URL配置错误
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**解决方案**：
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1. 尝试访问其他接口，如`/api/kline`
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2. 检查.env中的TDX_BASE_URL是否正确
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3. 确认服务地址和端口
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### Q3: K线数据获取失败？
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**原因**：
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- 股票代码不存在
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- 接口参数错误
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- 数据源未就绪
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**解决方案**：
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1. 使用已知存在的股票代码测试（如000001）
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2. 检查参数格式是否正确
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3. 查看TDX API服务日志
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### Q4: 数据量不足，无法计算MA20？
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**原因**：
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- TDX API返回的K线数据少于20条
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- 新上市股票数据不足
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**解决方案**：
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1. 这是正常情况，系统会自动跳过
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2. 等待股票积累更多交易日数据
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3. 监控服务会在下次扫描时重试
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### Q5: 如何使用远程TDX API服务？
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如果TDX API服务部署在其他服务器上：
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```bash
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# .env配置
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TDX_BASE_URL=http://192.168.1.100:5000
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# 或使用域名
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TDX_BASE_URL=http://tdx-api.example.com:5000
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```
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### Q6: 如何修改扫描间隔？
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在.env文件中修改：
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```bash
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# 扫描间隔（秒）
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LOW_PRICE_BULL_SCAN_INTERVAL=60 # 默认60秒，可改为30-300秒
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```
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或在监控面板的"⚙️ 监控配置"中动态修改。
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## 配置检查清单
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- [ ] ✅ .env文件中已配置TDX_BASE_URL
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- [ ] ✅ TDX API服务已启动
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- [ ] ✅ 可以访问http://127.0.0.1:5000/api/health
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- [ ] ✅ 运行test_tdx_api.py测试通过
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- [ ] ✅ 在监控面板启动监控服务
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- [ ] ✅ 配置了Webhook通知（可选）
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## 下一步
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配置完成后：
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1. 进入"低价擒牛"选股板块
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2. 执行选股，查看结果
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3. 点击"➕ 加入策略监控"
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4. 进入"📊 策略监控"面板
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5. 点击"▶️ 启动监控服务"
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6. 监控服务将每60秒扫描一次
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## 需要帮助？
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如果仍然遇到问题，请：
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1. 查看控制台错误日志
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2. 运行`python test_tdx_api.py`获取详细错误信息
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3. 检查TDX API服务日志
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4. 确认网络连接正常
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---
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**文档版本**: v1.0
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**更新日期**: 2024-12-12
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@@ -0,0 +1,416 @@
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'''
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三重滤网交易系统 V2.0
|
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by Alexander Elder (优化版)
|
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|
||||
核心改进：
|
||||
1. 第一重滤网：月线MACD金叉/多头（更稳定的趋势判断）
|
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2. 第二重滤网：日线RSI/KDJ超卖回调（放宽条件）
|
||||
3. 第三重滤网：价格企稳或突破（更灵活的入场）
|
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'''
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import jqdata
|
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import numpy as np
|
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|
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## 初始化函数
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||||
def initialize(context):
|
||||
set_benchmark('000300.XSHG')
|
||||
set_option('use_real_price', True)
|
||||
set_option('order_volume_ratio', 1)
|
||||
set_order_cost(OrderCost(open_tax=0, close_tax=0.001,
|
||||
open_commission=0.0003, close_commission=0.0003,
|
||||
close_today_commission=0, min_commission=5), type='stock')
|
||||
|
||||
# ========== 策略参数 ==========
|
||||
g.stocknum = 10 # 增加持仓数
|
||||
g.max_position_ratio = 0.98
|
||||
|
||||
# 第一重滤网参数（月线MACD）
|
||||
g.macd_fast = 12
|
||||
g.macd_slow = 26
|
||||
g.macd_signal = 9
|
||||
|
||||
# 第二重滤网参数（日线震荡 - 放宽）
|
||||
g.rsi_period = 14
|
||||
g.rsi_oversold = 50 # 放宽到50
|
||||
g.rsi_low = 35 # 极度超卖
|
||||
g.kdj_oversold = 40 # 放宽到40
|
||||
|
||||
# 第三重滤网参数（入场确认 - 放宽）
|
||||
g.ma_short = 5 # 5日均线
|
||||
g.ma_mid = 20 # 20日均线
|
||||
|
||||
# 止盈止损参数
|
||||
g.stop_loss = 0.06
|
||||
g.take_profit = 0.20
|
||||
g.trailing_stop = 0.08
|
||||
|
||||
# 市值筛选（亿）
|
||||
g.min_market_cap = 20
|
||||
g.max_market_cap = 1500
|
||||
|
||||
# 记录
|
||||
g.highest_profit = {}
|
||||
g.hold_days = {}
|
||||
|
||||
# 定时任务
|
||||
run_daily(morning_screen, '09:35')
|
||||
run_daily(check_positions, '14:00')
|
||||
run_daily(afternoon_trade, '14:50')
|
||||
|
||||
## 早盘筛选
|
||||
def morning_screen(context):
|
||||
g.buy_list = triple_screen_filter(context)
|
||||
log.info("【三重滤网】筛选出 %d 只股票" % len(g.buy_list))
|
||||
|
||||
## 三重滤网筛选
|
||||
def triple_screen_filter(context):
|
||||
# 基础选股
|
||||
q = query(
|
||||
valuation.code,
|
||||
valuation.market_cap
|
||||
).filter(
|
||||
valuation.market_cap.between(g.min_market_cap, g.max_market_cap)
|
||||
).order_by(
|
||||
valuation.market_cap.asc()
|
||||
).limit(500)
|
||||
|
||||
df = get_fundamentals(q)
|
||||
if df.empty:
|
||||
return []
|
||||
|
||||
stock_list = list(df['code'])
|
||||
stock_list = filter_basic(stock_list)
|
||||
|
||||
# 三重滤网筛选
|
||||
candidates = []
|
||||
for stock in stock_list[:150]:
|
||||
result = check_triple_screen(stock)
|
||||
if result['pass']:
|
||||
candidates.append((stock, result['score']))
|
||||
|
||||
# 按评分排序
|
||||
candidates.sort(key=lambda x: x[1], reverse=True)
|
||||
return [c[0] for c in candidates[:g.stocknum * 3]]
|
||||
|
||||
## 检查三重滤网信号
|
||||
def check_triple_screen(stock):
|
||||
"""
|
||||
三重滤网检查 V2.0：
|
||||
1. 第一重：月线MACD金叉/多头
|
||||
2. 第二重：日线RSI/KDJ回调
|
||||
3. 第三重：价格企稳或突破
|
||||
"""
|
||||
result = {'pass': False, 'score': 0}
|
||||
|
||||
try:
|
||||
# 获取更多日线数据（计算月线MACD需要约130天）
|
||||
df = attribute_history(stock, 150, '1d', ['close', 'high', 'low'], skip_paused=True)
|
||||
if len(df) < 130:
|
||||
return result
|
||||
|
||||
close = df['close'].values
|
||||
high = df['high'].values
|
||||
low = df['low'].values
|
||||
|
||||
# ========== 第一重滤网：月线MACD ==========
|
||||
# 模拟月线：每20天取收盘价（或用月末价格）
|
||||
monthly_close = []
|
||||
for i in range(19, len(close), 20):
|
||||
monthly_close.append(close[i])
|
||||
|
||||
if len(monthly_close) < 6:
|
||||
# 数据不足，改用日线MACD判断大趋势
|
||||
dif, dea, macd = calculate_macd(close)
|
||||
if dif[-1] <= dea[-1]: # MACD死叉
|
||||
return result
|
||||
if dif[-1] <= 0: # DIF在零轴下方
|
||||
return result
|
||||
else:
|
||||
# 计算月线MACD
|
||||
m_dif, m_dea, m_macd = calculate_macd(np.array(monthly_close))
|
||||
|
||||
# 月线MACD条件（放宽）：
|
||||
# 1. MACD金叉（DIF上穿DEA）或
|
||||
# 2. MACD柱由绿变红 或
|
||||
# 3. DIF > 0 且 DIF > DEA（多头）
|
||||
macd_golden = m_dif[-1] > m_dea[-1] and m_dif[-2] <= m_dea[-2]
|
||||
macd_turn_red = m_macd[-1] > 0 and m_macd[-2] <= 0
|
||||
macd_bullish = m_dif[-1] > 0 and m_dif[-1] > m_dea[-1]
|
||||
|
||||
if not (macd_golden or macd_turn_red or macd_bullish):
|
||||
return result
|
||||
|
||||
result['score'] += 30 # 趋势分
|
||||
|
||||
# ========== 第二重滤网：日线震荡指标 ==========
|
||||
rsi = calculate_rsi(close, g.rsi_period)
|
||||
k, d, j = calculate_kdj(high, low, close)
|
||||
|
||||
# RSI条件（放宽）：
|
||||
# 1. RSI < 50（相对低位）或
|
||||
# 2. RSI从低位回升
|
||||
rsi_current = rsi[-1] if len(rsi) > 0 else 50
|
||||
rsi_prev = rsi[-2] if len(rsi) > 1 else 50
|
||||
|
||||
rsi_signal = (rsi_current < g.rsi_oversold) or \
|
||||
(rsi_prev < g.rsi_low and rsi_current > rsi_prev) or \
|
||||
(rsi_current < 60 and rsi_current > rsi_prev)
|
||||
|
||||
# KDJ条件（放宽）：
|
||||
# 1. K < D 后金叉 或
|
||||
# 2. J < 40 或
|
||||
# 3. K/D都在低位回升
|
||||
kdj_golden = k[-1] > d[-1] and k[-2] <= d[-2]
|
||||
kdj_oversold = j[-1] < g.kdj_oversold or k[-1] < g.kdj_oversold
|
||||
kdj_rising = k[-1] > k[-2] and d[-1] > d[-2] and k[-1] < 60
|
||||
|
||||
kdj_signal = kdj_golden or kdj_oversold or kdj_rising
|
||||
|
||||
if not (rsi_signal or kdj_signal):
|
||||
return result
|
||||
|
||||
result['score'] += 30
|
||||
|
||||
# ========== 第三重滤网：入场确认（放宽）==========
|
||||
current_price = close[-1]
|
||||
ma5 = np.mean(close[-g.ma_short:])
|
||||
ma20 = np.mean(close[-g.ma_mid:])
|
||||
|
||||
# 入场条件（满足任一）：
|
||||
# 1. 价格站上5日均线
|
||||
# 2. 价格接近20日均线（±3%）
|
||||
# 3. 价格突破近5日高点
|
||||
recent_high = np.max(high[-6:-1])
|
||||
|
||||
price_above_ma5 = current_price > ma5
|
||||
price_near_ma20 = abs(current_price - ma20) / ma20 < 0.03
|
||||
price_breakout = current_price > recent_high * 0.99
|
||||
|
||||
if not (price_above_ma5 or price_near_ma20 or price_breakout):
|
||||
return result
|
||||
|
||||
result['score'] += 40
|
||||
|
||||
# 额外加分
|
||||
if rsi_current < 35:
|
||||
result['score'] += 15
|
||||
if kdj_golden:
|
||||
result['score'] += 10
|
||||
if current_price > ma5 > ma20:
|
||||
result['score'] += 10
|
||||
|
||||
result['pass'] = True
|
||||
return result
|
||||
|
||||
except:
|
||||
return result
|
||||
|
||||
## 计算MACD
|
||||
def calculate_macd(close, fast=12, slow=26, signal=9):
|
||||
ema_fast = calculate_ema(close, fast)
|
||||
ema_slow = calculate_ema(close, slow)
|
||||
dif = ema_fast - ema_slow
|
||||
dea = calculate_ema(dif, signal)
|
||||
macd = (dif - dea) * 2
|
||||
return dif, dea, macd
|
||||
|
||||
## 计算EMA
|
||||
def calculate_ema(data, period):
|
||||
ema = np.zeros(len(data))
|
||||
ema[0] = data[0]
|
||||
multiplier = 2 / (period + 1)
|
||||
for i in range(1, len(data)):
|
||||
ema[i] = (data[i] - ema[i-1]) * multiplier + ema[i-1]
|
||||
return ema
|
||||
|
||||
## 计算RSI
|
||||
def calculate_rsi(close, period=14):
|
||||
delta = np.diff(close)
|
||||
gain = np.where(delta > 0, delta, 0)
|
||||
loss = np.where(delta < 0, -delta, 0)
|
||||
|
||||
avg_gain = np.zeros(len(delta))
|
||||
avg_loss = np.zeros(len(delta))
|
||||
|
||||
avg_gain[period-1] = np.mean(gain[:period])
|
||||
avg_loss[period-1] = np.mean(loss[:period])
|
||||
|
||||
for i in range(period, len(delta)):
|
||||
avg_gain[i] = (avg_gain[i-1] * (period-1) + gain[i]) / period
|
||||
avg_loss[i] = (avg_loss[i-1] * (period-1) + loss[i]) / period
|
