Initial commit: A股量化交易系统
主要功能: - K线形态策略: 两阳+阴+阳突破形态识别 - 信号时间修复: 使用K线时间而非发送时间 - 换手率约束: 最后阳线换手率不超过40% - 汇总通知: 钉钉webhook单次发送所有信号 - 数据获取: 支持AKShare数据源 - 舆情分析: 北向资金、热门股票等 技术特性: - 支持日线/周线/月线多时间周期 - EMA20趋势确认 - 实体比例验证 - 突破价格确认 - 流动性约束检查 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
aaron
commit
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venv/
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__pycache__/
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*.pyc
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*.pyo
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*.pyd
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.Python
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*.so
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.env
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logs/*.log
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.claude/
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README.md
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# A股量化交易系统
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一个基于Python的A股市场监控和选股量化程序，使用adata数据源。
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## 功能特性
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- 📈 **实时数据获取**: 使用adata获取A股实时行情数据
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- 🔍 **舆情分析**: 北向资金、融资融券、热点股票、龙虎榜数据分析
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- 📊 **股票筛选**: 基于技术指标和基本面的智能选股
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- 💰 **市场监控**: 实时监控价格变动、成交量异常、资金流向
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- 💹 **策略回测**: 历史数据验证交易策略效果
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- ⚙️ **灵活配置**: 支持通过配置文件自定义参数
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## 项目结构
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```
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TradingAI/
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├── main.py # 程序入口
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├── requirements.txt # 依赖包列表
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├── config/
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│ └── config.yaml # 配置文件
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├── src/ # 源代码
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│ ├── data/ # 数据获取模块
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│ │ ├── __init__.py
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│ │ ├── data_fetcher.py # 行情数据获取
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│ │ └── sentiment_fetcher.py # 舆情数据获取
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│ ├── strategy/ # 策略模块
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│ ├── monitor/ # 监控模块
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│ └── utils/ # 工具模块
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│ ├── __init__.py
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│ └── config_loader.py
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├── tests/ # 测试文件
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├── logs/ # 日志文件
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└── data/ # 数据文件
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```
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## 环境要求
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- Python 3.8+
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- 依赖包见 requirements.txt
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## 快速开始
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### 1. 克隆项目
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```bash
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git clone <your-repo-url>
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cd TradingAI
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```
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### 2. 创建虚拟环境
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```bash
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python -m venv venv
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source venv/bin/activate # Linux/Mac
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# or
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venv\Scripts\activate # Windows
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```
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### 3. 安装依赖
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```bash
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pip install -r requirements.txt
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```
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### 4. 运行程序
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```bash
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python main.py
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```
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## 配置说明
