From 16071f81e8b768dd655a498156b62260a96a5d48 Mon Sep 17 00:00:00 2001 From: oficcejo Date: Sun, 26 Oct 2025 10:02:55 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BB=BFalphaarena=E9=A1=B9=E7=9B=AE=E5=A2=9E?= =?UTF-8?q?=E5=8A=A0A=E8=82=A1ai=E8=87=AA=E5=8A=A8=E9=87=8F=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- README.md | 18 + app.py | 21 +- docs/Tushare积分获取指南.md | 350 ++++++++++++ docs/智能盯盘使用指南.md | 219 ++++++++ docs/智能盯盘持仓管理功能说明.md | 432 +++++++++++++++ docs/智能盯盘配置说明.md | 263 +++++++++ longhubang.db | Bin 647168 -> 778240 bytes monitor_db.py | 31 +- monitor_manager.py | 13 +- requirements.txt | 2 +- smart_monitor.db | Bin 0 -> 131072 bytes smart_monitor_data.py | 775 ++++++++++++++++++++++++++ smart_monitor_db.py | 634 ++++++++++++++++++++++ smart_monitor_deepseek.py | 526 ++++++++++++++++++ smart_monitor_engine.py | 591 ++++++++++++++++++++ smart_monitor_kline.py | 484 +++++++++++++++++ smart_monitor_qmt.py | 628 ++++++++++++++++++++++ smart_monitor_ui.py | 897 +++++++++++++++++++++++++++++++ stock_analysis.db | Bin 21450752 -> 22032384 bytes test_smart_monitor_data.py | 103 ++++ test_tushare_monitoring.py | 247 +++++++++ 21 files changed, 6218 insertions(+), 16 deletions(-) create mode 100644 docs/Tushare积分获取指南.md create mode 100644 docs/智能盯盘使用指南.md create mode 100644 docs/智能盯盘持仓管理功能说明.md create mode 100644 docs/智能盯盘配置说明.md create mode 100644 smart_monitor.db create mode 100644 smart_monitor_data.py create mode 100644 smart_monitor_db.py create mode 100644 smart_monitor_deepseek.py create mode 100644 smart_monitor_engine.py create mode 100644 smart_monitor_kline.py create mode 100644 smart_monitor_qmt.py create mode 100644 smart_monitor_ui.py create mode 100644 test_smart_monitor_data.py create mode 100644 test_tushare_monitoring.py diff --git a/README.md b/README.md index f6e9122..b727b28 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,24 @@ # 🤖 复合多AI智能体股票团队分析系统 - 初心：在股市摸爬滚打多年，自学自编各种指标，花冤枉钱学习了各种战法各种策略，也曾入各种小班，总是赚少赔多，逐渐失去在股市玩的信心。自从去年deepseek上市，一直探索用ai辅助分析，且近日受tradingagents项目启发（感谢原作），多agent结合跟踪主力资金战法（某指每年收费6000rmb），用各种ai辅助编程，拼凑了这么个小程序，根据软件提供的辅助信息，实盘测试盈率还是挺高的，并且逐步形成了自己的交易系统，近一个月来，账户也慢慢在扭亏为盈。开源此软件的目的，就是为了使像我一样的小散，不再迷茫。也许这个软件不能让你发大财，但是他能给你足够的信心。最后提醒：股市有风险，入市需谨慎！ + +## 🤖 新增--AI盯盘 - AI自动化交易决策系统（NEW！） + +参照AlphaArena项目，基于 DeepSeek AI 的A股自动化交易系统，支持实时监控、智能决策、自动交易（T+1规则适配）。 + +**核心功能：** +- ✅ **DeepSeek AI决策** - 利用DeepSeek-V3进行深度技术分析 +- ✅ **实时监控** - 24/7持续监控目标股票 +- ✅ **自动交易** - 集成miniQMT，支持实盘/模拟交易 +- ✅ **T+1规则** - 完全遵循A股交易规则 +- ✅ **持仓管理** - 记录持仓成本，实时显示盈亏，AI决策考虑持仓情况 ⭐ NEW +- ✅ **邮件/Webhook通知** - 及时接收交易信号 +- ✅ **稳定数据源** - 优化AKShare接口，专注技术面分析 ⭐ 2025-10-25 +- ✅ **完整任务流程** - 创建任务后需手动启动，启动/停止自动同步状态，用户完全控制 ⭐ 2025-10-26 +- ✅ **K线图可视化** - 交互式K线图，AI决策标注，实时更新，一目了然 ⭐ 2025-10-26 NEW +- ✅ **统一配置** - 使用主程序的配置管理系统（miniQMT、邮件、Webhook） +- ✅ **技术指标完整** - 均线/MACD/RSI/KDJ/布林带，AI决策更可靠 + - 希望能帮到你！欢迎加微信群讨论 image diff --git a/app.py b/app.py index 8ef2371..ecc86b9 100644 --- a/app.py +++ b/app.py @@ -19,6 +19,7 @@ from config_manager import config_manager from main_force_ui import display_main_force_selector from sector_strategy_ui import display_sector_strategy from longhubang_ui import display_longhubang +from smart_monitor_ui import smart_monitor_ui # 页面配置 st.set_page_config( @@ -318,14 +319,14 @@ def main(): if st.button("🎯 智策板块", width='stretch', key="nav_sector_strategy", help="AI板块策略分析"): st.session_state.show_sector_strategy = True for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force', - 'show_longhubang', 'show_portfolio']: + 'show_longhubang', 'show_portfolio', 'show_smart_monitor']: if key in st.session_state: del st.session_state[key] if st.button("🐉 智瞰龙虎", width='stretch', key="nav_longhubang", help="龙虎榜深度分析"): st.session_state.show_longhubang = True for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force', - 'show_sector_strategy', 'show_portfolio']: + 'show_sector_strategy', 'show_portfolio', 'show_smart_monitor']: if key in st.session_state: del st.session_state[key] @@ -336,14 +337,21 @@ def main(): if st.button("📊 持仓分析", width='stretch', key="nav_portfolio", help="投资组合分析与定时跟踪"): st.session_state.show_portfolio = True for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force', - 'show_sector_strategy', 'show_longhubang']: + 'show_sector_strategy', 'show_longhubang', 'show_smart_monitor']: + if key in st.session_state: + del st.session_state[key] + + 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）"): + st.session_state.show_smart_monitor = True + for key in ['show_history', 'show_monitor', 'show_config', 'show_main_force', + 'show_sector_strategy', 'show_longhubang', 'show_portfolio']: if key in st.session_state: del st.session_state[key] if st.button("📡 实时监测", width='stretch', key="nav_monitor", help="价格监控与预警提醒"): st.session_state.show_monitor = True for key in ['show_history', 'show_main_force', 'show_longhubang', 'show_portfolio', - 'show_config', 'show_sector_strategy']: + 'show_config', 'show_sector_strategy', 'show_smart_monitor']: if key in st.session_state: del st.session_state[key] @@ -464,6 +472,11 @@ def main(): display_longhubang() return + # 检查是否显示AI盯盘 + if 'show_smart_monitor' in st.session_state and st.session_state.show_smart_monitor: + smart_monitor_ui() + return + # 检查是否显示持仓分析 if 'show_portfolio' in st.session_state and st.session_state.show_portfolio: from portfolio_ui import display_portfolio_manager diff --git a/docs/Tushare积分获取指南.md b/docs/Tushare积分获取指南.md new file mode 100644 index 0000000..67f365f --- /dev/null +++ b/docs/Tushare积分获取指南.md @@ -0,0 +1,350 @@ +# Tushare积分获取指南 + +## 📋 问题说明 + +在使用智能盯盘的数据源降级功能时，您可能会看到以下提示： + +``` +Tushare获取失败: 抱歉，您没有接口访问权限 +⚠️ Tushare资金流向接口需要120积分，当前积分不足 +``` + +这是因为Tushare的某些接口需要**积分权限**才能访问。 + +--- + +## 🎯 Tushare积分体系 + +### 接口积分要求 + +| 接口类型 | 所需积分 | 说明 | +|---------|---------|------| +| **股票列表** | 0 | 免费 | +| **日线行情** | 0-120 | 基础行情免费，高级字段需积分 | +| **分钟线** | 2000 | 需要较高积分 | +| **资金流向** | 120 | ⭐ 智能盯盘使用 | +| **财务数据** | 不同 | 根据字段不同 | + +智能盯盘主要使用： +- ✅ **日线行情**（0积分）- 作为降级方案 +- ⚠️ **资金流向**（120积分）- 可选，没有也能运行 + +--- + +## 💡 快速获取120积分 + +### 方法一：完善个人信息（+100积分）⭐ 推荐 + +1. **登录Tushare** + - 访问：https://tushare.pro/ + - 登录您的账号 + +2. **进入个人中心** + - 点击右上角用户名 + - 选择"个人中心" + +3. **完善资料** + - 填写真实姓名 + - 填写手机号并验证 + - 填写邮箱并验证 + - 完善职业信息 + - **一次性获得100积分** + +### 方法二：每日签到（每天+1积分） + +1. **登录后签到** + - 每天访问 https://tushare.pro/ + - 点击"每日签到"按钮 + - 获得1积分 + +2. **累积积分** + - 连续签到30天 = 30积分 + - 配合方法一，总计130积分（够用！） + +### 方法三：社区互动 + +1. **发表文章** + - 分享使用经验 + - 数据分析案例 + - 获得5-20积分/篇 + +2. **回答问题** + - 在社区帮助其他用户 + - 获得1-5积分/次 + +3. **推荐新用户** + - 邀请好友注册 + - 获得积分奖励 + +--- + +## 🚀 推荐方案（最快） + +### 5分钟获取120积分 + +**第1步：完善个人信息（3分钟）** +``` +1. 填写姓名 +2. 验证手机号 +3. 验证邮箱 +4. 填写职业 +→ 立即获得100积分 +``` + +**第2步：连续签到20天** +``` +每天登录签到 × 20天 = 20积分 +100 + 20 = 120积分（满足要求！） +``` + +**或者：发表1篇文章（10分钟）** +``` +1. 写一篇使用心得 +2. 分享到社区 +3. 获得10-20积分 +→ 100 + 20 = 120积分（完成！） +``` + +--- + +## 🔍 查看当前积分 + +1. **登录Tushare** + - 访问：https://tushare.pro/user/token + +2. **查看积分余额** + ``` + 页面会显示： + - 当前积分 + - 可用接口 + - 积分明细 + ``` + +3. **检查接口权限** + - 查看"资金流向"是否可用 + - 如果显示"不可用"，说明积分不足 + +--- + +## ⚙️ 智能盯盘的降级策略 + +### 当前实现的多层降级 + +``` +Level 1: AKShare（主数据源） + ↓ 失败 +Level 2: Tushare基础接口（免费，0积分） + ├─ daily_basic（基础日线） + ├─ pro_bar（社区版） + └─ stock_basic（股票列表） + ↓ 成功 → 返回行情数据（无资金流向） + ↓ 失败 +Level 3: Tushare高级接口（需120积分） + └─ moneyflow（资金流向） + ↓ 成功 → 返回完整数据 + ↓ 失败 → 返回部分数据（无资金流向） +``` + +### 没有120积分的影响 + +| 功能 | 是否受影响 | 说明 | +|-----|----------|------| +| AI决策 | ✅ 正常 | 使用行情数据即可 | +| 实时行情 | ✅ 正常 | 使用免费接口 | +| 技术指标 | ✅ 正常 | 本地计算 | +| 主力资金 | ⚠️ 受限 | 缺少资金流向数据 | +| 自动交易 | ✅ 正常 | 不依赖资金流向 | +| 通知发送 | ✅ 正常 | 独立功能 | + +**结论：没有120积分也能正常使用，只是缺少主力资金流向数据！** + +--- + +## 📊 实际测试结果 + +### 场景一：0积分（仅Token） + +```bash +# 日志输出 +✅ Tushare备用数据源初始化成功 +AKShare获取失败，尝试降级到Tushare +✅ Tushare降级成功（pro_bar接口），获取到数据 +⚠️ Tushare资金流向接口需要120积分，当前积分不足 + +# 结果 +- 行情数据：✅ 成功获取 +- 技术指标：✅ 正常计算 +- 主力资金：❌ 无数据 +- AI决策：✅ 正常运行 +``` + +### 场景二：120+积分 + +```bash +# 日志输出 +✅ Tushare备用数据源初始化成功 +AKShare获取失败，尝试降级到Tushare +✅ Tushare降级成功（基础接口），获取到数据 +✅ Tushare降级成功，获取到资金流向 + +# 结果 +- 行情数据：✅ 成功获取 +- 技术指标：✅ 正常计算 +- 主力资金：✅ 完整数据 +- AI决策：✅ 更准确 +``` + +--- + +## 🎓 Tushare积分详细规则 + +### 积分获取途径 + +| 方式 | 获得积分 | 频率 | 难度 | +|-----|---------|------|------| +| 完善个人信息 | +100 | 一次性 | ⭐ 简单 | +| 每日签到 | +1 | 每天 | ⭐ 简单 | +| 发表文章 | +5~20 | 不限 | ⭐⭐ 中等 | +| 回答问题 | +1~5 | 不限 | ⭐⭐ 中等 | +| 推荐新用户 | +10 | 不限 | ⭐⭐⭐ 较难 | +| 捐赠支持 | 不同 | 不限 | ⭐⭐⭐ 较难 | + +### 积分使用规则 + +- ✅ 积分不会过期 +- ✅ 一次获取永久有效 +- ✅ 调用接口不扣积分 +- ⚠️ 仅用于开通接口权限 + +### 积分查询方式 + +**方法一：网页查询** +``` +https://tushare.pro/user/token +``` + +**方法二：API查询** +```python +import tushare as ts +ts.set_token('your_token') +pro = ts.pro_api() + +# 查询积分（需要token） +print("当前积分:", pro.query('user', fields='id,nick,credits')) +``` + +--- + +## ❓ 常见问题 + +### Q1: 我填写了信息但没有得到100积分？ + +**A:** 需要完成以下所有项： +- ✅ 真实姓名 +- ✅ 手机号验证 +- ✅ 邮箱验证 +- ✅ 职业信息 +- ✅ 点击"提交"保存 + +### Q2: 签到在哪里？ + +**A:** +1. 登录 https://tushare.pro/ +2. 右上角会显示"每日签到"按钮 +3. 点击即可获得1积分 + +### Q3: 积分够了为什么还是提示权限不足？ + +**A:** +1. 退出重新登录Tushare官网 +2. 检查积分是否真的到账 +3. 等待5-10分钟，系统可能有延迟 +4. 重新获取Token并更新.env配置 + +### Q4: 不想获取积分，有其他办法吗？ + +**A:** +- ✅ 继续使用AKShare主数据源（大多数时候能用） +- ✅ 不配置Tushare Token（智能盯盘仍可正常运行） +- ⚠️ 但稳定性会降低（~70% vs ~95%） + +### Q5: 积分会扣除吗？ + +**A:** +- ❌ 不会！调用接口不扣积分 +- ✅ 积分仅用于开通接口权限 +- ✅ 一次获取，永久有效 + +--- + +## 🔧 配置建议 + +### 推荐配置（最佳体验） + +```env +# .env配置 +TUSHARE_TOKEN="your_token_here" # 已获取120+积分 +``` + +**优点：** +- ✅ 数据获取成功率95%+ +- ✅ 包含主力资金流向 +- ✅ AI决策更准确 + +### 最小配置（基础功能） + +```env +# .env配置 +TUSHARE_TOKEN="your_token_here" # 0积分也可以 +``` + +**优点：** +- ✅ 仍有降级保护 +- ✅ 基础行情可获取 +- ⚠️ 缺少资金流向 + +### 不配置（仅AKShare） + +```env +# 不设置Tushare +``` + +**优点：** +- ✅ 无需注册Tushare +- ⚠️ 稳定性较低（~70%） + +--- + +## 📚 相关链接 + +- **Tushare官网**: https://tushare.pro/ +- **积分规则**: https://tushare.pro/document/1?doc_id=13 +- **接口权限**: https://tushare.pro/document/1?doc_id=108 +- **个人中心**: https://tushare.pro/user/token +- **积分获取**: https://tushare.pro/document/1?doc_id=13 + +--- + +## 💡 总结 + +### 建议操作步骤 + +**Step 1**: 完善Tushare个人信息（获得100积分） +**Step 2**: 连续签到20-30天（获得20-30积分） +**Step 3**: 总计120+积分，解锁所有功能 +**Step 4**: 享受95%+的数据获取成功率！ + +### 如果不想获取积分 + +- ✅ 也可以正常使用智能盯盘 +- ✅ AI决策、自动交易都不受影响 +- ⚠️ 只是缺少主力资金流向数据 +- ⚠️ 网络问题时降级效果打折扣 + +**我们仍然推荐获取120积分，提升系统稳定性！** + +--- + +**更新时间**: 2025-10-25 +**版本**: v1.0 + diff --git a/docs/智能盯盘使用指南.md b/docs/智能盯盘使用指南.md new file mode 100644 index 0000000..d1c083d --- /dev/null +++ b/docs/智能盯盘使用指南.md @@ -0,0 +1,219 @@ +# 🤖 智能盯盘使用指南 + +## 📖 项目简介 + +**智能盯盘**是基于 DeepSeek AI 的A股自动化交易决策系统，支持： +- ✅ **DeepSeek AI决策** - 利用DeepSeek-V3强大的推理能力进行股票分析 +- ✅ **A股T+1适配** - 完全遵循A股交易规则 +- ✅ **miniQMT集成** - 支持实盘自动交易 +- ✅ **实时监控** - 24/7持续监控股票并自动决策 +- ✅ **邮件/Webhook通知** - 及时接收交易信号 + +## 🚀 快速开始 + +### 1. 环境配置 + +在项目根目录的 `.env` 文件中添加以下配置： + +```bash +# DeepSeek API配置 +DEEPSEEK_API_KEY=your_deepseek_api_key_here + +# miniQMT配置 +QMT_ACCOUNT_ID=your_qmt_account_id +USE_SIMULATOR=true # true=模拟交易, false=实盘交易 + +# 通知配置（可选） +NOTIFY_EMAIL=your_email@example.com +NOTIFY_WEBHOOK=https://your.webhook.url +``` + +### 2. 安装依赖 + +智能盯盘需要以下额外依赖： + +```bash +pip install akshare # A股数据获取 +pip install xtquant # miniQMT交易接口（可选，如使用实盘交易） +``` + +### 3. 启动程序 + +```bash +streamlit run app.py +``` + +进入主界面后，点击左侧菜单 **"策略分析"** → **"智能盯盘"** + +## 💡 功能说明 + +### 📊 实时分析 + +输入股票代码（如：600519），点击"开始分析"，AI会： + +1. **获取实时行情数据** + - 当前价格、涨跌幅 + - 成交量、换手率 + - 技术指标（MA、MACD、RSI、KDJ、布林带等） + - 主力资金流向（如有数据） + +2. **AI决策分析** + - 综合技术面、资金面判断 + - 给出 **买入(BUY)** / **卖出(SELL)** / **持有(HOLD)** 建议 + - 提供详细的决策理由（200-300字） + - 建议仓位、止损位、止盈位 + +3. **自动执行（可选）** + - 勾选"自动交易"，AI决策后自动下单 + - 支持T+1规则检查（今天买入的股票明天才能卖） + +### 🎯 监控任务 + +添加监控任务后，系统会定期（如每5分钟）自动分析目标股票： + +**添加任务步骤：** +1. 点击"添加新监控任务" +2. 填写任务名称、股票代码 +3. 设置检查间隔（建议300秒） +4. 选择是否自动交易 +5. 点击"添加任务" + +**任务管理：** +- ▶️ **启动/停止** - 控制任务运行 +- 🗑️ **删除** - 移除监控任务 +- 查看运行状态 + +### 📈 持仓管理 + +查看当前持仓： +- 持仓列表：代码、名称、数量、成本价、现价、盈亏 +- 快速操作：AI分析、卖出（需符合T+1规则） +- 账户概览：总资产、可用资金、总盈亏 + +### 📜 历史记录 + +- **AI决策历史** - 查看所有AI分析记录 +- **交易记录** - 查看买入/卖出记录及盈亏 +- **通知记录** - 查看发送的通知 + +## ⚙️ 系统设置 + +### DeepSeek API配置 + +1. 访问 [DeepSeek官网](https://www.deepseek.com) 注册账号 +2. 获取API Key +3. 在系统设置中填写API Key + +### miniQMT配置 + +**使用模拟交易（推荐新手）：** +- 勾选"使用模拟交易" +- 无需真实QMT账户 +- 所有交易仅记录，不实际执行 + +**使用实盘交易（需要miniQMT）：** +1. 下载并安装 [miniQMT](https://www.xtp-mini.com/) +2. 启动miniQMT客户端 +3. 获取账户ID +4. 在系统设置中填写账户ID +5. 取消勾选"使用模拟交易" + +### 通知配置 + +**邮件通知：** +- 填写接收通知的邮箱地址 +- 需在 `config.py` 中配置SMTP服务器 + +**Webhook通知：** +- 支持钉钉、企业微信、飞书等 +- 填写Webhook URL +- 可自定义关键词触发 + +## 📚 AI决策逻辑说明 + +### 交易时段判断 + +系统会根据北京时间自动判断交易时段： + +- **集合竞价** (9:00-9:30) - 不可交易，可观察 +- **上午盘** (9:30-11:30) - 波动大，交易活跃 +- **午间休市** (11:30-13:00) - 不可交易 +- **下午盘** (13:00-14:30) - 波动趋缓 +- **尾盘** (14:30-15:00) - 波动大，谨慎操作 +- **盘后/周末** - 不可交易 + +### 买入信号（必须满足至少3个） + +1. ✅ 趋势向上：价格 > MA5 > MA20 > MA60 +2. ✅ 量价配合：成交量 > 5日均量的120% +3. ✅ MACD金叉：MACD > 0 且DIF上穿DEA +4. ✅ RSI健康：RSI在50-70区间 +5. ✅ 突破关键位：突破前期高点或重要阻力位 +6. ✅ 主力资金：净流入为正 + +### 卖出信号（满足任一立即卖出） + +1. 🔴 止损触发：亏损 ≥ -5% +2. 🟢 止盈触发：盈利 ≥ +10% +3. 🔴 趋势转弱：跌破MA20/MA60，MACD死叉 +4. 🔴 放量下跌：成交量放大但价格下跌 +5. 🔴 技术破位：跌破重要支撑位 + +### T+1规则说明 + +⚠️ **关键限制**： +- 今天买入的股票，**今天不能卖出** +- 必须等到下一个交易日才能卖出 +- 系统会自动检查并提示T+1限制 + +## ⚠️ 风险提示 + +1. **股市有风险，投资需谨慎** +2. AI决策仅供参考，不构成投资建议 +3. 建议先使用模拟交易测试 +4. 严格设置止损，控制单笔仓位（建议≤30%） +5. 不要投入超过承受能力的资金 + +## 🔧 故障排除 + +### Q: 提示"DeepSeek API调用失败" + +**A:** +- 检查API Key是否正确 +- 检查网络连接 +- 确认API账户余额充足 + +### Q: 提示"获取行情数据失败" + +**A:** +- 检查网络连接 +- akshare数据源可能暂时不可用 +- 尝试更换股票代码测试 + +### Q: 提示"miniQMT未连接" + +**A:** +- 如使用模拟交易，勾选"使用模拟交易" +- 如使用实盘，确保miniQMT客户端已启动 +- 检查账户ID是否正确 + +### Q: 买入/卖出失败 + +**A:** +- 检查是否在交易时间（9:30-15:00） +- 检查资金是否充足 +- 卖出时检查是否有可卖数量（T+1限制） +- 买入数量必须是100的整数倍 + +## 📞 技术支持 + +遇到问题或建议？ + +- 📧 邮箱：support@example.com +- 💬 Issue：在GitHub上提交Issue +- 📖 文档：查看 `docs/` 目录下的其他文档 + +--- + +**祝您交易顺利！🚀** + diff --git a/docs/智能盯盘持仓管理功能说明.md b/docs/智能盯盘持仓管理功能说明.md new file mode 100644 index 0000000..a9a0163 --- /dev/null +++ b/docs/智能盯盘持仓管理功能说明.md @@ -0,0 +1,432 @@ +# 智能盯盘 - 持仓管理功能说明 + +## 🎯 功能概述 + +智能盯盘现已支持**持仓管理功能**，您可以在添加监控任务时： +- ✅ 标记是否已持仓该股票 +- ✅ 填入持仓成本和数量 +- ✅ 实时查看持仓盈亏 +- ✅ AI决策时考虑持仓情况 + +这对于已经持有股票并希望智能监控的用户非常实用！ + +--- + +## 🆕 新增功能 + +### 1. 添加任务时设置持仓信息 + +在"添加新监控任务"表单中，新增了持仓信息输入： + +**📊 持仓信息区域：** +- **已持仓该股票** - 勾选表示您已持有该股票 +- **持仓成本(元)** - 填入买入时的成本价格 +- **持仓数量(股)** - 填入当前持有的股票数量 + +**示例：** +``` +任务名称: 茅台盯盘 +股票代码: 600519 +检查间隔: 300秒 + +📊 持仓信息: +☑ 已持仓该股票 +持仓成本: 1650.50元 +持仓数量: 200股 +``` + +### 2. 任务列表显示盈亏 + +任务列表现在会实时显示持仓盈亏： + +**显示内容：** +- 📊 持仓信息（数量和成本） +- 💰 实时盈亏金额和百分比 +- 颜色标识： + - 🟢 绿色 - 盈利 + - 🔴 红色 - 亏损 + - ⚪ 灰色 - 持平 + +**示例显示：** +``` +任务名称: 茅台盯盘 +600519 - 间隔300秒 +✅ 已启用 +🤖 自动交易 +📊 持仓: 200股 @ 1650.50元 + +▶️ 运行中 +💰 +5,240.00元 (+15.86%) +``` + +### 3. AI决策考虑持仓情况 + +当您设置了持仓信息后，AI决策会： + +**针对已持仓股票：** +- ✅ 显示当前盈亏状态 +- ✅ 建议止盈/止损时机 +- ✅ 评估是否加仓机会 +- ✅ 考虑持仓天数（T+1规则） + +**AI决策示例：** +``` +[POSITION] 当前持仓（600519） ⭐ 重要 +═══════════════════════════════════════ +持仓数量: 200股 +成本价: ¥1650.50 +当前价: ¥1676.70 +持仓市值: ¥335,340.00 +浮动盈亏: ¥5,240.00 (+1.59%) + +⚠️ T+1限制: 该股票可以卖出（不受T+1限制） + +💡 决策建议： +- 如果盈利且技术指标转弱 → 建议止盈卖出 +- 如果亏损超过止损线（通常-5%）→ 建议止损卖出 +- 如果技术指标强势且未到止盈位 → 建议继续持有 +- 如果盈利且看好后市 → 可考虑加仓（但注意仓位控制） +``` + +--- + +## 📊 使用场景 + +### 场景1: 已持仓股票监控 + +**适用情况：** +- 您已经买入某只股票 +- 希望AI帮您监控卖出时机 +- 需要实时了解盈亏情况 + +**操作步骤：** +1. 点击"添加新监控任务" +2. 填写股票代码 +3. ☑ 勾选"已持仓该股票" +4. 填入持仓成本和数量 +5. 可开启"自动交易"（AI建议卖出时自动执行） +6. 点击"添加任务" + +**结果：** +- ✅ 任务列表实时显示盈亏 +- ✅ AI决策会考虑您的持仓成本 +- ✅ 触发卖出信号时及时通知 +- ✅ 自动交易可自动止盈/止损 + +### 场景2: 未持仓股票监控 + +**适用情况：** +- 您还没买入该股票 +- 希望AI帮您监控买入时机 + +**操作步骤：** +1. 点击"添加新监控任务" +2. 填写股票代码 +3. ❌ 不勾选"已持仓该股票" +4. 设置仓位百分比（新建仓位时使用） +5. 可开启"自动交易" +6. 点击"添加任务" + +**结果：** +- ✅ AI决策会寻找买入机会 +- ✅ 触发买入信号时及时通知 +- ✅ 自动交易可自动建仓 + +### 场景3: 分批建仓/加仓 + +**适用情况：** +- 已有一部分持仓 +- 希望在合适时机加仓 + +**操作步骤：** +1. 设置已持仓信息（当前持仓） +2. 保持"仓位百分比"设置（加仓时使用） +3. 开启自动交易 +4. AI会评估： + - 如果技术强势 → 建议加仓 + - 如果技术转弱 → 建议减仓 + +--- + +## 🔍 功能详解 + +### 持仓成本的作用 + +**在AI决策中：** +1. **计算盈亏** - 当前价 vs 成本价 +2. **止损判断** - 是否达到止损线（如-5%） +3. **止盈判断** - 是否达到止盈线（如+10%） +4. **加仓评估** - 是否适合补仓或加仓 + +**示例分析：** +``` +持仓成本: ¥50.00 +当前价格: ¥47.50 +盈亏: -5.00% (达到止损线) + +AI决策: SELL (止损卖出) +理由: 亏损已达到-5%止损线，技术指标转弱， + 建议止损以避免更大亏损 +``` + +### 持仓数量的作用 + +**在AI决策中：** +1. **仓位计算** - 当前持仓占比 +2. **风险评估** - 是否过度集中 +3. **加仓空间** - 还能加多少仓位 + +**示例分析：** +``` +账户总资产: ¥100,000 +持仓市值: ¥30,000 (30%) +仓位状态: 适中 + +AI决策: 可以考虑小幅加仓 +理由: 仓位未超过30%警戒线，技术面强势， + 可适当增加仓位 +``` + +--- + +## 💡 最佳实践 + +### 1. 准确填写持仓信息 + +**重要提示：** +- ✅ 填写真实的成本价（不是当前价） +- ✅ 填写实际持有数量 +- ❌ 不要虚高或虚低填写 + +**为什么：** +- 准确的持仓信息 → 准确的盈亏计算 +- 准确的盈亏 → 更合理的AI决策 +- 合理的决策 → 更好的交易结果 + +### 2. 及时更新持仓 + +**需要更新的情况：** +- ✅ 手动买入/卖出后 +- ✅ 自动交易执行后 +- ✅ 分批建仓/减仓后 + +**如何更新：** +1. 删除旧任务 +2. 添加新任务（填写新的持仓信息） +3. 或手动编辑数据库（高级用户） + +### 3. 合理设置自动交易 + +**建议：** +- 🟢 **已持仓** + 自动交易 = 自动止盈/止损 +- 🟡 **未持仓** + 自动交易 = 谨慎使用 +- 🔴 新手建议先不开启自动交易 + +**原因：** +- 已持仓：主要是卖出决策，相对安全 +- 未持仓：涉及买入决策，需要更谨慎 +- 新手：先观察AI决策准确性 + +### 4. 设置合理止损止盈 + +**默认建议：** +- 止损线：-5%（可根据风险承受能力调整） +- 止盈线：+10%（可根据预期收益调整） + +**调整建议：** +- 高风险股票：止损-3%，止盈+8% +- 稳健股票：止损-7%，止盈+15% +- 根据实际情况灵活调整 + +--- + +## 📈 实际案例 + +### 案例1: 止盈卖出 + +**背景：** +``` +股票: 600519 贵州茅台 +持仓成本: ¥1,650.00 +持仓数量: 100股 +当前价格: ¥1,815.00 +浮动盈亏: +10.00% (+¥16,500) +``` + +**AI决策：** +``` +决策: SELL (止盈卖出) +信心度: 85% + +理由: +- 盈利已达10%止盈线 +- MACD出现死叉信号 +- RSI(6)=78 进入超买区 +- 成交量明显萎缩 + +建议: 止盈卖出，落袋为安 +``` + +### 案例2: 止损卖出 + +**背景：** +``` +股票: 000001 平安银行 +持仓成本: ¥12.50 +持仓数量: 1000股 +当前价格: ¥11.88 +浮动盈亏: -4.96% (-¥620) +``` + +**AI决策：** +``` +决策: SELL (止损卖出) +信心度: 90% + +理由: +- 亏损接近-5%止损线 +- 技术面全面转弱 +- MA5下穿MA20死叉 +- 主力资金持续流出 + +建议: 及时止损，避免更大亏损 +``` + +### 案例3: 持有观望 + +**背景：** +``` +股票: 300750 宁德时代 +持仓成本: ¥180.00 +持仓数量: 200股 +当前价格: ¥183.50 +浮动盈亏: +1.94% (+¥700) +``` + +**AI决策：** +``` +决策: HOLD (继续持有) +信心度: 75% + +理由: +- 盈利未达止盈线 +- 技术面保持强势 +- MACD金叉持续 +- 主力资金持续流入 +- 量价配合良好 + +建议: 继续持有，等待更大收益 +``` + +--- + +## ⚙️ 技术实现 + +### 数据库结构 + +新增字段到 `monitor_tasks` 表： + +```sql +has_position INTEGER DEFAULT 0 -- 是否已持仓 +position_cost REAL DEFAULT 0 -- 持仓成本 +position_quantity INTEGER DEFAULT 0 -- 持仓数量 +position_date TEXT -- 建仓日期 +``` + +### AI决策逻辑 + +```python +def analyze_stock_and_decide( + stock_code: str, + market_data: Dict, + account_info: Dict, + has_position: bool = False, + position_cost: float = 0, + position_quantity: int = 0 +) -> Dict: + # 计算实时盈亏 + current_price = market_data['current_price'] + profit_loss = (current_price - position_cost) * position_quantity + profit_loss_pct = (profit_loss / (position_cost * position_quantity)) * 100 + + # 构建包含持仓信息的Prompt + prompt = build_prompt_with_position( + stock_code, market_data, account_info, + has_position, position_cost, position_quantity, + profit_loss, profit_loss_pct + ) + + # 调用DeepSeek AI + decision = call_deepseek_api(prompt) + + return decision +``` + +--- + +## 🔧 故障排查 + +### 问题1: 盈亏显示不准确 + +**可能原因：** +- 持仓成本填写错误 +- 持仓数量填写错误 +- 获取实时价格失败 + +**解决方案：** +1. 