||||
|
||||
rs = avg_gain / (avg_loss + 1e-10)
|
||||
rsi = 100 - 100 / (1 + rs)
|
||||
return rsi
|
||||
|
||||
## 计算KDJ
|
||||
def calculate_kdj(high, low, close, n=9, m1=3, m2=3):
|
||||
length = len(close)
|
||||
rsv = np.zeros(length)
|
||||
k = np.zeros(length)
|
||||
d = np.zeros(length)
|
||||
j = np.zeros(length)
|
||||
|
||||
for i in range(n-1, length):
|
||||
hn = np.max(high[i-n+1:i+1])
|
||||
ln = np.min(low[i-n+1:i+1])
|
||||
rsv[i] = (close[i] - ln) / (hn - ln + 1e-10) * 100
|
||||
|
||||
k[n-1] = 50
|
||||
d[n-1] = 50
|
||||
|
||||
for i in range(n, length):
|
||||
k[i] = (m1-1)/m1 * k[i-1] + 1/m1 * rsv[i]
|
||||
d[i] = (m2-1)/m2 * d[i-1] + 1/m2 * k[i]
|
||||
j[i] = 3 * k[i] - 2 * d[i]
|
||||
|
||||
return k, d, j
|
||||
|
||||
## 检查持仓
|
||||
def check_positions(context):
|
||||
if len(context.portfolio.positions) == 0:
|
||||
return
|
||||
|
||||
for stock in list(context.portfolio.positions.keys()):
|
||||
position = context.portfolio.positions[stock]
|
||||
if position.closeable_amount <= 0:
|
||||
continue
|
||||
|
||||
cost = position.avg_cost
|
||||
current_price = position.price
|
||||
if cost <= 0:
|
||||
continue
|
||||
|
||||
profit_ratio = (current_price - cost) / cost
|
||||
g.hold_days[stock] = g.hold_days.get(stock, 0) + 1
|
||||
|
||||
if stock not in g.highest_profit:
|
||||
g.highest_profit[stock] = profit_ratio
|
||||
else:
|
||||
g.highest_profit[stock] = max(g.highest_profit[stock], profit_ratio)
|
||||
|
||||
highest = g.highest_profit[stock]
|
||||
|
||||
# === 止损 ===
|
||||
if profit_ratio < -g.stop_loss:
|
||||
log.info("【止损】%s 亏损 %.2f%%" % (stock, profit_ratio * 100))
|
||||
order_target(stock, 0)
|
||||
clean_stock_data(stock)
|
||||
continue
|
||||
|
||||
# === 趋势反转卖出 ===
|
||||
if check_trend_reversal(stock):
|
||||
log.info("【趋势反转】%s 周线趋势转弱" % stock)
|
||||
order_target(stock, 0)
|
||||
clean_stock_data(stock)
|
||||
continue
|
||||
|
||||
# === 移动止盈 ===
|
||||
if highest >= g.take_profit:
|
||||
drawdown = highest - profit_ratio
|
||||
allowed_drawdown = min(g.trailing_stop + highest * 0.4, 0.18)
|
||||
if drawdown >= allowed_drawdown:
|
||||
log.info("【移动止盈】%s 最高%.1f%% 回撤%.1f%%" %
|
||||
(stock, highest*100, drawdown*100))
|
||||
order_target(stock, 0)
|
||||
clean_stock_data(stock)
|
||||
continue
|
||||
|
||||
# === 分批止盈 ===
|
||||
if profit_ratio >= g.take_profit * 2:
|
||||
sell_amount = int(position.closeable_amount * 0.3 / 100) * 100
|
||||
if sell_amount >= 100:
|
||||
log.info("【止盈】%s +%.1f%%" % (stock, profit_ratio*100))
|
||||
order(stock, -sell_amount)
|
||||
|
||||
## 检查趋势反转（使用MACD）
|
||||
def check_trend_reversal(stock):
|
||||
try:
|
||||
df = attribute_history(stock, 60, '1d', ['close'], skip_paused=True)
|
||||
if len(df) < 50:
|
||||
return False
|
||||
|
||||
close = df['close'].values
|
||||
dif, dea, macd = calculate_macd(close)
|
||||
|
||||
# MACD死叉且DIF < 0
|
||||
if dif[-1] < dea[-1] and dif[-1] < 0:
|
||||
return True
|
||||
|
||||
# MACD连续3天下降且为负
|
||||
if macd[-1] < 0 and macd[-2] < 0 and macd[-3] < 0:
|
||||
if macd[-1] < macd[-2] < macd[-3]:
|
||||
return True
|
||||
|
||||
return False
|
||||
except:
|
||||
return False
|
||||
|
||||
## 清理数据
|
||||
def clean_stock_data(stock):
|
||||
for d in [g.highest_profit, g.hold_days]:
|
||||
if stock in d:
|
||||
del d[stock]
|
||||
|
||||
## 尾盘交易
|
||||
def afternoon_trade(context):
|
||||
buy_stocks(context)
|
||||
|
||||
## 买入函数
|
||||
def buy_stocks(context):
|
||||
if not hasattr(g, 'buy_list') or not g.buy_list:
|
||||
return
|
||||
|
||||
position_count = len(context.portfolio.positions)
|
||||
if position_count >= g.stocknum:
|
||||
return
|
||||
|
||||
available_cash = context.portfolio.available_cash * g.max_position_ratio
|
||||
buy_count = min(g.stocknum - position_count, len(g.buy_list))
|
||||
if buy_count <= 0 or available_cash < 10000:
|
||||
return
|
||||
|
||||
cash_per_stock = available_cash / buy_count
|
||||
|
||||
bought = 0
|
||||
for stock in g.buy_list:
|
||||
if bought >= buy_count:
|
||||
break
|
||||
if stock in context.portfolio.positions:
|
||||
continue
|
||||
|
||||
# 再次确认三重滤网信号
|
||||
result = check_triple_screen(stock)
|
||||
if result['pass']:
|
||||
order_value(stock, cash_per_stock)
|
||||
log.info("【买入】%s 评分:%.0f" % (stock, result['score']))
|
||||
g.highest_profit[stock] = 0
|
||||
g.hold_days[stock] = 0
|
||||
bought += 1
|
||||
|
||||
## 基础过滤
|
||||
def filter_basic(stock_list):
|
||||
if not stock_list:
|
||||
return []
|
||||
|
||||
current_data = get_current_data()
|
||||
filtered = []
|
||||
|
||||
for stock in stock_list:
|
||||
if current_data[stock].paused:
|
||||
continue
|
||||
if current_data[stock].is_st:
|
||||
continue
|
||||
if 'ST' in current_data[stock].name or '*' in current_data[stock].name:
|
||||
continue
|
||||
if stock.startswith('688') or stock.startswith('8') or stock.startswith('4'):
|
||||
continue
|
||||
if current_data[stock].last_price >= current_data[stock].high_limit:
|
||||
continue
|
||||
if current_data[stock].last_price <= current_data[stock].low_limit:
|
||||
continue
|
||||
|
||||
filtered.append(stock)
|
||||
|
||||
return filtered
|
||||
|
||||
@@ -0,0 +1,240 @@
|
||||
# 低价擒牛功能说明
|
||||
|
||||
## 功能概述
|
||||
|
||||
**低价擒牛**是一个专注于筛选低价高成长股票的选股策略板块，结合量化交易策略，帮助投资者发现潜在的价值洼地。
|
||||
|
||||
## 选股策略
|
||||
|
||||
### 筛选条件
|
||||
|
||||
| 条件 | 说明 |
|
||||
|------|------|
|
||||
| 股价 | < 10元 |
|
||||
| 净利润增长率 | ≥ 100%（净利润同比增长率） |
|
||||
| 市场板块 | 深圳A股 |
|
||||
| 排除项 | ST股票、科创板、创业板 |
|
||||
| 排序方式 | 按成交额由小至大排名 |
|
||||
|
||||
### 策略理念
|
||||
|
||||
1. **低价优势**：股价低于10元，易于散户参与，具有更大的上涨空间
|
||||
2. **高成长性**：净利润增长率≥100%，说明公司业绩高速增长
|
||||
3. **流动性考虑**：优先选择成交额较小的股票，避免过度热门
|
||||
4. **风险控制**：排除ST、科创板、创业板，降低风险
|
||||
|
||||
## 量化交易策略
|
||||
|
||||
### 资金与仓位管理
|
||||
|
||||
| 参数 | 配置 | 说明 |
|
||||
|------|------|------|
|
||||
| 初始资金 | 100万元 | 策略启动资金 |
|
||||
| 持股周期 | 5天 | 单只股票最长持有时间 |
|
||||
| 交易仓位 | 满仓 | 使用所有可用资金 |
|
||||
| 个股最大持仓 | 40% | 单只股票最多占用40%资金 |
|
||||
| 账户最大持股数 | 4只 | 同时最多持有4只股票 |
|
||||
| 单日最大买入数 | 2只 | 每天最多买入2只新股票 |
|
||||
|
||||
### 买卖择时规则
|
||||
|
||||
#### 买入时机
|
||||
- **时间点**：开盘买入
|
||||
- **优先级**：按成交额由小到大排序
|
||||
- **执行条件**：
|
||||
- 未达到最大持股数（4只）
|
||||
- 当日买入数量未达上限（2只）
|
||||
- 有足够的可用资金
|
||||
|
||||
#### 卖出时机
|
||||
满足以下**任一**条件即卖出：
|
||||
1. **技术信号**：MA5日线下穿MA20日线
|
||||
2. **时间到期**：持股满5天
|
||||
|
||||
### 策略优势
|
||||
|
||||
1. ✅ **严格的仓位控制**：避免单一股票风险过大
|
||||
2. ✅ **轮动机制**：卖出后释放资金，继续买入新标的
|
||||
3. ✅ **纪律性强**：明确的买卖规则，避免情绪化交易
|
||||
4. ✅ **风险分散**：最多持有4只股票，分散风险
|
||||
|
||||
## 使用步骤
|
||||
|
||||
### 1. 进入功能
|
||||
在左侧菜单栏点击：**🎯 选股板块** → **🐂 低价擒牛**
|
||||
|
||||
### 2. 设置参数
|
||||
- **筛选数量**：选择展示的股票数量（默认5只）
|
||||
|
||||
### 3. 开始选股
|
||||
点击 **🚀 开始低价擒牛选股** 按钮
|
||||
|
||||
系统将：
|
||||
- 调用问财接口获取符合条件的股票
|
||||
- 按成交额排序
|
||||
- 展示前N只股票
|
||||
|
||||
### 4. 查看结果
|
||||
|
||||
#### 股票列表
|
||||
- 展示每只股票的基本信息
|
||||
- 包含股价、净利润增长率、成交额等关键指标
|
||||
- 可下载完整数据CSV文件
|
||||
|
||||
#### 钉钉通知
|
||||
如果配置了Webhook，系统会自动发送钉钉消息，包含：
|
||||
- 筛选策略说明
|
||||
- 精选股票列表
|
||||
- 关键财务指标
|
||||
|
||||
### 5. 量化交易模拟
|
||||
|
||||
#### 策略模拟
|
||||
点击 **🎮 开始策略模拟** 可以查看：
|
||||
- 模拟买入信号
|
||||
- 当前持仓情况
|
||||
- 账户资金状态
|
||||
|
||||
#### 实盘交易（可选）
|
||||
如需使用MiniQMT进行实盘交易：
|
||||
1. 配置环境变量（详见环境配置）
|
||||
2. 点击 **🔗 连接MiniQMT实盘**
|
||||
3. 系统将自动执行买卖操作
|
||||
|
||||
## 配置说明
|
||||
|
||||
### Webhook通知配置
|
||||
|
||||
在 `.env` 文件中配置：
|
||||
|
||||
```bash
|
||||
# Webhook通知配置
|
||||
WEBHOOK_ENABLED=true
|
||||
WEBHOOK_URL=your_dingtalk_webhook_url
|
||||
WEBHOOK_TYPE=dingtalk
|
||||
WEBHOOK_KEYWORD=aiagents通知
|
||||
```
|
||||
|
||||
### MiniQMT交易配置
|
||||
|
||||
在 `.env` 文件中配置：
|
||||
|
||||
```bash
|
||||
# MiniQMT配置
|
||||
MINIQMT_ENABLED=true
|
||||
MINIQMT_ACCOUNT_ID=your_account_id
|
||||
```
|
||||
|
||||
## 风险提示
|
||||
|
||||
⚠️ **重要提醒**：
|
||||
|
||||
1. **历史业绩不代表未来收益**
|
||||
- 净利润高增长可能不可持续
|
||||
- 低价股波动性较大
|
||||
|
||||
2. **交易成本**
|
||||
- 实际交易存在滑点
|
||||
- 需要考虑手续费和印花税
|
||||
|
||||
3. **市场风险**
|
||||
- 深圳A股市场波动较大
|
||||
- 小盘股流动性风险
|
||||
|
||||
4. **技术风险**
|
||||
- MA均线信号可能滞后
|
||||
- 5天持股周期可能错过更好的卖点
|
||||
|
||||
5. **策略局限**
|
||||
- 本策略为机械化交易策略
|
||||
- 无法应对突发事件（如重大利空）
|
||||
|
||||
## 策略改进建议
|
||||
|
||||
### 可优化方向
|
||||
|
||||
1. **动态仓位管理**
|
||||
- 根据市场情绪调整仓位
|
||||
- 引入风险评分机制
|
||||
|
||||
2. **止损机制**
|
||||
- 添加固定比例止损（如-5%）
|
||||
- 避免单只股票亏损过大
|
||||
|
||||
3. **择时优化**
|
||||
- 结合更多技术指标（MACD、RSI等）
|
||||
- 考虑大盘走势
|
||||
|
||||
4. **基本面过滤**
|
||||
- 添加财务指标筛选
|
||||
- 排除财务造假风险股
|
||||
|
||||
## 常见问题
|
||||
|
||||
### Q1: 为什么只选深圳A股？
|
||||
A: 深圳A股集中了大量中小盘股票，符合"低价高成长"的特征。上海主板以大盘蓝筹为主，不太符合本策略定位。
|
||||
|
||||
### Q2: 成交额排序的意义是什么？
|
||||
A: 成交额小的股票往往还未被市场充分发现，具有更大的潜在空间。但也要注意流动性风险。
|
||||
|
||||
### Q3: 持股5天的依据是什么？
|
||||
A: 5天是一个交易周（一周）的时间，可以让股票有足够的时间展现其趋势，同时避免长期持有的风险。
|
||||
|
||||
### Q4: 能否调整策略参数？
|
||||
A: 当前版本参数固定，未来版本将支持自定义参数配置。
|
||||
|
||||
### Q5: 模拟交易和实盘有什么区别？
|
||||
A: 模拟交易不考虑滑点、手续费等实际成本，仅供策略验证。实盘交易需要接入MiniQMT，会有实际的资金变动。
|
||||
|
||||
## 技术实现
|
||||
|
||||
### 核心模块
|
||||
|
||||
1. **low_price_bull_selector.py**
|
||||
- 使用pywencai获取数据
|
||||
- 数据清洗和格式化
|
||||
|
||||
2. **low_price_bull_strategy.py**
|
||||
- 量化交易策略实现
|
||||
- 仓位管理和买卖信号
|
||||
|
||||
3. **low_price_bull_ui.py**
|
||||
- Streamlit界面展示
|
||||
- 用户交互逻辑
|
||||
|
||||
### 数据流程
|
||||
|
||||
```
|
||||
用户输入参数
|
||||
↓
|
||||
调用pywencai接口
|
||||
↓
|
||||
获取符合条件的股票
|
||||
↓
|
||||
按成交额排序
|
||||
↓
|
||||
展示股票列表
|
||||
↓
|
||||
发送钉钉通知（可选）
|
||||
↓
|
||||
执行量化策略（可选）
|
||||
```
|
||||
|
||||
## 更新日志
|
||||
|
||||
### v1.0 (2024-12-12)
|
||||
- ✅ 初始版本发布
|
||||
- ✅ 实现基础选股功能
|
||||
- ✅ 集成钉钉通知
|
||||
- ✅ 量化策略模拟
|
||||
- ✅ MiniQMT接口支持
|
||||
|
||||
## 联系与反馈
|
||||
|
||||
如有问题或建议，请通过以下方式反馈：
|
||||
- GitHub Issues
|
||||
- 系统内置反馈功能
|
||||
|
||||
---
|
||||
|
||||
**免责声明**：本功能仅供学习和研究使用，不构成任何投资建议。股市有风险，投资需谨慎。
|
||||
@@ -0,0 +1,260 @@
|
||||
# 低价擒牛选股板块 - 实现总结
|
||||
|
||||
## 实现日期
|
||||
2024-12-12
|
||||
|
||||
## 功能概述
|
||||
在左侧菜单栏"选股板块"下新增"低价擒牛"选股策略，支持低价高成长股票筛选和量化交易模拟。
|
||||
|
||||
## 核心功能
|
||||
|
||||
### 1. 选股策略
|
||||
- ✅ 股价 < 10元
|
||||
- ✅ 净利润增长率 ≥ 100%
|
||||
- ✅ 非ST、非科创板、非创业板
|
||||
- ✅ 深圳A股
|
||||
- ✅ 按成交额由小至大排名
|
||||
- ✅ 筛选前5名（可配置3-10只）
|
||||
|
||||
### 2. 钉钉通知
|
||||
- ✅ 读取.env中的Webhook配置
|
||||
- ✅ 自动发送选股结果到钉钉群
|
||||
- ✅ 包含股票代码、价格、净利增长率等信息
|
||||
|
||||
### 3. 量化交易策略
|
||||
- ✅ 初始资金：100万元
|
||||
- ✅ 持股周期：5天
|
||||
- ✅ 仓位控制：满仓
|
||||
- ✅ 个股最大持仓：40%
|
||||
- ✅ 最大持股数：4只
|
||||
- ✅ 单日最大买入：2只
|
||||
- ✅ 买入时机：开盘买入
|
||||
- ✅ 卖出信号：MA5下穿MA20 或 持股满5天
|
||||
|
||||
### 4. 交易模拟
|
||||
- ✅ 支持策略模拟（无需配置）
|
||||
- ✅ 支持MiniQMT实盘对接（需配置）
|
||||
- ✅ 显示买入信号、持仓、账户状态
|
||||
|
||||
## 新增文件
|
||||
|
||||
### Python模块
|
||||
1. **low_price_bull_selector.py** (158行)
|
||||
- 使用pywencai获取符合条件的股票
|
||||
- 数据清洗和格式化
|
||||
- 股票代码列表生成
|
||||
|
||||
2. **low_price_bull_strategy.py** (278行)
|
||||
- 量化交易策略实现
|
||||
- 买卖信号判断
|
||||
- 仓位管理
|
||||
- 交易历史记录
|
||||
|
||||
3. **low_price_bull_ui.py** (382行)
|
||||
- Streamlit界面展示
|
||||
- 用户交互逻辑
|
||||
- 钉钉通知集成
|
||||
- 策略模拟展示
|
||||
|
||||
### 文档
|
||||
1. **docs/低价擒牛功能说明.md** (241行)
|
||||
- 完整功能文档
|
||||
- 策略详解
|
||||
- 配置说明
|
||||
- 风险提示
|
||||
|
||||
2. **docs/低价擒牛快速开始.md** (197行)
|
||||
- 快速上手指南
|
||||
- 典型使用场景
|
||||
- 常见问题解答
|
||||
|
||||
## 修改文件
|
||||
|
||||
### app.py
|
||||
修改位置：第293-380行（菜单导航区域）
|
||||
|
||||
**修改内容**：
|
||||
1. 在"选股板块"下添加"🐂 低价擒牛"按钮
|
||||
2. 更新所有按钮的状态清除列表，增加`show_low_price_bull`
|
||||
3. 添加路由检查，显示低价擒牛界面
|
||||
|
||||
**代码变更**：
|
||||
```python
|
||||
# 添加导航按钮
|
||||
if st.button("🐂 低价擒牛", width='stretch', key="nav_low_price_bull", help="低价高成长股票筛选策略"):
|
||||
st.session_state.show_low_price_bull = True
|
||||
# 清除其他页面标志
|
||||
|
||||
# 添加路由
|
||||
if 'show_low_price_bull' in st.session_state and st.session_state.show_low_price_bull:
|
||||
from low_price_bull_ui import display_low_price_bull
|
||||
display_low_price_bull()
|
||||
return
|
||||
```
|
||||
|
||||
## 技术实现
|
||||
|
||||
### 数据获取
|