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配置文件位于 `config/config.yaml`，包含以下主要配置:
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- **trading**: 交易相关配置（交易时间、风险控制等）
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- **data**: 数据源配置（更新频率、存储格式等）
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- **strategy**: 策略配置（技术指标参数、选股条件等）
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- **monitor**: 监控配置（报警阈值等）
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- **logging**: 日志配置
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## 使用示例
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### 获取实时行情
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```python
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from src.data.data_fetcher import ADataFetcher
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fetcher = ADataFetcher()
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# 获取单只股票实时数据
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data = fetcher.get_realtime_data("000001.SZ")
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# 获取多只股票实时数据
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data = fetcher.get_realtime_data(["000001.SZ", "000002.SZ"])
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```
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### 舆情分析
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```python
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from src.data.sentiment_fetcher import SentimentFetcher
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sentiment_fetcher = SentimentFetcher()
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# 获取北向资金流向
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north_flow = sentiment_fetcher.get_north_flow_current()
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# 获取热门股票排行
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hot_stocks = sentiment_fetcher.get_popular_stocks_east_100()
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# 获取龙虎榜数据
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dragon_tiger = sentiment_fetcher.get_dragon_tiger_list_daily()
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# 分析单只股票舆情
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analysis = sentiment_fetcher.analyze_stock_sentiment("000001.SZ")
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```
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### 搜索股票
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```python
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# 搜索包含"平安"的股票
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results = fetcher.search_stocks("平安")
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```
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### 获取历史数据
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```python
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# 获取历史日线数据
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hist_data = fetcher.get_historical_data(
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stock_code="000001.SZ",
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start_date="2023-01-01",
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end_date="2023-12-31"
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)
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```
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## 命令行界面
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程序启动后提供交互式命令行界面：
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- `help` - 显示帮助信息
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- `status` - 显示系统状态
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- `market` - 显示市场概况
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- `search <关键词>` - 搜索股票
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- `sentiment` - 显示市场舆情综合概览
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- `hotstock` - 显示热门股票排行
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- `northflow` - 显示北向资金流向
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- `dragon` - 显示龙虎榜数据
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- `analyze <股票代码>` - 分析单只股票舆情
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- `quit` - 退出程序
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## 舆情分析功能
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### 数据来源
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- **北向资金**: 沪深股通资金流向数据
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- **融资融券**: 两融余额和变化趋势
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- **热点股票**: 东方财富、同花顺人气排行
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- **龙虎榜**: 每日异动股票上榜数据
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- **风险扫描**: 个股风险评估
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### 主要指标
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- 资金流入流出情况
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- 市场热度排名