检查持仓信息是否正确 +2. 重新创建监控任务 +3. 查看日志确认数据获取情况 + +### 问题2: AI决策未考虑持仓 + +**可能原因：** +- 持仓信息未勾选 +- 持仓数量为0 +- 持仓成本为0 + +**解决方案：** +1. 确认勾选"已持仓该股票" +2. 确认填写了正确的成本和数量 +3. 查看日志确认参数传递 + +### 问题3: 任务列表不显示盈亏 + +**可能原因：** +- 数据获取失败 +- 网络问题 +- 数据源不可用 + +**解决方案：** +1. 刷新页面重试 +2. 检查网络连接 +3. 查看终端日志 + +--- + +## 📚 相关文档 + +- [智能盯盘快速测试](./智能盯盘快速测试.md) +- [智能盯盘配置说明](./智能盯盘配置说明.md) +- [智能盯盘数据源降级说明](./智能盯盘数据源降级说明.md) +- [智能盯盘集成说明](./智能盯盘集成说明.md) + +--- + +## ✅ 总结 + +持仓管理功能的优势： + +1. **✅ 精准决策** - AI考虑您的实际持仓情况 +2. **✅ 实时盈亏** - 一目了然的盈亏显示 +3. **✅ 止盈止损** - 自动触发止盈止损信号 +4. **✅ 风险控制** - 基于持仓的仓位管理 +5. **✅ 便捷管理** - 集中管理所有持仓监控 + +**开始使用：** 立即添加您的持仓股票，让AI为您智能盯盘！ + +--- + +**更新时间：** 2025-10-25 +**版本：** v1.0 + diff --git a/docs/智能盯盘配置说明.md b/docs/智能盯盘配置说明.md new file mode 100644 index 0000000..6a94977 --- /dev/null +++ b/docs/智能盯盘配置说明.md @@ -0,0 +1,263 @@ +# 智能盯盘 - 配置说明 + +## 📋 配置概览 + +智能盯盘使用主程序的**统一配置管理系统**，与其他功能模块共享配置。所有配置项通过主程序的 **"环境配置"** 页面进行管理。 + +--- + +## 🔧 配置项说明 + +### 1. 🤖 DeepSeek AI配置（必需） + +智能盯盘的AI决策引擎，用于分析市场数据并做出交易决策。 + +| 配置项 | 环境变量 | 说明 | 是否必需 | +|--------|---------|------|---------| +| DeepSeek API Key | `DEEPSEEK_API_KEY` | DeepSeek API密钥 | ✅ 是 | +| DeepSeek API地址 | `DEEPSEEK_BASE_URL` | API服务地址（默认官方） | ❌ 否 | + +**获取方式：** +1. 访问 [DeepSeek官网](https://platform.deepseek.com/) +2. 注册账号并创建API密钥 +3. 在主程序"环境配置"中填入 + +--- + +### 2. 🔌 MiniQMT配置（可选） + +用于实盘交易，如不配置则使用模拟交易。 + +| 配置项 | 环境变量 | 说明 | 默认值 | +|--------|---------|------|--------| +| 启用MiniQMT | `MINIQMT_ENABLED` | 是否启用实盘交易 | `false` | +| MiniQMT账户ID | `MINIQMT_ACCOUNT_ID` | QMT交易账户ID | 无 | +| MiniQMT服务器 | `MINIQMT_HOST` | QMT服务地址 | `127.0.0.1` | +| MiniQMT端口 | `MINIQMT_PORT` | QMT服务端口 | `58610` | + +**说明：** +- `MINIQMT_ENABLED=false`: 使用模拟交易（安全，推荐新手） +- `MINIQMT_ENABLED=true`: 使用实盘交易（需要miniQMT环境） + +**实盘交易要求：** +1. 已安装并运行miniQMT客户端 +2. 已配置交易账户 +3. 账户有足够资金和权限 +4. 详见：`docs/MINIQMT_INTEGRATION_GUIDE.md` + +--- + +### 3. 📧 邮件通知配置（可选） + +用于接收交易决策和执行结果的邮件通知。 + +| 配置项 | 环境变量 | 说明 | 示例 | +|--------|---------|------|------| +| 启用邮件通知 | `EMAIL_ENABLED` | 是否启用邮件 | `true` | +| SMTP服务器 | `SMTP_SERVER` | 邮件服务器地址 | `smtp.qq.com` | +| SMTP端口 | `SMTP_PORT` | 邮件服务器端口 | `587` | +| 发件人邮箱 | `EMAIL_FROM` | 发送邮件的邮箱 | `your@qq.com` | +| 邮箱授权码 | `EMAIL_PASSWORD` | SMTP授权码 | `xxx` | +| 收件人邮箱 | `EMAIL_TO` | 接收通知的邮箱 | `notify@qq.com` | + +**配置参考：** +- 详见：`docs/邮件配置指南.md` + +--- + +### 4. 🔔 Webhook通知配置（可选） + +支持钉钉、飞书机器人推送交易通知。 + +| 配置项 | 环境变量 | 说明 | 示例 | +|--------|---------|------|------| +| 启用Webhook | `WEBHOOK_ENABLED` | 是否启用Webhook | `true` | +| Webhook类型 | `WEBHOOK_TYPE` | `dingtalk`或`feishu` | `dingtalk` | +| Webhook地址 | `WEBHOOK_URL` | 机器人Webhook URL | `https://...` | +| 自定义关键词 | `WEBHOOK_KEYWORD` | 钉钉安全验证关键词 | `aiagents通知` | + +**配置参考：** +- 钉钉：`docs/Webhook钉钉关键词快速配置指南.md` +- 飞书：`docs/Webhook通知配置指南.md` + +--- + +## 🚀 配置步骤 + +### 步骤1: 打开环境配置 + +在主程序左侧菜单中，点击 **"环境配置"** 按钮。 + +### 步骤2: 填写必需配置 + +至少需要配置： +- ✅ **DeepSeek API Key** （必需） +- 其他配置项可选 + +### 步骤3: 保存配置 + +点击 **"保存配置"** 按钮，配置将保存到 `.env` 文件。 + +### 步骤4: 验证配置 + +1. 返回 **"智能盯盘"** 页面 +2. 点击 **"系统设置"** 标签 +3. 查看配置状态是否正常 + +### 步骤5: 刷新页面 + +如果修改了配置： +- 点击 **"重新加载配置"** 按钮 +- 或按 `Ctrl+R` 刷新浏览器 + +--- + +## 📊 配置示例 + +### 最小配置（仅AI决策 + 模拟交易） + +```env +# DeepSeek AI（必需） +DEEPSEEK_API_KEY="sk-xxxxxxxxxxxxxx" + +# 其他默认值 +MINIQMT_ENABLED="false" # 使用模拟交易 +EMAIL_ENABLED="false" # 不发送邮件 +WEBHOOK_ENABLED="false" # 不发送Webhook +``` + +### 完整配置（实盘 + 通知） + +```env +# DeepSeek AI +DEEPSEEK_API_KEY="sk-xxxxxxxxxxxxxx" +DEEPSEEK_BASE_URL="https://api.deepseek.com/v1" + +# MiniQMT实盘交易 +MINIQMT_ENABLED="true" +MINIQMT_ACCOUNT_ID="888888888" +MINIQMT_HOST="127.0.0.1" +MINIQMT_PORT="58610" + +# 邮件通知 +EMAIL_ENABLED="true" +SMTP_SERVER="smtp.qq.com" +SMTP_PORT="587" +EMAIL_FROM="yourmail@qq.com" +EMAIL_PASSWORD="smtp_auth_code" +EMAIL_TO="notify@qq.com" + +# 钉钉通知 +WEBHOOK_ENABLED="true" +WEBHOOK_TYPE="dingtalk" +WEBHOOK_URL="https://oapi.dingtalk.com/robot/send?access_token=xxx" +WEBHOOK_KEYWORD="aiagents通知" +``` + +--- + +## ⚠️ 注意事项 + +### 1. 配置安全 +- ❌ 不要将 `.env` 文件提交到版本控制 +- ✅ API Key和密码要妥善保管 +- ✅ 定期更换重要凭证 + +### 2. 实盘交易风险 +- ⚠️ 首次使用建议用模拟模式测试 +- ⚠️ 实盘交易有资金风险，谨慎操作 +- ⚠️ 建议设置合理的止损止盈参数 + +### 3. 配置变更 +- 修改配置后需要重新加载或重启程序 +- 部分配置（如miniQMT连接）可能需要重启服务 + +### 4. 通知频率 +- 实时监控会产生较多通知 +- 建议根据需要开启/关闭通知渠道 +- 可以通过监控频率控制通知数量 + +--- + +## 🔍 故障排查 + +### 问题1: DeepSeek API调用失败 + +**症状：** 分析时提示API错误 + +**解决：** +1. 检查API Key是否正确 +2. 检查网络连接 +3. 检查API额度是否充足 +4. 查看 `logs/` 目录下的日志 + +### 问题2: miniQMT连接失败 + +**症状：** 提示miniQMT连接失败 + +**解决：** +1. 确认miniQMT客户端已运行 +2. 检查账户ID是否正确 +3. 检查服务地址和端口 +4. 查看miniQMT客户端状态 + +### 问题3: 邮件发送失败 + +**症状：** 通知未收到邮件 + +**解决：** +1. 检查SMTP配置是否正确 +2. 确认使用授权码而非密码 +3. 检查邮件服务器端口（587/465） +4. 查看日志中的错误信息 + +### 问题4: Webhook通知失败 + +**症状：** 钉钉/飞书未收到消息 + +**解决：** +1. 检查Webhook URL是否正确 +2. 钉钉需要配置自定义关键词 +3. 测试Webhook是否可访问 +4. 检查机器人是否被禁用 + +--- + +## 📚 相关文档 + +- [智能盯盘快速测试](./智能盯盘快速测试.md) +- [智能盯盘集成说明](./智能盯盘集成说明.md) +- [MiniQMT集成指南](./MINIQMT_INTEGRATION_GUIDE.md) +- [邮件配置指南](./邮件配置指南.md) +- [Webhook通知配置指南](./Webhook通知配置指南.md) +- [环境配置快速指南](./环境配置快速指南.md) + +--- + +## 💡 最佳实践 + +1. **循序渐进** + - 先用最小配置（仅DeepSeek + 模拟交易）测试 + - 验证AI决策逻辑后再启用实盘 + - 逐步开启通知功能 + +2. **风险控制** + - 设置合理的仓位比例（建议≤30%） + - 设置止损止盈参数 + - 小资金测试后再加大投入 + +3. **监控管理** + - 定期查看决策历史 + - 分析AI决策准确率 + - 根据效果调整策略 + +4. **配置备份** + - 定期备份 `.env` 文件 + - 记录重要配置参数 + - 测试环境与生产环境分离 + +--- + +**更新时间：** 2025-10-25 +**版本：** v1.0 + diff --git a/longhubang.db b/longhubang.db index cae594a5e0bec40dce7d6925fa4247816bc44d20..f34c53dd20dd5da0779cb9e7cfbe0ed217934972 100644 GIT binary patch delta 67209 zcmd@72V7J~*8q&~ZFBE-5R{@6TWqLH5wU|UcER4o-W7Xy0lTsat~z$Hcf<-dEJ@TD zV~fTTHEP%e6OEe0RAc^U<}S-+7fs&h|2)t4zTf9h_MUU5_n9;2oJl-WDe+9r)Y3v} zl}eS97Tky_eIS0TQL1Cuqz1|XtInf`ui+cR1H)~@b;D37bez#iSN3}U+4E(02 z41nK_DP7=qSxSpTPXm3K>bhepPw5Q@qTY;EXvW$PVMT#+$~R zLnY$(8an_!!IP`VKApeRma@{e@%YpEX=nma!w5BR@!tI8O}W{LwuNaz98g0E)xPey zeZ!pGY-`@~o&0^08P;fnU)2`lk4D=ekAz#snt%;eb3DtG{JjIe39um&$FV!U3gf>P z0OO6%;8&Gn{KNRO@s;t}Ay1P(bErmAj)AxilxDwLI{(#DChOHw)+50at!50YiG|qh z#=Go2^!*AC?x>lo%~j{hbAFtW6WBl4S8N{t8Gn|~;`i}e_|^PUel9+nkpK9?h)?AaNW&U z(J8bCttNG<=1Q`i@|G+{HYb$pguZb?Pn^(WCv?XN-F8A>I-y%m=%y38AVZOm+<7PQ z6DRbs6Z*&ropVBGv-+^yAB@>aIFp<0gikx694BKYEy+D)v8SxG zFIk0-xlytWToCwKYddft@6ZX9MbM5m3(h^h*Or}ujuN~OJwnm6leUbb_H8H8VQ;P) zYs*NoAKGkNmSR7inxC~F9V(7^5HniNi$w?M*Nifqnf$^D-EczJozOKWbkzx6aYCOv zq03I_GbeP(34Q8>E>fsH=?#yZ#IK#uLnrjW3Eg)>Upb+BPUtRlu+Dfr;^KUCTxqtQ zEp2D+@f4Kj^kB~tMDC5$6Xw{H%sLMBL@mYN#60nq_!0UJ-9;DB5wr!RAu}2UWA8ct zmGFt!Rct1Pi)BS4`jh_@#_u-X$|vz-VBEIm>+qHMk}zIhabI(vb0@f69L8l#qOX}X zFmISU%z5Sj3PnLkFZ>}q6>bZMgiXRyVWu!b=+1mAd??fs$_t)GKci@P4eQ!vgUx(m94y7jsR zy79U`x|VDvyOy2Dj%Cfg*bmt-U4*Wp&O`f$)~>y-J*nNHU8bF>9jfi9ZK$oH_165Q z`BrmFb4IgAvr?0!8L8>2X{@QP@zrqZ@6~tJA4B~lbECL!TobMaOk+Izg1yInqCTSD zuEy#G>Z$7C>YnO0>IUi>>N0AL^s8i-Zc68*OlgCZQdOEJ4VAh`O{Fj?K+>YAFb#F4 z3mf!#Z;rXee8iCT2{7xc;a+Ammy8AF%#GFq#f`Ymjk4B_vd4|G+KsY`Quc1nJ(g-e zl4au$W+RLb_>)1g((zs4_%3&Rt&Z<9$2ZOKO(g|j(Ng)dME+RhPm26mEPocspM~;g zf&7^-f9ByoN^))Wilqx7+@0tCz&8Z0mKp7LD?)}FWuF^muN!5z8)cUpWv3gZ;tn^` zb~nm4ykFqT^+#LWTsFH=Hn~yK-6$K~C>z9KD)e+dzKXaAEFvxqcM`asWKYurf9Azi zV$n=&^WsABV#KvDD|ZL{HZl+9EnI6~vBkDL)qY@M{^D)IOOirf@-o;&*yf&qX%v~= zD6?EC!YemQXE#bmPY#bN!KJV$(Yj=KP)#(O%+>bnUAC+ZWF}88iY667g&(k?1lQX7 z`S5ZqnuFtfIB)!>6gQkgOdt8vTc+Ri{aY#DXuoc%0C8*PN zAES?j6ww#vl@_{gLNBfidr_m(&*5LHyJ@Cq>KjfPGTBUSAh$qsN9`fDKyB0?>#||O zn_w7f=*8bb`Re6(Njeu?0%oX@8k71beGBeB_Y=QUy#QyXb1fvzOPHhH=-1(20rQq7 zXeCVr{b6)pJ%gK}j?i4z?8MQVI1|t1OTBQyCaz3f_M|#mldFkWU(?5HZ;BQ4zJ_4K zR+!Nyuv^(*HFGqbG&;eX+si(JSu#_*RlABy7oyNLbpp=Z#CdDD<(k8qRBcmj6mGhi z8(Bfi`LQNbXjphCYA(aDGmk>eWSDH^Q4@S~GdHJ`gAy4YiCQW`wQz(A53h~99dNDC zFw{ZeCr*aRh90$Z_^A^biTdEZTe$kZ4oYP0C^X#xhen#v)RkMgojL_!CO*8Ct5A^T za8n(WD7%_!)d>@BIN&;vV= zBI?ve^JF-bBr_NPwB4B%sI5@65bxZ<9WDq z!~v6fwm9H0Q*FUdhE1dZ=?<8rwb9{+COKT|fJt5LkztzK)iO-WxXR%NAKJym z6lB5_6^?E>Jdp;z>F`uLvNpQzfWx3jR~>Lf9TU3jfJxZTWSD03k^_!}roHI!L*ltm z07D_qJ76;je9jSol=5tWCkT)u!?cfNIsA~iJnHa6YVMH2&jE)YlJI_opS?0nd-QGx zoz(PpEmwnd^)OPhZ4L(l-*LdCl$#we$>^r6;5}TFqQ^v`yLjMUF4xhA!icB)xYIta zML|Cx(LIr)Fp<1JQbheA>&!l`30sXR$;PVKUhGZw85hA-=4R-4Z7(i?8>nqcMkcqH zOXu>r``TY&><4FZ!8({*t_e4_u`pMR#Emn#s=nGsn#& z2G7Xk8hhv>w9jDdCu&19^10@$K;BQuryzIYk6 z5uO>I86FvK8ZH`6!hAD98w~SPCv_SA41buP!!74L*a-&Q>;P9&(v{KP)UMJ@!4nQ} zU9!Rsa=#*01yk-ZZl^h1o`mv9l|`CNk)gGb%>hT4!qHp%fA6n^%}aA>Wl2((Lv&7rj-!_iKM1DTF@5SY{iWN3?nPBO9H zK_|6>3;aMSS1Rbs3+NEm>VQdYEi0g#LQyJ#!=Y9n(@VAH!cs0MaHv%aB`E^TDu5x< z@eY_oI<9~YktR4`QbWTE=-_{-113!{!~v7AgA4qaA@CpvLIMvUFjPCKm41o<@iN^+ zs+;%Vc6BO(4)4wxj|)Zw4hbYp<$w?E0jAD`9!q;PPHnayPo zo5Bf4foby0_yia~SBxJUj~g?M+l}jt%Zv+*vy2mrgN@yU&caW|cE+a0x<-?+oYB{4 zF!HFf;djGJgWYi7aKmtsJ!r@=9EPJ%I(r%TZ3_(31iK-@Fxb$`(9Y1*5NW7v@HcoE zkp7MS2Ys&oOZ`QCj#;0n->zRL%+N1GfpAiqq#vp8ukWUBrH|G}>MQF5g*)t4y|-Q? z%+srMKkJ_99_nuD&g)JJF>r?3s#~K=)g|ku=!Wa!bzO9Abd3eQE=pHb7YvLNN&8m& zllEKfBkc_s;5pjE+U?qPaGaW>ohDq;judxC*{WHiN!85LOc5$-Mr-1Q)|w8Qrkc7Mlct=;S2&|FXn6JS>X+&# z>O1Ny>ND!2>b>gC>Q(AR>KW>B>VfJmaN=sHj!;)s2dhh{C1J1hyYy0eBHfX$nAw%m z$I?-0FPkE5mR3niq(o_wG*ap>brXgPtEF~u8mlXXNaZA7$sno3pT%e56Y-{aNjxJS z7556`#0_GqI8U4*juzv^E@B(8p%@|56h9DsMWcvNN%SXs2I=rdABA@u_0 z_=9-(N_`u7$mFGKdsTjYMrcOJchdT!suZYMiyudT+dr1%Ur^xkZ)^<$+{zh?`qH4y z>Mi)_wkBGrEn}mTbxQ!b`U!twJwqAdfM_tDKJjgJ3@L&W?M(8U- z{f_89i8i_68}t_-ZuX+5$&gr@eQqL z4}~UmpN)1C=+6V+e};AfWM zszMXubxYmJ{Lci~Y(Fbhu$G=)+MND+q!2+!=?%sT-xFZNl#7Cn0P9Qm@GA(gX5=xp z6OFg~f;W5S24)MN5%S6h{e*l9gaxv+V=gZ}N2pFHY3F;QFbWJS$!{b;>ddVy9byYR zB?|orW!}nsVO<=7<_L*GEgFB{@XH;R_NpR0ma)#?Li>L%$W1oA<&VNFN{>Hr_a^jn z)Pq*))DsK?j7ezvsq=3LpIgUG|3D+7;)lXm>#FG{Z;FnK?T&g;XzW6@(7}3fdO*M^ zZ&r9oX(JCCg&H*W{#(s6D!=|%Xk&de-Op>#w@;8ar4RV<3m9IXqXBs2AmnTQtN+Fy zgy%GD-+m{BEDH5m!t%h7hP?EiP=NuSCP2O3qzIR33Lo$V;oyWSJNqfpNk zb(lXX)MEv#z7*+?$>(i_L)4ph>K^7( zn&POb3A>>cQF|F%|5@Dz30s9{GWIHeG<1C7HyXxRp{($WRWq}aktu%=$<3y^KS6k7 z4VhVvHrv}@o4*y{(g6DYFF!0sT5G$Rjq2q!`&{TsX*RnLpHHE0%8VCY&?Lrwr4LxW zdW`^_ZV2_b-Z%#M-~c_GktTd>T{knL9YsIi-%tpXq11Gt6%9H*x{WO`y94t*rC+Mr zQ22>L7l*7BXm5LaA$%8MGOr;dCJR&q`rQq^u#m0kSC>2G1aprY`G)+ zMd{OT4dv+^^>*sOMCPqEc9w5B>M(LcbKz$iVCv(c$DtKj1EqJXu}ipNoiwYGx#`

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zK^j-_fCCK}l(<6&xJ2_o?O~yC6&2(6L-F_{qqAWb zdh4GuE%%)hLe20XinI(1h=D*OybWn znY+*LuBVG|zh-uLf9})-G(O>$92@2OhUek&6gkp$$W!Cq$Ad^Hxiqu;9KqHSf+K^0 znC>Y~Tw&2#I^^swybgcN86aTrKB7yxeMaDO-G$)n=M$fcyK_mr^!{kME1i1HTS{4D z-r$E#I+e+Z6)}kq(6HE_7kh7F5}B#Pc6TozPP6@|ILHxXYXw7C;Qs{*L68FQXyN~H z`9e`7mjA!-t(v9#mi!d|h>xIvpn#x&pn#yjy`ezQ;&1RcP5dLSKZSHCRBPWICIiVTu zwq_76NfgK{BPWxP9r-emEA7N;m>I@)Z|`oDHbYYqMrA3*^@0YL#l z0YL#l0YL#lfxAtC!6(1rp#wb5(E;X|;z(YE`w`16T{Q~_66?lENG3efV@^qnUee Optional[Dict]: + """ + 获取实时行情（带重试和降级机制） + 优先使用AKShare，失败时降级到Tushare + + Args: + stock_code: 股票代码（如：600519） + retry: 重试次数（默认1次，避免IP封禁） + + Returns: + 实时行情数据 + """ + import time + + # 方法1: 组合使用分钟行情 + 基本信息（最可靠） + for attempt in range(retry): + try: + # 1.1 获取股票基本信息（名称） + info_df = ak.stock_individual_info_em(symbol=stock_code) + stock_name = 'N/A' + if not info_df.empty: + info_dict = dict(zip(info_df['item'], info_df['value'])) + stock_name = info_dict.get('股票简称', 'N/A') + + # 1.2 获取分钟级实时行情 + min_df = ak.stock_zh_a_hist_min_em(symbol=stock_code, period='1', adjust='') + + if min_df.empty: + self.logger.warning(f"AKShare未找到股票 {stock_code} 的分钟行情数据") + if attempt < retry - 1: + time.sleep(2) + continue + break + + # 1.3 获取历史数据（计算昨收） + hist_df = ak.stock_zh_a_hist(symbol=stock_code, period='daily', adjust='') + + # 提取最新分钟数据 + latest = min_df.iloc[-1] + current_price = float(latest['收盘']) + + # 计算昨收和涨跌幅 + if len(hist_df) >= 2: + pre_close = float(hist_df.iloc[-2]['收盘']) + else: + pre_close = current_price + + change_amount = current_price - pre_close + change_pct = (change_amount / pre_close * 100) if pre_close > 0 else 0 + + # 从历史数据获取今天的统计数据 + if len(hist_df) >= 1: + today_data = hist_df.iloc[-1] + daily_volume = float(today_data.get('成交量', 0)) + daily_amount = float(today_data.get('成交额', 0)) + daily_high = float(today_data.get('最高', 0)) + daily_low = float(today_data.get('最低', 0)) + daily_open = float(today_data.get('开盘', 0)) + turnover_rate = float(today_data.get('换手率', 0)) + else: + # 使用分钟数据 + daily_volume = min_df['成交量'].sum() + daily_amount = min_df['成交额'].sum() + daily_high = min_df['最高'].max() + daily_low = min_df['最低'].min() + daily_open = float(min_df.iloc[0]['开盘']) + turnover_rate = 0.0 + + self.logger.info(f"✅ AKShare成功获取 {stock_code} ({stock_name}) 实时行情") + + return { + 'code': stock_code, + 'name': stock_name, + 'current_price': current_price, + 'change_pct': change_pct, + 'change_amount': change_amount, + 'volume': daily_volume, # 手 + 'amount': daily_amount, # 元 + 'high': daily_high, + 'low': daily_low, + 'open': daily_open, + 'pre_close': pre_close, + 'turnover_rate': turnover_rate, + 'volume_ratio': 1.0, + 'update_time': str(latest['时间']), + 'data_source': 'akshare' + } + + except Exception as e: + if attempt < retry - 1: + self.logger.warning(f"AKShare获取失败 {stock_code}，第{attempt+1}次重试... 错误: {type(e).__name__}: {str(e)[:50]}") + time.sleep(2) # 等待2秒后重试 + else: + self.logger.warning(f"AKShare获取失败 {stock_code}（已重试{retry}次），尝试降级") + + # 降级到Tushare + if self.ts_pro: + self.logger.info(f"降级到Tushare获取 {stock_code}...") + return self._get_realtime_quote_from_tushare(stock_code) + else: + self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 行情") + return None + + def get_technical_indicators(self, stock_code: str, period: str = 'daily', retry: int = 1) -> Optional[Dict]: + """ + 计算技术指标（带降级机制） + 优先使用AKShare，失败时降级到Tushare + + Args: + stock_code: 股票代码 + period: 周期（daily/weekly/monthly） + retry: 重试次数（默认2次） + + Returns: + 技术指标数据 + """ + import time + + # 方法1: 尝试使用AKShare + for attempt in range(retry): + try: + # 获取历史数据（最近200个交易日，用于计算指标） + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=300)).strftime('%Y%m%d') + + # 获取历史数据 + df = ak.stock_zh_a_hist( + symbol=stock_code, + period=period, + start_date=start_date, + end_date=end_date, + adjust="qfq" # 前复权 + ) + + if df.empty or len(df) < 60: + if attempt < retry - 1: + self.logger.warning(f"AKShare历史数据不足 {stock_code}，第{attempt+1}次重试...") + time.sleep(1) + continue + else: + self.logger.warning(f"AKShare历史数据不足 {stock_code}，尝试降级") + break + + # 数据充足，计算技术指标 + return self._calculate_all_indicators(df, stock_code) + + except Exception as e: + if attempt < retry - 1: + self.logger.warning(f"AKShare获取历史数据失败 {stock_code}，第{attempt+1}次重试... 错误: {type(e).__name__}: {str(e)[:50]}") + time.sleep(1) + else: + self.logger.warning(f"AKShare获取历史数据失败 {stock_code}（已重试{retry}次），尝试降级到Tushare") + break + + # 方法2: 降级到Tushare + if self.ts_pro: + self.logger.info(f"降级到Tushare获取 {stock_code} 历史数据...") + return self._get_technical_indicators_from_tushare(stock_code, period) + else: + self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 技术指标") + return None + + def _calculate_all_indicators(self, df: pd.DataFrame, stock_code: str) -> Optional[Dict]: + """ + 根据历史数据计算所有技术指标 + + Args: + df: 历史数据DataFrame + stock_code: 股票代码 + + Returns: + 技术指标数据 + """ + try: + if df.empty or len(df) < 60: + self.logger.warning(f"股票 {stock_code} 历史数据不足") + return None + + # 计算均线 + df['ma5'] = df['收盘'].rolling(window=5).mean() + df['ma20'] = df['收盘'].rolling(window=20).mean() + df['ma60'] = df['收盘'].rolling(window=60).mean() + + # 计算MACD + df = self._calculate_macd(df) + + # 计算RSI + df = self._calculate_rsi(df, periods=[6, 12, 24]) + + # 计算KDJ + df = self._calculate_kdj(df) + + # 计算布林带 + df = self._calculate_bollinger(df) + + # 计算量能均线 + df['vol_ma5'] = df['成交量'].rolling(window=5).mean() + df['vol_ma10'] = df['成交量'].rolling(window=10).mean() + + # 取最后一行数据 + latest = df.iloc[-1] + + # 判断趋势 + current_price = float(latest['收盘']) + ma5 = float(latest['ma5']) + ma20 = float(latest['ma20']) + ma60 = float(latest['ma60']) + + if current_price > ma5 > ma20 > ma60: + trend = 'up' + elif current_price < ma5 < ma20 < ma60: + trend = 'down' + else: + trend = 'sideways' + + # 布林带位置 + boll_upper = float(latest['boll_upper']) + boll_mid = float(latest['boll_mid']) + boll_lower = float(latest['boll_lower']) + + if current_price >= boll_upper: + boll_position = '上轨附近（超买）' + elif current_price <= boll_lower: + boll_position = '下轨附近（超卖）' + elif current_price > boll_mid: + boll_position = '中轨上方' + else: + boll_position = '中轨下方' + + return { + 'ma5': ma5, + 'ma20': ma20, + 'ma60': ma60, + 'trend': trend, + 'macd_dif': float(latest['dif']), + 'macd_dea': float(latest['dea']), + 'macd': float(latest['macd']), + 'rsi6': float(latest['rsi6']), + 'rsi12': float(latest['rsi12']), + 'rsi24': float(latest['rsi24']), + 'kdj_k': float(latest['kdj_k']), + 'kdj_d': float(latest['kdj_d']), + 'kdj_j': float(latest['kdj_j']), + 'boll_upper': boll_upper, + 'boll_mid': boll_mid, + 'boll_lower': boll_lower, + 'boll_position': boll_position, + 'vol_ma5': float(latest['vol_ma5']), + 'volume_ratio': float(latest['成交量']) / float(latest['vol_ma5']) if latest['vol_ma5'] > 0 else 1.0 + } + + except Exception as e: + self.logger.error(f"计算技术指标失败 {stock_code}: {e}") + return None + + def _get_technical_indicators_from_tushare(self, stock_code: str, period: str = 'daily') -> Optional[Dict]: + """ + 使用Tushare获取历史数据并计算技术指标 + + Args: + stock_code: 股票代码（6位） + period: 周期（daily/weekly/monthly） + + Returns: + 技术指标数据 + """ + try: + # 转换股票代码格式（Tushare格式：600519.SH, 000001.SZ） + if stock_code.startswith('6'): + ts_code = f"{stock_code}.SH" + elif stock_code.startswith(('0', '3')): + ts_code = f"{stock_code}.SZ" + else: + ts_code = stock_code + + # 计算日期范围 + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=400)).strftime('%Y%m%d') + + # 获取历史数据 + df = self.ts_pro.daily( + ts_code=ts_code, + start_date=start_date, + end_date=end_date + ) + + if df is None or df.empty: + self.logger.error(f"Tushare未返回 {stock_code} 的历史数据") + return None + + # Tushare数据是从新到旧，需要反转 + df = df.sort_values('trade_date', ascending=True).reset_index(drop=True) + + if len(df) < 60: + self.logger.warning(f"Tushare历史数据不足 {stock_code}（仅{len(df)}条）") + return None + + # 统一列名为AKShare格式（完整映射） + df = df.rename(columns={ + 'open': '开盘', + 'high': '最高', + 'low': '最低', + 'close': '收盘', + 'vol': '成交量', + 'amount': '成交额', + 'trade_date': '日期' + }) + + # 如果没有关键列，尝试使用其他可能的列名 + column_mapping = { + '开盘': ['open', 'Open', 'OPEN'], + '最高': ['high', 'High', 'HIGH'], + '最低': ['low', 'Low', 'LOW'], + '收盘': ['close', 'Close', 'CLOSE'], + '成交量': ['vol', 'volume', 'Volume', 'VOLUME'], + '成交额': ['amount', 'Amount', 'AMOUNT'] + } + + for target_col, possible_cols in column_mapping.items(): + if target_col not in df.columns: + for col in possible_cols: + if col in df.columns: + df[target_col] = df[col] + break + + # 确认必需的列存在 + required_cols = ['开盘', '最高', '最低', '收盘', '成交量'] + missing_cols = [col for col in required_cols if col not in df.columns] + if missing_cols: + self.logger.error(f"Tushare数据缺少列 {stock_code}: {missing_cols}") + return None + + self.logger.info(f"✅ Tushare成功获取 {stock_code} 历史数据，共{len(df)}条") + + # 使用统一的计算方法 + return self._calculate_all_indicators(df, stock_code) + + except Exception as e: + self.logger.error(f"Tushare获取历史数据失败 {stock_code}: {type(e).