||||
- 使用pywencai模块调用问财接口
|
||||
- 查询语句：`"股价<10元，净利润增长率(净利润同比增长率)≥100%，非st，非科创板，非创业板，深圳A股，成交额由小至大排名"`
|
||||
- 智能解析返回结果，兼容DataFrame/dict/list格式
|
||||
|
||||
### 通知集成
|
||||
- 复用notification_service模块
|
||||
- 支持钉钉/飞书Webhook
|
||||
- 自动包含关键词"aiagents通知"
|
||||
- Markdown格式消息展示
|
||||
|
||||
### 策略实现
|
||||
- 基于面向对象设计
|
||||
- 严格的仓位管理
|
||||
- 清晰的买卖信号
|
||||
- 完整的交易记录
|
||||
|
||||
### UI展示
|
||||
- Streamlit expander组件展示详情
|
||||
- DataFrame展示完整数据
|
||||
- Metric组件展示关键指标
|
||||
- CSV下载功能
|
||||
|
||||
## 环境变量配置
|
||||
|
||||
### Webhook通知（可选）
|
||||
```bash
|
||||
WEBHOOK_ENABLED=true
|
||||
WEBHOOK_URL=your_dingtalk_webhook_url
|
||||
WEBHOOK_TYPE=dingtalk
|
||||
WEBHOOK_KEYWORD=aiagents通知
|
||||
```
|
||||
|
||||
### MiniQMT实盘（可选）
|
||||
```bash
|
||||
MINIQMT_ENABLED=true
|
||||
MINIQMT_ACCOUNT_ID=your_account_id
|
||||
```
|
||||
|
||||
## 使用流程
|
||||
|
||||
1. **启动系统**
|
||||
```bash
|
||||
streamlit run app.py
|
||||
```
|
||||
|
||||
2. **进入功能**
|
||||
- 左侧菜单 → 🎯 选股板块 → 🐂 低价擒牛
|
||||
|
||||
3. **开始选股**
|
||||
- 调整筛选数量
|
||||
- 点击"🚀 开始低价擒牛选股"
|
||||
|
||||
4. **查看结果**
|
||||
- 股票列表
|
||||
- 统计信息
|
||||
- 钉钉通知（如已配置）
|
||||
|
||||
5. **策略模拟**（可选）
|
||||
- 点击"🎮 开始策略模拟"
|
||||
- 查看买入信号和持仓
|
||||
|
||||
6. **实盘交易**（可选）
|
||||
- 配置MiniQMT
|
||||
- 点击"🔗 连接MiniQMT实盘"
|
||||
|
||||
## 特色亮点
|
||||
|
||||
### 1. 智能筛选
|
||||
- 多维度条件过滤
|
||||
- 成交额排序优先低热度股票
|
||||
- 自动排除风险股（ST、科创板）
|
||||
|
||||
### 2. 完整策略
|
||||
- 清晰的买卖规则
|
||||
- 严格的风险控制
|
||||
- 可复现的交易逻辑
|
||||
|
||||
### 3. 即时通知
|
||||
- 自动发送钉钉消息
|
||||
- 无需人工查看
|
||||
- 移动端即时接收
|
||||
|
||||
### 4. 灵活配置
|
||||
- 筛选数量可调
|
||||
- 策略参数可改
|
||||
- 支持模拟和实盘
|
||||
|
||||
## 注意事项
|
||||
|
||||
### 使用限制
|
||||
1. ⚠️ 问财接口有频率限制，建议每次间隔30秒以上
|
||||
2. ⚠️ 净利润增长率≥100%的股票可能较少
|
||||
3. ⚠️ 策略模拟不包含手续费和滑点
|
||||
|
||||
### 风险提示
|
||||
1. ⚠️ 本功能仅供学习研究使用
|
||||
2. ⚠️ 不构成任何投资建议
|
||||
3. ⚠️ 股市有风险，投资需谨慎
|
||||
4. ⚠️ 历史业绩不代表未来收益
|
||||
|
||||
### 技术限制
|
||||
1. ⚠️ 需要稳定的网络连接
|
||||
2. ⚠️ pywencai模块需要正确安装
|
||||
3. ⚠️ MiniQMT需要额外配置
|
||||
|
||||
## 后续优化方向
|
||||
|
||||
### 短期优化
|
||||
- [ ] 添加历史选股记录
|
||||
- [ ] 支持自定义筛选条件
|
||||
- [ ] 增加更多技术指标
|
||||
|
||||
### 中期优化
|
||||
- [ ] 回测功能
|
||||
- [ ] 策略参数优化
|
||||
- [ ] 多策略对比
|
||||
|
||||
### 长期优化
|
||||
- [ ] AI选股建议
|
||||
- [ ] 实时监控
|
||||
- [ ] 自动化交易
|
||||
|
||||
## 测试建议
|
||||
|
||||
### 功能测试
|
||||
1. ✅ 测试选股功能是否正常
|
||||
2. ✅ 验证数据格式是否正确
|
||||
3. ✅ 检查钉钉通知是否发送
|
||||
4. ✅ 确认策略模拟逻辑
|
||||
|
||||
### 边界测试
|
||||
1. ✅ 筛选不到股票时的处理
|
||||
2. ✅ 网络异常时的错误处理
|
||||
3. ✅ Webhook未配置时的提示
|
||||
|
||||
### 性能测试
|
||||
1. ✅ 数据获取速度
|
||||
2. ✅ UI渲染性能
|
||||
3. ✅ 并发访问支持
|
||||
|
||||
## 相关文档
|
||||
|
||||
- [低价擒牛功能说明](./docs/低价擒牛功能说明.md)
|
||||
- [低价擒牛快速开始](./docs/低价擒牛快速开始.md)
|
||||
- [Webhook通知配置指南](./docs/Webhook通知配置指南.md)
|
||||
- [MiniQMT集成指南](./docs/MINIQMT_INTEGRATION_GUIDE.md)
|
||||
|
||||
## 总结
|
||||
|
||||
本次实现完整地添加了"低价擒牛"选股板块，包含：
|
||||
- ✅ 完整的选股策略
|
||||
- ✅ 钉钉通知集成
|
||||
- ✅ 量化交易策略
|
||||
- ✅ 策略模拟功能
|
||||
- ✅ 详细的文档说明
|
||||
|
||||
所有功能均已集成到现有系统中，与主力选股等其他板块保持一致的交互体验。
|
||||
|
||||
---
|
||||
|
||||
**实现完成时间**：2024-12-12
|
||||
**版本**：v1.0
|
||||
**状态**：✅ 已完成并可使用
|
||||
@@ -0,0 +1,196 @@
|
||||
# 低价擒牛 - 快速开始指南
|
||||
|
||||
## 🚀 5分钟上手
|
||||
|
||||
### 第一步：进入功能
|
||||
|
||||
1. 启动系统：`streamlit run app.py`
|
||||
2. 在左侧菜单栏找到 **🎯 选股板块**
|
||||
3. 点击 **🐂 低价擒牛**
|
||||
|
||||
### 第二步：开始选股
|
||||
|
||||
1. 调整筛选数量（默认5只，可调整3-10只）
|
||||
2. 点击 **🚀 开始低价擒牛选股**
|
||||
3. 等待30-60秒，系统自动获取数据
|
||||
|
||||
### 第三步：查看结果
|
||||
|
||||
系统会展示：
|
||||
- ✅ 筛选数量和统计信息
|
||||
- ✅ 精选股票详细列表
|
||||
- ✅ 完整数据表格（可下载CSV）
|
||||
|
||||
### 第四步：接收通知（可选）
|
||||
|
||||
如果配置了钉钉Webhook，会自动收到消息通知
|
||||
|
||||
## 📊 选股策略一览
|
||||
|
||||
| 条件 | 值 |
|
||||
|------|-----|
|
||||
| 股价 | < 10元 |
|
||||
| 净利润增长率 | ≥ 100% |
|
||||
| 市场 | 深圳A股 |
|
||||
| 排除 | ST、科创板、创业板 |
|
||||
| 排序 | 成交额由小到大 |
|
||||
|
||||
## 🎯 量化策略参数
|
||||
|
||||
| 参数 | 值 |
|
||||
|------|-----|
|
||||
| 初始资金 | 100万元 |
|
||||
| 持股周期 | 5天 |
|
||||
| 最大持股数 | 4只 |
|
||||
| 个股最大仓位 | 40% |
|
||||
| 单日最大买入 | 2只 |
|
||||
| 买入时机 | 开盘 |
|
||||
| 卖出信号 | MA5下穿MA20 或 持股5天 |
|
||||
|
||||
## 🔧 配置Webhook通知（可选）
|
||||
|
||||
### 1. 获取钉钉机器人Webhook URL
|
||||
|
||||
1. 打开钉钉群 → 群设置 → 智能群助手
|
||||
2. 添加机器人 → 自定义
|
||||
3. 添加安全设置 → 自定义关键词：`aiagents通知`
|
||||
4. 复制Webhook URL
|
||||
|
||||
### 2. 配置环境变量
|
||||
|
||||
编辑项目根目录的 `.env` 文件：
|
||||
|
||||
```bash
|
||||
# Webhook通知配置
|
||||
WEBHOOK_ENABLED=true
|
||||
WEBHOOK_URL=https://oapi.dingtalk.com/robot/send?access_token=xxxxx
|
||||
WEBHOOK_TYPE=dingtalk
|
||||
WEBHOOK_KEYWORD=aiagents通知
|
||||
```
|
||||
|
||||
### 3. 重启系统
|
||||
|
||||
配置完成后重启Streamlit即可
|
||||
|
||||
## 🎮 使用量化策略模拟
|
||||
|
||||
### 策略模拟（无需配置）
|
||||
|
||||
1. 完成选股后，在页面底部找到 **量化交易策略模拟**
|
||||
2. 点击 **🎮 开始策略模拟**
|
||||
3. 系统会展示：
|
||||
- 模拟买入信号
|
||||
- 当前持仓情况
|
||||
- 账户资金状态
|
||||
|
||||
### MiniQMT实盘（需要配置）
|
||||
|
||||
**前提条件**：
|
||||
- 已安装MiniQMT客户端
|
||||
- 已开通量化交易账户
|
||||
|
||||
**配置步骤**：
|
||||
1. 编辑 `.env` 文件：
|
||||
```bash
|
||||
MINIQMT_ENABLED=true
|
||||
MINIQMT_ACCOUNT_ID=your_account_id
|
||||
```
|
||||
2. 重启系统
|
||||
3. 点击 **🔗 连接MiniQMT实盘**
|
||||
|
||||
## 📋 典型使用场景
|
||||
|
||||
### 场景1：每日早盘选股
|
||||
|
||||
**时间**：每天早上9:15-9:25
|
||||
|
||||
**操作**：
|
||||
1. 打开低价擒牛功能
|
||||
2. 开始选股，获取最新数据
|
||||
3. 查看筛选结果
|
||||
4. 开盘后根据买入信号下单
|
||||
|
||||
### 场景2：周末策略回测
|
||||
|
||||
**时间**：周末
|
||||
|
||||
**操作**：
|
||||
1. 选股获取数据
|
||||
2. 导出CSV文件
|
||||
3. 使用Excel或Python进行历史数据分析
|
||||
4. 优化策略参数
|
||||
|
||||
### 场景3：自动通知监控
|
||||
|
||||
**时间**：工作日定时
|
||||
|
||||
**操作**：
|
||||
1. 配置Webhook
|
||||
2. 设置定时任务（cron或Task Scheduler）
|
||||
3. 每日自动选股并推送钉钉消息
|
||||
|
||||
## 💡 使用技巧
|
||||
|
||||
### 技巧1：成交额排序的意义
|
||||
- 成交额小 = 市场关注度低 = 可能被低估
|
||||
- 优先买入排名靠前的股票（成交额最小）
|
||||
|
||||
### 技巧2：持股周期管理
|
||||
- 5天是一个交易周期
|
||||
- 可在第3-4天关注卖出信号
|
||||
- 避免持股到期临近时盲目持有
|
||||
|
||||
### 技巧3：仓位控制
|
||||
- 建议新手先用小资金测试
|
||||
- 逐步增加至策略设定的资金量
|
||||
- 严格执行单股40%的仓位限制
|
||||
|
||||
### 技巧4：结合大盘走势
|
||||
- 大盘上涨时：可适当激进
|
||||
- 大盘下跌时：减少买入或观望
|
||||
- 策略不能替代市场判断
|
||||
|
||||
## ⚠️ 常见问题
|
||||
|
||||
### Q: 为什么有时候筛选不到股票？
|
||||
A: 可能原因：
|
||||
- 市场环境不符合条件（如整体估值偏高）
|
||||
- 净利润增长率≥100%的股票较少
|
||||
- 建议适当放宽筛选条件
|
||||
|
||||
### Q: 钉钉通知没收到？
|
||||
A: 检查清单：
|
||||
- ✅ WEBHOOK_ENABLED 设置为 true
|
||||
- ✅ WEBHOOK_URL 正确
|
||||
- ✅ 钉钉机器人关键词包含"aiagents通知"
|
||||
- ✅ 系统已重启
|
||||
|
||||
### Q: 策略模拟和实盘有什么区别？
|
||||
A: 主要区别：
|
||||
- 模拟：不实际下单，无手续费
|
||||
- 实盘：真实下单，有滑点和手续费
|
||||
- 建议先用模拟验证策略
|
||||
|
||||
### Q: 如何调整策略参数？
|
||||
A: 当前版本参数固定在代码中，如需调整：
|
||||
1. 编辑 `low_price_bull_strategy.py`
|
||||
2. 修改 `__init__` 方法中的参数
|
||||
3. 重启系统
|
||||
|
||||
## 📚 进阶阅读
|
||||
|
||||
- [低价擒牛功能说明](./低价擒牛功能说明.md) - 完整功能文档
|
||||
- [Webhook配置指南](./Webhook通知配置指南.md) - 详细配置步骤
|
||||
- [MiniQMT集成指南](./MINIQMT_INTEGRATION_GUIDE.md) - 量化交易配置
|
||||
|
||||
## 🎯 下一步
|
||||
|
||||
1. ✅ 熟悉基础选股功能
|
||||
2. ✅ 配置钉钉通知
|
||||
3. ✅ 使用策略模拟
|
||||
4. ✅ 尝试MiniQMT实盘（谨慎）
|
||||
5. ✅ 根据实际情况优化策略
|
||||
|
||||
---
|
||||
|
||||
**祝您投资顺利！** 🚀📈💰
|
||||
@@ -0,0 +1,365 @@
|
||||
# 低价擒牛策略监控功能实现总结
|
||||
|
||||
## 实现日期
|
||||
2024-12-12
|
||||
|
||||
## 功能概述
|
||||
为"低价擒牛"选股板块添加策略监控功能，实现自动监控持仓股票并在满足卖出条件时发送提醒。
|
||||
|
||||
## 核心功能
|
||||
|
||||
### 1. 策略监控
|
||||
- ✅ 加入监控：在选出的股票列表中点击"加入策略监控"按钮
|
||||
- ✅ 自动扫描：每分钟扫描1次（可配置）
|
||||
- ✅ 持股天数监控：持股满5天第6天开盘提醒卖出
|
||||
- ✅ 均线监控：MA5下穿MA20提醒卖出
|
||||
- ✅ 自动移除：提醒卖出后自动移出监控列表
|
||||
|
||||
### 2. 数据源集成
|
||||
- ✅ TDX数据源：读取股票K线数据
|
||||
- ✅ 均线计算：自动计算MA5和MA20
|
||||
- ✅ 实时价格：获取最新股票价格
|
||||
|
||||
### 3. 通知系统
|
||||
- ✅ 钉钉通知：自动发送卖出提醒到钉钉群
|
||||
- ✅ 面板提醒：在监控面板显示待处理提醒
|
||||
- ✅ 历史记录：保存所有提醒记录
|
||||
|
||||
## 新增文件
|
||||
|
||||
### 核心模块（3个Python文件）
|
||||
|
||||
1. **low_price_bull_monitor.py** (363行)
|
||||
- 监控数据库管理
|
||||
- 股票添加/移除
|
||||
- 提醒生成和管理
|
||||
- 持有天数更新
|
||||
|
||||
2. **low_price_bull_service.py** (310行)
|
||||
- 后台监控服务
|
||||
- 定时扫描循环
|
||||
- TDX数据获取
|
||||
- MA均线检测
|
||||
- 钉钉通知发送
|
||||
|
||||
3. **low_price_bull_monitor_ui.py** (273行)
|
||||
- 监控面板UI
|
||||
- 服务控制（启动/停止）
|
||||
- 监控列表展示
|
||||
- 卖出提醒展示
|
||||
- 历史记录查询
|
||||
|
||||
### 文档（2个Markdown文件）
|
||||
|
||||
1. **低价擒牛策略监控配置说明.md**
|
||||
- 环境变量配置
|
||||
- TDX数据源配置
|
||||
- 使用流程说明
|
||||
- 常见问题解答
|
||||
|
||||
2. **低价擒牛策略监控功能实现总结.md**
|
||||
- 功能概述
|
||||
- 技术实现
|
||||
- 使用说明
|
||||
|
||||
## 修改文件
|
||||
|
||||
### low_price_bull_ui.py
|
||||
- ✅ 添加导入语句
|
||||
- ✅ 添加"策略监控"按钮（右上角）
|
||||
- ✅ 在股票详情中添加"加入策略监控"按钮
|
||||
- ✅ 集成监控面板切换
|
||||
- ✅ 更新策略说明
|
||||
|
||||
## 数据库设计
|
||||
|
||||
### 表1: monitored_stocks（监控列表）
|
||||
```sql
|
||||
CREATE TABLE monitored_stocks (
|
||||
id INTEGER PRIMARY KEY,
|
||||
stock_code TEXT NOT NULL, -- 股票代码
|
||||
stock_name TEXT NOT NULL, -- 股票名称
|
||||
buy_price REAL NOT NULL, -- 买入价格
|
||||
buy_date TEXT NOT NULL, -- 买入日期
|
||||
holding_days INTEGER DEFAULT 0, -- 持有天数
|
||||
status TEXT DEFAULT 'holding', -- 状态（holding/removed）
|
||||
add_time TEXT NOT NULL, -- 加入时间
|
||||
remove_time TEXT, -- 移除时间
|
||||
remove_reason TEXT, -- 移除原因
|
||||
UNIQUE(stock_code, status)
|
||||
)
|
||||
```
|
||||
|
||||
### 表2: sell_alerts（卖出提醒）
|
||||
```sql
|
||||
CREATE TABLE sell_alerts (
|
||||
id INTEGER PRIMARY KEY,
|
||||
stock_code TEXT NOT NULL, -- 股票代码
|
||||
stock_name TEXT NOT NULL, -- 股票名称
|
||||
alert_type TEXT NOT NULL, -- 提醒类型（holding_days/ma_cross）
|
||||
alert_reason TEXT NOT NULL, -- 提醒原因
|
||||
current_price REAL, -- 当前价格
|
||||
ma5 REAL, -- MA5值
|
||||
ma20 REAL, -- MA20值
|
||||
holding_days INTEGER, -- 持有天数
|
||||
alert_time TEXT NOT NULL, -- 提醒时间
|
||||
is_sent INTEGER DEFAULT 0 -- 是否已发送
|
||||
)
|
||||
```
|
||||
|
||||
## 技术实现
|
||||
|
||||
### 1. 监控服务架构
|
||||
```
|
||||
┌─────────────────────────────────────┐
|
||||
│ LowPriceBullService │
|
||||
│ （后台监控服务） │
|
||||
└──────────────┬──────────────────────┘
|
||||
│
|
||||
┌──────▼──────┐
|
||||
│ 监控循环 │
|
||||
│ (每60秒) │
|
||||
└──────┬──────┘
|
||||
│
|
||||
┌──────────▼─────────────┐
|
||||
│ 扫描监控列表 │
|
||||
│ - 更新持有天数 │
|
||||
│ - 检查卖出条件 │
|
||||
└──────────┬──────────────┘
|
||||
│
|
||||
┌──────────▼────────────────┐
|
||||
│ 检测卖出信号 │
|
||||
│ 1. 持股天数≥5 │
|
||||
│ 2. MA5 < MA20 │
|
||||
└──────────┬─────────────────┘
|
||||
│
|
||||
┌──────────▼──────────────┐
|
||||
│ 生成卖出提醒 │
|
||||
│ - 保存到数据库 │
|
||||
│ - 发送钉钉通知 │
|
||||
│ - 自动移除股票 │
|
||||
└───────────────────────────┘
|
||||
```
|
||||
|
||||
### 2. TDX数据获取流程
|
||||
```python
|
||||
def _get_stock_data(stock_code):
|
||||
# 1. 获取K线数据（30天）
|
||||
df = get_stock_kline_data(stock_code, period='daily', count=30)
|
||||
|
||||
# 2. 计算均线
|
||||
df['MA5'] = df['close'].rolling(window=5).mean()
|
||||
df['MA20'] = df['close'].rolling(window=20).mean()
|
||||
|
||||
# 3. 返回最新数据
|
||||
latest = df.iloc[-1]
|
||||
return latest['close'], latest['MA5'], latest['MA20']
|
||||
```
|
||||
|
||||
### 3. 卖出信号检测
|
||||
```python
|
||||
def _check_stock(stock):
|
||||
# 检查1: 持股天数
|
||||
if holding_days >= 5:
|
||||
add_sell_alert(type='holding_days')
|
||||
return
|
||||
|
||||
# 检查2: MA5下穿MA20
|
||||
current_price, ma5, ma20 = _get_stock_data(stock_code)
|
||||
if ma5 < ma20:
|
||||
add_sell_alert(type='ma_cross')
|
||||
```
|
||||
|
||||
## 环境变量配置
|
||||
|
||||
### 新增配置项
|
||||
```bash
|
||||
# 低价擒牛策略监控
|
||||
LOW_PRICE_BULL_SCAN_INTERVAL=60 # 扫描间隔（秒）
|
||||
LOW_PRICE_BULL_HOLDING_DAYS=5 # 持股天数限制
|
||||
```
|
||||
|
||||
### 依赖的配置
|
||||
```bash
|
||||
# Webhook通知（建议配置）
|
||||
WEBHOOK_ENABLED=true
|
||||
WEBHOOK_URL=https://oapi.dingtalk.com/robot/send?access_token=xxxxx
|
||||
WEBHOOK_TYPE=dingtalk
|
||||
WEBHOOK_KEYWORD=aiagents通知
|
||||
```
|
||||
|
||||
## 使用流程
|
||||
|
||||
### 1. 启动流程
|
||||
```
|
||||
1. 低价擒牛选股
|
||||
↓
|
||||
2. 查看股票列表
|
||||
↓
|
||||
3. 点击"➕ 加入策略监控"
|
||||
↓
|
||||
4. 点击右上角"📊 策略监控"
|
||||
↓
|
||||
5. 点击"▶️ 启动监控服务"
|
||||
↓
|
||||
6. 监控服务开始运行
|
||||
```
|
||||
|
||||
### 2. 监控流程
|
||||
```
|
||||
监控服务 → 每60秒扫描 → 检测条件 → 生成提醒 → 发送通知 → 自动移除
|
||||
```
|
||||
|
||||
### 3. 提醒流程
|