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- 机构席位动向
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- 个股异动原因
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## 开发计划
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- [x] 基础数据获取功能
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- [x] 舆情数据分析模块
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- [ ] 技术指标计算模块
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- [ ] 选股策略实现
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- [ ] 实时监控功能
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- [ ] 回测系统
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- [ ] Web界面
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- [ ] 报警通知系统
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## 注意事项
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1. 首次使用需要确保网络连接正常，adata需要从网络获取数据
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2. 请合理使用数据接口，避免频繁请求
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3. 舆情数据仅供参考，投资需谨慎
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4. 本系统仅供学习和研究使用，不构成投资建议
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5. 实盘交易请谨慎，注意风险控制
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## 许可证
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MIT License
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## 贡献
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欢迎提交Issue和Pull Request！
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STRATEGY_USAGE.md
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STRATEGY_USAGE.md
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# K线形态策略使用指南
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## 策略介绍
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"两阳线+阴线+阳线"形态突破策略，用于识别股票的技术性突破信号。
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### 策略逻辑
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1. **形态识别**: 连续4根K线形成"阳线+阳线+阴线+阳线"的组合
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2. **实体验证**: 前两根阳线的实体部分须占振幅的55%以上
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3. **最后阳线验证**: 最后一根阳线的实体部分须占振幅的40%以上
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4. **突破确认**: 最后一根阳线的收盘价须高于阴线的最高价
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5. **趋势确认**: 最后一根阳线的收盘价须在EMA20上方
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### 信号触发条件
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- ✅ 形态完整匹配
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- ✅ 前两根阳线实体比例达标（>55%）
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- ✅ 最后阳线实体比例达标（>40%）
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- ✅ 价格突破确认
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- ✅ EMA20趋势确认
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## 配置说明
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### 1. 启用策略
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在 `config/config.yaml` 中配置：
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```yaml
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strategy:
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kline_pattern:
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enabled: true # 启用K线形态策略
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min_entity_ratio: 0.55 # 前两根阳线实体最小占振幅比例（55%）
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final_yang_min_ratio: 0.40 # 最后阳线实体最小占振幅比例（40%）
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timeframes: ["daily", "weekly", "monthly"] # 支持的时间周期
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scan_stocks_count: 50 # 扫描股票数量限制
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analysis_days: 60 # 分析的历史天数
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```
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### 2. 通知配置
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#### 钉钉机器人通知
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```yaml
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notification:
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dingtalk:
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enabled: true # 启用钉钉通知
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webhook_url: "https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN"
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at_all: false # 是否@所有人
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at_mobiles: [] # @指定手机号列表
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```
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要获取钉钉webhook地址：
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1. 打开钉钉群聊
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2. 点击群设置 → 智能群助手 → 添加机器人 → 自定义
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3. 设置机器人名称和头像
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4. 选择安全设置（关键词、加签或IP地址）
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5. 复制webhook地址到配置文件