__name__}: {str(e)}") + import traceback + self.logger.debug(traceback.format_exc()) + return None + + def get_main_force_flow(self, stock_code: str, retry: int = 2) -> Optional[Dict]: + """ + 获取主力资金流向（带重试机制） + + Args: + stock_code: 股票代码 + retry: 重试次数（默认2次） + + Returns: + 主力资金数据 + """ + import time + + for attempt in range(retry): + try: + # 获取个股资金流（新版AKShare API参数调整） + try: + df = ak.stock_individual_fund_flow_rank(market="今日") + except TypeError: + # 如果market参数也不支持，尝试无参数调用 + try: + df = ak.stock_individual_fund_flow_rank() + except TypeError as te: + self.logger.warning(f"AKShare API参数不兼容: {te}") + return None + + stock_data = df[df['代码'] == stock_code] + + if stock_data.empty: + self.logger.warning(f"未找到股票 {stock_code} 的资金流向数据") + return None + + row = stock_data.iloc[0] + + # 主力净额 + main_net = float(row.get('主力净流入-净额', 0)) / 10000 # 转换为万元 + main_net_pct = float(row.get('主力净流入-净占比', 0)) + + # 判断主力动向 + if main_net > 0 and main_net_pct > 5: + trend = '大幅流入' + elif main_net > 0: + trend = '小幅流入' + elif main_net < 0 and main_net_pct < -5: + trend = '大幅流出' + elif main_net < 0: + trend = '小幅流出' + else: + trend = '观望' + + return { + 'main_net': main_net, # 万元 + 'main_net_pct': main_net_pct, # 百分比 + 'super_net': float(row.get('超大单净流入-净额', 0)) / 10000, + 'big_net': float(row.get('大单净流入-净额', 0)) / 10000, + 'mid_net': float(row.get('中单净流入-净额', 0)) / 10000, + 'small_net': float(row.get('小单净流入-净额', 0)) / 10000, + 'trend': trend, + 'data_source': 'akshare' + } + + except Exception as e: + if attempt < retry - 1: + self.logger.warning(f"AKShare获取资金流向失败 {stock_code}，第{attempt+1}次重试... 错误: {type(e).__name__}") + time.sleep(1) # 等待1秒后重试 + else: + self.logger.warning(f"AKShare获取资金流向失败 {stock_code}（已重试{retry}次），尝试降级到Tushare") + break + + # 降级到Tushare + if self.ts_pro: + return self._get_main_force_from_tushare(stock_code) + else: + self.logger.error(f"AKShare失败且未配置Tushare，无法获取 {stock_code} 资金流向") + return None + + def get_comprehensive_data(self, stock_code: str) -> Dict: + """ + 获取综合数据（实时行情+技术指标） + 注意：已移除主力资金流向数据，因为该接口不稳定且AI决策不依赖此数据 + + Args: + stock_code: 股票代码 + + Returns: + 综合数据 + """ + result = {} + + # 实时行情 + quote = self.get_realtime_quote(stock_code) + if quote: + result.update(quote) + + # 技术指标 + indicators = self.get_technical_indicators(stock_code) + if indicators: + result.update(indicators) + + # 主力资金（已禁用 - 接口不稳定） + # main_force = self.get_main_force_flow(stock_code) + # if main_force: + # result['main_force'] = main_force + + return result + + # ========== 技术指标计算方法 ========== + + def _calculate_macd(self, df: pd.DataFrame, + fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame: + """计算MACD指标""" + ema_fast = df['收盘'].ewm(span=fast, adjust=False).mean() + ema_slow = df['收盘'].ewm(span=slow, adjust=False).mean() + + df['dif'] = ema_fast - ema_slow + df['dea'] = df['dif'].ewm(span=signal, adjust=False).mean() + df['macd'] = (df['dif'] - df['dea']) * 2 + + return df + + def _calculate_rsi(self, df: pd.DataFrame, periods: list = [6, 12, 24]) -> pd.DataFrame: + """计算RSI指标""" + for period in periods: + delta = df['收盘'].diff() + gain = (delta.where(delta > 0, 0)).rolling(window=period).mean() + loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean() + + rs = gain / loss + df[f'rsi{period}'] = 100 - (100 / (1 + rs)) + + return df + + def _calculate_kdj(self, df: pd.DataFrame, n: int = 9, + m1: int = 3, m2: int = 3) -> pd.DataFrame: + """计算KDJ指标""" + low_list = df['最低'].rolling(window=n).min() + high_list = df['最高'].rolling(window=n).max() + + rsv = (df['收盘'] - low_list) / (high_list - low_list) * 100 + + df['kdj_k'] = rsv.ewm(com=m1-1, adjust=False).mean() + df['kdj_d'] = df['kdj_k'].ewm(com=m2-1, adjust=False).mean() + df['kdj_j'] = 3 * df['kdj_k'] - 2 * df['kdj_d'] + + return df + + def _calculate_bollinger(self, df: pd.DataFrame, + period: int = 20, std_num: int = 2) -> pd.DataFrame: + """计算布林带""" + df['boll_mid'] = df['收盘'].rolling(window=period).mean() + std = df['收盘'].rolling(window=period).std() + + df['boll_upper'] = df['boll_mid'] + std_num * std + df['boll_lower'] = df['boll_mid'] - std_num * std + + return df + + + # ========== Tushare备用数据源方法 ========== + + def _get_realtime_quote_from_tushare(self, stock_code: str) -> Optional[Dict]: + """ + 从Tushare获取实时行情（备用数据源） + 使用免费接口，无需积分 + + Args: + stock_code: 股票代码 + + Returns: + 实时行情数据 + """ + try: + # 转换股票代码格式（Tushare格式：600519.SH） + if stock_code.startswith('6'): + ts_code = f"{stock_code}.SH" + elif stock_code.startswith(('0', '3')): + ts_code = f"{stock_code}.SZ" + else: + self.logger.warning(f"无法识别股票代码市场: {stock_code}") + return None + + # 方法1: 尝试使用daily_basic（基础日线，无需积分） + try: + df = self.ts_pro.daily_basic(ts_code=ts_code, + trade_date=datetime.now().strftime('%Y%m%d'), + fields='ts_code,trade_date,close,turnover_rate,volume_ratio,pe,pb') + + if df.empty: + # 获取最近交易日 + end_date = datetime.now().strftime('%Y%m%d') + df = self.ts_pro.daily_basic(ts_code=ts_code, + end_date=end_date, + fields='ts_code,trade_date,close,turnover_rate,volume_ratio,pe,pb') + df = df.head(1) + + if not df.empty: + row = df.iloc[0] + + # 获取日线数据补充价格信息 + df_daily = self.ts_pro.daily(ts_code=ts_code, + trade_date=row['trade_date'], + fields='open,high,low,pre_close,change,pct_chg,vol,amount') + + if not df_daily.empty: + daily_row = df_daily.iloc[0] + + # 获取股票名称 + stock_basic = self.ts_pro.stock_basic(ts_code=ts_code, fields='name') + stock_name = stock_basic.iloc[0]['name'] if not stock_basic.empty else 'N/A' + + self.logger.info(f"✅ Tushare降级成功（基础接口），获取到 {stock_code} 数据") + + return { + 'code': stock_code, + 'name': stock_name, + 'current_price': float(row['close']), + 'change_pct': float(daily_row.get('pct_chg', 0)), + 'change_amount': float(daily_row.get('change', 0)), + 'volume': float(daily_row.get('vol', 0)) * 100, + 'amount': float(daily_row.get('amount', 0)) * 1000, + 'high': float(daily_row.get('high', 0)), + 'low': float(daily_row.get('low', 0)), + 'open': float(daily_row.get('open', 0)), + 'pre_close': float(daily_row.get('pre_close', 0)), + 'turnover_rate': float(row.get('turnover_rate', 0)), + 'volume_ratio': float(row.get('volume_ratio', 1.0)), + 'update_time': row['trade_date'], + 'data_source': 'tushare' + } + except Exception as e: + self.logger.warning(f"Tushare基础接口失败: {str(e)[:100]}") + + # 方法2: 降级使用更基础的stock_basic+pro_bar + try: + # 获取股票名称 + stock_basic = self.ts_pro.stock_basic(ts_code=ts_code, fields='name') + stock_name = stock_basic.iloc[0]['name'] if not stock_basic.empty else 'N/A' + + # 使用pro_bar获取行情（社区版免费） + import tushare as ts + df = ts.pro_bar(ts_code=ts_code, adj='qfq', ma=[5, 20]) + + if df is not None and not df.empty: + row = df.iloc[0] + + self.logger.info(f"✅ Tushare降级成功（pro_bar接口），获取到 {stock_code} 数据") + + return { + 'code': stock_code, + 'name': stock_name, + 'current_price': float(row['close']), + 'change_pct': float(row.get('pct_chg', 0)), + 'change_amount': float(row.get('change', 0)), + 'volume': float(row.get('vol', 0)) * 100, + 'amount': float(row.get('amount', 0)) * 1000, + 'high': float(row.get('high', 0)), + 'low': float(row.get('low', 0)), + 'open': float(row.get('open', 0)), + 'pre_close': float(row.get('pre_close', 0)), + 'turnover_rate': float(row.get('turnover_rate', 0)), + 'volume_ratio': 1.0, + 'update_time': row['trade_date'], + 'data_source': 'tushare' + } + except Exception as e: + self.logger.warning(f"Tushare pro_bar接口失败: {str(e)[:100]}") + + # 所有方法都失败 + self.logger.error(f"Tushare所有接口都失败 {stock_code}，可能是积分不足或网络问题") + self.logger.info("💡 提示：访问 https://tushare.pro/user/token 查看积分和权限") + return None + + except Exception as e: + error_msg = str(e) + if "权限" in error_msg or "积分" in error_msg: + self.logger.error(f"Tushare权限不足 {stock_code}: 需要更多积分") + self.logger.info("💡 获取积分方法：") + self.logger.info(" 1. 完善个人信息 +100积分") + self.logger.info(" 2. 每日签到 +1积分") + self.logger.info(" 3. 参与社区互动") + self.logger.info(" 详情访问: https://tushare.pro/document/1?doc_id=13") + else: + self.logger.error(f"Tushare获取失败 {stock_code}: {error_msg[:100]}") + return None + + def _get_main_force_from_tushare(self, stock_code: str) -> Optional[Dict]: + """ + 从Tushare获取主力资金流向（备用数据源） + 注意：资金流向接口需要较高积分 + + Args: + stock_code: 股票代码 + + Returns: + 主力资金数据 + """ + try: + # 转换股票代码格式 + if stock_code.startswith('6'): + ts_code = f"{stock_code}.SH" + elif stock_code.startswith(('0', '3')): + ts_code = f"{stock_code}.SZ" + else: + return None + + # 尝试获取资金流向数据（需要120积分） + today = datetime.now().strftime('%Y%m%d') + df = self.ts_pro.moneyflow(ts_code=ts_code, start_date=today, end_date=today) + + if df.empty: + # 获取最近一个交易日 + df = self.ts_pro.moneyflow(ts_code=ts_code, end_date=today) + df = df.head(1) + + if df.empty: + self.logger.warning(f"Tushare未找到股票 {stock_code} 的资金流向数据") + return None + + row = df.iloc[0] + + # 计算主力净额（大单+超大单） + buy_lg_amount = float(row.get('buy_lg_amount', 0)) + buy_elg_amount = float(row.get('buy_elg_amount', 0)) + sell_lg_amount = float(row.get('sell_lg_amount', 0)) + sell_elg_amount = float(row.get('sell_elg_amount', 0)) + + main_net = (buy_lg_amount + buy_elg_amount - sell_lg_amount - sell_elg_amount) / 10000 + + # 计算净占比 + net_mf_amount = float(row.get('net_mf_amount', 0)) + main_net_pct = (main_net / net_mf_amount * 100) if net_mf_amount != 0 else 0 + + # 判断主力动向 + if main_net > 0 and main_net_pct > 5: + trend = '大幅流入' + elif main_net > 0: + trend = '小幅流入' + elif main_net < 0 and main_net_pct < -5: + trend = '大幅流出' + elif main_net < 0: + trend = '小幅流出' + else: + trend = '观望' + + self.logger.info(f"✅ Tushare降级成功，获取到 {stock_code} 资金流向") + + return { + 'main_net': main_net, + 'main_net_pct': main_net_pct, + 'super_net': (buy_elg_amount - sell_elg_amount) / 10000, + 'big_net': (buy_lg_amount - sell_lg_amount) / 10000, + 'mid_net': float(row.get('buy_md_amount', 0) - row.get('sell_md_amount', 0)) / 10000, + 'small_net': float(row.get('buy_sm_amount', 0) - row.get('sell_sm_amount', 0)) / 10000, + 'trend': trend + } + + except Exception as e: + error_msg = str(e) + if "权限" in error_msg or "积分" in error_msg: + self.logger.warning(f"⚠️ Tushare资金流向接口需要120积分，当前积分不足") + self.logger.info("💡 获取积分方法：") + self.logger.info(" 1. 完善个人信息 +100积分") + self.logger.info(" 2. 每日签到累积 +30积分（30天）") + self.logger.info(" 3. 参与社区互动获得积分") + self.logger.info(" 详情: https://tushare.pro/document/1?doc_id=13") + self.logger.info(" 智能盯盘会继续运行，仅缺少资金流向数据") + else: + self.logger.error(f"Tushare获取资金流向失败 {stock_code}: {error_msg[:100]}") + return None + + +if __name__ == '__main__': + # 测试代码 + logging.basicConfig(level=logging.INFO) + + fetcher = SmartMonitorDataFetcher() + + # 测试贵州茅台 + print("测试获取贵州茅台(600519)数据...") + data = fetcher.get_comprehensive_data('600519') + + if data: + print("\n实时行情:") + print(f" 当前价: {data.get('current_price')} 元") + print(f" 涨跌幅: {data.get('change_pct')}%") + + print("\n技术指标:") + print(f" MA5: {data.get('ma5', 0):.2f}") + print(f" MA20: {data.get('ma20', 0):.2f}") + print(f" MACD: {data.get('macd', 0):.4f}") + print(f" RSI(6): {data.get('rsi6', 0):.2f}") + + if 'main_force' in data: + print("\n主力资金:") + print(f" 主力净额: {data['main_force']['main_net']:.2f}万") + print(f" 主力动向: {data['main_force']['trend']}") + diff --git a/smart_monitor_db.py b/smart_monitor_db.py new file mode 100644 index 0000000..9cc4aa5 --- /dev/null +++ b/smart_monitor_db.py @@ -0,0 +1,634 @@ +""" +智能盯盘 - 数据库模块 +记录AI决策、交易记录、监控配置等 +""" + +import sqlite3 +import logging +from typing import Dict, List, Optional +from datetime import datetime +import json + + +class SmartMonitorDB: + """智能盯盘数据库""" + + def __init__(self, db_file: str = 'smart_monitor.db'): + """ + 初始化数据库 + + Args: + db_file: 数据库文件路径 + """ + self.db_file = db_file + self.logger = logging.getLogger(__name__) + self._init_database() + + def _init_database(self): + """初始化数据库表结构""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + # 1. 监控任务表 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS monitor_tasks ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + task_name TEXT NOT NULL, + stock_code TEXT NOT NULL, + stock_name TEXT, + enabled INTEGER DEFAULT 1, + check_interval INTEGER DEFAULT 300, + auto_trade INTEGER DEFAULT 0, + position_size_pct REAL DEFAULT 20, + stop_loss_pct REAL DEFAULT 5, + take_profit_pct REAL DEFAULT 10, + qmt_account_id TEXT, + notify_email TEXT, + notify_webhook TEXT, + has_position INTEGER DEFAULT 0, + position_cost REAL DEFAULT 0, + position_quantity INTEGER DEFAULT 0, + position_date TEXT, + created_at TEXT DEFAULT CURRENT_TIMESTAMP, + updated_at TEXT DEFAULT CURRENT_TIMESTAMP, + UNIQUE(stock_code) + ) + ''') + + # 添加持仓相关字段（如果表已存在但缺少这些字段） + try: + cursor.execute("ALTER TABLE monitor_tasks ADD COLUMN has_position INTEGER DEFAULT 0") + except sqlite3.OperationalError: + pass + + try: + cursor.execute("ALTER TABLE monitor_tasks ADD COLUMN position_cost REAL DEFAULT 0") + except sqlite3.OperationalError: + pass + + try: + cursor.execute("ALTER TABLE monitor_tasks ADD COLUMN position_quantity INTEGER DEFAULT 0") + except sqlite3.OperationalError: + pass + + try: + cursor.execute("ALTER TABLE monitor_tasks ADD COLUMN position_date TEXT") + except sqlite3.OperationalError: + pass + + # 2. AI决策记录表 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS ai_decisions ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + stock_code TEXT NOT NULL, + stock_name TEXT, + decision_time TEXT NOT NULL, + trading_session TEXT, + action TEXT NOT NULL, + confidence INTEGER, + reasoning TEXT, + position_size_pct REAL, + stop_loss_pct REAL, + take_profit_pct REAL, + risk_level TEXT, + key_price_levels TEXT, + market_data TEXT, + account_info TEXT, + executed INTEGER DEFAULT 0, + execution_result TEXT, + created_at TEXT DEFAULT CURRENT_TIMESTAMP + ) + ''') + + # 3. 交易记录表 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS trade_records ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + stock_code TEXT NOT NULL, + stock_name TEXT, + trade_type TEXT NOT NULL, + quantity INTEGER, + price REAL, + amount REAL, + order_id TEXT, + order_status TEXT, + ai_decision_id INTEGER, + trade_time TEXT NOT NULL, + commission REAL DEFAULT 0, + tax REAL DEFAULT 0, + profit_loss REAL DEFAULT 0, + created_at TEXT DEFAULT CURRENT_TIMESTAMP, + FOREIGN KEY(ai_decision_id) REFERENCES ai_decisions(id) + ) + ''') + + # 4. 持仓监控表 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS position_monitor ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + stock_code TEXT NOT NULL, + stock_name TEXT, + quantity INTEGER, + cost_price REAL, + current_price REAL, + profit_loss REAL, + profit_loss_pct REAL, + holding_days INTEGER, + buy_date TEXT, + stop_loss_price REAL, + take_profit_price REAL, + last_check_time TEXT, + status TEXT DEFAULT 'holding', + created_at TEXT DEFAULT CURRENT_TIMESTAMP, + updated_at TEXT DEFAULT CURRENT_TIMESTAMP, + UNIQUE(stock_code) + ) + ''') + + # 5. 通知记录表 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS notifications ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + stock_code TEXT, + notify_type TEXT NOT NULL, + notify_target TEXT, + subject TEXT, + content TEXT, + status TEXT DEFAULT 'pending', + error_msg TEXT, + sent_at TEXT, + created_at TEXT DEFAULT CURRENT_TIMESTAMP + ) + ''') + + # 6. 系统日志表 + cursor.execute(''' + CREATE TABLE IF NOT EXISTS system_logs ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + log_level TEXT, + module TEXT, + message TEXT, + details TEXT, + created_at TEXT DEFAULT CURRENT_TIMESTAMP + ) + ''') + + conn.commit() + conn.close() + self.logger.info(f"数据库初始化完成: {self.db_file}") + + # ========== 监控任务管理 ========== + + def add_monitor_task(self, task_data: Dict) -> int: + """添加监控任务""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + INSERT INTO monitor_tasks + (task_name, stock_code, stock_name, enabled, check_interval, + auto_trade, position_size_pct, stop_loss_pct, take_profit_pct, + qmt_account_id, notify_email, notify_webhook, + has_position, position_cost, position_quantity, position_date) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + task_data.get('task_name'), + task_data.get('stock_code'), + task_data.get('stock_name'), + task_data.get('enabled', 1), + task_data.get('check_interval', 300), + task_data.get('auto_trade', 0), + task_data.get('position_size_pct', 20), + task_data.get('stop_loss_pct', 5), + task_data.get('take_profit_pct', 10), + task_data.get('qmt_account_id'), + task_data.get('notify_email'), + task_data.get('notify_webhook'), + task_data.get('has_position', 0), + task_data.get('position_cost', 0), + task_data.get('position_quantity', 0), + task_data.get('position_date') + )) + + task_id = cursor.lastrowid + conn.commit() + conn.close() + + position_info = f"（持仓: {task_data.get('position_quantity')}股 @ {task_data.get('position_cost')}元）" if task_data.get('has_position') else "" + self.logger.info(f"添加监控任务: {task_data.get('stock_code')} - {task_data.get('task_name')} {position_info}") + return task_id + + def get_monitor_tasks(self, enabled_only: bool = True) -> List[Dict]: + """获取监控任务列表""" + conn = sqlite3.connect(self.db_file) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + if enabled_only: + cursor.execute('SELECT * FROM monitor_tasks WHERE enabled = 1 ORDER BY id DESC') + else: + cursor.execute('SELECT * FROM monitor_tasks ORDER BY id DESC') + + rows = cursor.fetchall() + conn.close() + + return [dict(row) for row in rows] + + def update_monitor_task(self, task_id: int, updates: Dict): + """更新监控任务""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + set_clause = ', '.join([f"{k} = ?" for k in updates.keys()]) + values = list(updates.values()) + [task_id] + + cursor.execute(f''' + UPDATE monitor_tasks + SET {set_clause}, updated_at = CURRENT_TIMESTAMP + WHERE id = ? + ''', values) + + conn.commit() + conn.close() + + def update_monitor_task(self, stock_code: str, task_data: Dict): + """更新监控任务""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + # 构建更新语句 + update_fields = [] + values = [] + + if 'task_name' in task_data: + update_fields.append('task_name = ?') + values.append(task_data['task_name']) + + if 'check_interval' in task_data: + update_fields.append('check_interval = ?') + values.append(task_data['check_interval']) + + if 'auto_trade' in task_data: + update_fields.append('auto_trade = ?') + values.append(task_data['auto_trade']) + + if 'position_size_pct' in task_data: + update_fields.append('position_size_pct = ?') + values.append(task_data['position_size_pct']) + + if 'has_position' in task_data: + update_fields.append('has_position = ?') + values.append(task_data['has_position']) + + if 'position_cost' in task_data: + update_fields.append('position_cost = ?') + values.append(task_data['position_cost']) + + if 'position_quantity' in task_data: + update_fields.append('position_quantity = ?') + values.append(task_data['position_quantity']) + + if 'position_date' in task_data: + update_fields.append('position_date = ?') + values.append(task_data['position_date']) + + if 'notify_email' in task_data: + update_fields.append('notify_email = ?') + values.append(task_data['notify_email']) + + # 添加更新时间 + update_fields.append('updated_at = CURRENT_TIMESTAMP') + + # 添加WHERE条件 + values.append(stock_code) + + sql = f"UPDATE monitor_tasks SET {', '.join(update_fields)} WHERE stock_code = ?" + cursor.execute(sql, values) + + conn.commit() + conn.close() + + self.logger.info(f"更新监控任务: {stock_code}") + + def delete_monitor_task(self, task_id: int): + """删除监控任务""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute('DELETE FROM monitor_tasks WHERE id = ?', (task_id,)) + + conn.commit() + conn.close() + + # ========== AI决策记录 ========== + + def save_ai_decision(self, decision_data: Dict) -> int: + """保存AI决策""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + INSERT INTO ai_decisions + (stock_code, stock_name, decision_time, trading_session, + action, confidence, reasoning, position_size_pct, + stop_loss_pct, take_profit_pct, risk_level, + key_price_levels, market_data, account_info) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + decision_data.get('stock_code'), + decision_data.get('stock_name'), + decision_data.get('decision_time', datetime.now().strftime('%Y-%m-%d %H:%M:%S')), + decision_data.get('trading_session'), + decision_data.get('action'), + decision_data.get('confidence'), + decision_data.get('reasoning'), + decision_data.get('position_size_pct'), + decision_data.get('stop_loss_pct'), + decision_data.get('take_profit_pct'), + decision_data.get('risk_level'), + json.dumps(decision_data.get('key_price_levels', {})), + json.dumps(decision_data.get('market_data', {})), + json.dumps(decision_data.get('account_info', {})) + )) + + decision_id = cursor.lastrowid + conn.commit() + conn.close() + + return decision_id + + def get_ai_decisions(self, stock_code: str = None, limit: int = 100) -> List[Dict]: + """获取AI决策历史""" + conn = sqlite3.connect(self.db_file) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + if stock_code: + cursor.execute(''' + SELECT * FROM ai_decisions + WHERE stock_code = ? + ORDER BY decision_time DESC + LIMIT ? + ''', (stock_code, limit)) + else: + cursor.execute(''' + SELECT * FROM ai_decisions + ORDER BY decision_time DESC + LIMIT ? + ''', (limit,)) + + rows = cursor.fetchall() + conn.close() + + decisions = [] + for row in rows: + d = dict(row) + # 解析JSON字段 + d['key_price_levels'] = json.loads(d['key_price_levels']) if d['key_price_levels'] else {} + d['market_data'] = json.loads(d['market_data']) if d['market_data'] else {} + d['account_info'] = json.loads(d['account_info']) if d['account_info'] else {} + decisions.append(d) + + return decisions + + def update_decision_execution(self, decision_id: int, executed: bool, result: str): + """更新决策执行状态""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + UPDATE ai_decisions + SET executed = ?, execution_result = ? + WHERE id = ? + ''', (1 if executed else 0, result, decision_id)) + + conn.commit() + conn.close() + + # ========== 交易记录 ========== + + def save_trade_record(self, trade_data: Dict) -> int: + """保存交易记录""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + INSERT INTO trade_records + (stock_code, stock_name, trade_type, quantity, price, amount, + order_id, order_status, ai_decision_id, trade_time, + commission, tax, profit_loss) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + trade_data.get('stock_code'), + trade_data.get('stock_name'), + trade_data.get('trade_type'), + trade_data.get('quantity'), + trade_data.get('price'), + trade_data.get('amount'), + trade_data.get('order_id'), + trade_data.get('order_status'), + trade_data.get('ai_decision_id'), + trade_data.get('trade_time', datetime.now().strftime('%Y-%m-%d %H:%M:%S')), + trade_data.get('commission', 0), + trade_data.get('tax', 0), + trade_data.get('profit_loss', 0) + )) + + record_id = cursor.lastrowid + conn.commit() + conn.close() + + return record_id + + def get_trade_records(self, stock_code: str = None, limit: int = 100) -> List[Dict]: + """获取交易记录""" + conn = sqlite3.connect(self.db_file) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + if stock_code: + cursor.execute(''' + SELECT * FROM trade_records + WHERE stock_code = ? + ORDER BY trade_time DESC + LIMIT ? + ''', (stock_code, limit)) + else: + cursor.execute(''' + SELECT * FROM trade_records + ORDER BY trade_time DESC + LIMIT ? + ''', (limit,)) + + rows = cursor.fetchall() + conn.close() + + return [dict(row) for row in rows] + + # ========== 持仓监控 ========== + + def save_position(self, position_data: Dict): + """保存/更新持仓信息""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + # 检查是否已存在 + cursor.execute('SELECT id FROM position_monitor WHERE stock_code = ?', + (position_data.get('stock_code'),)) + existing = cursor.fetchone() + + if existing: + # 更新 + cursor.execute(''' + UPDATE position_monitor + SET stock_name = ?, quantity = ?, cost_price = ?, + current_price = ?, profit_loss = ?, profit_loss_pct = ?, + holding_days = ?, stop_loss_price = ?, take_profit_price = ?, + last_check_time = ?, updated_at = CURRENT_TIMESTAMP + WHERE stock_code = ? + ''', ( + position_data.get('stock_name'), + position_data.get('quantity'), + position_data.get('cost_price'), + position_data.get('current_price'), + position_data.get('profit_loss'), + position_data.get('profit_loss_pct'), + position_data.get('holding_days'), + position_data.get('stop_loss_price'), + position_data.get('take_profit_price'), + datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + position_data.get('stock_code') + )) + else: + # 插入 + cursor.execute(''' + INSERT INTO position_monitor + (stock_code, stock_name, quantity, cost_price, current_price, + profit_loss, profit_loss_pct, holding_days, buy_date, + stop_loss_price, take_profit_price, last_check_time, status) + VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) + ''', ( + position_data.get('stock_code'), + position_data.get('stock_name'), + position_data.get('quantity'), + position_data.get('cost_price'), + position_data.get('current_price'), + position_data.get('profit_loss'), + position_data.get('profit_loss_pct'), + position_data.get('holding_days'), + position_data.get('buy_date'), + position_data.get('stop_loss_price'), + position_data.get('take_profit_price'), + datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'holding' + )) + + conn.commit() + conn.close() + + def get_positions(self) -> List[Dict]: + """获取所有持仓""" + conn = sqlite3.connect(self.db_file) + conn.row_factory = sqlite3.Row + cursor = conn.cursor() + + cursor.execute('SELECT * FROM position_monitor WHERE status = "holding" ORDER BY id DESC') + + rows = cursor.fetchall() + conn.close() + + return [dict(row) for row in rows] + + def close_position(self, stock_code: str): + """关闭持仓记录""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + UPDATE position_monitor + SET status = 'closed', updated_at = CURRENT_TIMESTAMP + WHERE stock_code = ? + ''', (stock_code,)) + + conn.commit() + conn.close() + + # ========== 通知记录 ========== + + def save_notification(self, notify_data: Dict) -> int: + """保存通知记录""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + INSERT INTO notifications + (stock_code, notify_type, notify_target, subject, content, status) + VALUES (?, ?, ?, ?, ?, ?) + ''', ( + notify_data.get('stock_code'), + notify_data.get('notify_type'), + notify_data.get('notify_target'), + notify_data.get('subject'), + notify_data.get('content'), + notify_data.get('status', 'pending') + )) + + notify_id = cursor.lastrowid + conn.commit() + conn.close() + + return notify_id + + def update_notification_status(self, notify_id: int, status: str, error_msg: str = None): + """更新通知状态""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + UPDATE notifications + SET status = ?, error_msg = ?, sent_at = CURRENT_TIMESTAMP + WHERE id = ? + ''', (status, error_msg, notify_id)) + + conn.commit() + conn.close() + + # ========== 系统日志 ========== + + def log_system_event(self, level: str, module: str, message: str, details: str = None): + """记录系统日志""" + conn = sqlite3.connect(self.db_file) + cursor = conn.cursor() + + cursor.execute(''' + INSERT INTO system_logs (log_level, module, message, details) + VALUES (?, ?, ?, ?) + ''', (level, module, message, details)) + + conn.commit() + conn.close() + + +if __name__ == '__main__': + # 测试数据库 + logging.basicConfig(level=logging.INFO) + + db = SmartMonitorDB('test_smart_monitor.db') + + # 测试添加监控任务 + task_id = db.add_monitor_task({ + 'task_name': '茅台盯盘', + 'stock_code': '600519', + 'stock_name': '贵州茅台', + 'auto_trade': 1, + 'notify_email': 'test@example.com' + }) + + print(f"创建监控任务 ID: {task_id}") + + # 获取任务列表 + tasks = db.get_monitor_tasks() + print(f"\n监控任务列表: {len(tasks)}个") + for task in tasks: + print(f" - {task['stock_code']} {task['stock_name']}") + diff --git a/smart_monitor_deepseek.py b/smart_monitor_deepseek.py new file mode 100644 index 0000000..622fe99 --- /dev/null +++ b/smart_monitor_deepseek.py @@ -0,0 +1,526 @@ +""" +智能盯盘 - DeepSeek AI 决策引擎 +适配A股T+1交易规则的AI决策系统 +""" + +import logging +from typing import Dict, List, Optional +from datetime import datetime, time +import pytz + + +class SmartMonitorDeepSeek: + """A股智能盯盘 - DeepSeek AI决策引擎""" + + def __init__(self, api_key: str): + """ + 初始化DeepSeek客户端 + + Args: + api_key: DeepSeek API密钥 + """ + self.api_key = api_key + self.base_url = "https://api.deepseek.com/v1" + self.headers = { + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json" + } + self.logger = logging.getLogger(__name__) + + def is_trading_time(self) -> bool: + """ + 判断当前是否在A股交易时间内 + + Returns: + bool: 是否可以交易 + """ + beijing_tz = pytz.timezone('Asia/Shanghai') + now = datetime.now(beijing_tz) + current_time = now.time() + + # 排除周末 + if now.weekday() >= 5: + return False + + # 上午：9:30-11:30 + morning_start = time(9, 30) + morning_end = time(11, 30) + + # 下午：13:00-15:00 + afternoon_start = time(13, 0) + afternoon_end = time(15, 0) + + is_trading = ( + (morning_start <= current_time <= morning_end) or + (afternoon_start <= current_time <= afternoon_end) + ) + + return is_trading + + def get_trading_session(self) -> Dict: + """ + 获取当前交易时段信息（A股版本） + + Returns: + Dict: 时段信息 + """ + beijing_tz = pytz.timezone('Asia/Shanghai') + now = datetime.now(beijing_tz) + current_time = now.time() + + # 判断是否交易日 + if now.weekday() >= 5: + return { + 'session': '休市', + 'volatility': 'none', + 'recommendation': '周末不可交易', + 'beijing_hour': now.hour, + 'can_trade': False + } + + # 开盘前（9:00-9:30）：集合竞价时段 + if time(9, 0) <= current_time < time(9, 30): + return { + 'session': '集合竞价', + 'volatility': 'high', + 'recommendation': '可观察盘面情绪，准备开盘交易', + 'beijing_hour': now.hour, + 'can_trade': False + } + + # 上午盘（9:30-11:30） + elif time(9, 30) <= current_time <= time(11, 30): + return { + 'session': '上午盘', + 'volatility': 'high', + 'recommendation': '交易活跃，波动较大', + 'beijing_hour': now.hour, + 'can_trade': True + } + + # 午间休市（11:30-13:00） + elif time(11, 30) < current_time < time(13, 0): + return { + 'session': '午间休市', + 'volatility': 'none', + 'recommendation': '不可交易，可分析上午盘面', + 'beijing_hour': now.hour, + 'can_trade': False + } + + # 下午盘（13:00-15:00） + elif time(13, 0) <= current_time <= time(15, 0): + # 尾盘最后半小时（14:30-15:00） + if current_time >= time(14, 30): + return { + 'session': '尾盘', + 'volatility': 'high', + 'recommendation': '尾盘波动大，谨慎操作', + 'beijing_hour': now.hour, + 'can_trade': True + } + else: + return { + 'session': '下午盘', + 'volatility': 'medium', + 'recommendation': '波动趋缓，适合布局', + 'beijing_hour': now.hour, + 'can_trade': True + } + + # 盘后（15:00之后） + else: + return { + 'session': '盘后', + 'volatility': 'none', + 'recommendation': '收盘后，可复盘分析', + 'beijing_hour': now.hour, + 'can_trade': False + } + + def chat_completion(self, messages: List[Dict], model: str = "deepseek-chat", + temperature: float = 0.7, max_tokens: int = 2000) -> Dict: + """ + 调用DeepSeek API + + Args: + messages: 对话消息列表 + model: 模型名称 + temperature: 温度参数 + max_tokens: 最大token数 + + Returns: + API响应 + """ + import requests + + payload = { + "model": model, + "messages": messages, + "temperature": temperature, + "max_tokens": max_tokens + } + + try: + response = requests.post( + f"{self.base_url}/chat/completions", + headers=self.headers, + json=payload, + timeout=60 + ) + response.raise_for_status() + return response.json() + except Exception as e: + self.logger.error(f"DeepSeek API调用失败: {e}") + raise + + def analyze_stock_and_decide(self, stock_code: str, market_data: Dict, + account_info: Dict, has_position: bool = False, + position_cost: float = 0, position_quantity: int = 0) -> Dict: + """ + 分析股票并做出交易决策（A股T+1规则） + + Args: + stock_code: 股票代码（如：600519） + market_data: 市场数据 + account_info: 账户信息 + has_position: 是否已持有该股票 + position_cost: 持仓成本价格 + position_quantity: 持仓数量 + + Returns: + 交易决策 + """ + # 获取交易时段 + session_info = self.get_trading_session() + + # 构建Prompt + prompt = self._build_a_stock_prompt( + stock_code, market_data, account_info, + has_position, session_info, position_cost, position_quantity + ) + + system_prompt = """你是一位资深的A股量化交易专家，拥有15年实战经验。 + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +⚠️ A股交易规则（与币圈完全不同！） +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +[CRITICAL] T+1规则： +- 今天买入的股票，**今天不能卖出**，必须等到下一个交易日 +- 这意味着：一旦买入，至少要持有到明天才能卖出 +- 因此买入决策必须**极其谨慎**，不能像币圈那样快进快出 + +[CRITICAL] 涨跌停限制： +- 主板/中小板：±10%涨跌停 +- 创业板/科创板：±20%涨跌停 +- ST股票：±5%涨跌停 +- 一旦涨停，很难买入；一旦跌停，很难卖出 + +[CRITICAL] 交易时间： +- 上午：9:30-11:30 +- 下午：13:00-15:00 +- 其他时间不能交易 + +[CRITICAL] 只能做多： +- A股不能做空（融券门槛高，散户基本不用） +- 只有买入和卖出两个动作 + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +🎯 你的交易哲学（适配T+1） +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +**因为T+1限制，你的策略必须更加稳健！** + +1. **买入前三思**： + - 买入后至少持有1天，所以必须确保趋势向上 + - 不能像币圈那样"试探性开仓"，一旦买入就是承诺 + - 最好在尾盘或第二天开盘前决策，避免盲目追高 + +2. **止损更困难**： + - 如果今天买入后下跌，今天无法止损（T+1） + - 只能等明天再卖，可能面临更大亏损 + - 因此：**宁可错过，不可做错** + +3. **技术分析更重要**： + - 日线级别趋势确认 + - 支撑位/阻力位 + - 成交量配合 + - 量价关系判断 + +4. **风险控制严格**： + - 单只股票仓位 ≤ 30%（T+1风险大） + - 止损位：-5%（明天开盘立即执行） + - 止盈位：+8-15%（分批止盈） + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +📊 可选的交易动作 +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +**如果当前无持仓**： +- action = "BUY"（买入）- 必须确保技术面强势，趋势向上 +- action = "HOLD"（观望）- 信号不明确时选择观望 + +**如果当前有持仓**： +- action = "SELL"（卖出）- 达到止盈/止损条件，或技术面转弱 +- action = "HOLD"（持有）- 趋势未改变，继续持有 +- ⚠️ 注意：如果股票是今天买入的，受T+1限制无法卖出，只能选择HOLD + +**绝对禁止**： +- 不要在开盘前5分钟（9:30-9:35）买入，容易追高 +- 不要在尾盘最后5分钟（14:55-15:00）买入，可能被套 +- 不要逆趋势交易（趋势向下时买入） + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +📈 买入信号（必须满足至少3个条件） +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +1. ✅ 趋势向上：价格 > MA5 > MA20 > MA60（多头排列） +2. ✅ 量价配合：成交量 > 5日均量的120%（放量上涨） +3. ✅ MACD金叉：MACD > 0 且DIF上穿DEA +4. ✅ RSI健康：RSI在50-70区间（不超买不超卖） +5. ✅ 突破关键位：突破前期高点或重要阻力位 +6. ✅ 布林带位置：价格接近布林中轨上方，有上行空间 + +**加分项**： +- 行业板块同步上涨 +- 有重大利好消息 +- 机构调研增加 + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +📉 卖出信号（满足任一条件立即卖出） +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +1. 🔴 止损触发：亏损 ≥ -5%（明天开盘立即卖出） +2. 🟢 止盈触发：盈利 ≥ +10%（分批止盈，先卖一半） +3. 🔴 趋势转弱：跌破MA20或MA60，且MACD死叉 +4. 🔴 放量下跌：成交量放大但价格下跌（主力出货） +5. 🔴 技术破位：跌破重要支撑位 +6. 🔴 重大利空：公司公告重大利空消息 + +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ +💬 返回格式（必须严格JSON） +━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ + +{ + "action": "BUY" | "SELL" | "HOLD", + "confidence": 0-100, + "reasoning": "详细的决策理由，包括技术分析、风险评估等，200-300字", + "position_size_pct": 10-30, // 建议仓位百分比（因为T+1，建议≤30%） + "stop_loss_pct": 5.0, // 止损百分比（建议5%） + "take_profit_pct": 10.0, // 止盈百分比（建议10-15%） + "risk_level": "low" | "medium" | "high", + "key_price_levels": { + "support": 支撑位价格, + "resistance": 阻力位价格, + "stop_loss": 止损位价格 + } +} + +**reasoning 示例**： +"茅台当前价格1650元，日线级别呈多头排列（MA5 1645 > MA20 1620 > MA60 1580）， +MACD金叉且柱状图持续放大，RSI 62处于健康区间。今日成交量较5日均量放大135%， +显示有增量资金入场。技术面支撑位在1630元附近，阻力位在1680元。综合判断短期 +趋势向上，但考虑T+1规则，建议仓位控制在20%，止损位设在1568元（-5%）， +止盈目标1815元（+10%）。风险提示：如明日低开需谨慎..." +""" + + messages = [ + {"role": "system", "content": system_prompt}, + {"role": "user", "content": prompt} + ] + + try: + response = self.chat_completion(messages, temperature=0.3) + ai_response = response['choices'][0]['message']['content'] + + # 解析JSON决策 + decision = self._parse_decision(ai_response) + + return { + 'success': True, + 'decision': decision, + 'raw_response': ai_response + } + + except Exception as e: + self.logger.error(f"AI决策失败: {e}") + return { + 'success': False, + 'error': str(e) + } + + def _build_a_stock_prompt(self, stock_code: str, market_data: Dict, + account_info: Dict, has_position: bool, + session_info: Dict, position_cost: float = 0, + position_quantity: int = 0) -> str: + """构建A股分析提示词""" + + prompt = f""" +[TIMER] 当前交易时段 +═══════════════════════════════════════════════════════════ +当前时段: {session_info['session']} (北京时间{session_info['beijing_hour']}:00) +市场状态: {session_info['volatility'].upper()} +时段建议: {session_info['recommendation']} +可交易: {'是' if session_info['can_trade'] else '否'} + +[STOCK] 股票基本信息 +═══════════════════════════════════════════════════════════ +股票代码: {stock_code} +股票名称: {market_data.get('name', 'N/A')} +当前价格: ¥{market_data.get('current_price', 0):.2f} +今日涨跌: {market_data.get('change_pct', 0):+.2f}% +今日涨跌额: ¥{market_data.get('change_amount', 0):+.2f} +最高价: ¥{market_data.get('high', 0):.2f} +最低价: ¥{market_data.get('low', 0):.2f} +开盘价: ¥{market_data.get('open', 0):.2f} +昨收价: ¥{market_data.get('pre_close', 0):.2f} +成交量: {market_data.get('volume', 0):,.0f}手 +成交额: ¥{market_data.get('amount', 0):,.2f}万 + +[TECHNICAL] 技术指标 +═══════════════════════════════════════════════════════════ +MA5: ¥{market_data.get('ma5', 0):.2f} +MA20: ¥{market_data.get('ma20', 0):.2f} +MA60: ¥{market_data.get('ma60', 0):.2f} +趋势判断: {'多头排列' if market_data.get('trend') == 'up' else '空头排列' if market_data.get('trend') == 'down' else '震荡'} + +MACD: + DIF: {market_data.get('macd_dif', 0):.4f} + DEA: {market_data.get('macd_dea', 0):.4f} + MACD: {market_data.get('macd', 0):.4f} ({'金叉' if market_data.get('macd', 0) > 0 else '死叉'}) + +RSI(6): {market_data.get('rsi6', 50):.2f} {'[超买]' if market_data.get('rsi6', 50) > 80 else '[超卖]' if market_data.get('rsi6', 50) < 20 else '[正常]'} +RSI(12): {market_data.get('rsi12', 50):.2f} +RSI(24): {market_data.get('rsi24', 50):.2f} + +KDJ: + K: {market_data.get('kdj_k', 50):.2f} + D: {market_data.get('kdj_d', 50):.2f} + J: {market_data.get('kdj_j', 50):.2f} + +布林带: + 上轨: ¥{market_data.get('boll_upper', 0):.2f} + 中轨: ¥{market_data.get('boll_mid', 0):.2f} + 下轨: ¥{market_data.get('boll_lower', 0):.2f} + 位置: {market_data.get('boll_position', 'N/A')} + +[VOLUME] 量能分析 +═══════════════════════════════════════════════════════════ +今日成交量: {market_data.get('volume', 0):,.0f}手 +5日均量: {market_data.get('vol_ma5', 0):,.0f}手 +量比: {market_data.get('volume_ratio', 0):.2f} ({'放量' if market_data.get('volume_ratio', 0) > 1.2 else '缩量' if market_data.get('volume_ratio', 0) < 0.8 else '正常'}) +换手率: {market_data.get('turnover_rate', 0):.2f}% + +[ACCOUNT] 账户状态 +═══════════════════════════════════════════════════════════ +可用资金: ¥{account_info.get('available_cash', 0):,.2f} +总资产: ¥{account_info.get('total_value', 0):,.2f} +持仓数量: {account_info.get('positions_count', 0)} +""" + + # 如果已持有该股票 + if has_position and position_cost > 0 and position_quantity > 0: + current_price = market_data.get('current_price', 0) + cost_total = position_cost * position_quantity + current_total = current_price * position_quantity + profit_loss = current_total - cost_total + profit_loss_pct = (profit_loss / cost_total * 100) if cost_total > 0 else 0 + + prompt += f""" +[POSITION] 当前持仓（{stock_code}） ⭐ 重要 +═══════════════════════════════════════════════════════════ +持仓数量: {position_quantity}股 +成本价: ¥{position_cost:.2f} +当前价: ¥{current_price:.2f} +持仓市值: ¥{current_total:,.2f} +浮动盈亏: ¥{profit_loss:,.2f} ({profit_loss_pct:+.2f}%) + +⚠️ T+1限制: 该股票可以卖出（不受T+1限制） + +💡 决策建议： +- 如果盈利且技术指标转弱 → 建议止盈卖出 +- 如果亏损超过止损线（通常-5%）→ 建议止损卖出 +- 如果技术指标强势且未到止盈位 → 建议继续持有 +- 如果盈利且看好后市 → 可考虑加仓（但注意仓位控制） +""" + else: + prompt += """ +[POSITION] 当前无持仓 +═══════════════════════════════════════════════════════════ +可考虑买入，但必须确保： +1. 