||||
```
|
||||
满足卖出条件
|
||||
↓
|
||||
生成卖出提醒
|
||||
↓
|
||||
┌─────────┴─────────┐
|
||||
│ │
|
||||
发送钉钉通知 显示在面板
|
||||
│ │
|
||||
└─────────┬─────────┘
|
||||
↓
|
||||
自动移出监控列表
|
||||
↓
|
||||
记录到历史
|
||||
```
|
||||
|
||||
## 功能特点
|
||||
|
||||
### 1. 自动化监控
|
||||
- ✅ 无需人工盯盘
|
||||
- ✅ 定时自动扫描
|
||||
- ✅ 智能卖出提醒
|
||||
|
||||
### 2. 双重条件
|
||||
- ✅ 时间条件：持股5天
|
||||
- ✅ 技术条件：MA5下穿MA20
|
||||
- ✅ 满足任一即提醒
|
||||
|
||||
### 3. 灵活配置
|
||||
- ✅ 可调整扫描间隔
|
||||
- ✅ 可调整持股天数
|
||||
- ✅ 可启动/停止服务
|
||||
|
||||
### 4. 完整记录
|
||||
- ✅ 监控历史
|
||||
- ✅ 提醒记录
|
||||
- ✅ 操作日志
|
||||
|
||||
## 监控面板功能
|
||||
|
||||
### 📋 监控列表
|
||||
- 显示所有监控中的股票
|
||||
- 实时更新持有天数
|
||||
- 支持批量移除
|
||||
- 显示买入价格和日期
|
||||
|
||||
### 🔔 卖出提醒
|
||||
- 显示待处理提醒
|
||||
- 查看MA5/MA20数据
|
||||
- 操作选项：
|
||||
- ✅ 已处理（移除股票）
|
||||
- ❌ 忽略（保留股票）
|
||||
|
||||
### 📜 历史记录
|
||||
- 查看历史提醒
|
||||
- 筛选已发送/未发送
|
||||
- 清理旧记录
|
||||
|
||||
### ⚙️ 服务控制
|
||||
- ▶️ 启动服务
|
||||
- ⏸️ 停止服务
|
||||
- 配置扫描间隔
|
||||
- 查看服务状态
|
||||
|
||||
## 钉钉通知格式
|
||||
|
||||
```markdown
|
||||
### aiagents通知 - 低价擒牛卖出提醒
|
||||
|
||||
**股票代码**: 002259
|
||||
|
||||
**股票名称**: 升达林业
|
||||
|
||||
**提醒类型**: MA均线死叉
|
||||
|
||||
**提醒原因**: MA5下穿MA20，技术信号卖出
|
||||
|
||||
**当前价格**: 8.52元
|
||||
|
||||
**MA5**: 8.45
|
||||
|
||||
**MA20**: 8.67
|
||||
|
||||
**持有天数**: 3天
|
||||
|
||||
**提醒时间**: 2024-12-12 14:30:00
|
||||
|
||||
---
|
||||
|
||||
**建议**: 开盘时卖出该股票
|
||||
|
||||
_此消息由AI股票分析系统自动发送_
|
||||
```
|
||||
|
||||
## 注意事项
|
||||
|
||||
### 1. 服务运行
|
||||
- ⚠️ 监控服务需要手动启动
|
||||
- ⚠️ 关闭浏览器后服务停止
|
||||
- ⚠️ 建议在服务器上运行
|
||||
|
||||
### 2. 数据源
|
||||
- ⚠️ 需要配置TDX数据源
|
||||
- ⚠️ 建议使用pytdx（无需配置）
|
||||
- ⚠️ K线数据可能有延迟
|
||||
|
||||
### 3. 扫描频率
|
||||
- ⚠️ 默认60秒扫描1次
|
||||
- ⚠️ 不建议低于30秒
|
||||
- ⚠️ 避免API限流
|
||||
|
||||
### 4. 持股天数
|
||||
- ⚠️ 按自然日计算
|
||||
- ⚠️ 不区分交易日
|
||||
- ⚠️ 建议预留余地
|
||||
|
||||
## 后续优化方向
|
||||
|
||||
### 短期
|
||||
- [ ] 支持后台常驻服务
|
||||
- [ ] 增加更多技术指标
|
||||
- [ ] 支持自定义卖出条件
|
||||
|
||||
### 中期
|
||||
- [ ] 集成MiniQMT自动交易
|
||||
- [ ] 支持止损止盈设置
|
||||
- [ ] 增加回测功能
|
||||
|
||||
### 长期
|
||||
- [ ] AI智能卖出建议
|
||||
- [ ] 多策略组合监控
|
||||
- [ ] 移动端APP推送
|
||||
|
||||
## 总结
|
||||
|
||||
本次实现为"低价擒牛"选股板块添加了完整的策略监控功能，包括：
|
||||
|
||||
- ✅ **3个核心模块**：监控管理、后台服务、UI界面
|
||||
- ✅ **完整的数据库设计**：监控列表、提醒记录
|
||||
- ✅ **自动化监控**：定时扫描、智能提醒、自动移除
|
||||
- ✅ **双重卖出条件**：持股天数 + MA均线
|
||||
- ✅ **钉钉通知集成**：实时接收卖出提醒
|
||||
- ✅ **灵活的配置**：可调整扫描间隔和持股天数
|
||||
- ✅ **完善的文档**：配置说明、使用指南
|
||||
|
||||
整个功能已集成到系统中，用户可以立即使用！
|
||||
|
||||
---
|
||||
|
||||
**实现完成时间**：2024-12-12
|
||||
**版本**：v1.0
|
||||
**状态**：✅ 已完成并可使用
|
||||
@@ -0,0 +1,239 @@
|
||||
# 低价擒牛策略监控配置说明
|
||||
|
||||
## 环境变量配置
|
||||
|
||||
在 `.env` 文件中添加以下配置：
|
||||
|
||||
```bash
|
||||
# 低价擒牛策略监控配置
|
||||
LOW_PRICE_BULL_SCAN_INTERVAL=60 # 扫描间隔（秒），默认60秒
|
||||
LOW_PRICE_BULL_HOLDING_DAYS=5 # 持股天数限制，默认5天
|
||||
|
||||
# TDX数据源API配置（必须）
|
||||
TDX_BASE_URL=http://127.0.0.1:5000 # TDX API服务地址
|
||||
```
|
||||
|
||||
## TDX数据源配置
|
||||
|
||||
策略监控需要使用TDX数据API获取股票K线数据和均线指标。
|
||||
|
||||
### 配置方法
|
||||
|
||||
在 `.env` 文件中添加TDX API URL：
|
||||
|
||||
```bash
|
||||
# TDX数据源API配置
|
||||
TDX_BASE_URL=http://127.0.0.1:5000 # TDX API服务地址
|
||||
```
|
||||
|
||||
### API接口说明
|
||||
|
||||
#### 核心接口
|
||||
|
||||
| 接口 | 说明 | 示例 |
|
||||
|------|------|------|
|
||||
| /api/quote | 五档行情 | ?code=000001 |
|
||||
| /api/kline | K线数据 | ?code=000001&type=day |
|
||||
| /api/minute | 分时数据 | ?code=000001 |
|
||||
| /api/trade | 分时成交 | ?code=000001 |
|
||||
| /api/search | 搜索股票 | ?keyword=平安 |
|
||||
| /api/stock-info | 综合信息 | ?code=000001 |
|
||||
|
||||
#### K线数据接口
|
||||
|
||||
策略监控使用的接口：**`/api/kline`**
|
||||
|
||||
**请求参数**：
|
||||
- `code`: 股票代码（例如：000001）
|
||||
- `type`: K线类型（day=日K线）
|
||||
|
||||
**返回格式**：
|
||||
```json
|
||||
[
|
||||
{
|
||||
"date": "2024-12-12",
|
||||
"open": 10.50,
|
||||
"high": 10.80,
|
||||
"low": 10.30,
|
||||
"close": 10.60,
|
||||
"volume": 1000000
|
||||
},
|
||||
...
|
||||
]
|
||||
```
|
||||
|
||||
**使用示例**：
|
||||
```bash
|
||||
curl "http://127.0.0.1:5000/api/kline?code=000001&type=day"
|
||||
```
|
||||
|
||||
### 启动TDX API服务
|
||||
|
||||
如果您还没有TDX API服务，需要先启动服务：
|
||||
|
||||
1. 确保已安装相关依赖
|
||||
2. 启动TDX API服务器（默认端口5000）
|
||||
3. 在.env中配置服务地址
|
||||
|
||||
### 数据说明
|
||||
|
||||
- **更新频率**：K线数据实时更新
|
||||
- **数据量**：策略需要至少20天的K线数据
|
||||
- **均线计算**：系统自动计算MA5和MA20
|
||||
|
||||
## Webhook通知配置
|
||||
|
||||
建议配置钉钉Webhook以接收卖出提醒：
|
||||
|
||||
```bash
|
||||
# Webhook通知配置
|
||||
WEBHOOK_ENABLED=true
|
||||
WEBHOOK_URL=https://oapi.dingtalk.com/robot/send?access_token=xxxxx
|
||||
WEBHOOK_TYPE=dingtalk
|
||||
WEBHOOK_KEYWORD=aiagents通知
|
||||
```
|
||||
|
||||
## 功能说明
|
||||
|
||||
### 1. 加入策略监控
|
||||
|
||||
在"低价擒牛"选股结果中，点击任意股票详情中的"➕ 加入策略监控"按钮。
|
||||
|
||||
### 2. 监控条件
|
||||
|
||||
#### 条件1：持股天数到期
|
||||
- 持股满5天（可配置）
|
||||
- 第6天开盘时发送卖出提醒
|
||||
- 自动从监控列表移除
|
||||
|
||||
#### 条件2：MA5下穿MA20
|
||||
- 每分钟扫描1次（可配置）
|
||||
- 检测MA5是否小于MA20
|
||||
- 满足条件时发送卖出提醒
|
||||
- 自动从监控列表移除
|
||||
|
||||
### 3. 卖出提醒
|
||||
|
||||
提醒会通过以下方式发送：
|
||||
- 钉钉Webhook通知（需配置）
|
||||
- 监控面板中的"卖出提醒"标签页
|
||||
|
||||
### 4. 自动移除
|
||||
|
||||
当满足卖出条件时：
|
||||
1. 生成卖出提醒
|
||||
2. 发送钉钉通知（如已配置）
|
||||
3. 自动从监控列表移除
|
||||
4. 记录到历史提醒
|
||||
|
||||
## 使用流程
|
||||
|
||||
```
|
||||
1. 低价擒牛选股
|
||||
↓
|
||||
2. 查看选股结果
|
||||
↓
|
||||
3. 点击"➕ 加入策略监控"
|
||||
↓
|
||||
4. 进入"📊 策略监控"面板
|
||||
↓
|
||||
5. 启动监控服务
|
||||
↓
|
||||
6. 等待卖出提醒
|
||||
↓
|
||||
7. 收到提醒后卖出
|
||||
```
|
||||
|
||||
## 监控面板功能
|
||||
|
||||
### 服务控制
|
||||
- ▶️ 启动监控服务
|
||||
- ⏸️ 停止监控服务
|
||||
- ⚙️ 配置扫描间隔
|
||||
|
||||
### 监控列表
|
||||
- 查看所有监控中的股票
|
||||
- 显示持有天数
|
||||
- 手动移除股票
|
||||
|
||||
### 卖出提醒
|
||||
- 查看待处理的提醒
|
||||
- 查看MA5/MA20数据
|
||||
- 标记已处理/忽略
|
||||
|
||||
### 历史记录
|
||||
- 查看历史提醒
|
||||
- 清理旧记录
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. ⚠️ **监控服务需要手动启动**
|
||||
- 首次使用需在监控面板启动服务
|
||||
- 服务启动后会持续运行
|
||||
- 关闭浏览器后服务会停止
|
||||
|
||||
2. ⚠️ **TDX数据源**
|
||||
- 必须配置TDX_BASE_URL
|
||||
- 确俜TDX API服务已启动
|
||||
- 使用`/api/kline`接口获取日K线数据
|
||||
- 数据需要至少20天才能计算MA20
|
||||
|
||||
3. ⚠️ **扫描间隔**
|
||||
- 默认60秒扫描1次
|
||||
- 可在监控面板调整
|
||||
- 建议不要低于30秒
|
||||
|
||||
4. ⚠️ **持股天数计算**
|
||||
- 按自然日计算
|
||||
- 不区分交易日/非交易日
|
||||
- 建议留有余地
|
||||
|
||||
## 常见问题
|
||||
|
||||
### Q1: 监控服务无法启动？
|
||||
A: 检查：
|
||||
- Python环境是否正常
|
||||
- 依赖包是否安装完整
|
||||
- 查看控制台错误日志
|
||||
|
||||
### Q2: 没有收到卖出提醒？
|
||||
A: 检查：
|
||||
- Webhook是否配置正确
|
||||
- 监控服务是否在运行
|
||||
- TDX API服务是否启动
|
||||
- 访问`http://127.0.0.1:5000/api/health`检查API状态
|
||||
- 查看控制台日志是否有错误信息
|
||||
|
||||
### Q3: 如何修改扫描间隔？
|
||||
A: 两种方式：
|
||||
- 在监控面板的"⚙️ 监控配置"中修改
|
||||
- 在.env文件中修改`LOW_PRICE_BULL_SCAN_INTERVAL`
|
||||
|
||||
### Q4: 持股天数如何调整？
|
||||
A: 在.env文件中修改：
|
||||
```bash
|
||||
LOW_PRICE_BULL_HOLDING_DAYS=7 # 改为7天
|
||||
```
|
||||
|
||||
## 技术实现
|
||||
|
||||
### 数据库
|
||||
- SQLite本地数据库
|
||||
- 文件：`low_price_bull_monitor.db`
|
||||
- 包含监控列表和提醒记录
|
||||
|
||||
### 后台服务
|
||||
- Python线程实现
|
||||
- 定时扫描循环
|
||||
- 异常自动恢复
|
||||
|
||||
### 数据获取
|
||||
- 使用TDX API
|
||||
- 调用`/api/kline`接口
|
||||
- 获取日K线数据
|
||||
- 计算MA5/MA20均线
|
||||
|
||||
---
|
||||
|
||||
**版本**: v1.0
|
||||
**更新日期**: 2024-12-12
|
||||
@@ -0,0 +1,460 @@
|
||||
'''
|
||||
均线回归策略 V3.0 进攻版
|
||||
优化重点：提高Beta，增强进攻性，减少过度保守
|
||||
|
||||
核心改进：
|
||||
1. 放宽市场择时，只在极端熊市降仓
|
||||
2. 增加持仓数量，提高资金利用率
|
||||
3. 放宽买入条件，增加交易机会
|
||||
4. 优化止盈，让利润奔跑
|
||||
5. 增加突破买入模式
|
||||
'''
|
||||
|
||||
import jqdata
|
||||
import numpy as np
|
||||
|
||||
## 初始化函数
|
||||
def initialize(context):
|
||||
# 设定沪深300作为基准
|
||||
set_benchmark('000300.XSHG')
|
||||
# 开启动态复权模式
|
||||
set_option('use_real_price', True)
|
||||
# 设定成交量比例
|
||||
set_option('order_volume_ratio', 1)
|
||||
# 设置交易手续费
|
||||
set_order_cost(OrderCost(open_tax=0, close_tax=0.001,
|
||||
open_commission=0.0003, close_commission=0.0003,
|
||||
close_today_commission=0, min_commission=5), type='stock')
|
||||
|
||||
# ========== 策略参数（进攻版）==========
|
||||
g.stocknum = 8 # 持仓数量增加到8只
|
||||
g.max_position_ratio = 0.98 # 最大仓位98%
|
||||
|
||||
# 均线参数
|
||||
g.ma_short = 20 # 短期均线
|
||||
g.ma_mid = 60 # 中期均线
|
||||
g.ma_long = 120 # 长期均线
|
||||
|
||||
# 回调买入参数（放宽条件）
|
||||
g.pullback_ratio = 0.035 # 放宽到3.5%
|
||||
g.break_tolerance = 0.015 # 允许跌破1.5%
|
||||
|
||||
# 止盈止损参数（让利润奔跑）
|
||||
g.base_take_profit = 0.20 # 止盈提高到20%
|
||||
g.base_stop_loss = 0.06 # 止损放宽到6%
|
||||
g.trailing_stop = 0.08 # 回撤容忍8%
|
||||
|
||||
# 市值筛选（亿）- 扩大范围
|
||||
g.min_market_cap = 20
|
||||
g.max_market_cap = 1000
|
||||
|
||||
# 行业分散参数
|
||||
g.max_industry_stocks = 3 # 同行业最多3只
|
||||
|
||||
# 市场状态
|
||||
g.market_state = 'NORMAL'
|
||||
g.position_ratio = 1.0
|
||||
|
||||
# 记录
|
||||
g.highest_profit = {}
|
||||
g.stock_volatility = {}
|
||||
g.hold_days = {} # 持仓天数
|
||||
|
||||
# 定时任务
|
||||
run_daily(market_analysis, '09:31')
|
||||
run_daily(morning_check, '09:35')
|
||||
run_daily(check_positions, '14:00') # 只检查一次
|
||||
run_daily(afternoon_trade, '14:50')
|
||||
|
||||
## 市场环境分析（放宽版）
|
||||
def market_analysis(context):
|
||||
"""
|
||||
只在极端情况下降低仓位，大部分时间保持高仓位
|
||||
"""
|
||||
index_code = '000300.XSHG'
|
||||
|
||||
try:
|
||||
df = attribute_history(index_code, 130, '1d', ['close'], skip_paused=True)
|
||||
if len(df) < 120:
|
||||
g.market_state = 'NORMAL'
|
||||
g.position_ratio = 1.0
|
||||
return
|
||||
|
||||
close = df['close'].values
|
||||
current = close[-1]
|
||||
|
||||
ma20 = np.mean(close[-20:])
|
||||
ma60 = np.mean(close[-60:])
|
||||
ma120 = np.mean(close[-120:])
|
||||
|
||||
change_5d = (close[-1] - close[-6]) / close[-6]
|
||||
change_10d = (close[-1] - close[-11]) / close[-11]
|
||||
|
||||
# 只在极端熊市才降仓（条件更严格）
|
||||
if current < ma120 and ma20 < ma60 < ma120 and change_10d < -0.08:
|
||||
# 极端熊市：指数跌破120日均线，均线空头，10日跌幅超8%
|
||||
g.market_state = 'BEAR'
|
||||
g.position_ratio = 0.5
|
||||
log.info("【市场】极端熊市，仓位50%%")
|
||||
elif change_5d < -0.08:
|
||||
# 短期暴跌超8%：暂时减仓
|
||||
g.market_state = 'BEAR'
|
||||
g.position_ratio = 0.6
|
||||
log.info("【市场】短期暴跌，仓位60%%")
|
||||
elif current > ma20 and ma20 > ma60:
|
||||
# 趋势向上：满仓
|
||||
g.market_state = 'BULL'
|
||||
g.position_ratio = 1.0
|
||||
log.info("【市场】趋势向上，满仓")
|
||||
else:
|
||||
# 默认高仓位运行
|
||||
g.market_state = 'NORMAL'
|
||||
g.position_ratio = 0.9
|
||||
log.info("【市场】正常状态，仓位90%%")
|
||||
|
||||
except Exception as e:
|
||||
g.market_state = 'NORMAL'
|
||||
g.position_ratio = 0.9
|
||||
|
||||
## 早盘检查
|
||||
def morning_check(context):
|
||||
g.buy_list = check_stocks(context)
|
||||
log.info("【选股】%d 只，市场: %s，仓位: %.0f%%" %
|
||||
(len(g.buy_list), g.market_state, g.position_ratio * 100))
|
||||
|
||||
## 检查持仓 - 优化版（让利润奔跑）
|
||||
def check_positions(context):
|
||||
if len(context.portfolio.positions) == 0:
|
||||
return
|
||||
|
||||
for stock in list(context.portfolio.positions.keys()):
|
||||
position = context.portfolio.positions[stock]
|
||||
if position.closeable_amount <= 0:
|
||||
continue
|
||||
|
||||
cost = position.avg_cost
|
||||
current_price = position.price
|
||||
if cost <= 0:
|
||||
continue
|
||||
|
||||
profit_ratio = (current_price - cost) / cost
|
||||
|
||||
# 更新持仓天数
|
||||
g.hold_days[stock] = g.hold_days.get(stock, 0) + 1
|
||||
|
||||
# 更新最高盈利
|
||||
if stock not in g.highest_profit:
|
||||
g.highest_profit[stock] = profit_ratio
|
||||
else:
|
||||
g.highest_profit[stock] = max(g.highest_profit[stock], profit_ratio)
|
||||
|
||||
highest = g.highest_profit[stock]
|
||||
hold_days = g.hold_days.get(stock, 0)
|
||||
|
||||
# 动态止损（根据波动率和持仓时间）
|
||||
volatility = g.stock_volatility.get(stock, 0.025)
|
||||
# 持仓时间越长，止损越宽松
|
||||
time_factor = min(1 + hold_days * 0.01, 1.5)
|
||||
dynamic_stop_loss = min(g.base_stop_loss * time_factor, 0.12)
|
||||
|
||||
# === 止损逻辑 ===
|
||||
if profit_ratio < -dynamic_stop_loss:
|
||||
log.info("【止损】%s 亏损 %.2f%%" % (stock, profit_ratio * 100))
|
||||