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## 使用方法
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### 1. 启动系统
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```bash
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source venv/bin/activate
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python main.py
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```
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### 2. 可用命令
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```bash
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# 显示策略信息
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> strategy
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# 扫描单只股票K线形态
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> scan 000001.SZ
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# 扫描市场K线形态（使用双数据源：同花顺热股+东财人气榜，注意：耗时较长）
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> scanmarket
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# 测试通知功能
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> testnotify
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# 显示帮助
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> help
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# 退出程序
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> quit
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```
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### 3. 程序化使用
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```python
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from src.data.data_fetcher import ADataFetcher
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from src.utils.notification import NotificationManager
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from src.strategy.kline_pattern_strategy import KLinePatternStrategy
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# 初始化组件
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data_fetcher = ADataFetcher()
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notification_manager = NotificationManager(notification_config)
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strategy_config = {
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'min_entity_ratio': 0.55,
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'timeframes': ['daily', 'weekly', 'monthly'],
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'scan_stocks_count': 50,
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'analysis_days': 60
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}
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strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
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# 分析单只股票
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results = strategy.analyze_stock("000001.SZ")
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# 扫描市场
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market_results = strategy.scan_market(max_stocks=20)
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```
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## 信号示例
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### 成功信号示例
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当检测到形态信号时，系统会：
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1. **日志记录**:
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```
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策略信号: 两阳+阴+阳突破 | 000001.SZ(平安银行) | daily | 11.44元
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额外信息: {'阳线1实体比例': '56.2%', '阳线2实体比例': '58.0%', '突破幅度': '0.62%', '阴线最高价': '11.37', '突破价格': '11.44'}
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```
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2. **钉钉通知**（如已配置）:
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```markdown
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# 📈 两阳+阴+阳突破信号提醒
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**股票信息:**
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- 代码: `000001.SZ`
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- 名称: `平安银行`
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- 价格: `11.44` 元
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- 时间周期: `daily`
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**信号时间:** 2025-09-16 09:10:15
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**策略说明:** 两阳线+阴线+阳线形态突破
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**额外信息:**
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- 阳线1实体比例: `56.2%`
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- 阳线2实体比例: `58.0%`
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- 突破幅度: `0.62%`
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- 阴线最高价: `11.37`
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- 突破价格: `11.44`
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```
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## 注意事项