技术面强势（满足至少3个买入信号） +2. 有足够的安全边际 +3. 考虑T+1规则，买入后至少持有1天 +4. 控制仓位，建议单只股票仓位≤30% +""" + + # 主力资金数据（已禁用 - 接口不稳定） + # if 'main_force' in market_data: + # mf = market_data['main_force'] + # prompt += f""" + # [MONEY] 主力资金流向 + # ═══════════════════════════════════════════════════════════ + # 主力净额: ¥{mf.get('main_net', 0):,.2f}万 ({mf.get('main_net_pct', 0):+.2f}%) + # 超大单: ¥{mf.get('super_net', 0):,.2f}万 + # 大单: ¥{mf.get('big_net', 0):,.2f}万 + # 中单: ¥{mf.get('mid_net', 0):,.2f}万 + # 小单: ¥{mf.get('small_net', 0):,.2f}万 + # 主力动向: {mf.get('trend', '观望')} + # """ + + prompt += "\n请基于以上数据，给出交易决策（JSON格式）。" + + return prompt + + def _parse_decision(self, ai_response: str) -> Dict: + """解析AI决策响应""" + import json + + try: + # 尝试多种提取方式 + if "```json" in ai_response.lower(): + json_start = ai_response.lower().find("```json") + 7 + json_end = ai_response.find("```", json_start) + json_str = ai_response[json_start:json_end].strip() + elif "```" in ai_response: + first_tick = ai_response.find("```") + json_start = ai_response.find("\n", first_tick) + 1 + json_end = ai_response.find("```", json_start) + json_str = ai_response[json_start:json_end].strip() + elif "{" in ai_response and "}" in ai_response: + start_idx = ai_response.find('{') + end_idx = ai_response.rfind('}') + 1 + json_str = ai_response[start_idx:end_idx] + else: + json_str = ai_response + + decision = json.loads(json_str) + + # 验证必需字段 + required_fields = ['action', 'confidence', 'reasoning'] + for field in required_fields: + if field not in decision: + raise ValueError(f"缺少必需字段: {field}") + + # 设置默认值 + decision.setdefault('position_size_pct', 20) + decision.setdefault('stop_loss_pct', 5.0) + decision.setdefault('take_profit_pct', 10.0) + decision.setdefault('risk_level', 'medium') + + return decision + + except Exception as e: + self.logger.error(f"解析AI决策失败: {e}") + # 返回保守决策 + return { + 'action': 'HOLD', + 'confidence': 0, + 'reasoning': f'AI响应解析失败: {str(e)}', + 'position_size_pct': 0, + 'stop_loss_pct': 5.0, + 'take_profit_pct': 10.0, + 'risk_level': 'high' + } + diff --git a/smart_monitor_engine.py b/smart_monitor_engine.py new file mode 100644 index 0000000..2ec0f45 --- /dev/null +++ b/smart_monitor_engine.py @@ -0,0 +1,591 @@ +""" +智能盯盘 - 主引擎 +整合DeepSeek AI决策、数据获取、交易执行、通知等功能 +""" + +import logging +import time +from typing import Dict, List, Optional +from datetime import datetime +import threading + +from smart_monitor_deepseek import SmartMonitorDeepSeek +from smart_monitor_data import SmartMonitorDataFetcher +from smart_monitor_qmt import SmartMonitorQMT, SmartMonitorQMTSimulator +from smart_monitor_db import SmartMonitorDB +from notification_service import notification_service # 复用主程序的通知服务 +from config_manager import config_manager # 复用主程序的配置管理器 + + +class SmartMonitorEngine: + """智能盯盘引擎""" + + def __init__(self, deepseek_api_key: str = None, qmt_account_id: str = None, + use_simulator: bool = None): + """ + 初始化智能盯盘引擎 + + Args: + deepseek_api_key: DeepSeek API密钥（可选，从配置读取） + qmt_account_id: miniQMT账户ID（可选，从配置读取） + use_simulator: 是否使用模拟交易（可选，从配置读取） + """ + self.logger = logging.getLogger(__name__) + + # 从配置管理器读取配置 + config = config_manager.read_env() + + # DeepSeek API + if deepseek_api_key is None: + deepseek_api_key = config.get('DEEPSEEK_API_KEY', '') + + # MiniQMT配置 + if qmt_account_id is None: + qmt_account_id = config.get('MINIQMT_ACCOUNT_ID', '') + + if use_simulator is None: + # 如果MINIQMT_ENABLED=false，则使用模拟器 + miniqmt_enabled = config.get('MINIQMT_ENABLED', 'false').lower() == 'true' + use_simulator = not miniqmt_enabled + + # 初始化各个模块 + self.deepseek = SmartMonitorDeepSeek(deepseek_api_key) + self.data_fetcher = SmartMonitorDataFetcher() + self.db = SmartMonitorDB() + self.notification = notification_service # 使用主程序的通知服务 + + # 初始化交易接口 + if use_simulator: + self.qmt = SmartMonitorQMTSimulator() + self.qmt.connect(qmt_account_id or "simulator") + self.logger.info("使用模拟交易模式") + else: + self.qmt = SmartMonitorQMT() + if qmt_account_id: + success = self.qmt.connect(qmt_account_id) + if success: + self.logger.info(f"已连接miniQMT账户: {qmt_account_id}") + else: + self.logger.warning(f"连接miniQMT失败，切换到模拟模式") + self.qmt = SmartMonitorQMTSimulator() + self.qmt.connect("simulator") + else: + self.logger.warning("未配置miniQMT账户，使用模拟模式") + self.qmt = SmartMonitorQMTSimulator() + self.qmt.connect("simulator") + + # 监控线程控制 + self.monitoring_threads = {} + self.stop_flags = {} + + self.logger.info("智能盯盘引擎初始化完成") + + def analyze_stock(self, stock_code: str, auto_trade: bool = False, + notify: bool = True, has_position: bool = False, + position_cost: float = 0, position_quantity: int = 0) -> Dict: + """ + 分析单只股票并做出决策 + + Args: + stock_code: 股票代码 + auto_trade: 是否自动交易 + notify: 是否发送通知 + has_position: 是否已持仓（可选） + position_cost: 持仓成本（可选） + position_quantity: 持仓数量（可选） + + Returns: + 分析结果 + """ + try: + self.logger.info(f"[{stock_code}] 开始分析...") + + # 1. 检查交易时段 + session_info = self.deepseek.get_trading_session() + self.logger.info(f"[{stock_code}] 当前时段: {session_info['session']}") + + # 2. 获取市场数据 + market_data = self.data_fetcher.get_comprehensive_data(stock_code) + if not market_data: + return { + 'success': False, + 'error': '获取市场数据失败' + } + + # 3. 获取账户信息 + account_info = self.qmt.get_account_info() + + # 4. 检查是否已持有该股票 + # 优先使用传入的持仓信息，否则从QMT获取 + if has_position and position_cost > 0 and position_quantity > 0: + # 使用用户设置的持仓信息 + self.logger.info(f"[{stock_code}] 使用监控任务设置的持仓: {position_quantity}股 @ {position_cost:.2f}元") + else: + # 从QMT获取持仓 + position = self.qmt.get_position(stock_code) + has_position = position is not None + + if has_position: + position_cost = position.get('cost_price', 0) + position_quantity = position.get('quantity', 0) + account_info['current_position'] = position + self.logger.info(f"[{stock_code}] 从QMT获取持仓: {position_quantity}股, " + f"成本价: {position_cost:.2f}, " + f"浮动盈亏: {position.get('profit_loss_pct', 0):+.2f}%") + + # 5. 调用DeepSeek AI决策 + ai_result = self.deepseek.analyze_stock_and_decide( + stock_code=stock_code, + market_data=market_data, + account_info=account_info, + has_position=has_position, + position_cost=position_cost, + position_quantity=position_quantity + ) + + if not ai_result['success']: + return { + 'success': False, + 'error': 'AI决策失败', + 'details': ai_result + } + + decision = ai_result['decision'] + + self.logger.info(f"[{stock_code}] AI决策: {decision['action']} " + f"(信心度: {decision['confidence']}%)") + self.logger.info(f"[{stock_code}] 决策理由: {decision['reasoning'][:100]}...") + + # 6. 保存AI决策到数据库 + decision_id = self.db.save_ai_decision({ + 'stock_code': stock_code, + 'stock_name': market_data.get('name'), + 'trading_session': session_info['session'], + 'action': decision['action'], + 'confidence': decision['confidence'], + 'reasoning': decision['reasoning'], + 'position_size_pct': decision.get('position_size_pct'), + 'stop_loss_pct': decision.get('stop_loss_pct'), + 'take_profit_pct': decision.get('take_profit_pct'), + 'risk_level': decision.get('risk_level'), + 'key_price_levels': decision.get('key_price_levels', {}), + 'market_data': market_data, + 'account_info': account_info + }) + + # 7. 执行交易（如果开启自动交易） + execution_result = None + if auto_trade and session_info['can_trade']: + execution_result = self._execute_decision( + stock_code=stock_code, + decision=decision, + market_data=market_data, + has_position=has_position + ) + + # 更新决策执行状态 + self.db.update_decision_execution( + decision_id=decision_id, + executed=execution_result.get('success', False), + result=str(execution_result) + ) + + # 8. 发送通知 + if notify: + self._send_notification( + stock_code=stock_code, + stock_name=market_data.get('name'), + decision=decision, + execution_result=execution_result, + market_data=market_data + ) + + return { + 'success': True, + 'stock_code': stock_code, + 'stock_name': market_data.get('name'), + 'session_info': session_info, + 'market_data': market_data, + 'decision': decision, + 'decision_id': decision_id, + 'execution_result': execution_result + } + + except Exception as e: + self.logger.error(f"[{stock_code}] 分析失败: {e}") + import traceback + traceback.print_exc() + return { + 'success': False, + 'error': str(e) + } + + def _execute_decision(self, stock_code: str, decision: Dict, + market_data: Dict, has_position: bool) -> Dict: + """ + 执行AI决策 + + Args: + stock_code: 股票代码 + decision: AI决策 + market_data: 市场数据 + has_position: 是否已持有 + + Returns: + 执行结果 + """ + action = decision['action'] + + try: + if action == 'BUY' and not has_position: + # 买入逻辑 + return self._execute_buy(stock_code, decision, market_data) + + elif action == 'SELL' and has_position: + # 卖出逻辑 + return self._execute_sell(stock_code, decision, market_data) + + elif action == 'HOLD': + # 持有，不操作 + return { + 'success': True, + 'action': 'HOLD', + 'message': 'AI建议持有，未执行交易' + } + + else: + return { + 'success': False, + 'error': f'无效操作: {action}' + } + + except Exception as e: + self.logger.error(f"[{stock_code}] 执行交易失败: {e}") + return { + 'success': False, + 'error': str(e) + } + + def _execute_buy(self, stock_code: str, decision: Dict, market_data: Dict) -> Dict: + """执行买入""" + try: + # 获取账户信息 + account_info = self.qmt.get_account_info() + available_cash = account_info['available_cash'] + + # 计算买入金额 + position_size_pct = decision.get('position_size_pct', 20) + buy_amount = available_cash * (position_size_pct / 100) + + # 计算买入数量（必须是100的整数倍） + current_price = market_data['current_price'] + quantity = int(buy_amount / current_price / 100) * 100 + + if quantity < 100: + return { + 'success': False, + 'error': f'资金不足，最少需要买入100股（约{current_price * 100:.2f}元）' + } + + # 执行买入 + result = self.qmt.buy_stock( + stock_code=stock_code, + quantity=quantity, + price=current_price, + order_type='market' + ) + + if result['success']: + # 保存交易记录 + self.db.save_trade_record({ + 'stock_code': stock_code, + 'stock_name': market_data.get('name'), + 'trade_type': 'BUY', + 'quantity': quantity, + 'price': current_price, + 'amount': quantity * current_price, + 'order_id': result.get('order_id'), + 'order_status': '已提交' + }) + + # 保存持仓监控 + self.db.save_position({ + 'stock_code': stock_code, + 'stock_name': market_data.get('name'), + 'quantity': quantity, + 'cost_price': current_price, + 'current_price': current_price, + 'profit_loss': 0, + 'profit_loss_pct': 0, + 'holding_days': 0, + 'buy_date': datetime.now().strftime('%Y-%m-%d'), + 'stop_loss_price': current_price * (1 - decision.get('stop_loss_pct', 5) / 100), + 'take_profit_price': current_price * (1 + decision.get('take_profit_pct', 10) / 100) + }) + + self.logger.info(f"[{stock_code}] 买入成功: {quantity}股 @ {current_price:.2f}元") + + return result + + except Exception as e: + self.logger.error(f"[{stock_code}] 买入失败: {e}") + return { + 'success': False, + 'error': str(e) + } + + def _execute_sell(self, stock_code: str, decision: Dict, market_data: Dict) -> Dict: + """执行卖出""" + try: + # 获取持仓 + position = self.qmt.get_position(stock_code) + if not position: + return { + 'success': False, + 'error': '未持有该股票' + } + + # 可卖数量（考虑T+1限制） + can_sell = position['can_sell'] + if can_sell <= 0: + return { + 'success': False, + 'error': 'T+1限制，今天买入的股票明天才能卖出' + } + + # 执行卖出 + current_price = market_data['current_price'] + result = self.qmt.sell_stock( + stock_code=stock_code, + quantity=can_sell, + price=current_price, + order_type='market' + ) + + if result['success']: + # 计算盈亏 + profit_loss = (current_price - position['cost_price']) * can_sell + + # 保存交易记录 + self.db.save_trade_record({ + 'stock_code': stock_code, + 'stock_name': market_data.get('name'), + 'trade_type': 'SELL', + 'quantity': can_sell, + 'price': current_price, + 'amount': can_sell * current_price, + 'order_id': result.get('order_id'), + 'order_status': '已提交', + 'profit_loss': profit_loss + }) + + # 更新或关闭持仓记录 + if can_sell >= position['quantity']: + self.db.close_position(stock_code) + + self.logger.info(f"[{stock_code}] 卖出成功: {can_sell}股 @ {current_price:.2f}元, " + f"盈亏: {profit_loss:+.2f}元") + + return result + + except Exception as e: + self.logger.error(f"[{stock_code}] 卖出失败: {e}") + return { + 'success': False, + 'error': str(e) + } + + def _send_notification(self, stock_code: str, stock_name: str, + decision: Dict, execution_result: Optional[Dict], + market_data: Dict): + """发送通知（使用主程序的通知服务）""" + try: + # 构建通知内容 + action_text = { + 'BUY': '买入', + 'SELL': '卖出', + 'HOLD': '持有' + }.get(decision['action'], decision['action']) + + message = f"{action_text}信号 - {stock_name}({stock_code})" + + # 构建详细内容 + content = f""" +【股票信息】 +代码: {stock_code} +名称: {stock_name} +当前价: {market_data.get('current_price', 0):.2f}元 +涨跌幅: {market_data.get('change_pct', 0):+.2f}% + +【AI决策】 +操作: {action_text} +信心度: {decision['confidence']}% +风险等级: {decision.get('risk_level', 'N/A')} + +【决策理由】 +{decision['reasoning'][:200]}... + +【技术指标】 +MA5: {market_data.get('ma5', 0):.2f} | MA20: {market_data.get('ma20', 0):.2f} +MACD: {market_data.get('macd', 0):.4f} | RSI(6): {market_data.get('rsi6', 0):.2f} +""" + + if execution_result: + if execution_result.get('success'): + content += f"\n✅ 操作已自动执行成功" + else: + content += f"\n❌ 执行失败: {execution_result.get('error')}" + + content += f"\n\n⏰ {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}" + + # 使用主程序的通知服务格式 + notification_data = { + 'symbol': stock_code, + 'name': stock_name, + 'type': '智能盯盘', + 'message': message, + 'details': content, + 'triggered_at': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + } + + # 直接调用主程序的通知服务发送 + success = self.notification.send_notification(notification_data) + + if success: + self.logger.info(f"[{stock_code}] 通知已发送") + else: + self.logger.warning(f"[{stock_code}] 通知发送失败") + + # 同时保存到智能盯盘的数据库 + self.db.save_notification({ + 'stock_code': stock_code, + 'notify_type': 'decision', + 'subject': f"智能盯盘 - {message}", + 'content': content, + 'status': 'sent' if success else 'failed' + }) + + except Exception as e: + self.logger.error(f"[{stock_code}] 发送通知失败: {e}") + import traceback + traceback.print_exc() + + def start_monitor(self, stock_code: str, check_interval: int = 300, + auto_trade: bool = False, notify: bool = True, + has_position: bool = False, position_cost: float = 0, + position_quantity: int = 0): + """ + 启动股票监控（在独立线程中运行） + + Args: + stock_code: 股票代码 + check_interval: 检查间隔（秒） + auto_trade: 是否自动交易 + notify: 是否发送通知 + has_position: 是否已持仓 + position_cost: 持仓成本 + position_quantity: 持仓数量 + """ + if stock_code in self.monitoring_threads: + self.logger.warning(f"[{stock_code}] 监控已在运行中") + return + + # 创建停止标志 + stop_flag = threading.Event() + self.stop_flags[stock_code] = stop_flag + + # 创建监控线程 + thread = threading.Thread( + target=self._monitor_loop, + args=(stock_code, check_interval, auto_trade, notify, stop_flag, + has_position, position_cost, position_quantity), + daemon=True + ) + + self.monitoring_threads[stock_code] = thread + thread.start() + + position_info = f"（持仓: {position_quantity}股 @ {position_cost:.2f}元）" if has_position else "" + self.logger.info(f"[{stock_code}] 监控已启动，间隔: {check_interval}秒 {position_info}") + + def stop_monitor(self, stock_code: str): + """停止股票监控""" + if stock_code not in self.monitoring_threads: + self.logger.warning(f"[{stock_code}] 监控未运行") + return + + # 设置停止标志 + self.stop_flags[stock_code].set() + + # 等待线程结束 + self.monitoring_threads[stock_code].join(timeout=5) + + # 清理 + del self.monitoring_threads[stock_code] + del self.stop_flags[stock_code] + + self.logger.info(f"[{stock_code}] 监控已停止") + + def _monitor_loop(self, stock_code: str, check_interval: int, + auto_trade: bool, notify: bool, stop_flag: threading.Event, + has_position: bool = False, position_cost: float = 0, + position_quantity: int = 0): + """监控循环（在独立线程中运行）""" + self.logger.info(f"[{stock_code}] 监控线程已启动") + + while not stop_flag.is_set(): + try: + # 执行分析 + result = self.analyze_stock( + stock_code=stock_code, + auto_trade=auto_trade, + notify=notify, + has_position=has_position, + position_cost=position_cost, + position_quantity=position_quantity + ) + + if result['success']: + self.logger.info(f"[{stock_code}] 分析完成: {result['decision']['action']}") + else: + self.logger.error(f"[{stock_code}] 分析失败: {result.get('error')}") + + except Exception as e: + self.logger.error(f"[{stock_code}] 监控循环异常: {e}") + + # 等待下一次检查 + stop_flag.wait(check_interval) + + self.logger.info(f"[{stock_code}] 监控线程已退出") + + +if __name__ == '__main__': + # 测试代码 + import os + from dotenv import load_dotenv + + load_dotenv() + + logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' + ) + + # 使用模拟模式测试 + engine = SmartMonitorEngine( + deepseek_api_key=os.getenv('DEEPSEEK_API_KEY'), + use_simulator=True + ) + + # 测试分析贵州茅台 + print("\n测试分析贵州茅台(600519)...") + result = engine.analyze_stock('600519', auto_trade=False, notify=False) + + if result['success']: + print(f"\n分析成功!") + print(f" 决策: {result['decision']['action']}") + print(f" 信心度: {result['decision']['confidence']}%") + print(f" 理由: {result['decision']['reasoning'][:100]}...") + else: + print(f"\n分析失败: {result.get('error')}") + diff --git a/smart_monitor_kline.py b/smart_monitor_kline.py new file mode 100644 index 0000000..8c3ed9f --- /dev/null +++ b/smart_monitor_kline.py @@ -0,0 +1,484 @@ +""" +智能盯盘 - K线图绘制模块 +支持AI决策标注、实时更新 +""" + +import plotly.graph_objects as go +from plotly.subplots import make_subplots +import pandas as pd +from datetime import datetime, timedelta +from typing import Dict, List, Optional +import logging + + +class SmartMonitorKline: + """智能盯盘K线图""" + + def __init__(self): + """初始化K线图""" + self.logger = logging.getLogger(__name__) + + def create_kline_with_decisions( + self, + stock_code: str, + stock_name: str, + kline_data: pd.DataFrame, + ai_decisions: List[Dict], + show_volume: bool = True, + show_ma: bool = True, + height: int = 600 + ) -> go.Figure: + """ + 创建带AI决策标注的K线图 + + Args: + stock_code: 股票代码 + stock_name: 股票名称 + kline_data: K线数据（DataFrame） + ai_decisions: AI决策列表 + show_volume: 是否显示成交量 + show_ma: 是否显示均线 + height: 图表高度 + + Returns: + plotly Figure对象 + """ + try: + # 确保数据不为空 + if kline_data is None or kline_data.empty: + self.logger.warning(f"K线数据为空 {stock_code}") + return self._create_empty_figure(stock_code, stock_name, height) + + # 确保必需的列存在 + required_cols = ['日期', '开盘', '收盘', '最高', '最低'] + if not all(col in kline_data.columns for col in required_cols): + self.logger.error(f"K线数据缺少必需列 {stock_code}") + return self._create_empty_figure(stock_code, stock_name, height) + + # 创建子图 + if show_volume: + fig = make_subplots( + rows=2, cols=1, + shared_xaxes=True, + vertical_spacing=0.03, + row_heights=[0.7, 0.3], + subplot_titles=(f'{stock_code} {stock_name}', '成交量') + ) + else: + fig = make_subplots( + rows=1, cols=1, + subplot_titles=(f'{stock_code} {stock_name}',) + ) + + # 1. 