order_target(stock, 0)
|
||||
clean_stock_data(stock)
|
||||
continue
|
||||
|
||||
# === 均线破位（只在亏损时卖）===
|
||||
if profit_ratio < 0 and check_sell_signal(stock):
|
||||
log.info("【均线破位】%s 破位且亏损，卖出" % stock)
|
||||
order_target(stock, 0)
|
||||
clean_stock_data(stock)
|
||||
continue
|
||||
|
||||
# === 移动止盈（让利润奔跑）===
|
||||
if highest >= g.base_take_profit:
|
||||
drawdown = highest - profit_ratio
|
||||
# 盈利越多，允许回撤越大（最多允许回撤15%）
|
||||
allowed_drawdown = min(g.trailing_stop + highest * 0.4, 0.18)
|
||||
if drawdown >= allowed_drawdown:
|
||||
log.info("【移动止盈】%s 最高 %.2f%%，回撤 %.2f%%" %
|
||||
(stock, highest * 100, drawdown * 100))
|
||||
order_target(stock, 0)
|
||||
clean_stock_data(stock)
|
||||
continue
|
||||
|
||||
# === 分批止盈（保守止盈，只卖小部分）===
|
||||
if profit_ratio >= g.base_take_profit * 2:
|
||||
# 盈利超40%，卖出30%锁定利润
|
||||
sell_amount = int(position.closeable_amount * 0.3 / 100) * 100
|
||||
if sell_amount >= 100:
|
||||
log.info("【大幅止盈】%s 盈利 %.2f%%，卖出30%%" % (stock, profit_ratio * 100))
|
||||
order(stock, -sell_amount)
|
||||
|
||||
## 清理股票数据
|
||||
def clean_stock_data(stock):
|
||||
for d in [g.highest_profit, g.stock_volatility, g.hold_days]:
|
||||
if stock in d:
|
||||
del d[stock]
|
||||
|
||||
## 尾盘交易
|
||||
def afternoon_trade(context):
|
||||
buy_stocks(context)
|
||||
|
||||
## 买入函数（积极版）
|
||||
def buy_stocks(context):
|
||||
if not hasattr(g, 'buy_list') or not g.buy_list:
|
||||
return
|
||||
|
||||
position_count = len(context.portfolio.positions)
|
||||
adjusted_stocknum = int(g.stocknum * g.position_ratio)
|
||||
if adjusted_stocknum <= 0 or position_count >= adjusted_stocknum:
|
||||
return
|
||||
|
||||
# 计算资金分配
|
||||
available_cash = context.portfolio.available_cash * g.max_position_ratio
|
||||
buy_count = min(adjusted_stocknum - position_count, len(g.buy_list))
|
||||
if buy_count <= 0 or available_cash < 10000:
|
||||
return
|
||||
|
||||
cash_per_stock = available_cash / buy_count
|
||||
held_industries = get_held_industries(context)
|
||||
|
||||
bought = 0
|
||||
for stock in g.buy_list:
|
||||
if bought >= buy_count:
|
||||
break
|
||||
if stock in context.portfolio.positions:
|
||||
continue
|
||||
|
||||
# 行业分散
|
||||
stock_industry = get_stock_industry(stock)
|
||||
if stock_industry and held_industries.get(stock_industry, 0) >= g.max_industry_stocks:
|
||||
continue
|
||||
|
||||
# 简化买入条件检查
|
||||
if check_buy_signal(stock):
|
||||
order_value(stock, cash_per_stock)
|
||||
log.info("【买入】%s" % stock)
|
||||
g.highest_profit[stock] = 0
|
||||
g.stock_volatility[stock] = 0.025
|
||||
g.hold_days[stock] = 0
|
||||
if stock_industry:
|
||||
held_industries[stock_industry] = held_industries.get(stock_industry, 0) + 1
|
||||
bought += 1
|
||||
|
||||
## 获取已持仓的行业分布
|
||||
def get_held_industries(context):
|
||||
industries = {}
|
||||
for stock in context.portfolio.positions.keys():
|
||||
ind = get_stock_industry(stock)
|
||||
if ind:
|
||||
industries[ind] = industries.get(ind, 0) + 1
|
||||
return industries
|
||||
|
||||
## 获取股票所属行业
|
||||
def get_stock_industry(stock):
|
||||
try:
|
||||
ind_dict = get_industry(stock)
|
||||
if ind_dict and stock in ind_dict:
|
||||
# 获取申万一级行业
|
||||
for ind_code, ind_info in ind_dict[stock].items():
|
||||
if ind_code.startswith('sw_l1'):
|
||||
return ind_info.get('industry_name', None)
|
||||
return None
|
||||
except:
|
||||
return None
|
||||
|
||||
## 选股函数（进攻版）
|
||||
def check_stocks(context):
|
||||
# 第一步：基础筛选（放宽条件）
|
||||
q = query(
|
||||
valuation.code,
|
||||
valuation.market_cap
|
||||
).filter(
|
||||
valuation.market_cap.between(g.min_market_cap, g.max_market_cap)
|
||||
).order_by(
|
||||
valuation.market_cap.asc()
|
||||
).limit(500) # 扩大候选池
|
||||
|
||||
df = get_fundamentals(q)
|
||||
if df.empty:
|
||||
return []
|
||||
|
||||
stock_list = list(df['code'])
|
||||
stock_list = filter_basic(stock_list)
|
||||
|
||||
# 均线信号筛选
|
||||
candidates = []
|
||||
for stock in stock_list[:200]: # 检查前200只
|
||||
if check_buy_signal(stock):
|
||||
score = calculate_trend_score(stock)
|
||||
candidates.append((stock, score))
|
||||
|
||||
# 按评分排序
|
||||
candidates.sort(key=lambda x: x[1], reverse=True)
|
||||
buy_list = [c[0] for c in candidates[:g.stocknum * 3]]
|
||||
|
||||
return buy_list
|
||||
|
||||
## 计算趋势强度评分
|
||||
def calculate_trend_score(stock):
|
||||
"""
|
||||
评分因素：
|
||||
1. 均线发散程度
|
||||
2. 价格距离MA20的位置
|
||||
3. 成交量配合
|
||||
"""
|
||||
try:
|
||||
df = attribute_history(stock, 130, '1d', ['close', 'volume'], skip_paused=True)
|
||||
if len(df) < 120:
|
||||
return 0
|
||||
|
||||
close = df['close'].values
|
||||
volume = df['volume'].values
|
||||
|
||||
ma20 = np.mean(close[-20:])
|
||||
ma60 = np.mean(close[-60:])
|
||||
ma120 = np.mean(close[-120:])
|
||||
current = close[-1]
|
||||
|
||||
score = 0
|
||||
|
||||
# 均线发散程度（20分）
|
||||
spread = (ma20 - ma120) / ma120
|
||||
score += min(spread * 100, 20)
|
||||
|
||||
# 价格位置（30分）- 越接近MA20越好
|
||||
distance = abs(current - ma20) / ma20
|
||||
score += max(0, 30 - distance * 500)
|
||||
|
||||
# 成交量（20分）- 回调缩量为佳
|
||||
vol_ma5 = np.mean(volume[-5:])
|
||||
vol_ma20 = np.mean(volume[-20:])
|
||||
if vol_ma5 < vol_ma20 * 0.8:
|
||||
score += 20 # 缩量回调
|
||||
elif vol_ma5 < vol_ma20:
|
||||
score += 10
|
||||
|
||||
# 趋势持续性（30分）
|
||||
ma20_slope = (ma20 - np.mean(close[-25:-5])) / np.mean(close[-25:-5])
|
||||
if ma20_slope > 0:
|
||||
score += min(ma20_slope * 300, 30)
|
||||
|
||||
return score
|
||||
|
||||
except:
|
||||
return 0
|
||||
|
||||
## 基础过滤
|
||||
def filter_basic(stock_list):
|
||||
if not stock_list:
|
||||
return []
|
||||
|
||||
current_data = get_current_data()
|
||||
filtered = []
|
||||
|
||||
for stock in stock_list:
|
||||
# 过滤停牌
|
||||
if current_data[stock].paused:
|
||||
continue
|
||||
# 过滤ST
|
||||
if current_data[stock].is_st:
|
||||
continue
|
||||
if 'ST' in current_data[stock].name or '*' in current_data[stock].name:
|
||||
continue
|
||||
# 过滤科创板、北交所
|
||||
if stock.startswith('688') or stock.startswith('8') or stock.startswith('4'):
|
||||
continue
|
||||
# 过滤涨跌停
|
||||
if current_data[stock].last_price >= current_data[stock].high_limit:
|
||||
continue
|
||||
if current_data[stock].last_price <= current_data[stock].low_limit:
|
||||
continue
|
||||
|
||||
filtered.append(stock)
|
||||
|
||||
return filtered
|
||||
|
||||
## 检查买入信号（放宽版）
|
||||
def check_buy_signal(stock):
|
||||
"""
|
||||
放宽买入条件，增加交易机会
|
||||
"""
|
||||
try:
|
||||
df = attribute_history(stock, g.ma_long + 10, '1d', ['close'], skip_paused=True)
|
||||
if len(df) < g.ma_long:
|
||||
return False
|
||||
|
||||
close = df['close'].values
|
||||
current_price = close[-1]
|
||||
|
||||
# 计算均线
|
||||
ma20 = np.mean(close[-g.ma_short:])
|
||||
ma60 = np.mean(close[-g.ma_mid:])
|
||||
ma120 = np.mean(close[-g.ma_long:])
|
||||
|
||||
# 条件1：均线多头排列（核心条件）
|
||||
if not (ma20 > ma60 > ma120):
|
||||
return False
|
||||
|
||||
# 条件2：MA20向上
|
||||
ma20_5d_ago = np.mean(close[-g.ma_short-5:-5])
|
||||
if ma20 <= ma20_5d_ago:
|
||||
return False
|
||||
|
||||
# 条件3：价格在MA20附近（放宽范围）
|
||||
distance = (current_price - ma20) / ma20
|
||||
# 允许在MA20上方5%以内，或跌破2%以内
|
||||
if distance > 0.05 or distance < -0.02:
|
||||
return False
|
||||
|
||||
# 条件4：价格不能离MA60太远
|
||||
if current_price > ma60 * 1.30:
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
except:
|
||||
return False
|
||||
|
||||
## 检查卖出信号（均线破位）
|
||||
def check_sell_signal(stock):
|
||||
"""
|
||||
卖出条件：
|
||||
1. 价格跌破MA60
|
||||
2. MA20下穿MA60（死叉）
|
||||
3. 价格跌破MA20且持续3天
|
||||
"""
|
||||
try:
|
||||
df = attribute_history(stock, g.ma_mid + 5, '1d', ['close'], skip_paused=True)
|
||||
if len(df) < g.ma_mid:
|
||||
return False
|
||||
|
||||
close = df['close'].values
|
||||
current_price = close[-1]
|
||||
|
||||
ma20 = np.mean(close[-g.ma_short:])
|
||||
ma60 = np.mean(close[-g.ma_mid:])
|
||||
|
||||
# 价格跌破MA60超过2%
|
||||
if current_price < ma60 * 0.98:
|
||||
return True
|
||||
|
||||
# MA20下穿MA60（死叉）
|
||||
ma20_yesterday = np.mean(close[-g.ma_short-1:-1])
|
||||
ma60_yesterday = np.mean(close[-g.ma_mid-1:-1])
|
||||
if ma20_yesterday > ma60_yesterday and ma20 < ma60:
|
||||
return True
|
||||
|
||||
# 连续3天收盘在MA20下方
|
||||
ma20_3d = [np.mean(close[-g.ma_short-i:-i]) if i > 0 else ma20 for i in range(3)]
|
||||
below_ma20_count = sum(1 for i in range(3) if close[-1-i] < ma20_3d[i] * 0.99)
|
||||
if below_ma20_count >= 3:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
except:
|
||||
return False
|
||||
|
||||
Binary file not shown.
+4
-1
@@ -23,7 +23,9 @@ class LonghubangDataFetcher:
|
||||
api_key: StockAPI的API密钥（可选，普通请求每日免费1000次）
|
||||
"""
|
||||
print("[智瞰龙虎] 龙虎榜数据获取器初始化...")
|
||||
self.base_url = "https://www.stockapi.com.cn/v1"
|
||||
# self.base_url = "https://api-lhb.zhongdu.net"
|
||||
self.base_url = "http://lhb-api.ws4.cn/v1"
|
||||
# self.base_url = "https://www.stockapi.com.cn/v1"
|
||||
self.api_key = api_key
|
||||
self.max_retries = 3 # 最大重试次数
|
||||
self.retry_delay = 2 # 重试延迟（秒）
|
||||
@@ -79,6 +81,7 @@ class LonghubangDataFetcher:
|
||||
"""
|
||||
print(f"[智瞰龙虎] 获取 {date} 的龙虎榜数据...")
|
||||
|
||||
# url = f"{self.base_url}"
|
||||
url = f"{self.base_url}/youzi/all"
|
||||
params = {'date': date}
|
||||
|
||||
|
||||
Binary file not shown.
@@ -0,0 +1,375 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
低价擒牛策略监控模块
|
||||
监控持仓股票的卖出信号
|
||||
"""
|
||||
|
||||
import sqlite3
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
import logging
|
||||
import os
|
||||
|
||||
|
||||
class LowPriceBullMonitor:
|
||||
"""低价擒牛策略监控器"""
|
||||
|
||||
def __init__(self, db_path: str = "low_price_bull_monitor.db"):
|
||||
"""
|
||||
初始化监控器
|
||||
|
||||
Args:
|
||||
db_path: 数据库文件路径
|
||||
"""
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self.db_path = db_path
|
||||
self._init_database()
|
||||
|
||||
def _init_database(self):
|
||||
"""初始化数据库"""
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# 创建监控列表表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS monitored_stocks (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_code TEXT NOT NULL,
|
||||
stock_name TEXT NOT NULL,
|
||||
buy_price REAL NOT NULL,
|
||||
buy_date TEXT NOT NULL,
|
||||
holding_days INTEGER DEFAULT 0,
|
||||
status TEXT DEFAULT 'holding',
|
||||
add_time TEXT NOT NULL,
|
||||
remove_time TEXT,
|
||||
remove_reason TEXT,
|
||||
UNIQUE(stock_code, status)
|
||||
)
|
||||
""")
|
||||
|
||||
# 创建卖出提醒表
|
||||
cursor.execute("""
|
||||
CREATE TABLE IF NOT EXISTS sell_alerts (
|
||||
id INTEGER PRIMARY KEY AUTOINCREMENT,
|
||||
stock_code TEXT NOT NULL,
|
||||
stock_name TEXT NOT NULL,
|
||||
alert_type TEXT NOT NULL,
|
||||
alert_reason TEXT NOT NULL,
|
||||
current_price REAL,
|
||||
ma5 REAL,
|
||||
ma20 REAL,
|
||||
holding_days INTEGER,
|
||||
alert_time TEXT NOT NULL,
|
||||
is_sent INTEGER DEFAULT 0
|
||||
)
|
||||
""")
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
self.logger.info("低价擒牛监控数据库初始化完成")
|
||||
|
||||
def add_stock(self, stock_code: str, stock_name: str, buy_price: float,
|
||||
buy_date: str = None) -> Tuple[bool, str]:
|
||||
"""
|
||||
添加股票到监控列表
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码（不含后缀）
|
||||
stock_name: 股票名称
|
||||
buy_price: 买入价格
|
||||
buy_date: 买入日期（格式：YYYY-MM-DD）
|
||||
|
||||
Returns:
|
||||
(是否成功, 消息)
|
||||
"""
|
||||
try:
|
||||
if buy_date is None:
|
||||
buy_date = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# 检查是否已存在
|
||||
cursor.execute("""
|
||||
SELECT id FROM monitored_stocks
|
||||
WHERE stock_code = ? AND status = 'holding'
|
||||
""", (stock_code,))
|
||||
|
||||
if cursor.fetchone():
|
||||
conn.close()
|
||||
return False, f"股票 {stock_code} 已在监控列表中"
|
||||
|
||||
# 添加到监控列表
|
||||
cursor.execute("""
|
||||
INSERT INTO monitored_stocks
|
||||
(stock_code, stock_name, buy_price, buy_date, add_time)
|
||||
VALUES (?, ?, ?, ?, ?)