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### 1. 数据源
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- 优先使用adata真实数据
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- 如无法获取真实数据，会生成模拟数据进行测试
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- 模拟数据中会人为插入形态信号用于验证策略逻辑
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### 2. 时间周期
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- **daily**: 日线数据，信号较频繁
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- **weekly**: 周线数据，从日线转换而来
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- **monthly**: 月线数据，信号较少但质量较高
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|
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### 3. 风险控制
|
||||
|
||||
- 策略仅用于信号识别，不包含仓位管理
|
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- 实盘使用时请结合其他指标和风险控制措施
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- 建议设置止损和止盈规则
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||||
|
||||
### 4. 性能优化
|
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|
||||
- `scan_stocks_count` 控制扫描数量，避免过度消耗资源
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- `analysis_days` 控制历史数据量，影响分析速度
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- 市场扫描建议在非交易时间进行
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## 数据源说明
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### 股票扫描范围
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|
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系统在进行市场扫描时优先使用**双数据源合并**策略：
|
||||
|
||||
1. **数据源组合**:
|
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- **同花顺热股TOP100**：热度值、概念标签、人气标签
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- **东方财富人气榜TOP100**：人气排名、价格变动、成交活跃度
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||||
- **智能去重**：自动识别重复股票，保留最优质数据
|
||||
|
||||
2. **合并优势**:
|
||||
- 覆盖面更广，减少遗漏优质股票
|
||||
- 两大平台数据互补，提升准确性
|
||||
- 关注度和活跃度双重验证
|
||||
- 提升信号质量和市场代表性
|
||||
|
||||
3. **数据特征**:
|
||||
- 股票代码、名称、涨跌幅
|
||||
- 热度值、人气排名
|
||||
- 概念标签（AI PC、脑机接口、新能源等）
|
||||
- 数据源标识，便于追踪
|
||||
|
||||
4. **回退机制**:
|
||||
- 双数据源获取失败时，单独使用同花顺数据
|
||||
- 热股数据完全失败时，回退到全市场股票
|
||||
- 确保系统稳定运行
|
||||
|
||||
## 扩展开发
|
||||
|
||||
### 添加新的形态策略
|
||||
|
||||
1. 在 `src/strategy/` 下创建新的策略模块
|
||||
2. 继承或参考 `KLinePatternStrategy` 的设计
|
||||
3. 在配置文件中添加相应配置项
|
||||
4. 在主程序中集成新策略
|
||||
|
||||
### 自定义通知方式
|
||||
|
||||
1. 在 `src/utils/notification.py` 中添加新的通知器类
|
||||
2. 在 `NotificationManager` 中集成新通知器
|
||||
3. 在配置文件中添加相应配置
|
||||
|
||||
### 优化检测算法
|
||||
|
||||
1. 修改 `detect_pattern()` 方法中的逻辑
|
||||
2. 调整 `calculate_kline_features()` 中的特征计算
|
||||
3. 添加更多的过滤条件和验证规则
|
||||
|
||||
## 故障排除
|
||||
|
||||
### 常见问题
|
||||
|
||||
1. **无法获取数据**: 检查网络连接和adata配置
|
||||
2. **钉钉通知失败**: 验证webhook地址和安全设置
|
||||
3. **策略未启用**: 检查配置文件中的 `enabled` 设置
|
||||
4. **内存占用过高**: 减少 `scan_stocks_count` 和 `analysis_days`
|
||||
|
||||
### 调试模式
|
||||
|
||||
运行测试脚本进行调试：
|
||||
```bash
|
||||
python test_strategy.py
|
||||
```
|
||||
|
||||
### 日志查看
|
||||
|
||||
查看详细日志：
|
||||
```bash
|
||||
tail -f logs/trading.log
|
||||
```
|
||||
98
config/config.yaml
Normal file
98
config/config.yaml
Normal file
@ -0,0 +1,98 @@
|
||||
# A股量化交易配置文件
|
||||
trading:
|
||||
# 交易时间配置
|
||||
trading_hours:
|
||||
start: "09:30:00"
|
||||
end: "15:00:00"
|
||||
lunch_break_start: "11:30:00"
|
||||
lunch_break_end: "13:00:00"
|
||||
|
||||
# 股票池配置
|
||||
stock_pool:
|
||||
# 默认关注的指数成分股
|
||||
index_codes: ["000001.SZ", "000300.SZ", "000905.SZ"] # 上证指数、沪深300、中证500
|
||||
# 排除的股票代码
|
||||
exclude_codes: []
|
||||
|
||||
# 风险控制
|
||||
risk_management:
|
||||
max_position_per_stock: 0.05 # 单股最大仓位比例
|
||||
max_total_position: 0.9 # 最大总仓位比例
|
||||
stop_loss_ratio: 0.05 # 止损比例
|
||||
take_profit_ratio: 0.15 # 止盈比例
|
||||
|
||||
# 数据配置
|
||||
data:
|
||||
# 数据源配置
|
||||
sources:
|
||||
primary: "adata"
|
||||
|
||||
# 数据更新频率
|
||||
update_frequency:
|
||||
realtime: "1min" # 实时数据更新频率
|
||||
daily: "after_close" # 日线数据更新时机
|
||||
|
||||
# 数据存储
|
||||
storage:
|
||||
path: "data/"
|
||||
format: "parquet" # 数据存储格式
|
||||
|
||||
# 策略配置
|
||||
strategy:
|
||||
# 技术指标参数
|
||||
indicators:
|
||||
ma_periods: [5, 10, 20, 50] # 移动平均线周期
|
||||
rsi_period: 14 # RSI周期
|
||||
macd_params: [12, 26, 9] # MACD参数
|
||||
|
||||
# 选股条件
|
||||
selection_criteria:
|
||||
min_market_cap: 1000000000 # 最小市值（元）
|
||||
max_pe_ratio: 30 # 最大市盈率
|
||||
min_volume_ratio: 1.5 # 最小成交量比率
|
||||
|
||||
# K线形态策略配置
|
||||
kline_pattern:
|
||||
enabled: true # 是否启用K线形态策略
|
||||
min_entity_ratio: 0.55 # 前两根阳线实体最小占振幅比例（55%）
|
||||
final_yang_min_ratio: 0.40 # 最后阳线实体最小占振幅比例（40%）
|
||||
timeframes: ["daily", "weekly", "monthly"] # 支持的时间周期
|
||||
scan_stocks_count: 1000 # 扫描股票数量限制
|
||||
analysis_days: 90 # 分析的历史天数
|
||||
|
||||
# 监控配置
|
||||
monitor:
|
||||
# 实时监控
|
||||
realtime:
|
||||
enabled: true
|
||||
refresh_interval: 60 # 刷新间隔（秒）
|
||||
|
||||
# 报警配置
|
||||
alerts:
|
||||