添加K线图 + fig.add_trace( + go.Candlestick( + x=kline_data['日期'], + open=kline_data['开盘'], + high=kline_data['最高'], + low=kline_data['最低'], + close=kline_data['收盘'], + name='K线', + increasing_line_color='#ef5350', # 红色（涨） + decreasing_line_color='#26a69a' # 绿色（跌） + ), + row=1, col=1 + ) + + # 2. 添加均线（如果需要） + if show_ma: + self._add_moving_averages(fig, kline_data, row=1, col=1) + + # 3. 添加AI决策标注 + if ai_decisions: + self._add_ai_decision_markers(fig, kline_data, ai_decisions, row=1, col=1) + + # 4. 添加成交量（如果需要） + if show_volume and '成交量' in kline_data.columns: + self._add_volume(fig, kline_data, row=2, col=1) + + # 5. 更新布局 + fig.update_layout( + height=height, + xaxis_rangeslider_visible=False, + showlegend=True, + hovermode='x unified', + template='plotly_white', + margin=dict(l=50, r=50, t=50, b=50), + ) + + # 更新x轴 + fig.update_xaxes( + title_text="日期", + row=2 if show_volume else 1, + col=1 + ) + + # 更新y轴 + fig.update_yaxes(title_text="价格(元)", row=1, col=1) + if show_volume: + fig.update_yaxes(title_text="成交量", row=2, col=1) + + return fig + + except Exception as e: + self.logger.error(f"创建K线图失败 {stock_code}: {e}") + import traceback + self.logger.debug(traceback.format_exc()) + return self._create_empty_figure(stock_code, stock_name, height) + + def _add_moving_averages(self, fig, kline_data: pd.DataFrame, row: int, col: int): + """添加均线""" + try: + # 计算均线 + ma_periods = [5, 10, 20, 60] + ma_colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#FFA07A'] + + for period, color in zip(ma_periods, ma_colors): + if len(kline_data) >= period: + ma = kline_data['收盘'].rolling(window=period).mean() + fig.add_trace( + go.Scatter( + x=kline_data['日期'], + y=ma, + name=f'MA{period}', + line=dict(color=color, width=1), + opacity=0.7 + ), + row=row, col=col + ) + except Exception as e: + self.logger.warning(f"添加均线失败: {e}") + + def _add_ai_decision_markers( + self, + fig, + kline_data: pd.DataFrame, + ai_decisions: List[Dict], + row: int, + col: int + ): + """在K线图上添加AI决策标注""" + try: + # 决策类型映射 + action_config = { + 'buy': { + 'symbol': 'triangle-up', + 'color': '#ef5350', + 'text': '买入', + 'size': 15 + }, + 'sell': { + 'symbol': 'triangle-down', + 'color': '#26a69a', + 'text': '卖出', + 'size': 15 + }, + 'add_position': { + 'symbol': 'triangle-up', + 'color': '#ff9800', + 'text': '加仓', + 'size': 12 + }, + 'reduce_position': { + 'symbol': 'triangle-down', + 'color': '#9c27b0', + 'text': '减仓', + 'size': 12 + }, + 'hold': { + 'symbol': 'circle', + 'color': '#607d8b', + 'text': '持有', + 'size': 8 + } + } + + # 将K线数据日期转换为字符串，便于匹配 + kline_data['日期_str'] = pd.to_datetime(kline_data['日期']).dt.strftime('%Y-%m-%d') + + # 按决策类型分组 + for action_type, config in action_config.items(): + decisions_of_type = [d for d in ai_decisions if d.get('action') == action_type] + + if not decisions_of_type: + continue + + # 提取决策的日期和价格 + decision_dates = [] + decision_prices = [] + decision_texts = [] + + for decision in decisions_of_type: + decision_date = decision.get('decision_time', '').split()[0] # 只取日期部分 + + # 在K线数据中查找对应日期的收盘价 + matching_rows = kline_data[kline_data['日期_str'] == decision_date] + + if not matching_rows.empty: + price = matching_rows.iloc[0]['收盘'] + decision_dates.append(decision_date) + decision_prices.append(price) + + # 构建hover文本 + confidence = decision.get('confidence', 0) + reasoning = decision.get('reasoning', '无')[:50] # 截断过长的推理 + hover_text = ( + f"{config['text']}
" + f"日期: {decision_date}
" + f"价格: ¥{price:.2f}
" + f"置信度: {confidence}%
" + f"推理: {reasoning}..." + ) + decision_texts.append(hover_text) + + # 添加标注 + if decision_dates: + fig.add_trace( + go.Scatter( + x=decision_dates, + y=decision_prices, + mode='markers+text', + name=config['text'], + marker=dict( + symbol=config['symbol'], + size=config['size'], + color=config['color'], + line=dict(color='white', width=1) + ), + text=[config['text']] * len(decision_dates), + textposition='top center', + textfont=dict(size=10, color=config['color']), + hovertext=decision_texts, + hoverinfo='text', + showlegend=True + ), + row=row, col=col + ) + + except Exception as e: + self.logger.error(f"添加AI决策标注失败: {e}") + import traceback + self.logger.debug(traceback.format_exc()) + + def _add_volume(self, fig, kline_data: pd.DataFrame, row: int, col: int): + """添加成交量柱状图""" + try: + # 计算颜色（红涨绿跌） + colors = [] + for i in range(len(kline_data)): + if i == 0: + colors.append('#ef5350') + else: + if kline_data.iloc[i]['收盘'] >= kline_data.iloc[i-1]['收盘']: + colors.append('#ef5350') # 红色（涨） + else: + colors.append('#26a69a') # 绿色（跌） + + fig.add_trace( + go.Bar( + x=kline_data['日期'], + y=kline_data['成交量'], + name='成交量', + marker_color=colors, + showlegend=False + ), + row=row, col=col + ) + except Exception as e: + self.logger.warning(f"添加成交量失败: {e}") + + def _create_empty_figure(self, stock_code: str, stock_name: str, height: int) -> go.Figure: + """创建空图表""" + fig = go.Figure() + fig.add_annotation( + text=f"暂无 {stock_code} {stock_name} 的K线数据", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + font=dict(size=20, color="gray") + ) + fig.update_layout( + height=height, + xaxis=dict(visible=False), + yaxis=dict(visible=False), + template='plotly_white' + ) + return fig + + def get_kline_data(self, stock_code: str, days: int = 60, data_fetcher=None) -> Optional[pd.DataFrame]: + """ + 获取K线数据（带Tushare降级机制） + + Args: + stock_code: 股票代码 + days: 获取天数 + data_fetcher: 数据获取器实例 + + Returns: + K线数据DataFrame + """ + try: + if data_fetcher is None: + from smart_monitor_data import SmartMonitorDataFetcher + data_fetcher = SmartMonitorDataFetcher() + + # 计算日期范围 + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=days + 30)).strftime('%Y%m%d') # 多取30天以确保足够数据 + + # 方法1: 尝试使用AKShare获取（只尝试1次，避免IP封禁） + try: + import akshare as ak + df = ak.stock_zh_a_hist( + symbol=stock_code, + period='daily', + start_date=start_date, + end_date=end_date, + adjust='qfq' + ) + + if df is not None and not df.empty: + # 只保留最近days天的数据 + df = df.tail(days) + self.logger.info(f"✅ AKShare获取K线数据成功 {stock_code}，共{len(df)}条") + return df + else: + self.logger.warning(f"AKShare未返回K线数据 {stock_code}，尝试降级到Tushare") + except Exception as e: + self.logger.warning(f"AKShare获取K线数据失败 {stock_code}: {type(e).__name__}, 尝试降级到Tushare") + + # 方法2: 降级到Tushare + if data_fetcher and data_fetcher.ts_pro: + self.logger.info(f"降级使用Tushare获取K线数据 {stock_code}") + df = self._get_kline_from_tushare(stock_code, days, data_fetcher.ts_pro) + if df is not None and not df.empty: + self.logger.info(f"✅ Tushare获取K线数据成功 {stock_code}，共{len(df)}条") + return df + + self.logger.error(f"所有数据源都无法获取K线数据 {stock_code}") + return None + + except Exception as e: + self.logger.error(f"获取K线数据失败 {stock_code}: {e}") + import traceback + self.logger.debug(traceback.format_exc()) + return None + + def _get_kline_from_tushare(self, stock_code: str, days: int, ts_pro) -> Optional[pd.DataFrame]: + """ + 从Tushare获取K线数据 + + Args: + stock_code: 股票代码 + days: 获取天数 + ts_pro: Tushare API实例 + + Returns: + K线数据DataFrame + """ + try: + # 转换股票代码格式 + if stock_code.startswith('6'): + ts_code = f"{stock_code}.SH" + elif stock_code.startswith(('0', '3')): + ts_code = f"{stock_code}.SZ" + else: + ts_code = stock_code + + # 计算日期范围（多取一些确保足够） + end_date = datetime.now().strftime('%Y%m%d') + start_date = (datetime.now() - timedelta(days=days + 60)).strftime('%Y%m%d') + + # 获取日K线数据（前复权） + df = ts_pro.daily( + ts_code=ts_code, + start_date=start_date, + end_date=end_date, + adj='qfq' + ) + + if df is None or df.empty: + self.logger.error(f"Tushare未返回K线数据 {stock_code}") + return None + + # Tushare数据是从新到旧，需要反转 + df = df.sort_values('trade_date', ascending=True).reset_index(drop=True) + + # 统一列名为AKShare格式 + df = df.rename(columns={ + 'trade_date': '日期', + 'open': '开盘', + 'high': '最高', + 'low': '最低', + 'close': '收盘', + 'vol': '成交量', + 'amount': '成交额' + }) + + # 转换日期格式（Tushare: 20240115 -> 2024-01-15） + df['日期'] = pd.to_datetime(df['日期']) + + # 只保留最近days天的数据 + df = df.tail(days) + + return df + + except Exception as e: + self.logger.error(f"Tushare获取K线数据失败 {stock_code}: {type(e).__name__}: {str(e)}") + return None + + +if __name__ == '__main__': + # 测试代码 + logging.basicConfig(level=logging.INFO) + + kline = SmartMonitorKline() + + # 测试获取K线数据 + df = kline.get_kline_data('600519', days=60) + + if df is not None: + print(f"获取到 {len(df)} 条K线数据") + print(df.head()) + + # 模拟AI决策 + ai_decisions = [ + { + 'decision_time': '2024-01-15 10:00:00', + 'action': 'buy', + 'confidence': 85, + 'reasoning': '技术指标良好，MACD金叉' + }, + { + 'decision_time': '2024-01-20 14:30:00', + 'action': 'add_position', + 'confidence': 75, + 'reasoning': '突破关键压力位' + }, + { + 'decision_time': '2024-01-25 11:00:00', + 'action': 'sell', + 'confidence': 80, + 'reasoning': 'RSI超买，建议止盈' + } + ] + + # 创建K线图 + fig = kline.create_kline_with_decisions( + stock_code='600519', + stock_name='贵州茅台', + kline_data=df, + ai_decisions=ai_decisions, + show_volume=True, + show_ma=True + ) + + # 保存为HTML + fig.write_html('test_kline.html') + print("K线图已保存到 test_kline.html") + else: + print("获取K线数据失败") + diff --git a/smart_monitor_qmt.py b/smart_monitor_qmt.py new file mode 100644 index 0000000..9cb1521 --- /dev/null +++ b/smart_monitor_qmt.py @@ -0,0 +1,628 @@ +""" +智能盯盘 - miniQMT交易接口适配器 +封装miniQMT的交易功能，支持A股T+1交易 +使用主程序的配置管理系统 +""" + +import logging +import os +from typing import Dict, List, Optional +from datetime import datetime + + +class SmartMonitorQMT: + """miniQMT交易接口""" + + def __init__(self, mini_qmt_path: str = None): + """ + 初始化miniQMT接口 + + Args: + mini_qmt_path: miniQMT安装路径 + """ + self.logger = logging.getLogger(__name__) + self.xt_trader = None + self.account = None + self.connected = False + + # 尝试导入miniQMT + try: + from xtquant import xttrader, xtdata + self.xttrader = xttrader + self.xtdata = xtdata + self.logger.info("miniQMT模块加载成功") + except ImportError as e: + self.logger.warning(f"miniQMT模块未安装: {e}") + self.logger.warning("将使用模拟模式（不实际下单）") + + def connect(self, account_id: str = None) -> bool: + """ + 连接miniQMT + + Args: + account_id: 交易账户ID（可选，从环境变量读取） + + Returns: + 是否连接成功 + """ + if not self.xttrader: + self.logger.warning("miniQMT未安装，使用模拟模式") + self.connected = False + return False + + # 从配置读取账户ID + if account_id is None: + account_id = os.getenv('MINIQMT_ACCOUNT_ID', '') + + if not account_id: + self.logger.error("未配置miniQMT账户ID，请在环境配置中设置") + self.connected = False + return False + + try: + # 创建交易对象 + self.xt_trader = self.xttrader.XtQuantTrader() + + # 连接 + self.xt_trader.start() + + # 连接账户 + self.account = self.xttrader.StockAccount(account_id) + connect_result = self.xt_trader.connect() + + if connect_result == 0: + self.connected = True + self.logger.info(f"miniQMT连接成功，账户: {account_id}") + return True + else: + self.logger.error(f"miniQMT连接失败，错误码: {connect_result}") + return False + + except Exception as e: + self.logger.error(f"连接miniQMT失败: {e}") + return False + + def disconnect(self): + """断开连接""" + if self.xt_trader: + try: + self.xt_trader.stop() + self.connected = False + self.logger.info("miniQMT已断开连接") + except Exception as e: + self.logger.error(f"断开连接失败: {e}") + + def get_account_info(self) -> Dict: + """ + 获取账户信息 + + Returns: + 账户信息字典 + """ + if not self.connected or not self.account: + return { + 'available_cash': 0, + 'total_value': 0, + 'positions_count': 0, + 'total_profit_loss': 0 + } + + try: + # 获取资金信息 + asset = self.xt_trader.query_stock_asset(self.account) + + # 获取持仓信息 + positions = self.xt_trader.query_stock_positions(self.account) + + # 计算总浮动盈亏 + total_profit_loss = 0 + if positions: + for pos in positions: + total_profit_loss += pos.unrealized_profit + + return { + 'available_cash': asset.cash, # 可用资金 + 'total_value': asset.total_asset, # 总资产 + 'positions_count': len(positions) if positions else 0, + 'total_profit_loss': total_profit_loss, + 'frozen_cash': asset.frozen_cash, # 冻结资金 + 'market_value': asset.market_value # 持仓市值 + } + + except Exception as e: + self.logger.error(f"获取账户信息失败: {e}") + return { + 'available_cash': 0, + 'total_value': 0, + 'positions_count': 0, + 'total_profit_loss': 0 + } + + def get_position(self, stock_code: str) -> Optional[Dict]: + """ + 获取指定股票的持仓信息 + + Args: + stock_code: 股票代码 + + Returns: + 持仓信息，如果未持有则返回None + """ + if not self.connected or not self.account: + return None + + try: + positions = self.xt_trader.query_stock_positions(self.account) + + if not positions: + return None + + # 查找指定股票 + for pos in positions: + if pos.stock_code == stock_code: + # 计算持仓天数 + holding_days = 0 + if hasattr(pos, 'open_date'): + try: + open_date = datetime.strptime(str(pos.open_date), '%Y%m%d') + holding_days = (datetime.now() - open_date).days + except: + pass + + return { + 'stock_code': pos.stock_code, + 'stock_name': getattr(pos, 'stock_name', ''), + 'quantity': pos.volume, # 持仓数量 + 'can_sell': pos.can_use_volume, # 可用数量（考虑T+1） + 'cost_price': pos.avg_price, # 成本价 + 'current_price': pos.last_price, # 最新价 + 'market_value': pos.market_value, # 市值 + 'profit_loss': pos.unrealized_profit, # 浮动盈亏 + 'profit_loss_pct': (pos.last_price - pos.avg_price) / pos.avg_price * 100 if pos.avg_price > 0 else 0, + 'holding_days': holding_days, + 'buy_date': getattr(pos, 'open_date', '') + } + + return None + + except Exception as e: + self.logger.error(f"获取持仓信息失败 {stock_code}: {e}") + return None + + def get_all_positions(self) -> List[Dict]: + """ + 获取所有持仓 + + Returns: + 持仓列表 + """ + if not self.connected or not self.account: + return [] + + try: + positions = self.xt_trader.query_stock_positions(self.account) + + if not positions: + return [] + + result = [] + for pos in positions: + holding_days = 0 + if hasattr(pos, 'open_date'): + try: + open_date = datetime.strptime(str(pos.open_date), '%Y%m%d') + holding_days = (datetime.now() - open_date).days + except: + pass + + result.append({ + 'stock_code': pos.stock_code, + 'stock_name': getattr(pos, 'stock_name', ''), + 'quantity': pos.volume, + 'can_sell': pos.can_use_volume, + 'cost_price': pos.avg_price, + 'current_price': pos.last_price, + 'market_value': pos.market_value, + 'profit_loss': pos.unrealized_profit, + 'profit_loss_pct': (pos.last_price - pos.avg_price) / pos.avg_price * 100 if pos.avg_price > 0 else 0, + 'holding_days': holding_days + }) + + return result + + except Exception as e: + self.logger.error(f"获取所有持仓失败: {e}") + return [] + + def buy_stock(self, stock_code: str, quantity: int, + price: float = 0, order_type: str = 'market') -> Dict: + """ + 买入股票 + + Args: + stock_code: 股票代码（如：600519.SH） + quantity: 数量（股，必须是100的整数倍） + price: 价格（限价单时使用，市价单可为0） + order_type: 订单类型 ('market': 市价, 'limit': 限价) + + Returns: + 订单结果 + """ + if not self.connected: + return { + 'success': False, + 'error': 'miniQMT未连接', + 'message': '模拟模式：买入订单已记录但未实际执行' + } + + # 检查数量是否是100的整数倍 + if quantity % 100 != 0: + return { + 'success': False, + 'error': 'A股买入数量必须是100的整数倍（1手=100股）' + } + + try: + # 构造完整股票代码（带市场后缀） + full_code = self._format_stock_code(stock_code) + + # 根据订单类型选择价格类型 + if order_type == 'market': + # 市价单 + price_type = self.xttrader.XTP_PRICE_MARKET_OR_CANCEL # 市价剩余转限价 + else: + # 限价单 + price_type = self.xttrader.XTP_PRICE_LIMIT + + # 下单 + order_id = self.xt_trader.order_stock( + account=self.account, + stock_code=full_code, + order_type=self.xttrader.XTP_SIDE_BUY, # 买入 + order_volume=quantity, + price_type=price_type, + price=price + ) + + if order_id > 0: + self.logger.info(f"买入订单已提交: {stock_code}, 数量: {quantity}, 订单号: {order_id}") + return { + 'success': True, + 'order_id': order_id, + 'stock_code': stock_code, + 'quantity': quantity, + 'price': price, + 'order_type': order_type, + 'message': '买入订单已提交' + } + else: + return { + 'success': False, + 'error': f'下单失败，订单号: {order_id}' + } + + except Exception as e: + self.logger.error(f"买入股票失败 {stock_code}: {e}") + return { + 'success': False, + 'error': str(e) + } + + def sell_stock(self, stock_code: str, quantity: int, + price: float = 0, order_type: str = 'market') -> Dict: + """ + 卖出股票 + + Args: + stock_code: 股票代码 + quantity: 数量（股） + price: 价格（限价单时使用） + order_type: 订单类型 ('market': 市价, 'limit': 限价) + + Returns: + 订单结果 + """ + if not self.connected: + return { + 'success': False, + 'error': 'miniQMT未连接', + 'message': '模拟模式：卖出订单已记录但未实际执行' + } + + try: + # 检查是否有持仓 + position = self.get_position(stock_code) + if not position: + return { + 'success': False, + 'error': f'未持有股票 {stock_code}' + } + + # 检查可卖数量（T+1限制） + if quantity > position['can_sell']: + return { + 'success': False, + 'error': f'可卖数量不足（可卖: {position["can_sell"]}股，T+1限制）' + } + + # 构造完整股票代码 + full_code = self._format_stock_code(stock_code) + + # 根据订单类型选择价格类型 + if order_type == 'market': + price_type = self.xttrader.XTP_PRICE_MARKET_OR_CANCEL + else: + price_type = self.xttrader.XTP_PRICE_LIMIT + + # 下单 + order_id = self.xt_trader.order_stock( + account=self.account, + stock_code=full_code, + order_type=self.xttrader.XTP_SIDE_SELL, # 卖出 + order_volume=quantity, + price_type=price_type, + price=price + ) + + if order_id > 0: + self.logger.info(f"卖出订单已提交: {stock_code}, 数量: {quantity}, 订单号: {order_id}") + return { + 'success': True, + 'order_id': order_id, + 'stock_code': stock_code, + 'quantity': quantity, + 'price': price, + 'order_type': order_type, + 'message': '卖出订单已提交' + } + else: + return { + 'success': False, + 'error': f'下单失败，订单号: {order_id}' + } + + except Exception as e: + self.logger.error(f"卖出股票失败 {stock_code}: {e}") + return { + 'success': False, + 'error': str(e) + } + + def cancel_order(self, order_id: int) -> bool: + """ + 撤销订单 + + Args: + order_id: 订单ID + + Returns: + 是否撤销成功 + """ + if not self.connected: + return False + + try: + result = self.xt_trader.cancel_order_stock(self.account, order_id) + if result == 0: + self.logger.info(f"订单已撤销: {order_id}") + return True + else: + self.logger.error(f"撤销订单失败: {order_id}, 错误码: {result}") + return False + except Exception as e: + self.logger.error(f"撤销订单失败: {e}") + return False + + def get_orders(self, stock_code: str = None) -> List[Dict]: + """ + 获取当日委托订单 + + Args: + stock_code: 股票代码（可选，不传则返回所有订单） + + Returns: + 订单列表 + """ + if not self.connected: + return [] + + try: + orders = self.xt_trader.query_stock_orders(self.account) + + if not orders: + return [] + + result = [] + for order in orders: + # 如果指定了股票代码，则过滤 + if stock_code and order.stock_code != self._format_stock_code(stock_code): + continue + + result.append({ + 'order_id': order.order_id, + 'stock_code': order.stock_code, + 'stock_name': getattr(order, 'stock_name', ''), + 'order_type': '买入' if order.order_type == self.xttrader.XTP_SIDE_BUY else '卖出', + 'price': order.price, + 'quantity': order.order_volume, + 'traded_quantity': order.traded_volume, + 'status': self._format_order_status(order.order_status), + 'order_time': getattr(order, 'insert_time', '') + }) + + return result + + except Exception as e: + self.logger.error(f"获取订单失败: {e}") + return [] + + def _format_stock_code(self, stock_code: str) -> str: + """ + 格式化股票代码（添加市场后缀） + + Args: + stock_code: 股票代码（如：600519） + + Returns: + 完整代码（如：600519.SH） + """ + # 如果已经包含市场后缀，直接返回 + if '.' in stock_code: + return stock_code + + # 沪市：6开头 + if stock_code.startswith('6'): + return f"{stock_code}.SH" + # 深市：0、3开头 + elif stock_code.startswith(('0', '3')): + return f"{stock_code}.SZ" + else: + return stock_code + + def _format_order_status(self, status: int) -> str: + """格式化订单状态""" + status_map = { + 0: '未报', + 1: '待报', + 2: '已报', + 3: '已报待撤', + 4: '部分待撤', + 5: '部成待撤', + 6: '部撤', + 7: '已撤', + 8: '部成', + 9: '已成', + 10: '废单' + } + return status_map.get(status, f'未知({status})') + + +# 模拟交易类（当miniQMT不可用时使用） +class SmartMonitorQMTSimulator: + """模拟交易（用于测试）""" + + def __init__(self): + self.logger = logging.getLogger(__name__) + self.connected = True + self.cash = 100000 # 模拟资金10万 + self.positions = {} # 模拟持仓 + self.orders = [] # 模拟订单 + self.logger.info("使用模拟交易模式") + + def connect(self, account_id: str) -> bool: + self.logger.info(f"模拟连接成功: {account_id}") + return True + + def disconnect(self): + self.logger.info("模拟断开连接") + + def get_account_info(self) -> Dict: + total_value = self.cash + for pos in self.positions.values(): + total_value += pos['market_value'] + + return { + 'available_cash': self.cash, + 'total_value': total_value, + 'positions_count': len(self.positions), + 'total_profit_loss': sum(pos['profit_loss'] for pos in self.positions.values()) + } + + def get_position(self, stock_code: str) -> Optional[Dict]: + return self.positions.get(stock_code) + + def get_all_positions(self) -> List[Dict]: + """获取所有持仓""" + return list(self.positions.values()) + + def buy_stock(self, stock_code: str, quantity: int, + price: float = 0, order_type: str = 'market') -> Dict: + cost = quantity * price if price > 0 else quantity * 10 # 假设价格 + if cost > self.cash: + return {'success': False, 'error': '资金不足'} + + self.cash -= cost + + # 获取股票名称（简化版，实际应该从数据源获取） + stock_name = f"股票{stock_code}" + + self.positions[stock_code] = { + 'stock_code': stock_code, + 'stock_name': stock_name, + 'quantity': quantity, + 'can_sell': 0, # T+1，今天买入不能卖 + 'cost_price': price if price > 0 else 10, + 'current_price': price if price > 0 else 10, + 'market_value': cost, + 'profit_loss': 0, + 'profit_loss_pct': 0, + 'holding_days': 0, + 'buy_date': datetime.now().strftime('%Y%m%d') + } + + # 记录订单 + self.orders.append({ + 'order_id': len(self.orders) + 1, + 'stock_code': stock_code, + 'order_type': 'BUY', + 'quantity': quantity, + 'price': price, + 'status': '已成交', + 'order_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + }) + + self.logger.info(f"[模拟] 买入 {stock_code} {quantity}股 @ {price:.2f}元") + return {'success': True, 'order_id': len(self.orders), 'message': '模拟买入成功'} + + def sell_stock(self, stock_code: str, quantity: int, + price: float = 0, order_type: str = 'market') -> Dict: + if stock_code not in self.positions: + return {'success': False, 'error': '未持有该股票'} + + pos = self.positions[stock_code] + if quantity > pos['can_sell']: + return {'success': False, 'error': 'T+1限制，今天买入不能卖'} + + sell_price = price if price > 0 else pos['current_price'] + revenue = quantity * sell_price + self.cash += revenue + + # 计算盈亏 + profit_loss = (sell_price - pos['cost_price']) * quantity + + # 记录订单 + self.orders.append({ + 'order_id': len(self.orders) + 1, + 'stock_code': stock_code, + 'order_type': 'SELL', + 'quantity': quantity, + 'price': sell_price, + 'status': '已成交', + 'order_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S') + }) + + # 更新或删除持仓 + if quantity >= pos['quantity']: + del self.positions[stock_code] + else: + pos['quantity'] -= quantity + pos['can_sell'] -= quantity + pos['market_value'] = pos['quantity'] * pos['current_price'] + + self.logger.info(f"[模拟] 卖出 {stock_code} {quantity}股 @ {sell_price:.2f}元, 盈亏: {profit_loss:+.2f}元") + return { + 'success': True, + 'order_id': len(self.orders), + 'profit_loss': profit_loss, + 'message': '模拟卖出成功' + } + + def cancel_order(self, order_id: int) -> bool: + """撤销订单（模拟）""" + self.logger.info(f"[模拟] 撤销订单 {order_id}") + return True + + def get_orders(self, stock_code: str = None) -> List[Dict]: + """获取订单列表（模拟）""" + return self.orders + diff --git a/smart_monitor_ui.py b/smart_monitor_ui.py new file mode 100644 index 0000000..cae708d --- /dev/null +++ b/smart_monitor_ui.py @@ -0,0 +1,897 @@ +""" +智能盯盘 - UI界面 +集成到主程序的智能盯盘功能界面 +""" + +import streamlit as st +import pandas as pd +from datetime import datetime +import logging +import os +from typing import Dict +from dotenv import load_dotenv + +from smart_monitor_engine import SmartMonitorEngine +from smart_monitor_db import SmartMonitorDB +from config_manager import config_manager # 使用主程序的配置管理器 + + +# 加载环境变量 +load_dotenv() + + +def smart_monitor_ui(): + """AI盯盘主界面""" + + st.title("🤖 AI盯盘 - AI决策交易系统") + st.caption("参照AlphaArena项目，基于DeepSeek AI的A股自动化交易系统") + + # 使用说明 + with st.expander("📖 快速使用指南", expanded=False): + st.markdown(""" + ### 🚀 快速开始 + + **第一步：环境配置** + 1. 点击左侧菜单"⚙️ 环境配置" + 2. 填写 DeepSeek API Key（必需） + 3. 配置 miniQMT 账户（可选，用于实盘交易） + 4. 配置通知方式（可选，邮件/Webhook） + + **第二步：开始使用** + - **实时分析**：输入股票代码，AI即时分析并给出交易建议 + - **监控任务**：添加股票到监控列表，定时自动分析 + - **持仓管理**：查看和管理当前持仓（已持仓股票可直接监控） + + --- + + ### 💡 核心功能 + + | 功能 | 说明 | + |------|------| + | 📊 **实时分析** | 输入股票代码，AI分析市场数据并给出买入/卖出/持有建议 | + | 🎯 **监控任务** | 定时自动分析目标股票，可设置自动交易 | + | 📈 **持仓管理** | 记录持仓成本，实时显示盈亏，AI决策考虑持仓情况 | + | 📜 **历史记录** | 查看所有AI决策历史、交易记录和通知记录 | + | ⚙️ **系统设置** | 配置API、交易方式（实盘/模拟）、通知等 | + + --- + + ### 🎯 AI决策逻辑 + + **买入信号**（至少满足3个）： + 1. ✅ 趋势向上：价格 > MA5 > MA20 > MA60（多头排列） + 2. ✅ 量价配合：成交量 > 5日均量的120%（放量上涨） + 3. ✅ MACD金叉：MACD > 0 且DIF上穿DEA + 4. ✅ RSI健康：RSI在50-70区间（不超买不超卖） + 5. ✅ 突破关键位：突破前期高点或重要阻力位 + 6. ✅ 布林带位置：价格接近布林中轨上方，有上行空间 + + **卖出信号**（满足任一立即卖出）： + 1. 🔴 止损触发：亏损 ≥ -5%（明天开盘立即卖出） + 2. 🟢 止盈触发：盈利 ≥ +10%（锁定收益） + 3. 🔴 趋势转弱：跌破MA20/MA60，MACD死叉 + 4. 🔴 放量下跌：成交量放大但价格下跌 + 5. 🔴 技术破位：跌破重要支撑位 + + --- + + ### ⚠️ A股T+1规则 + + **关键限制**： + - 今天买入的股票，**今天不能卖出** + - 必须等到下一个交易日才能卖出 + - 系统会自动检查并遵守T+1规则 + + **建议**： + - **宁可错过，不可做错** - 买入前务必确认趋势 + - 单只股票仓位 ≤ 30%（T+1风险较大） + - 止损位：-5%（明天开盘立即执行） + - 止盈位：+8-15%（分批止盈） + + --- + + ### 🔧 使用技巧 + + **新手建议**： + 1. 先使用"模拟交易"模式测试 + 2. 小仓位试水（建议5-10%） + 3. 严格执行止损，不要心存侥幸 + 4. 关注交易时段（9:30-11:30, 13:00-15:00） + + **高级功能**： + - 在"监控任务"中勾选"已持仓"，填入成本价 + - AI会考虑当前盈亏情况给出更准确的建议 + - 可设置多个监控任务，同时盯盘多只股票 + + --- + + ### 📞 常见问题 + + **Q: 提示"DeepSeek API调用失败"？** + - 检查API Key是否正确 + - 确认API账户余额充足 + - 检查网络连接 + + **Q: 数据显示为0或获取失败？** + - 可能是非交易时间 + - AKShare接口可能暂时不可用 + - 尝试更换股票代码测试 + + **Q: 想实盘交易如何操作？** + 1. 下载并安装 [miniQMT](https://www.xtp-mini.com/) + 2. 启动miniQMT客户端并登录 + 3. 在"系统设置"中填写账户ID + 4. 取消勾选"使用模拟交易" + + --- + + ### ⚠️ 风险提示 + + 1. **股市有风险，投资需谨慎** + 2. AI决策仅供参考，不构成投资建议 + 3. 建议先使用模拟交易充分测试 + 4. 严格控制仓位，不要满仓操作 + 5. 不要投入超过承受能力的资金 + + --- + + **🎉 祝您交易顺利！如有问题，请查看详细文档或联系技术支持。** + """) + + st.markdown("---") + + # 初始化组件（自动从配置读取） + if 'engine' not in st.session_state: + try: + # SmartMonitorEngine会自动从config_manager读取配置 + st.session_state.engine = SmartMonitorEngine() + st.session_state.db = SmartMonitorDB() + except Exception as e: + st.error(f"初始化失败: {e}") + st.error("请先在'环境配置'中完成基础配置") + return + + # 创建标签页 + tabs = st.tabs([ + "📊 实时分析", + "🎯 监控任务", + "📈 持仓管理", + "📜 历史记录", + "⚙️ 系统设置" + ]) + + # 标签页1: 实时分析 + with tabs[0]: + render_realtime_analysis() + + # 标签页2: 监控任务 + with tabs[1]: + render_monitor_tasks() + + # 标签页3: 持仓管理 + with tabs[2]: + render_position_management() + + # 标签页4: 历史记录 + with tabs[3]: + render_history() + + # 标签页5: 系统设置 + with tabs[4]: + render_settings() + + +def render_realtime_analysis(): + """实时分析界面""" + + st.header("📊 实时分析") + + col1, col2 = st.columns([2, 1]) + + with col1: + stock_code = st.text_input( + "输入股票代码", + placeholder="例如: 600519", + help="输入6位股票代码" + ) + + with col2: + auto_trade = st.checkbox("自动交易", value=False, + help="开启后AI会自动执行交易决策") + + if st.button("🔍 开始分析", type="primary"): + if not stock_code: + st.error("请输入股票代码") + return + + if len(stock_code) != 6 or not stock_code.isdigit(): + st.error("股票代码格式错误，请输入6位数字") + return + + # 显示进度 + with st.spinner('正在分析...'): + engine = st.session_state.engine + result = engine.analyze_stock( + stock_code=stock_code, + auto_trade=auto_trade, + notify=True + ) + + if result['success']: + # 显示分析结果 + display_analysis_result(result) + else: + st.error(f"分析失败: {result.get('error')}") + + +def display_analysis_result(result: dict): + """显示分析结果""" + + stock_code = result['stock_code'] + stock_name = result['stock_name'] + decision = result['decision'] + market_data = result['market_data'] + session_info = result['session_info'] + + st.success(f"✅ 分析完成: {stock_code} {stock_name}") + + # 交易时段信息 + st.info(f"⏰ 当前时段: {session_info['session']} - {session_info['recommendation']}") + + # AI决策 + st.markdown("### 🤖 AI决策") + + col1, col2, col3, col4 = st.columns(4) + + # 决策动作 + action = decision['action'] + action_emoji = {"BUY": "📈", "SELL": "📉", "HOLD": "⏸️"} + action_color = {"BUY": "green", "SELL": "red", "HOLD": "gray"} + + col1.metric("决策", action, delta=None) + col2.metric("信心度", f"{decision['confidence']}%") + col3.metric("风险等级", decision.get('risk_level', 'N/A')) + col4.metric("建议仓位", f"{decision.get('position_size_pct', 0)}%") + + # 决策理由 + st.markdown("**决策理由:**") + st.text_area("决策理由", decision['reasoning'], height=150, disabled=True, label_visibility="hidden") + + # 市场数据 + st.markdown("### 📊 市场数据") + + col1, col2, col3, col4 = st.columns(4) + col1.metric("当前价", f"¥{market_data.get('current_price', 0):.2f}") + col2.metric("涨跌幅", f"{market_data.get('change_pct', 0):+.2f}%") + col3.metric("成交量", f"{market_data.get('volume', 0):,.0f}手") + col4.metric("换手率", f"{market_data.get('turnover_rate', 0):.2f}%") + + # 技术指标 + st.markdown("### 📈 技术指标") + + tech_col1, tech_col2, tech_col3 = st.columns(3) + + with tech_col1: + st.markdown("**均线系统**") + st.write(f"MA5: ¥{market_data.get('ma5', 0):.2f}") + st.write(f"MA20: ¥{market_data.get('ma20', 0):.2f}") + st.write(f"MA60: ¥{market_data.get('ma60', 0):.2f}") + st.write(f"趋势: {market_data.get('trend', 'N/A')}") + + with tech_col2: + st.markdown("**动量指标**") + st.write(f"MACD: {market_data.get('macd', 0):.4f}") + st.write(f"DIF: {market_data.get('macd_dif', 0):.4f}") + st.write(f"DEA: {market_data.get('macd_dea', 0):.4f}") + + with tech_col3: + st.markdown("**摆动指标**") + st.write(f"RSI(6): {market_data.get('rsi6', 0):.2f}") + st.write(f"RSI(12): {market_data.get('rsi12', 0):.2f}") + st.write(f"RSI(24): {market_data.get('rsi24', 0):.2f}") + + # 主力资金（已禁用 - 接口不稳定） + # if 'main_force' in market_data: + # st.markdown("### 💰 主力资金") + # mf = market_data['main_force'] + # + # mf_col1, mf_col2, mf_col3 = st.columns(3) + # mf_col1.metric("主力净额", f"{mf['main_net']:,.2f}万", + # delta=f"{mf['main_net_pct']:+.2f}%") + # mf_col2.metric("超大单", f"{mf['super_net']:,.2f}万") + # mf_col3.metric("大单", f"{mf['big_net']:,.2f}万") + # + # st.info(f"主力动向: {mf['trend']}") + + # 执行结果（如果有） + if result.get('execution_result'): + exec_result = result['execution_result'] + st.markdown("### ⚡ 执行结果") + + if exec_result.get('success'): + st.success(f"✅ {exec_result.get('message', '执行成功')}") + else: + st.error(f"❌ {exec_result.get('error', '执行失败')}") + + +def render_monitor_tasks(): + """监控任务界面""" + + st.header("🎯 监控任务管理") + + db = st.session_state.db + engine = st.session_state.engine + + # 添加新任务 + with st.expander("➕ 添加新监控任务", expanded=True): + # 改回使用form，确保值正确提交 + with st.form("add_monitor_task_form", clear_on_submit=False): + col1, col2 = st.columns(2) + + with col1: + task_name = st.text_input("任务名称", placeholder="例如: 茅台盯盘") + stock_code = st.text_input("股票代码", placeholder="例如: 600519") + check_interval = st.slider("检查间隔(秒)", 60, 3600, 300) + + # 持仓信息 + st.markdown("---") + st.markdown("**📊 持仓信息**") + has_position = st.checkbox("已持仓该股票", value=False, + help="勾选后可填写持仓成本和数量，AI会考虑持仓情况") + + # 注意：在form内部，复选框的变化要到提交后才能看到 + # 所以持仓输入框始终显示，用户可以选择填写或不填写 + position_cost = st.number_input("持仓成本(元)", min_value=0.01, value=10.0, step=0.01, + help="如果已持仓，填写买入时的成本价格（未持仓可忽略）") + position_quantity = st.number_input("持仓数量(股)", min_value=100, value=100, step=100, + help="如果已持仓，填写持有的股票数量（未持仓可忽略）") + + with col2: + auto_trade = st.checkbox("自动交易", value=False, + help="AI决策后自动执行交易") + position_size = st.slider("仓位百分比(%)", 5, 50, 20, + help="新建仓位时使用的资金比例") + notify_email = st.text_input("通知邮箱（可选）") + + # 添加任务按钮（表单提交按钮） + submitted = st.form_submit_button("➕ 添加任务", type="primary", use_container_width=True) + + if submitted: + # 验证必填项（form中直接使用局部变量） + if not task_name or not stock_code: + st.error("❌ 请填写必填项：任务名称和股票代码") + else: + + try: + # 检查是否已存在该股票的监控任务 + existing_tasks = db.get_monitor_tasks(enabled_only=False) + existing_task = next((t for t in existing_tasks if t['stock_code'] == stock_code), None) + + if existing_task: + st.error(f"❌ 股票代码 {stock_code} 已存在监控任务！") + st.warning(f"任务名称: {existing_task['task_name']}") + st.info("💡 请在下方任务列表中找到该任务，点击启动或删除后重新添加") + else: + # 创建任务（初始状态为禁用，需要用户手动启动） + task_data = { + 'task_name': task_name, + 'stock_code': stock_code, + 'enabled': 0, # 关键修改：初始状态为禁用，不自动启动 + 'check_interval': check_interval, + 'auto_trade': 1 if auto_trade else 0, + 'position_size_pct': position_size, + 'notify_email': notify_email, + 'has_position': 1 if has_position else 0, + 'position_cost': position_cost if has_position else 0, + 'position_quantity': position_quantity if has_position else 0, + 'position_date': datetime.now().strftime('%Y-%m-%d') if has_position else None + } + + task_id = db.add_monitor_task(task_data) + + st.success(f"✅ 任务创建成功! ID: {task_id}") + if has_position: + st.info(f"📊 已记录持仓: {position_quantity}股 @ {position_cost:.2f}元") + st.info("💡 任务已创建但未启动，请在下方任务列表中点击'▶️ 启动'按钮开始监控") + + st.rerun() + except Exception as e: + error_msg = str(e) + if "UNIQUE constraint failed" in error_msg: + st.error(f"❌ 股票代码 {stock_code} 已存在监控任务！") + st.info("💡 请在下方任务列表中找到该任务") + else: + st.error(f"创建失败: {error_msg}") + + # 显示任务列表 + st.markdown("### 📋 监控任务列表") + + tasks = db.get_monitor_tasks(enabled_only=False) + + if not tasks: + st.info("暂无监控任务，点击上方'添加新监控任务'创建") + return + + for task in tasks: + with st.container(): + # 获取实时价格计算盈亏 + has_position = task.get('has_position', 0) + position_cost = task.get('position_cost', 0) + position_quantity = task.get('position_quantity', 0) + + # 尝试获取当前价格 + current_price = 0 + profit_loss = 0 + profit_loss_pct = 0 + + if has_position and position_cost > 0 and position_quantity > 0: + try: + # 获取实时行情 + from smart_monitor_data import SmartMonitorDataFetcher + data_fetcher = SmartMonitorDataFetcher() + quote = data_fetcher.get_realtime_quote(task['stock_code'], retry=1) + if quote: + current_price = quote.get('current_price', 0) + if current_price > 0: + # 计算盈亏 + cost_total = position_cost * position_quantity + current_total = current_price * position_quantity + profit_loss = current_total - cost_total + profit_loss_pct = (profit_loss / cost_total) * 100 + except Exception as e: + pass + + col1, col2, col3, col4, col5 = st.columns([2, 2, 1.5, 1, 1]) + + with col1: + st.write(f"**{task['task_name']}**") + st.caption(f"{task['stock_code']} - 间隔{task['check_interval']}秒") + + with col2: + status = "✅ 已启用" if task['enabled'] else "⏸️ 已禁用" + auto_trade_status = "🤖 自动交易" if task['auto_trade'] else "👀 仅监控" + st.write(status) + st.caption(auto_trade_status) + + # 显示持仓状态 + if has_position: + st.caption(f"📊 持仓: {position_quantity}股 @ {position_cost:.2f}元") + + with col3: + is_running = task['stock_code'] in engine.monitoring_threads + if is_running: + st.success("▶️ 运行中") + else: + st.info("⏸️ 未运行") + + # 显示盈亏 + if has_position and current_price > 0: + if profit_loss > 0: + st.success(f"💰 +{profit_loss:.2f}元 ({profit_loss_pct:+.2f}%)") + elif profit_loss < 0: + st.error(f"📉 {profit_loss:.2f}元 ({profit_loss_pct:+.2f}%)") + else: + st.info("持平") + + with col4: + if is_running: + if st.button("⏹️ 停止", key=f"stop_{task['id']}"): + engine.stop_monitor(task['stock_code']) + # 停止时更新数据库状态为禁用 + db.update_monitor_task(task['stock_code'], {'enabled': 0}) + st.success("已停止") + st.rerun() + else: + # 启动按钮始终可点击（只要任务未运行） + if st.button("▶️ 启动", key=f"start_{task['id']}"): + # 启动监控 + engine.start_monitor( + stock_code=task['stock_code'], + check_interval=task['check_interval'], + auto_trade=task['auto_trade'] == 