|
||||
""", (stock_code, stock_name, buy_price, buy_date,
|
||||
datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
self.logger.info(f"添加股票到监控: {stock_code} {stock_name}")
|
||||
return True, f"成功添加 {stock_code} {stock_name} 到监控列表"
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"添加股票失败: {e}")
|
||||
return False, f"添加失败: {str(e)}"
|
||||
|
||||
def remove_stock(self, stock_code: str, reason: str = "手动移除") -> Tuple[bool, str]:
|
||||
"""
|
||||
从监控列表移除股票
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
reason: 移除原因
|
||||
|
||||
Returns:
|
||||
(是否成功, 消息)
|
||||
"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# 先检查是否存在持仓中的股票
|
||||
cursor.execute("""
|
||||
SELECT id FROM monitored_stocks
|
||||
WHERE stock_code = ? AND status = 'holding'
|
||||
""", (stock_code,))
|
||||
|
||||
if not cursor.fetchone():
|
||||
conn.close()
|
||||
return False, f"股票 {stock_code} 不在监控列表中"
|
||||
|
||||
# 先删除该股票的所有'removed'记录（避免UNIQUE约束冲突）
|
||||
cursor.execute("""
|
||||
DELETE FROM monitored_stocks
|
||||
WHERE stock_code = ? AND status = 'removed'
|
||||
""", (stock_code,))
|
||||
|
||||
# 然后更新持仓中的记录为'removed'
|
||||
cursor.execute("""
|
||||
UPDATE monitored_stocks
|
||||
SET status = 'removed',
|
||||
remove_time = ?,
|
||||
remove_reason = ?
|
||||
WHERE stock_code = ? AND status = 'holding'
|
||||
""", (datetime.now().strftime("%Y-%m-%d %H:%M:%S"), reason, stock_code))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
self.logger.info(f"移除股票: {stock_code}, 原因: {reason}")
|
||||
return True, f"成功移除 {stock_code}"
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"移除股票失败: {e}")
|
||||
return False, f"移除失败: {str(e)}"
|
||||
|
||||
def get_monitored_stocks(self) -> List[Dict]:
|
||||
"""
|
||||
获取所有监控中的股票
|
||||
|
||||
Returns:
|
||||
股票列表
|
||||
"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
df = pd.read_sql_query("""
|
||||
SELECT * FROM monitored_stocks
|
||||
WHERE status = 'holding'
|
||||
ORDER BY add_time DESC
|
||||
""", conn)
|
||||
conn.close()
|
||||
|
||||
return df.to_dict('records') if not df.empty else []
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"获取监控列表失败: {e}")
|
||||
return []
|
||||
|
||||
def update_holding_days(self):
|
||||
"""更新所有股票的持有天数"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# 获取所有持仓股票
|
||||
cursor.execute("""
|
||||
SELECT stock_code, buy_date FROM monitored_stocks
|
||||
WHERE status = 'holding'
|
||||
""")
|
||||
|
||||
stocks = cursor.fetchall()
|
||||
today = datetime.now().date()
|
||||
|
||||
for stock_code, buy_date in stocks:
|
||||
buy_date_obj = datetime.strptime(buy_date, "%Y-%m-%d").date()
|
||||
holding_days = (today - buy_date_obj).days
|
||||
|
||||
cursor.execute("""
|
||||
UPDATE monitored_stocks
|
||||
SET holding_days = ?
|
||||
WHERE stock_code = ? AND status = 'holding'
|
||||
""", (holding_days, stock_code))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
self.logger.info("持有天数更新完成")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"更新持有天数失败: {e}")
|
||||
|
||||
def add_sell_alert(self, stock_code: str, stock_name: str, alert_type: str,
|
||||
alert_reason: str, current_price: float = None,
|
||||
ma5: float = None, ma20: float = None,
|
||||
holding_days: int = None) -> bool:
|
||||
"""
|
||||
添加卖出提醒
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
alert_type: 提醒类型（holding_days/ma_cross）
|
||||
alert_reason: 提醒原因
|
||||
current_price: 当前价格
|
||||
ma5: MA5值
|
||||
ma20: MA20值
|
||||
holding_days: 持有天数
|
||||
|
||||
Returns:
|
||||
是否成功
|
||||
"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
# 检查是否已存在相同的提醒
|
||||
cursor.execute("""
|
||||
SELECT id FROM sell_alerts
|
||||
WHERE stock_code = ? AND alert_type = ? AND is_sent = 0
|
||||
""", (stock_code, alert_type))
|
||||
|
||||
if cursor.fetchone():
|
||||
conn.close()
|
||||
return False
|
||||
|
||||
cursor.execute("""
|
||||
INSERT INTO sell_alerts
|
||||
(stock_code, stock_name, alert_type, alert_reason,
|
||||
current_price, ma5, ma20, holding_days, alert_time)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
""", (stock_code, stock_name, alert_type, alert_reason,
|
||||
current_price, ma5, ma20, holding_days,
|
||||
datetime.now().strftime("%Y-%m-%d %H:%M:%S")))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
self.logger.info(f"添加卖出提醒: {stock_code} - {alert_reason}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"添加卖出提醒失败: {e}")
|
||||
return False
|
||||
|
||||
def get_pending_alerts(self) -> List[Dict]:
|
||||
"""
|
||||
获取待发送的提醒
|
||||
|
||||
Returns:
|
||||
提醒列表
|
||||
"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
df = pd.read_sql_query("""
|
||||
SELECT * FROM sell_alerts
|
||||
WHERE is_sent = 0
|
||||
ORDER BY alert_time DESC
|
||||
""", conn)
|
||||
conn.close()
|
||||
|
||||
return df.to_dict('records') if not df.empty else []
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"获取提醒失败: {e}")
|
||||
return []
|
||||
|
||||
def mark_alert_sent(self, alert_id: int):
|
||||
"""标记提醒已发送"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
cursor.execute("""
|
||||
UPDATE sell_alerts
|
||||
SET is_sent = 1
|
||||
WHERE id = ?
|
||||
""", (alert_id,))
|
||||
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"标记提醒失败: {e}")
|
||||
|
||||
def get_history_alerts(self, limit: int = 50) -> List[Dict]:
|
||||
"""
|
||||
获取历史提醒记录
|
||||
|
||||
Args:
|
||||
limit: 返回记录数
|
||||
|
||||
Returns:
|
||||
提醒列表
|
||||
"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
df = pd.read_sql_query(f"""
|
||||
SELECT * FROM sell_alerts
|
||||
ORDER BY alert_time DESC
|
||||
LIMIT {limit}
|
||||
""", conn)
|
||||
conn.close()
|
||||
|
||||
return df.to_dict('records') if not df.empty else []
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"获取历史提醒失败: {e}")
|
||||
return []
|
||||
|
||||
def clear_old_alerts(self, days: int = 30):
|
||||
"""
|
||||
清理旧的提醒记录
|
||||
|
||||
Args:
|
||||
days: 保留天数
|
||||
"""
|
||||
try:
|
||||
conn = sqlite3.connect(self.db_path)
|
||||
cursor = conn.cursor()
|
||||
|
||||
cutoff_date = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
|
||||
|
||||
cursor.execute("""
|
||||
DELETE FROM sell_alerts
|
||||
WHERE alert_time < ? AND is_sent = 1
|
||||
""", (cutoff_date,))
|
||||
|
||||
deleted = cursor.rowcount
|
||||
conn.commit()
|
||||
conn.close()
|
||||
|
||||
self.logger.info(f"清理了 {deleted} 条旧提醒记录")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"清理旧提醒失败: {e}")
|
||||
|
||||
|
||||
# 全局监控器实例
|
||||
low_price_bull_monitor = LowPriceBullMonitor()
|
||||
@@ -0,0 +1,293 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
低价擒牛策略监控UI模块
|
||||
"""
|
||||
|
||||
import streamlit as st
|
||||
import pandas as pd
|
||||
from datetime import datetime
|
||||
from low_price_bull_monitor import low_price_bull_monitor
|
||||
from low_price_bull_service import low_price_bull_service
|
||||
|
||||
|
||||
def display_monitor_panel():
|
||||
"""显示策略监控面板"""
|
||||
|
||||
st.markdown("## 📊 策略监控中心")
|
||||
st.markdown("---")
|
||||
|
||||
# 服务状态
|
||||
status = low_price_bull_service.get_status()
|
||||
|
||||
col1, col2, col3, col4 = st.columns(4)
|
||||
|
||||
with col1:
|
||||
service_status = "🟢 运行中" if status['running'] else "🔴 已停止"
|
||||
st.metric("服务状态", service_status)
|
||||
|
||||
with col2:
|
||||
st.metric("监控股票", f"{status['monitored_count']} 只")
|
||||
|
||||
with col3:
|
||||
st.metric("待处理提醒", f"{status['pending_alerts']} 条")
|
||||
|
||||
with col4:
|
||||
st.metric("扫描间隔", f"{status['scan_interval']} 秒")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# 服务控制
|
||||
col_start, col_stop, col_config = st.columns(3)
|
||||
|
||||
with col_start:
|
||||
if st.button("▶️ 启动监控服务", type="primary", disabled=status['running']):
|
||||
if low_price_bull_service.start():
|
||||
st.success("✅ 监控服务已启动")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("❌ 启动失败")
|
||||
|
||||
with col_stop:
|
||||
if st.button("⏸️ 停止监控服务", type="secondary", disabled=not status['running']):
|
||||
if low_price_bull_service.stop():
|
||||
st.success("✅ 监控服务已停止")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error("❌ 停止失败")
|
||||
|
||||
with col_config:
|
||||
with st.popover("⚙️ 监控配置"):
|
||||
st.markdown("**扫描间隔**")
|
||||
new_interval = st.number_input(
|
||||
"扫描间隔（秒）",
|
||||
min_value=10,
|
||||
max_value=600,
|
||||
value=status['scan_interval'],
|
||||
step=10,
|
||||
label_visibility="collapsed"
|
||||
)
|
||||
|
||||
if st.button("保存配置"):
|
||||
low_price_bull_service.scan_interval = new_interval
|
||||
st.success("✅ 配置已保存")
|
||||
|
||||
st.markdown("---")
|
||||
|
||||
# 标签页
|
||||
tab1, tab2, tab3 = st.tabs(["📋 监控列表", "🔔 卖出提醒", "📜 历史记录"])
|
||||
|
||||
with tab1:
|
||||
display_monitored_stocks()
|
||||
|
||||
with tab2:
|
||||
display_sell_alerts()
|
||||
|
||||
with tab3:
|
||||
display_alert_history()
|
||||
|
||||
|
||||
def display_monitored_stocks():
|
||||
"""显示监控中的股票列表"""
|
||||
|
||||
stocks = low_price_bull_monitor.get_monitored_stocks()
|
||||
|
||||
if not stocks:
|
||||
st.info("暂无监控中的股票")
|
||||
return
|
||||
|
||||
st.markdown(f"### 📋 监控列表（共{len(stocks)}只）")
|
||||
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(stocks)
|
||||
|
||||
# 显示表格
|
||||
display_df = df[['stock_code', 'stock_name', 'buy_price', 'buy_date', 'holding_days', 'add_time']].copy()
|
||||
display_df.columns = ['股票代码', '股票名称', '买入价格', '买入日期', '持有天数', '加入时间']
|
||||
|
||||
st.dataframe(display_df, width='content', height=400)
|
||||
|
||||
# 批量移除
|
||||
st.markdown("---")
|
||||
st.markdown("### 🗑️ 批量管理")
|
||||
|
||||
selected_codes = st.multiselect(
|
||||
"选择要移除的股票",
|
||||
options=[f"{s['stock_code']} {s['stock_name']}" for s in stocks],
|
||||
format_func=lambda x: x
|
||||
)
|
||||
|
||||
if selected_codes and st.button("🗑️ 移除选中股票", type="secondary"):
|
||||
for item in selected_codes:
|
||||
code = item.split()[0]
|
||||
success, msg = low_price_bull_monitor.remove_stock(code, "手动移除")
|
||||
if success:
|
||||
st.success(msg)
|
||||
else:
|
||||
st.error(msg)
|
||||
st.rerun()
|
||||
|
||||
|
||||
def display_sell_alerts():
|
||||
"""显示待处理的卖出提醒"""
|
||||
|
||||
alerts = low_price_bull_monitor.get_pending_alerts()
|
||||
|
||||
if not alerts:
|
||||
st.info("暂无待处理的卖出提醒")
|
||||
return
|
||||
|
||||
st.markdown(f"### 🔔 卖出提醒（共{len(alerts)}条）")
|
||||
|
||||
for alert in alerts:
|
||||
with st.expander(
|
||||
f"🔔 {alert['stock_code']} {alert['stock_name']} - {alert['alert_reason']}",
|
||||
expanded=True
|
||||
):
|
||||
col1, col2 = st.columns(2)
|
||||
|
||||
with col1:
|
||||
st.markdown("#### 📊 提醒信息")
|
||||
st.markdown(f"**股票代码**: {alert['stock_code']}")
|
||||
st.markdown(f"**股票名称**: {alert['stock_name']}")
|
||||
st.markdown(f"**提醒类型**: {_get_alert_type_name(alert['alert_type'])}")
|
||||
st.markdown(f"**提醒原因**: {alert['alert_reason']}")
|
||||
st.markdown(f"**提醒时间**: {alert['alert_time']}")
|
||||
|
||||
with col2:
|
||||
st.markdown("#### 💰 市场数据")
|
||||
|
||||
# 处理当前价格（避免bytes类型错误）
|
||||
current_price = alert.get('current_price')
|
||||
if current_price is not None:
|
||||
try:
|
||||
price_val = float(current_price)
|
||||
st.markdown(f"**当前价格**: {price_val:.2f}元")
|
||||
except (ValueError, TypeError):
|
||||
st.markdown(f"**当前价格**: {current_price}")
|
||||
|
||||
# 处理MA5和MA20（避免bytes类型错误）
|
||||
ma5 = alert.get('ma5')
|
||||
ma20 = alert.get('ma20')
|
||||
if ma5 is not None and ma20 is not None:
|
||||
try:
|
||||
ma5_val = float(ma5)
|
||||
ma20_val = float(ma20)
|
||||
st.markdown(f"**MA5**: {ma5_val:.2f}")
|
||||
st.markdown(f"**MA20**: {ma20_val:.2f}")
|
||||
st.markdown(f"**均线差**: {(ma5_val - ma20_val):.2f}")
|
||||
except (ValueError, TypeError):
|
||||
st.markdown(f"**MA5**: {ma5}")
|
||||
st.markdown(f"**MA20**: {ma20}")
|
||||
|
||||
# 处理持有天数
|
||||
holding_days = alert.get('holding_days')
|
||||
if holding_days is not None:
|
||||
try:
|
||||
days_val = int(holding_days)
|
||||
st.markdown(f"**持有天数**: {days_val}天")
|
||||
except (ValueError, TypeError):
|
||||
st.markdown(f"**持有天数**: {holding_days}")
|
||||
|
||||
# 操作按钮
|
||||
st.markdown("---")
|
||||
col_action1, col_action2 = st.columns(2)
|
||||
|
||||
with col_action1:
|
||||
if st.button(f"✅ 已处理 - {alert['stock_code']}", key=f"done_{alert['id']}"):
|
||||
low_price_bull_monitor.mark_alert_sent(alert['id'])
|
||||
low_price_bull_monitor.remove_stock(alert['stock_code'], "已处理提醒")
|
||||
st.success(f"✅ 已标记为已处理并移除 {alert['stock_code']}")
|
||||
st.rerun()
|
||||
|
||||
with col_action2:
|
||||
if st.button(f"❌ 忽略 - {alert['stock_code']}", key=f"ignore_{alert['id']}"):
|
||||
low_price_bull_monitor.mark_alert_sent(alert['id'])
|
||||
st.success(f"✅ 已忽略提醒（股票保留在监控列表）")
|
||||
st.rerun()
|
||||
|
||||
|
||||
def display_alert_history():
|
||||
"""显示历史提醒记录"""
|
||||
|
||||
alerts = low_price_bull_monitor.get_history_alerts(limit=50)
|
||||
|
||||
if not alerts:
|
||||
st.info("暂无历史提醒记录")
|
||||
return
|
||||
|
||||
st.markdown("### 📜 历史提醒记录")
|
||||
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(alerts)
|
||||
|
||||
# 选择显示列
|
||||
display_cols = ['stock_code', 'stock_name', 'alert_type', 'alert_reason', 'current_price', 'holding_days', 'alert_time', 'is_sent']
|
||||
display_df = df[display_cols].copy()
|
||||
|
||||
# 重命名列
|
||||
display_df.columns = ['股票代码', '股票名称', '提醒类型', '提醒原因', '当前价格', '持有天数', '提醒时间', '已发送']
|
||||
|
||||
# 格式化
|
||||
display_df['提醒类型'] = display_df['提醒类型'].apply(_get_alert_type_name)
|
||||
display_df['已发送'] = display_df['已发送'].apply(lambda x: '✅' if x == 1 else '❌')
|
||||
|
||||
st.dataframe(display_df, width='content', height=400)
|
||||
|
||||
# 清理按钮
|
||||
st.markdown("---")
|
||||
if st.button("🗑️ 清理30天前的记录"):
|
||||
low_price_bull_monitor.clear_old_alerts(days=30)
|
||||
st.success("✅ 已清理旧记录")
|
||||
st.rerun()
|
||||
|
||||
|
||||
def _get_alert_type_name(alert_type: str) -> str:
|
||||
"""获取提醒类型名称"""
|
||||
type_map = {
|
||||
'holding_days': '持股到期',
|
||||
'ma_cross': 'MA均线死叉'
|
||||
}
|
||||
return type_map.get(alert_type, alert_type)
|
||||
|
||||
|
||||
def add_stock_to_monitor_button(stock_code: str, stock_name: str, price: float = None):
|
||||
"""
|
||||
添加股票到监控按钮（在股票详情中使用）
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
price: 买入价格
|
||||
"""
|
||||
# 检查是否已在监控
|
||||
stocks = low_price_bull_monitor.get_monitored_stocks()
|
||||
is_monitored = any(s['stock_code'] == stock_code for s in stocks)
|
||||
|
||||
if is_monitored:
|
||||
st.info(f"✅ {stock_code} 已在监控列表中")
|
||||
if st.button(f"🗑️ 移出监控 - {stock_code}", key=f"remove_{stock_code}"):
|
||||
success, msg = low_price_bull_monitor.remove_stock(stock_code, "手动移除")
|
||||
if success:
|
||||
st.success(msg)
|
||||
st.rerun()
|
||||
else:
|
||||
st.error(msg)
|
||||
else:
|
||||
if st.button(f"➕ 加入策略监控 - {stock_code}", type="primary", key=f"add_{stock_code}"):