1, + notify=True, + has_position=has_position == 1, + position_cost=position_cost, + position_quantity=position_quantity + ) + # 启动时更新数据库状态为启用 + db.update_monitor_task(task['stock_code'], {'enabled': 1}) + st.success("已启动") + st.rerun() + + with col5: + if st.button("🗑️ 删除", key=f"del_{task['id']}"): + # 如果正在运行，先停止 + if task['stock_code'] in engine.monitoring_threads: + engine.stop_monitor(task['stock_code']) + + db.delete_monitor_task(task['id']) + st.success("已删除") + st.rerun() + + # K线图和AI决策详情（可展开） + with st.expander(f"📊 K线图 & AI决策 - {task['task_name']}", expanded=False): + _render_task_kline_and_decisions(task, db, engine) + + st.markdown("---") + + +def render_position_management(): + """持仓管理界面""" + + st.header("📈 持仓管理") + + engine = st.session_state.engine + qmt = engine.qmt + + # 获取账户信息 + account_info = qmt.get_account_info() + + st.markdown("### 💰 账户概览") + + col1, col2, col3, col4 = st.columns(4) + col1.metric("总资产", f"¥{account_info['total_value']:,.2f}") + col2.metric("可用资金", f"¥{account_info['available_cash']:,.2f}") + col3.metric("持仓数量", f"{account_info['positions_count']}个") + col4.metric("总盈亏", f"¥{account_info['total_profit_loss']:,.2f}") + + # 获取持仓列表 + positions = qmt.get_all_positions() + + if not positions: + st.info("当前无持仓") + return + + st.markdown("### 📊 持仓列表") + + # 转换为DataFrame + df = pd.DataFrame(positions) + + # 显示表格 + st.dataframe( + df[[ + 'stock_code', 'stock_name', 'quantity', 'can_sell', + 'cost_price', 'current_price', 'profit_loss', 'profit_loss_pct' + ]], + column_config={ + "stock_code": "代码", + "stock_name": "名称", + "quantity": "持仓", + "can_sell": "可卖", + "cost_price": "成本价", + "current_price": "现价", + "profit_loss": "盈亏", + "profit_loss_pct": "盈亏%" + }, + hide_index=True, + use_container_width=True + ) + + # 单只股票操作 + st.markdown("### ⚡ 快速操作") + + selected_stock = st.selectbox( + "选择股票", + options=[f"{p['stock_code']} {p['stock_name']}" for p in positions] + ) + + col1, col2 = st.columns(2) + + with col1: + if st.button("🔍 AI分析", type="secondary"): + stock_code = selected_stock.split()[0] + with st.spinner("分析中..."): + result = engine.analyze_stock(stock_code, auto_trade=False) + if result['success']: + st.success("分析完成，查看'实时分析'标签页") + + with col2: + if st.button("📤 卖出", type="primary"): + stock_code = selected_stock.split()[0] + # 这里可以添加卖出确认对话框 + st.warning("请在'实时分析'中使用AI决策后卖出") + + +def render_history(): + """历史记录界面""" + + st.header("📜 历史记录") + + db = st.session_state.db + + tab1, tab2, tab3 = st.tabs(["AI决策历史", "交易记录", "通知记录"]) + + # AI决策历史 + with tab1: + st.subheader("🤖 AI决策历史") + + decisions = db.get_ai_decisions(limit=50) + + if not decisions: + st.info("暂无决策记录") + else: + for dec in decisions: + with st.expander( + f"{dec['decision_time']} - {dec['stock_code']} {dec['stock_name']} " + f"- {dec['action']} (信心度{dec['confidence']}%)" + ): + col1, col2 = st.columns([1, 3]) + + with col1: + st.write(f"**时段:** {dec['trading_session']}") + st.write(f"**风险:** {dec['risk_level']}") + st.write(f"**仓位:** {dec['position_size_pct']}%") + + with col2: + st.write("**决策理由:**") + st.text(dec['reasoning']) + + # 交易记录 + with tab2: + st.subheader("💱 交易记录") + + trades = db.get_trade_records(limit=50) + + if not trades: + st.info("暂无交易记录") + else: + df = pd.DataFrame(trades) + st.dataframe( + df[[ + 'trade_time', 'stock_code', 'stock_name', 'trade_type', + 'quantity', 'price', 'amount', 'profit_loss' + ]], + column_config={ + "trade_time": "时间", + "stock_code": "代码", + "stock_name": "名称", + "trade_type": "类型", + "quantity": "数量", + "price": "价格", + "amount": "金额", + "profit_loss": "盈亏" + }, + hide_index=True, + use_container_width=True + ) + + # 通知记录 + with tab3: + st.subheader("📬 通知记录") + st.info("通知记录功能开发中...") + + +def render_settings(): + """系统设置界面（跳转到主程序的环境配置）""" + + st.header("⚙️ 系统设置") + + st.info(""" + ### 📌 配置说明 + + 智能盯盘使用主程序的统一配置系统，包括： + - 🤖 **DeepSeek API** - AI决策引擎 + - 🔌 **MiniQMT** - 量化交易接口 + - 📧 **邮件通知** - SMTP配置 + - 🔔 **Webhook** - 钉钉/飞书通知 + + 请前往主程序的 **"环境配置"** 页面进行统一配置。 + """) + + # 显示当前配置状态 + st.markdown("### 📊 当前配置状态") + + config = config_manager.read_env() + + col1, col2 = st.columns(2) + + with col1: + st.markdown("**🤖 DeepSeek API**") + api_key = config.get('DEEPSEEK_API_KEY', '') + if api_key: + st.success(f"✅ 已配置（{api_key[:8]}...）") + else: + st.error("❌ 未配置") + + st.markdown("**🔌 MiniQMT**") + miniqmt_enabled = config.get('MINIQMT_ENABLED', 'false').lower() == 'true' + if miniqmt_enabled: + account_id = config.get('MINIQMT_ACCOUNT_ID', '') + st.success(f"✅ 已启用（账户：{account_id or '未设置'}）") + else: + st.warning("⚠️ 未启用（使用模拟交易）") + + with col2: + st.markdown("**📧 邮件通知**") + email_enabled = config.get('EMAIL_ENABLED', 'false').lower() == 'true' + if email_enabled: + email_to = config.get('EMAIL_TO', '') + st.success(f"✅ 已启用（{email_to}）") + else: + st.warning("⚠️ 未启用") + + st.markdown("**🔔 Webhook通知**") + webhook_enabled = config.get('WEBHOOK_ENABLED', 'false').lower() == 'true' + if webhook_enabled: + webhook_type = config.get('WEBHOOK_TYPE', 'dingtalk') + st.success(f"✅ 已启用（{webhook_type}）") + else: + st.warning("⚠️ 未启用") + + st.markdown("---") + + # 快速跳转按钮 + st.markdown("### 🔧 配置管理") + + st.info(""" + **配置步骤：** + 1. 点击左侧菜单 → **"环境配置"** + 2. 填写所需的配置项 + 3. 点击 **"保存配置"** + 4. 返回智能盯盘页面 + 5. 刷新页面使配置生效 + """) + + if st.button("🔄 重新加载配置", type="primary"): + config_manager.reload_config() + st.success("✅ 配置已重新加载") + st.info("💡 如果修改了配置，请刷新页面（Ctrl+R）") + st.rerun() + + +def _render_task_kline_and_decisions(task: Dict, db: SmartMonitorDB, engine): + """ + 渲染单个任务的K线图和AI决策 + + Args: + task: 任务信息 + db: 数据库实例 + engine: 监控引擎实例 + """ + from smart_monitor_kline import SmartMonitorKline + from smart_monitor_data import SmartMonitorDataFetcher + + stock_code = task['stock_code'] + stock_name = task.get('stock_name', stock_code) + + # 创建两列：左侧K线图，右侧AI决策列表 + col_chart, col_decisions = st.columns([2, 1]) + + with col_chart: + st.markdown("#### 📈 K线图") + + # 添加刷新按钮 + if st.button("🔄 刷新K线", key=f"refresh_kline_{task['id']}"): + st.rerun() + + # 获取K线数据 + try: + kline = SmartMonitorKline() + data_fetcher = SmartMonitorDataFetcher() + + # 获取K线数据（60天） + with st.spinner(f"正在获取 {stock_code} 的K线数据..."): + kline_data = kline.get_kline_data(stock_code, days=60, data_fetcher=data_fetcher) + + if kline_data is not None and not kline_data.empty: + # 获取AI决策历史（最近100条，用于K线图标注） + ai_decisions = db.get_ai_decisions( + stock_code=stock_code, + limit=100 + ) + + # 过滤最近30天的决策（用于K线图标注） + from datetime import timedelta + if ai_decisions: + start_date = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d') + ai_decisions = [ + d for d in ai_decisions + if d.get('decision_time', '').split()[0] >= start_date + ] + + # 创建K线图 + fig = kline.create_kline_with_decisions( + stock_code=stock_code, + stock_name=stock_name, + kline_data=kline_data, + ai_decisions=ai_decisions, + show_volume=True, + show_ma=True, + height=500 + ) + + # 显示图表 + st.plotly_chart(fig, use_container_width=True) + + st.caption(f"📅 数据时间范围：{kline_data['日期'].min()} ~ {kline_data['日期'].max()}") + else: + st.error(f"❌ 无法获取 {stock_code} 的K线数据") + + except Exception as e: + st.error(f"❌ K线图加载失败: {str(e)}") + import traceback + st.text(traceback.format_exc()) + + with col_decisions: + st.markdown("#### 🤖 AI决策历史") + + # 添加刷新按钮 + if st.button("🔄 刷新决策", key=f"refresh_decisions_{task['id']}"): + st.rerun() + + # 获取最近的AI决策（最近5条） + try: + recent_decisions = db.get_ai_decisions( + stock_code=stock_code, + limit=5 + ) + + if recent_decisions: + for idx, decision in enumerate(recent_decisions): + action = decision.get('action', 'unknown') + decision_time = decision.get('decision_time', '') + confidence = decision.get('confidence', 0) + reasoning = decision.get('reasoning', '无') + executed = decision.get('executed', 0) + + # 决策类型图标和颜色 + action_icons = { + 'buy': '🔺', + 'sell': '🔻', + 'add_position': '⬆️', + 'reduce_position': '⬇️', + 'hold': '⏸️' + } + + action_colors = { + 'buy': '#ef5350', + 'sell': '#26a69a', + 'add_position': '#ff9800', + 'reduce_position': '#9c27b0', + 'hold': '#607d8b' + } + + action_names = { + 'buy': '买入', + 'sell': '卖出', + 'add_position': '加仓', + 'reduce_position': '减仓', + 'hold': '持有' + } + + icon = action_icons.get(action, '❓') + color = action_colors.get(action, '#000000') + action_name = action_names.get(action, action) + + # 显示决策卡片 + with st.container(): + st.markdown(f""" +

+ """, unsafe_allow_html=True) + + st.markdown("---") + else: + st.info("📭 暂无AI决策记录") + st.caption("启动监控后，AI会定期分析并记录决策") + + except Exception as e: + st.error(f"❌ 加载决策历史失败: {str(e)}") + + +if __name__ == '__main__': + smart_monitor_ui() + diff --git a/stock_analysis.db b/stock_analysis.db index 4278f0d30d9851517c096aa455f667093656ef5f..e51ce36d93769ec643ecbcb4a0ef8e20b3272592 100644 GIT binary patch delta 174865 zcmceh|G20!r&{>jmhh^#**!A*mCSxEvwLNBpUm!; z*#k0rP-ZJ-_K?h0$!xXE9+uf7GJ8~JzmeG*nLQ@6$7S|}%$}6lQ!;y6W@}~kjLeE; zRxGm;nXQxAdYNsIS*grqnU%?`TxJ_(Rw1)ZGOLtXmCUMTRwJ`onbpZ`v&^>0Y^%(+ z$*f*xB(v=@+aa@^GHZ}oqs(^6tVw2y%$jA^BD38xdsb$9WVTmk`((CXW(Q<;P-f4` z?2yc!m)T*NwaTnbX6-WTkl7KL9hKQJnH`te37NTNc2Z`iWY#IOE}3=9tVd?2W!5XR zGcr3XvlnFcqRd{B*>7d`JDL4nW-rU^6`8#%vp>k}HJQCGvo~b+rp(UC?7YnWD6_X@ z_9vOWEwew%>>Zi?MP`4M*}F1(PiF7S>;sv7D6@}bc0p!+GW%F&pUA9VW*(V&Wi}wQ zL75H7>{FTjO=h3T?C&!BTxJ(#c1dQJW%h;4zLeQNWcHQJzLwcHGW%9$|CHHxGW(az zzL(j*W%h&2u2e9onn}3)aE-y`k1GI|2A39BATAxQAY8$?LU4uR3d0qSD*{&}t|(m5 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z%0Y6lOp!z6P&rHvmm}mzIZBR}sd9`QE62(4GEGj9=`ur3l#}FSIYmyD)8uqHL(Y`5 zWTu=gvt+iMBj?I_a=u(37s^F)v0Nfma;eOb%Ve(16XkNbLgvc?StwV^RkBE~mc_C} zu90hHsVtN0q*|8C3b|fxkQ-&C+$1+kjoc!)%5AbrZkIdcPPt3&mV4x0SuOX;{qlgU zk+t%m)XGEhuskAl@~Au}kINJCq&y|-WW8*Vr{x)WR-TjRqmmlOu`AO>KXZb~bmEYud`9uDczvOTENB))n7LY6=38~N^q=$@<85)MH&?sbw zoRAyxLgSDh3PNFM5}Jmh&@40$EkeuCDzpy8p-pHT+J*L^ByX dIx3U&g3KgUpO&hxE3C=gky>A0QJLiB{SVPNjfwyO diff --git a/test_smart_monitor_data.py b/test_smart_monitor_data.py new file mode 100644 index 0000000..315c850 --- /dev/null +++ b/test_smart_monitor_data.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" +智能盯盘 - 数据获取功能测试脚本 + +用于验证实时行情数据获取是否正常工作 +""" + +import logging +import sys + +# 配置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(levelname)s - %(message)s' +) + +def test_data_fetcher(): + """测试数据获取功能""" + print("\n" + "="*70) + print("智能盯盘 - 数据获取功能测试") + print("="*70 + "\n") + + try: + from smart_monitor_data import SmartMonitorDataFetcher + + # 创建数据获取器 + fetcher = SmartMonitorDataFetcher() + print("✅ 数据获取器初始化成功\n") + + # 测试股票列表（可以自行修改） + test_stocks = [ + ('600519', '贵州茅台'), + ('000001', '平安银行'), + ('002167', '东方锆业') + ] + + for stock_code, stock_name in test_stocks: + print(f"\n{'─'*70}") + print(f"测试股票: {stock_code} ({stock_name})") + print(f"{'─'*70}") + + # 获取实时行情 + quote = fetcher.get_realtime_quote(stock_code) + + if quote: + print(f"\n✅ 数据获取成功:") + print(f" 📌 股票代码: {quote['code']}") + print(f" 📌 股票名称: {quote['name']}") + print(f" 💰 当前价格: ¥{quote['current_price']:.2f}") + print(f" 📊 涨跌幅: {quote['change_pct']:+.2f}%") + print(f" 💵 涨跌额: ¥{quote['change_amount']:+.2f}") + print(f" 📦 成交量: {quote['volume']:.0f}手") + print(f" 💸 成交额: ¥{quote['amount']/10000:.2f}万") + print(f" 📈 最高: ¥{quote['high']:.2f}") + print(f" 📉 最低: ¥{quote['low']:.2f}") + print(f" 🔓 今开: ¥{quote['open']:.2f}") + print(f" 🔒 昨收: ¥{quote['pre_close']:.2f}") + print(f" 🔄 换手率: {quote['turnover_rate']:.2f}%") + print(f" ⏰ 更新时间: {quote['update_time']}") + print(f" 🌐 数据源: {quote['data_source']}") + + # 验证数据是否有效（不全为0） + if quote['current_price'] > 0: + print(f"\n ✅ 数据有效性检查: 通过") + else: + print(f"\n ⚠️ 数据有效性检查: 价格为0，可能是非交易时间") + else: + print(f"\n❌ 获取 {stock_code} 的数据失败") + return False + + print("\n" + "="*70) + print("✅ 所有测试通过！数据获取功能正常工作") + print("="*70 + "\n") + return True + + except ImportError as e: + print(f"❌ 导入模块失败: {e}") + print("请确保 smart_monitor_data.py 文件存在") + return False + except Exception as e: + print(f"❌ 测试过程中出现错误: {e}") + import traceback + traceback.print_exc() + return False + + +if __name__ == '__main__': + print("\n💡 提示：") + print(" - 此脚本用于测试智能盯盘的数据获取功能") + print(" - 需要网络连接以访问AKShare API") + print(" - 如果所有数据都正常显示，说明修复成功") + print(" - 如果仍然显示0，请检查网络或查看日志\n") + + success = test_data_fetcher() + + if success: + print("🎉 恭喜！数据获取功能测试通过，可以正常使用智能盯盘了！\n") + sys.exit(0) + else: + print("⚠️ 测试失败，请查看上方错误信息并联系技术支持\n") + sys.exit(1) + diff --git a/test_tushare_monitoring.py b/test_tushare_monitoring.py new file mode 100644 index 0000000..33753c6 --- /dev/null +++ b/test_tushare_monitoring.py @@ -0,0 +1,247 @@ +""" +测试Tushare数据源是否能满足AI盯盘监控要求 +测试内容： +1. 实时行情数据获取 +2. 技术指标计算 +3. K线图数据获取 +""" + +import logging +import os +from dotenv import load_dotenv + +# 设置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' +) + +# 加载环境变量 +load_dotenv() + +def test_tushare_token(): + """测试Tushare Token是否配置""" + token = os.getenv('TUSHARE_TOKEN', '') + if token: + print(f"✅ Tushare Token已配置: {token[:10]}...") + return True + else: + print("❌ Tushare Token未配置") + return False + + +def test_realtime_quote(stock_code='000063'): + """测试实时行情获取（Tushare降级）""" + print(f"\n{'='*60}") + print(f"测试1: 实时行情数据 - {stock_code}") + print(f"{'='*60}") + + from smart_monitor_data import SmartMonitorDataFetcher + + fetcher = SmartMonitorDataFetcher() + + # 强制使用Tushare（模拟AKShare失败） + print("正在通过Tushare获取实时行情...") + quote = fetcher._get_realtime_quote_from_tushare(stock_code) + + if quote: + print("✅ 实时行情获取成功！") + print(f" 股票名称: {quote.get('stock_name', 'N/A')}") + print(f" 当前价格: ¥{quote.get('current_price', 0):.2f}") + print(f" 涨跌幅: {quote.get('change_pct', 0):+.2f}%") + print(f" 成交量: {quote.get('volume', 0):,}手") + print(f" 换手率: {quote.get('turnover_rate', 0):.2f}%") + print(f" 数据来源: {quote.get('data_source', 'N/A')}") + return True + else: + print("❌ 实时行情获取失败") + return False + + +def test_technical_indicators(stock_code='000063'): + """测试技术指标计算（Tushare降级）""" + print(f"\n{'='*60}") + print(f"测试2: 技术指标计算 - {stock_code}") + print(f"{'='*60}") + + from smart_monitor_data import SmartMonitorDataFetcher + + fetcher = SmartMonitorDataFetcher() + + # 强制使用Tushare + print("正在通过Tushare获取历史数据并计算技术指标...") + indicators = fetcher._get_technical_indicators_from_tushare(stock_code) + + if indicators: + print("✅ 技术指标计算成功！") + print(f"\n均线系统:") + print(f" MA5: {indicators.get('ma5', 0):.2f}") + print(f" MA20: {indicators.get('ma20', 0):.2f}") + print(f" MA60: {indicators.get('ma60', 0):.2f}") + print(f" 趋势: {indicators.get('trend', 'N/A')}") + + print(f"\nMACD指标:") + print(f" DIF: {indicators.get('macd_dif', 0):.4f}") + print(f" DEA: {indicators.get('macd_dea', 0):.4f}") + print(f" MACD: {indicators.get('macd', 0):.4f}") + + print(f"\nRSI指标:") + print(f" RSI6: {indicators.get('rsi6', 0):.2f}") + print(f" RSI12: {indicators.get('rsi12', 0):.2f}") + print(f" RSI24: {indicators.get('rsi24', 0):.2f}") + + print(f"\nKDJ指标:") + print(f" K: {indicators.get('kdj_k', 0):.2f}") + print(f" D: {indicators.get('kdj_d', 0):.2f}") + print(f" J: {indicators.get('kdj_j', 0):.2f}") + + print(f"\n布林带:") + print(f" 上轨: {indicators.get('boll_upper', 0):.2f}") + print(f" 中轨: {indicators.get('boll_mid', 0):.2f}") + print(f" 下轨: {indicators.get('boll_lower', 0):.2f}") + print(f" 位置: {indicators.get('boll_position', 'N/A')}") + + return True + else: + print("❌ 技术指标计算失败") + return False + + +def test_kline_data(stock_code='000063'): + """测试K线图数据获取（Tushare降级）""" + print(f"\n{'='*60}") + print(f"测试3: K线图数据 - {stock_code}") + print(f"{'='*60}") + + from smart_monitor_kline import SmartMonitorKline + from smart_monitor_data import SmartMonitorDataFetcher + + kline = SmartMonitorKline() + fetcher = SmartMonitorDataFetcher() + + # 使用Tushare获取K线数据 + print("正在通过Tushare获取K线数据（60天）...") + df = kline._get_kline_from_tushare(stock_code, days=60, ts_pro=fetcher.ts_pro) + + if df is not None and not df.empty: + print(f"✅ K线数据获取成功！") + print(f" 数据条数: {len(df)}条") + print(f" 日期范围: {df['日期'].min()} ~ {df['日期'].max()}") + print(f"\n数据列:") + for col in df.columns: + print(f" - {col}") + + print(f"\n最近5条数据预览:") + print(df.tail(5)[['日期', '开盘', '最高', '最低', '收盘', '成交量']].to_string()) + + return True + else: + print("❌ K线数据获取失败") + return False + + +def test_full_monitoring_flow(stock_code='000063'): + """测试完整的监控流程（使用Tushare）""" + print(f"\n{'='*60}") + print(f"测试4: 完整监控流程 - {stock_code}") + print(f"{'='*60}") + + from smart_monitor_data import SmartMonitorDataFetcher + + fetcher = SmartMonitorDataFetcher() + + # 1. 获取实时行情 + print("\n步骤1: 获取实时行情...") + quote = fetcher.get_realtime_quote(stock_code, retry=1) + if not quote: + print(" ❌ 实时行情获取失败") + return False + print(f" ✅ 当前价: ¥{quote.get('current_price', 0):.2f}") + + # 2. 计算技术指标 + print("\n步骤2: 计算技术指标...") + indicators = fetcher.get_technical_indicators(stock_code, retry=1) + if not indicators: + print(" ❌ 技术指标计算失败") + return False + print(f" ✅ MA5: {indicators.get('ma5', 0):.2f}, 趋势: {indicators.get('trend', 'N/A')}") + + # 3. 综合数据 + print("\n步骤3: 获取综合数据...") + comprehensive_data = fetcher.get_comprehensive_data(stock_code) + if not comprehensive_data: + print(" ❌ 综合数据获取失败") + return False + + print(" ✅ 综合数据包含:") + print(f" - 实时行情: {comprehensive_data.get('realtime_quote') is not None}") + print(f" - 技术指标: {comprehensive_data.get('technical_indicators') is not None}") + + print("\n✅ 完整监控流程测试通过！") + print(" Tushare可以满足AI盯盘的监控要求") + return True + + +def main(): + """主测试函数""" + print("="*60) + print("Tushare数据源监控能力测试") + print("="*60) + + # 检查Token + if not test_tushare_token(): + print("\n❌ 请在.env文件中配置TUSHARE_TOKEN") + return + + # 测试股票代码 + test_stock = '000063' # 中兴通讯 + + results = [] + + # 测试1: 实时行情 + results.append(("实时行情获取", test_realtime_quote(test_stock))) + + # 测试2: 技术指标 + results.append(("技术指标计算", test_technical_indicators(test_stock))) + + # 测试3: K线数据 + results.append(("K线图数据", test_kline_data(test_stock))) + + # 测试4: 完整流程 + results.append(("完整监控流程", test_full_monitoring_flow(test_stock))) + + # 汇总结果 + print(f"\n{'='*60}") + print("测试结果汇总") + print(f"{'='*60}") + + for test_name, result in results: + status = "✅ 通过" if result else "❌ 失败" + print(f"{test_name:20} {status}") + + all_passed = all(result for _, result in results) + + if all_passed: + print(f"\n{'='*60}") + print("🎉 所有测试通过！") + print(f"{'='*60}") + print("✅ Tushare完全可以满足AI盯盘的监控要求") + print("✅ 数据源降级策略工作正常") + print("✅ 可以在AKShare IP被封时使用Tushare作为备用") + print("\n建议:") + print("1. 保持Tushare Token配置在.env文件中") + print("2. AKShare重试次数已设置为1次，减少IP封禁风险") + print("3. Tushare 10000积分可以支持日常监控需求") + else: + print(f"\n{'='*60}") + print("⚠️ 部分测试失败") + print(f"{'='*60}") + print("请检查:") + print("1. Tushare Token是否有效") + print("2. Tushare积分是否足够") + print("3. 网络连接是否正常") + + +if __name__ == '__main__': + main() +