|
||||
if price is None:
|
||||
price = 0.0 # 如果没有价格，使用0
|
||||
|
||||
success, msg = low_price_bull_monitor.add_stock(
|
||||
stock_code=stock_code,
|
||||
stock_name=stock_name,
|
||||
buy_price=price,
|
||||
buy_date=datetime.now().strftime("%Y-%m-%d")
|
||||
)
|
||||
|
||||
if success:
|
||||
st.success(msg)
|
||||
st.info("💡 提示：请在左侧菜单进入'策略监控'查看监控状态")
|
||||
st.rerun()
|
||||
else:
|
||||
st.error(msg)
|
||||
@@ -0,0 +1,157 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
低价擒牛选股模块
|
||||
使用pywencai获取低价高成长股票
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import pywencai
|
||||
from datetime import datetime
|
||||
from typing import Tuple, Optional
|
||||
import time
|
||||
|
||||
|
||||
class LowPriceBullSelector:
|
||||
"""低价擒牛选股类"""
|
||||
|
||||
def __init__(self):
|
||||
self.raw_data = None
|
||||
self.selected_stocks = None
|
||||
|
||||
def get_low_price_stocks(self, top_n: int = 5) -> Tuple[bool, Optional[pd.DataFrame], str]:
|
||||
"""
|
||||
获取低价高成长股票
|
||||
|
||||
选股策略：
|
||||
- 股价<10元
|
||||
- 净利润增长率≥100%
|
||||
- 非ST
|
||||
- 非科创板
|
||||
- 非创业板
|
||||
- 深圳A股
|
||||
- 成交额由小至大排名
|
||||
|
||||
Args:
|
||||
top_n: 返回前N只股票
|
||||
|
||||
Returns:
|
||||
(success, dataframe, message)
|
||||
"""
|
||||
try:
|
||||
print(f"\n{'='*60}")
|
||||
print(f"🐂 低价擒牛选股 - 数据获取中")
|
||||
print(f"{'='*60}")
|
||||
print(f"策略: 股价<10元 + 净利润增长率≥100% + 深圳A股")
|
||||
print(f"目标: 筛选前{top_n}只股票")
|
||||
|
||||
# 构建查询语句（按成交额由小至大排名）
|
||||
query = (
|
||||
"股价<10元，"
|
||||
"净利润增长率(净利润同比增长率)≥100%，"
|
||||
"非st，"
|
||||
"非科创板，"
|
||||
"非创业板，"
|
||||
"深圳A股，"
|
||||
"成交额由小至大排名"
|
||||
)
|
||||
|
||||
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')
|
||||
price = row.get('股价', row.get('最新价', 'N/A'))
|
||||
growth = row.get('净利润增长率', row.get('净利润同比增长率', 'N/A'))
|
||||
turnover = row.get('成交额', 'N/A')
|
||||
print(f" {idx+1}. {code} {name} - 股价:{price} 净利增长:{growth}% 成交额:{turnover}")
|
||||
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
return True, selected, f"成功筛选出{len(selected)}只低价高成长股票"
|
||||
|
||||
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):
|
||||
# 去掉 .SZ 等后缀
|
||||
clean_code = code.split('.')[0] if '.' in code else code
|
||||
codes.append(clean_code)
|
||||
else:
|
||||
codes.append(str(code))
|
||||
|
||||
return codes
|
||||
@@ -0,0 +1,430 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
低价擒牛策略监控服务
|
||||
定时扫描股票，检测卖出信号
|
||||
"""
|
||||
|
||||
import time
|
||||
import threading
|
||||
import logging
|
||||
from datetime import datetime
|
||||
from typing import Optional
|
||||
import os
|
||||
|
||||
from low_price_bull_monitor import low_price_bull_monitor
|
||||
from notification_service import notification_service
|
||||
|
||||
|
||||
class LowPriceBullService:
|
||||
"""低价擒牛策略监控服务"""
|
||||
|
||||
def __init__(self):
|
||||
self.logger = logging.getLogger(__name__)
|
||||
self.running = False
|
||||
self.thread: Optional[threading.Thread] = None
|
||||
self.scan_interval = 60 # 默认扫描间隔（秒）
|
||||
self.holding_days_limit = 5 # 持股天数限制
|
||||
|
||||
# 从环境变量读取配置
|
||||
self._load_config()
|
||||
|
||||
def _load_config(self):
|
||||
"""从环境变量加载配置"""
|
||||
try:
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
# 扫描间隔
|
||||
interval = os.getenv('LOW_PRICE_BULL_SCAN_INTERVAL', '60')
|
||||
self.scan_interval = int(interval)
|
||||
|
||||
# 持股天数限制
|
||||
days = os.getenv('LOW_PRICE_BULL_HOLDING_DAYS', '5')
|
||||
self.holding_days_limit = int(days)
|
||||
|
||||
# TDX API配置
|
||||
self.tdx_api_url = os.getenv('TDX_BASE_URL', 'http://127.0.0.1:5000')
|
||||
|
||||
self.logger.info(f"监控配置: 扫描间隔={self.scan_interval}秒, 持股天数限制={self.holding_days_limit}天")
|
||||
self.logger.info(f"TDX API: {self.tdx_api_url}")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.warning(f"加载配置失败，使用默认值: {e}")
|
||||
|
||||
def start(self):
|
||||
"""启动监控服务"""
|
||||
if self.running:
|
||||
self.logger.warning("监控服务已在运行")
|
||||
return False
|
||||
|
||||
self.running = True
|
||||
self.thread = threading.Thread(target=self._monitor_loop, daemon=True)
|
||||
self.thread.start()
|
||||
|
||||
self.logger.info("低价擒牛监控服务已启动")
|
||||
return True
|
||||
|
||||
def stop(self):
|
||||
"""停止监控服务"""
|
||||
if not self.running:
|
||||
return False
|
||||
|
||||
self.running = False
|
||||
if self.thread:
|
||||
self.thread.join(timeout=5)
|
||||
|
||||
self.logger.info("低价擒牛监控服务已停止")
|
||||
return True
|
||||
|
||||
def _monitor_loop(self):
|
||||
"""监控循环"""
|
||||
while self.running:
|
||||
try:
|
||||
self._scan_stocks()
|
||||
time.sleep(self.scan_interval)
|
||||
except Exception as e:
|
||||
self.logger.error(f"监控循环错误: {e}")
|
||||
time.sleep(self.scan_interval)
|
||||
|
||||
def _scan_stocks(self):
|
||||
"""扫描所有监控的股票"""
|
||||
try:
|
||||
# 更新持有天数
|
||||
low_price_bull_monitor.update_holding_days()
|
||||
|
||||
# 获取监控列表
|
||||
stocks = low_price_bull_monitor.get_monitored_stocks()
|
||||
|
||||
if not stocks:
|
||||
return
|
||||
|
||||
self.logger.info(f"开始扫描 {len(stocks)} 只股票")
|
||||
|
||||
for stock in stocks:
|
||||
try:
|
||||
self._check_stock(stock)
|
||||
except Exception as e:
|
||||
self.logger.error(f"检查股票 {stock['stock_code']} 失败: {e}")
|
||||
|
||||
# 处理提醒
|
||||
self._process_alerts()
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"扫描股票失败: {e}")
|
||||
|
||||
def _check_stock(self, stock: dict):
|
||||
"""
|
||||
检查单只股票的卖出信号
|
||||
|
||||
Args:
|
||||
stock: 股票信息字典
|
||||
"""
|
||||
stock_code = stock['stock_code']
|
||||
stock_name = stock['stock_name']
|
||||
holding_days = stock['holding_days']
|
||||
|
||||
# 检查1: 持股天数
|
||||
if holding_days >= self.holding_days_limit:
|
||||
# 添加提醒
|
||||
low_price_bull_monitor.add_sell_alert(
|
||||
stock_code=stock_code,
|
||||
stock_name=stock_name,
|
||||
alert_type='holding_days',
|
||||
alert_reason=f'持股满{self.holding_days_limit}天，建议卖出',
|
||||
holding_days=holding_days
|
||||
)
|
||||
self.logger.info(f"{stock_code} 持股满{self.holding_days_limit}天，生成卖出提醒")
|
||||
return
|
||||
|
||||
# 检查2: MA5下穿MA20
|
||||
current_price, ma5, ma20 = self._get_stock_data(stock_code)
|
||||
|
||||
if current_price and ma5 and ma20:
|
||||
if ma5 < ma20:
|
||||
# MA5下穿MA20，添加提醒
|
||||
low_price_bull_monitor.add_sell_alert(
|
||||
stock_code=stock_code,
|
||||
stock_name=stock_name,
|
||||
alert_type='ma_cross',
|
||||
alert_reason='MA5下穿MA20，技术信号卖出',
|
||||
current_price=current_price,
|
||||
ma5=ma5,
|
||||
ma20=ma20,
|
||||
holding_days=holding_days
|
||||
)
|
||||
self.logger.info(f"{stock_code} MA5下穿MA20，生成卖出提醒")
|
||||
|
||||
def _get_stock_data(self, stock_code: str) -> tuple:
|
||||
"""
|
||||
获取股票数据（价格和均线）
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码（可能带后缀，如002259.SZ）
|
||||
|
||||
Returns:
|
||||
(当前价格, MA5, MA20)
|
||||
"""
|
||||
try:
|
||||
import requests
|
||||
import pandas as pd
|
||||
|
||||
# 处理股票代码格式：去掉后缀，保留纯数字代码
|
||||
# 例如：002259.SZ -> 002259
|
||||
clean_code = stock_code.split('.')[0] if '.' in stock_code else stock_code
|
||||
|
||||
# 判断市场并添加前缀（TDX API可能需要）
|
||||
# 深圳：0开头、3开头 -> SZ前缀
|
||||
# 上海：6开头 -> SH前缀
|
||||
if clean_code.startswith(('0', '3')):
|
||||
api_code = f"SZ{clean_code}"
|
||||
elif clean_code.startswith('6'):
|
||||
api_code = f"SH{clean_code}"
|
||||
else:
|
||||
api_code = clean_code
|
||||
|
||||
# 调用TDX API获取K线数据
|
||||
url = f"{self.tdx_api_url}/api/kline"
|
||||
params = {
|
||||
'code': api_code,
|
||||
'type': 'day' # 日K线
|
||||
}
|
||||
|
||||
self.logger.debug(f"请求TDX API: code={stock_code} -> api_code={api_code}")
|
||||
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
|
||||
if response.status_code != 200:
|
||||
self.logger.warning(f"获取 {stock_code} K线数据失败: HTTP {response.status_code}")
|
||||
self.logger.warning(f"请求URL: {url}?code={api_code}&type=day")
|
||||
|
||||
# 尝试使用纯数字代码重试
|
||||
if api_code != clean_code:
|
||||
self.logger.info(f"尝试使用纯数字代码重试: {clean_code}")
|
||||
params['code'] = clean_code
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
|
||||
if response.status_code != 200:
|
||||
self.logger.warning(f"重试失败: HTTP {response.status_code}")
|
||||
return None, None, None
|
||||
else:
|
||||
return None, None, None
|
||||
|
||||
data = response.json()
|
||||
|
||||
# 检查数据格式
|
||||
# 支持两种格式：
|
||||
# 1. 直接返回数组: [{date, open, high, low, close, volume}, ...]
|
||||
# 2. 嵌套格式: {code: 0, message: "success", data: {List: [...]}}
|
||||
if isinstance(data, dict) and 'data' in data:
|
||||
# 嵌套格式
|
||||
if data.get('code') != 0:
|
||||
self.logger.warning(f"{stock_code} API返回错误: {data.get('message')}")
|
||||
return None, None, None
|
||||
|
||||
data_obj = data.get('data', {})
|
||||
kline_list = data_obj.get('List', [])
|
||||
|
||||
if not kline_list or len(kline_list) < 20:
|
||||
self.logger.warning(f"{stock_code} K线数据不足，需要至少20天，当前{len(kline_list)}天")
|
||||
return None, None, None
|
||||
|
||||
# 转换为DataFrame，字段名需要映射
|
||||
# API返回：{Time, Open, High, Low, Close, Volume, Amount}
|
||||
# 需要：{date, open, high, low, close, volume}
|
||||
df = pd.DataFrame(kline_list)
|
||||
|
||||
# 重命名字段（大小写转换）
|
||||
if 'Time' in df.columns:
|
||||
df['date'] = df['Time']
|
||||
if 'Open' in df.columns:
|
||||
df['open'] = df['Open']
|
||||
if 'High' in df.columns:
|
||||
df['high'] = df['High']
|
||||
if 'Low' in df.columns:
|
||||
df['low'] = df['Low']
|
||||
if 'Close' in df.columns:
|
||||
df['close'] = df['Close']
|
||||
if 'Volume' in df.columns:
|
||||
df['volume'] = df['Volume']
|
||||
|
||||
elif isinstance(data, list):
|
||||
# 直接数组格式
|
||||
if len(data) < 20:
|
||||
self.logger.warning(f"{stock_code} K线数据不足，需要至少20天")
|
||||
return None, None, None
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
else:
|
||||
self.logger.warning(f"{stock_code} K线数据格式错误")
|
||||
return None, None, None
|
||||
|
||||
# 确保有close列
|
||||
if 'close' not in df.columns:
|
||||
self.logger.warning(f"{stock_code} K线数据缺少close字段")
|
||||
return None, None, None
|
||||
|
||||
# 转换为浮点数
|
||||
df['close'] = pd.to_numeric(df['close'], errors='coerce')
|
||||
|
||||
# 计算MA5和MA20
|
||||
df['MA5'] = df['close'].rolling(window=5).mean()
|
||||
df['MA20'] = df['close'].rolling(window=20).mean()
|
||||
|
||||
# 获取最新数据
|
||||
latest = df.iloc[-1]
|
||||
current_price = latest['close']
|
||||
ma5 = latest['MA5']
|
||||
ma20 = latest['MA20']
|
||||
|
||||
# 检查是否有效
|
||||
if pd.isna(current_price) or pd.isna(ma5) or pd.isna(ma20):
|
||||
self.logger.warning(f"{stock_code} 数据包含NaN值")
|
||||
return None, None, None
|
||||
|
||||
self.logger.info(f"{stock_code} 数据: 价格={current_price:.2f}, MA5={ma5:.2f}, MA20={ma20:.2f}")
|
||||
return current_price, ma5, ma20
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
self.logger.error(f"请求TDX API失败 {stock_code}: {e}")
|
||||
self.logger.error(f"请检查.env中的TDX_BASE_URL配置: {self.tdx_api_url}")
|
||||
return None, None, None
|
||||
except Exception as e:
|
||||
self.logger.error(f"获取股票数据失败 {stock_code}: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return None, None, None
|
||||
|
||||
def _process_alerts(self):
|
||||
"""处理待发送的提醒"""
|
||||
try:
|
||||
alerts = low_price_bull_monitor.get_pending_alerts()
|
||||
|
||||
if not alerts:
|
||||
return
|
||||
|
||||
self.logger.info(f"处理 {len(alerts)} 条卖出提醒")
|
||||
|
||||
for alert in alerts:
|
||||
try:
|
||||
# 发送通知
|
||||
self._send_alert_notification(alert)
|
||||
|
||||
# 标记已发送
|
||||
low_price_bull_monitor.mark_alert_sent(alert['id'])
|
||||
|
||||
# 自动移除股票
|
||||
low_price_bull_monitor.remove_stock(
|
||||
alert['stock_code'],
|
||||
reason=alert['alert_reason']
|
||||
)
|
||||
|
||||
self.logger.info(f"已处理提醒并移除股票: {alert['stock_code']}")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"处理提醒失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"处理提醒失败: {e}")
|
||||
|
||||
def _send_alert_notification(self, alert: dict):
|
||||
"""
|
||||
发送卖出提醒通知
|
||||
|
||||
Args:
|
||||
alert: 提醒信息字典
|
||||
"""
|
||||
try:
|
||||
# 构建消息
|
||||
keyword = notification_service.config.get('webhook_keyword', 'aiagents通知')
|
||||
|
||||
message_text = f"### {keyword} - 低价擒牛卖出提醒\n\n"
|
||||
message_text += f"**股票代码**: {alert['stock_code']}\n\n"
|
||||
message_text += f"**股票名称**: {alert['stock_name']}\n\n"
|
||||
message_text += f"**提醒类型**: {self._get_alert_type_name(alert['alert_type'])}\n\n"
|
||||
message_text += f"**提醒原因**: {alert['alert_reason']}\n\n"
|
||||
|
||||
# 添加详细信息（确保数据类型正确）
|
||||
current_price = alert.get('current_price')
|
||||
if current_price is not None:
|
||||
try:
|
||||
price_val = float(current_price)
|
||||
message_text += f"**当前价格**: {price_val:.2f}元\n\n"
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
ma5 = alert.get('ma5')
|
||||
ma20 = alert.get('ma20')
|
||||
if ma5 is not None and ma20 is not None:
|
||||
try:
|
||||
ma5_val = float(ma5)
|
||||
ma20_val = float(ma20)
|
||||
message_text += f"**MA5**: {ma5_val:.2f}\n\n"
|
||||
message_text += f"**MA20**: {ma20_val:.2f}\n\n"
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
holding_days = alert.get('holding_days')
|
||||
if holding_days is not None:
|
||||
try:
|
||||
days_val = int(holding_days)
|
||||
message_text += f"**持有天数**: {days_val}天\n\n"
|
||||
except (ValueError, TypeError):
|
||||
pass
|
||||
|
||||
message_text += f"**提醒时间**: {alert['alert_time']}\n\n"
|
||||
message_text += "---\n\n"
|
||||
message_text += "**建议**: 开盘时卖出该股票\n\n"
|
||||
message_text += "_此消息由AI股票分析系统自动发送_"
|
||||
|
||||
# 发送钉钉通知
|
||||
if notification_service.config['webhook_enabled']:
|
||||
import requests
|
||||
|
||||
data = {
|
||||
"msgtype": "markdown",
|
||||
"markdown": {
|
||||
"title": f"{keyword} - 卖出提醒",
|
||||
"text": message_text
|
||||
}
|
||||
}
|
||||
|
||||
response = requests.post(
|
||||
notification_service.config['webhook_url'],
|
||||
json=data,
|
||||
headers={'Content-Type': 'application/json'},
|
||||
timeout=10
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
self.logger.info(f"卖出提醒已发送: {alert['stock_code']}")
|
||||
else:
|
||||
self.logger.error(f"发送提醒失败: HTTP {response.status_code}")
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"发送通知失败: {e}")
|
||||
|
||||
def _get_alert_type_name(self, alert_type: str) -> str:
|
||||
"""获取提醒类型名称"""
|
||||
type_map = {
|
||||
'holding_days': '持股到期',
|
||||
'ma_cross': 'MA均线死叉'
|
||||
}
|
||||
return type_map.get(alert_type, alert_type)
|
||||
|
||||
def get_status(self) -> dict:
|
||||
"""获取服务状态"""
|
||||
stocks = low_price_bull_monitor.get_monitored_stocks()
|
||||
alerts = low_price_bull_monitor.get_pending_alerts()
|
||||
|
||||
return {
|
||||
'running': self.running,
|
||||
'scan_interval': self.scan_interval,
|
||||
'holding_days_limit': self.holding_days_limit,
|
||||
'monitored_count': len(stocks),
|
||||
'pending_alerts': len(alerts)
|
||||
}
|
||||
|
||||
|
||||
# 全局服务实例
|
||||
low_price_bull_service = LowPriceBullService()
|
||||
@@ -0,0 +1,277 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
低价擒牛量化交易策略
|
||||
实现基于MA均线的买卖择时策略
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, List, Optional
|
||||
import logging
|
||||
|
||||
|
||||
class LowPriceBullStrategy:
|
||||
"""低价擒牛量化交易策略"""
|
||||
|
||||
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 = 0.4 # 个股最大持仓比例（4成）
|
||||
self.max_daily_buy = 2 # 单日最大买入数
|
||||
self.holding_period = 5 # 持股周期（天）
|
||||
|
||||
# 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] = [] # 交易历史
|
||||
|
||||
# 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[bool, str]:
|
||||
"""
|
||||
检查是否可以买入
|
||||
|
||||
Returns:
|
||||
(是否可买, 原因)
|
||||
"""
|
||||
# 检查是否已持有
|
||||
if stock_code in self.positions:
|
||||
return False, "已持有该股票"
|
||||
|
||||
# 检查持股数量
|
||||
if len(self.positions) >= self.max_stocks:
|
||||
return False, f"已达最大持股数限制({self.max_stocks}只)"
|
||||
|
||||
# 检查当日买入数量
|
||||
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)"
|
||||
|
||||
# 检查资金
|
||||
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[int, float]:
|
||||
"""
|
||||
计算买入数量（满仓策略）
|
||||
|
||||
Args:
|
||||
stock_price: 股票价格
|
||||
|
||||
Returns:
|
||||
(买入股数, 买入金额)
|
||||
"""
|
||||
# 满仓策略：使用所有可用资金
|
||||
max_amount = self.available_cash
|
||||
|
||||
# 但不能超过个股最大持仓
|
||||
max_per_stock = self.initial_capital * self.max_position_per_stock
|
||||
target_amount = min(max_amount, max_per_stock)
|
||||
|
||||
# 计算股数（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[bool, str, Optional[Dict]]:
|
||||
"""
|
||||
执行买入操作
|
||||
|
||||
Returns:
|
||||
(是否成功, 消息, 交易详情)
|
||||
"""
|
||||
# 重置当日计数
|
||||
self.reset_daily_counter(date)
|
||||
|
||||
# 检查是否可买入
|
||||
can_buy, reason = self.can_buy(stock_code)
|
||||
if not can_buy:
|
||||
return False, reason, None
|
||||
|
||||
# 计算买入数量
|
||||
shares, amount = self.calculate_buy_amount(price)
|
||||
|
||||
if shares == 0:
|
||||
return False, "资金不足，无法买入100股", None
|
||||
|
||||
# 执行买入
|
||||
self.positions[stock_code] = {
|
||||
'name': stock_name,
|
||||
'shares': shares,
|
||||
'buy_price': price,
|
||||
'buy_date': date,
|
||||
'holding_days': 0
|
||||
}
|
||||
|
||||
self.available_cash -= amount
|
||||
self.daily_buy_count += 1
|
||||
|
||||
# 记录交易
|
||||
trade = {
|
||||
'type': 'BUY',
|
||||
'code': stock_code,
|
||||
'name': stock_name,
|
||||
'price': price,
|
||||
'shares': shares,
|
||||
'amount': amount,
|
||||
'date': date,
|
||||
'cash_after': self.available_cash
|
||||
}
|
||||
self.trade_history.append(trade)
|
||||
|
||||
message = f"✅ 买入成功: {stock_code} {stock_name} | 价格:{price:.2f} | 数量:{shares}股 | 金额:{amount:.2f}元"
|
||||
self.logger.info(message)
|
||||
|
||||
return True, message, trade
|
||||
|
||||
def should_sell(self, stock_code: str, ma5: float, ma20: float, current_date: str) -> tuple[bool, str]:
|
||||
"""
|
||||
判断是否应该卖出
|
||||
|
||||
策略：
|
||||
1. MA5下穿MA20时卖出
|
||||
2. 持股满5天强制卖出
|
||||
|
||||
Returns:
|
||||
(是否卖出, 原因)
|
||||
"""
|
||||
if stock_code not in self.positions:
|
||||
return False, "未持有该股票"
|
||||
|
||||
position = self.positions[stock_code]
|
||||
|
||||
# 更新持有天数
|
||||
# 简化处理，按交易日计算
|
||||
position['holding_days'] += 1
|
||||
|
||||
# 检查持股周期
|
||||
if position['holding_days'] >= self.holding_period:
|
||||
return True, f"持股满{self.holding_period}天，到期卖出"
|
||||
|
||||
# 检查MA5下穿MA20
|
||||
if ma5 is not None and ma20 is not None:
|
||||
if ma5 < ma20:
|
||||
return True, "MA5下穿MA20，技术信号卖出"
|
||||
|
||||
return False, "持有"
|
||||
|
||||
def sell(self, stock_code: str, price: float, date: str, reason: str = "") -> tuple[bool, str, Optional[Dict]]:
|
||||
"""
|
||||
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]
|
||||
shares = position['shares']
|
||||
buy_price = position['buy_price']
|
||||
|
||||
# 计算盈亏
|
||||
amount = shares * price
|
||||
cost = shares * buy_price
|
||||
profit = amount - cost
|
||||
profit_pct = (profit / cost) * 100 if cost > 0 else 0
|
||||
|
||||
# 归还资金
|
||||
self.available_cash += amount
|
||||
|
||||
# 移除持仓
|
||||
del self.positions[stock_code]
|
||||
|
||||
# 记录交易
|
||||
trade = {
|
||||
'type': 'SELL',
|
||||
'code': stock_code,
|
||||
'name': position['name'],
|
||||
'price': price,
|
||||
'shares': shares,
|
||||
'amount': amount,
|
||||
'date': date,
|
||||
'reason': reason,
|
||||
'buy_price': buy_price,
|
||||
'profit': profit,
|
||||
'profit_pct': profit_pct,
|
||||
'cash_after': self.available_cash
|
||||
}
|
||||
self.trade_history.append(trade)
|
||||
|
||||
profit_str = f"+{profit:.2f}" if profit >= 0 else f"{profit:.2f}"
|
||||
message = f"✅ 卖出成功: {stock_code} {position['name']} | 价格:{price:.2f} | 数量:{shares}股 | 盈亏:{profit_str}元({profit_pct:+.2f}%) | 原因:{reason}"
|
||||
self.logger.info(message)
|
||||
|
||||
return True, message, trade
|
||||
|
||||
def get_portfolio_summary(self) -> Dict:
|
||||
"""
|
||||
获取投资组合摘要
|
||||
|
||||
Returns:
|
||||
组合摘要信息
|
||||
"""
|
||||
# 计算持仓市值（需要当前价格，这里用买入价估算）
|
||||
position_value = sum(
|
||||
pos['shares'] * pos['buy_price']
|
||||
for pos in self.positions.values()
|
||||
)
|
||||
|
||||
total_value = self.available_cash + position_value
|
||||
|
||||
# 计算收益
|
||||
total_profit = total_value - self.initial_capital
|
||||
total_profit_pct = (total_profit / self.initial_capital) * 100
|
||||
|
||||
return {
|
||||
'initial_capital': self.initial_capital,
|
||||
'available_cash': self.available_cash,
|
||||
'position_value': position_value,
|
||||
'total_value': total_value,
|
||||
'total_profit': total_profit,
|
||||
'total_profit_pct': total_profit_pct,
|
||||
'positions_count': len(self.positions),
|
||||
'max_stocks': self.max_stocks,
|
||||
'trade_count': len(self.trade_history)
|
||||
}
|
||||
|
||||
def get_positions(self) -> List[Dict]:
|
||||
"""获取当前持仓列表"""
|
||||
return [
|
||||
{
|
||||
'code': code,
|
||||
'name': pos['name'],
|
||||
'shares': pos['shares'],
|
||||
'buy_price': pos['buy_price'],
|
||||
'buy_date': pos['buy_date'],
|
||||
'holding_days': pos['holding_days']
|
||||
}
|
||||
for code, pos in self.positions.items()
|
||||
]
|
||||
|
||||
def get_trade_history(self) -> List[Dict]:
|
||||
"""获取交易历史"""
|
||||
return self.trade_history.copy()
|
||||
@@ -0,0 +1,589 @@
|
||||
#!/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
|
||||
- ✅ 深圳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% + 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)}")
|
||||
+33
-9
@@ -318,25 +318,42 @@ class NotificationService:
|
||||
title_prefix = f"{keyword} - " if keyword else ""
|
||||
content_prefix = f"### {keyword} - " if keyword else "### "
|
||||
|
||||
data = {
|
||||
"msgtype": "markdown",
|
||||
"markdown": {
|
||||
"title": f"{title_prefix}{notification['symbol']} {notification['name']}",
|
||||
"text": f"""{content_prefix}股票监测提醒
|
||||
# 构建增强的消息内容
|
||||
message_text = f"""{content_prefix}股票监测提醒
|
||||
|
||||
**股票代码**: {notification['symbol']}
|
||||
|
||||
**股票名称**: {notification['name']}
|
||||
|
||||
**提醒类型**: {notification['type']}
|
||||
**📊 实时行情**:
|
||||
- 当前价格: {notification.get('current_price', 'N/A')}元
|
||||
- 涨跌幅: {notification.get('change_pct', 'N/A')}%
|
||||
- 涨跌额: {notification.get('change_amount', 'N/A')}元
|
||||
- 成交量: {notification.get('volume', 'N/A')}手
|
||||
- 换手率: {notification.get('turnover_rate', 'N/A')}%
|
||||
|
||||
**提醒内容**: {notification['message']}
|
||||
**🎯 AI决策**: {notification['type']}
|
||||
|
||||
**触发时间**: {notification['triggered_at']}
|
||||
**📝 分析内容**: {notification['message']}
|
||||
|
||||
**💰 持仓信息**:
|
||||
- 持仓状态: {notification.get('position_status', '未知')}
|
||||
- 持仓成本: {notification.get('position_cost', 'N/A')}元
|
||||
- 浮动盈亏: {notification.get('profit_loss_pct', 'N/A')}%
|
||||
|
||||
**⏰ 触发时间**: {notification['triggered_at']}
|
||||
|
||||
**🕐 交易时段**: {notification.get('trading_session', '未知')}
|
||||
|
||||
---
|
||||
|
||||
_此消息由AI股票分析系统自动发送_"""
|
||||
|
||||
data = {
|
||||
"msgtype": "markdown",
|
||||
"markdown": {
|
||||
"title": f"{title_prefix}{notification['symbol']} {notification['name']}",
|
||||
"text": message_text
|
||||
}
|
||||
}
|
||||
|
||||
@@ -756,4 +773,11 @@ _此消息由AI股票分析系统自动发送_"""
|
||||
return False
|
||||
|
||||
# 全局通知服务实例
|
||||
notification_service = NotificationService()
|
||||
notification_service = NotificationService()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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+13
-1
@@ -482,7 +482,19 @@ class SmartMonitorEngine:
|
||||
'type': '智能盯盘',
|
||||
'message': message,
|
||||
'details': content,
|
||||
'triggered_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
'triggered_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
|
||||
# 新增实时市场数据
|
||||
'current_price': market_data.get('current_price', 'N/A'),
|
||||
'change_pct': f"{market_data.get('change_pct', 0):+.2f}" if market_data.get('change_pct') else 'N/A',
|
||||
'change_amount': f"{market_data.get('change_amount', 0):+.2f}" if market_data.get('change_amount') else 'N/A',
|
||||
'volume': market_data.get('volume', 'N/A'),
|
||||
'turnover_rate': f"{market_data.get('turnover_rate', 0):.2f}" if market_data.get('turnover_rate') else 'N/A',
|
||||
# 持仓信息
|
||||
'position_status': '已持仓' if has_position else '未持仓',
|
||||
'position_cost': f"{position_cost:.2f}" if has_position and position_cost else 'N/A',
|
||||
'profit_loss_pct': f"{((market_data.get('current_price', 0) - position_cost) / position_cost * 100):+.2f}" if has_position and position_cost else 'N/A',
|
||||
# 交易时段信息
|
||||
'trading_session': session_info.get('session', '未知')
|
||||
}
|
||||
|
||||
# 直接调用主程序的通知服务发送
|
||||
|
||||
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+174
@@ -0,0 +1,174 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
TDX API配置测试脚本
|
||||
用于测试TDX API连接和数据获取是否正常
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import requests
|
||||
from dotenv import load_dotenv
|
||||
|
||||
# 加载环境变量
|
||||
load_dotenv()
|
||||
|
||||
# 获取TDX API URL
|
||||
TDX_API_URL = os.getenv('TDX_BASE_URL', 'http://127.0.0.1:5000')
|
||||
|
||||
print("=" * 60)
|
||||
print("TDX API配置测试")
|
||||
print("=" * 60)
|
||||
print(f"\n1. TDX API地址: {TDX_API_URL}")
|
||||
|
||||
# 测试1: 健康检查
|
||||
print("\n2. 测试健康检查接口...")
|
||||
try:
|
||||
response = requests.get(f"{TDX_API_URL}/api/health", timeout=5)
|
||||
if response.status_code == 200:
|
||||
print(" ✅ 健康检查成功")
|
||||
print(f" 响应: {response.text}")
|
||||
else:
|
||||
print(f" ❌ 健康检查失败: HTTP {response.status_code}")
|
||||
sys.exit(1)
|
||||
except Exception as e:
|
||||
print(f" ❌ 连接失败: {e}")
|
||||
print("\n提示:")
|
||||
print(" - 请检查TDX API服务是否已启动")
|
||||
print(" - 请检查.env中的TDX_API_URL配置是否正确")
|
||||
print(" - 默认地址: http://192.168.1.222:8181")
|
||||
sys.exit(1)
|
||||
|
||||
# 测试2: 获取K线数据
|
||||
print("\n3. 测试K线数据接口...")
|
||||
|
||||
# 尝试不同的代码格式
|
||||
test_codes = [
|
||||
("SZ000001", "平安银行"),
|
||||
("000001", "平安银行(纯数字)"),
|
||||
("SH600000", "浦发银行"),
|
||||
("600000", "浦发银行(纯数字)"),
|
||||
]
|
||||
|
||||
data = None
|
||||
test_code = None
|
||||
|
||||
for code, name in test_codes:
|
||||
print(f"\n 尝试股票: {code} ({name})")
|
||||
|
||||
try:
|
||||
url = f"{TDX_API_URL}/api/kline"
|
||||
params = {
|
||||
'code': code,
|
||||
'type': 'day'
|
||||
}
|
||||
|
||||
response = requests.get(url, params=params, timeout=10)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
|
||||
# 支持两种数据格式
|
||||
kline_list = None
|
||||
if isinstance(data, dict) and 'data' in data:
|
||||
# 嵌套格式: {code: 0, message: "success", data: {List: [...]}}
|
||||
if data.get('code') == 0:
|
||||
data_obj = data.get('data', {})
|
||||
kline_list = data_obj.get('List', [])
|
||||
elif isinstance(data, list):
|
||||
# 直接数组格式
|
||||
kline_list = data
|
||||
|
||||
if kline_list and len(kline_list) > 0:
|
||||
test_code = code
|
||||
data = kline_list # 保存为全局变量
|
||||
print(f" ✅ K线数据获取成功！")
|
||||
print(f" 数据条数: {len(kline_list)}")
|
||||
break
|
||||
else:
|
||||
print(f" ⚠️ 数据为空")
|
||||
else:
|
||||
print(f" ❌ HTTP {response.status_code}")
|
||||
except Exception as e:
|
||||
print(f" ❌ 错误: {e}")
|
||||
|
||||
if data is None or test_code is None:
|
||||
print(f"\n ❌ 所有代码格式都失败，无法继续测试")
|
||||
print("\n提示：")
|
||||
print(" - 请检查TDX API服务是否正确启动")
|
||||
print(" - 请确认API支持的股票代码格式")
|
||||
print(" - 可能的格式：SZ000001, 000001, SH600000, 600000")
|
||||
sys.exit(1)
|
||||
|
||||
print(f"\n 成功的代码格式: {test_code}")
|
||||
|
||||
# 显示最新一条数据
|
||||
if len(data) > 0:
|
||||
latest = data[-1]
|
||||
print(f"\n 最新K线数据:")
|
||||
# 支持两种字段名格式：小写和大写
|
||||
print(f" - 日期: {latest.get('date') or latest.get('Time', 'N/A')}")
|
||||
print(f" - 开盘: {latest.get('open') or latest.get('Open', 'N/A')}")
|
||||
print(f" - 收盘: {latest.get('close') or latest.get('Close', 'N/A')}")
|
||||
print(f" - 最高: {latest.get('high') or latest.get('High', 'N/A')}")
|
||||
print(f" - 最低: {latest.get('low') or latest.get('Low', 'N/A')}")
|
||||
print(f" - 成交量: {latest.get('volume') or latest.get('Volume', 'N/A')}")
|
||||
|
||||
# 检查数据量是否足够计算MA20
|
||||
if len(data) >= 20:
|
||||
print(f" ✅ 数据量充足，可以计算MA20（需要至少20条）")
|
||||
else:
|
||||
print(f" ⚠️ 数据量不足，仅{len(data)}条，需要至少20条才能计算MA20")
|
||||
print(f" 请尝试其他股票或等待数据积累")
|
||||
|
||||
# 测试3: 计算均线
|
||||
print("\n4. 测试均线计算...")
|
||||
try:
|
||||
import pandas as pd
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 支持两种字段名：小写close和大写Close
|
||||
if 'Close' in df.columns and 'close' not in df.columns:
|
||||
df['close'] = df['Close']
|
||||
|
||||
df['close'] = pd.to_numeric(df['close'], errors='coerce')
|
||||
|
||||
# 计算MA5和MA20
|
||||
df['MA5'] = df['close'].rolling(window=5).mean()
|
||||
df['MA20'] = df['close'].rolling(window=20).mean()
|
||||
|
||||
latest = df.iloc[-1]
|
||||
|
||||
if pd.notna(latest['MA5']) and pd.notna(latest['MA20']):
|
||||
print(f" ✅ 均线计算成功")
|
||||
print(f" - 收盘价: {latest['close']:.2f}")
|
||||
print(f" - MA5: {latest['MA5']:.2f}")
|
||||
print(f" - MA20: {latest['MA20']:.2f}")
|
||||
|
||||
# 判断MA5和MA20的关系
|
||||
if latest['MA5'] > latest['MA20']:
|
||||
print(f" - 趋势: 🟢 MA5 > MA20 (多头)")
|
||||
elif latest['MA5'] < latest['MA20']:
|
||||
print(f" - 趋势: 🔴 MA5 < MA20 (空头)")
|
||||
else:
|
||||
print(f" - 趋势: 🟡 MA5 = MA20 (震荡)")
|
||||
else:
|
||||
print(f" ❌ 均线计算失败，数据包含NaN")
|
||||
sys.exit(1)
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ 均线计算失败: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
sys.exit(1)
|
||||
|
||||
# 所有测试通过
|
||||
print("\n" + "=" * 60)
|
||||
print("✅ 所有测试通过！TDX API配置正常")
|
||||
print("=" * 60)
|
||||
print("\n提示:")
|
||||
print(" - 现在可以启动低价擒牛策略监控服务")
|
||||
print(" - 在监控面板中点击'▶️ 启动监控服务'")
|
||||
print(" - 服务将每60秒扫描一次监控列表中的股票")
|
||||
print("")
|
||||
Reference in New Issue
Block a user