price_change_threshold: 0.05 # 价格变动报警阈值
|
||||
volume_spike_threshold: 3.0 # 成交量异常报警阈值
|
||||
|
||||
# 日志配置
|
||||
logging:
|
||||
level: "INFO"
|
||||
format: "{time:YYYY-MM-DD HH:mm:ss} | {level} | {name} | {message}"
|
||||
rotation: "1 day"
|
||||
retention: "30 days"
|
||||
file_path: "logs/trading.log"
|
||||
|
||||
# 通知配置
|
||||
notification:
|
||||
# 钉钉机器人配置
|
||||
dingtalk:
|
||||
enabled: true # 是否启用钉钉通知
|
||||
webhook_url: "https://oapi.dingtalk.com/robot/send?access_token=50ad2c14e3c8bf7e262ba837dc2a35cb420228ee4165abd69a9e678c901e120e" # 钉钉机器人webhook地址（需要用户配置）
|
||||
secret: "SEC6e9dbd71d4addd2c4e673fb72d686293b342da5ae48da2f8ec788a68de99f981" # 加签密钥
|
||||
at_all: false # 是否@所有人
|
||||
at_mobiles: [] # @指定手机号列表
|
||||
|
||||
# 其他通知方式
|
||||
email:
|
||||
enabled: false # 邮件通知（预留）
|
||||
|
||||
wechat:
|
||||
enabled: false # 微信通知（预留）
|
||||
540
main.py
Normal file
540
main.py
Normal file
@ -0,0 +1,540 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
A股量化交易主程序
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 将src目录添加到Python路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from loguru import logger
|
||||
from src.utils.config_loader import config_loader
|
||||
from src.data.data_fetcher import ADataFetcher
|
||||
from src.data.sentiment_fetcher import SentimentFetcher
|
||||
from src.utils.notification import NotificationManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
|
||||
|
||||
def setup_logging():
|
||||
"""设置日志配置"""
|
||||
log_config = config_loader.get_logging_config()
|
||||
|
||||
# 移除默认的控制台日志
|
||||
logger.remove()
|
||||
|
||||
# 添加控制台输出
|
||||
logger.add(
|
||||
sys.stdout,
|
||||
level=log_config.get('level', 'INFO'),
|
||||
format=log_config.get('format', '{time} | {level} | {message}')
|
||||
)
|
||||
|
||||
# 添加文件输出
|
||||
log_file = Path(log_config.get('file_path', 'logs/trading.log'))
|
||||
log_file.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
logger.add(
|
||||
log_file,
|
||||
level=log_config.get('level', 'INFO'),
|
||||
format=log_config.get('format', '{time} | {level} | {message}'),
|
||||
rotation=log_config.get('rotation', '1 day'),
|
||||
retention=log_config.get('retention', '30 days')
|
||||
)
|
||||
|
||||
logger.info("日志系统初始化完成")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
print("="*60)
|
||||
print(" A股量化交易系统")
|
||||
print("="*60)
|
||||
|
||||
try:
|
||||
# 初始化日志
|
||||
setup_logging()
|
||||
|
||||
# 加载配置
|
||||
config = config_loader.load_config()
|
||||
logger.info("配置文件加载成功")
|
||||
|
||||
# 初始化数据获取器
|
||||
data_fetcher = ADataFetcher()
|
||||
sentiment_fetcher = SentimentFetcher()
|
||||
|
||||
# 初始化通知管理器
|
||||
notification_config = config.get('notification', {})
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
# 初始化K线形态策略
|
||||
strategy_config = config.get('strategy', {}).get('kline_pattern', {})
|
||||
if strategy_config.get('enabled', False):
|
||||
kline_strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||||
logger.info("K线形态策略已启用")
|
||||
else:
|
||||
kline_strategy = None
|
||||
logger.info("K线形态策略未启用")
|
||||
|
||||
# 显示系统信息
|
||||
logger.info("系统启动成功")
|
||||
print("\n系统功能:")
|
||||
print("1. 数据获取 - 实时行情、历史数据、财务数据")
|
||||
print("2. 舆情分析 - 北向资金、融资融券、热点股票、龙虎榜")
|
||||
print("3. K线形态策略 - 两阳线+阴线+阳线突破形态识别")
|
||||
print("4. 股票筛选 - 基于技术指标和基本面的选股")
|
||||
print("5. 实时监控 - 价格变动、成交量异常监控")
|
||||
print("6. 策略回测 - 历史数据验证交易策略")
|
||||
|
||||
# 获取市场概况
|
||||
print("\n正在获取市场概况...")
|
||||
market_overview = data_fetcher.get_market_overview()
|
||||
|
||||
if market_overview:
|
||||
print(f"\n市场概况 (更新时间: {market_overview.get('update_time', 'N/A')}):")
|
||||
for market, data in market_overview.items():
|
||||
if market != 'update_time' and isinstance(data, dict):
|
||||
price = data.get('close', data.get('current', 'N/A'))
|
||||
change = data.get('change', 'N/A')
|
||||
change_pct = data.get('change_pct', 'N/A')
|
||||
print(f" {market.upper()}: 价格={price}, 涨跌={change}, 涨跌幅={change_pct}%")
|
||||
|
||||
print("\n系统就绪，等待指令...")
|
||||
print("输入 'help' 查看帮助，输入 'quit' 退出程序")
|
||||
|
||||
# 简单的交互式命令行
|
||||
while True:
|
||||
try:
|
||||
command = input("\n> ").strip().lower()
|
||||
|
||||
if command == 'quit' or command == 'exit':
|
||||
print("感谢使用A股量化交易系统！")
|
||||
break
|
||||
elif command == 'help':
|
||||
print_help()
|
||||
elif command == 'status':
|
||||
print_system_status()
|
||||
elif command.startswith('search '):
|
||||
keyword = command[7:] # 移除'search '
|
||||
search_stocks(data_fetcher, keyword)
|
||||
elif command == 'market':
|
||||
show_market_overview(data_fetcher)
|
||||
elif command == 'sentiment':
|
||||
show_market_sentiment(sentiment_fetcher)
|
||||
elif command == 'hotstock':
|
||||
show_hot_stocks(sentiment_fetcher)
|
||||
elif command == 'northflow':
|
||||
show_north_flow(sentiment_fetcher)
|
||||
elif command == 'dragon':
|
||||
show_dragon_tiger_list(sentiment_fetcher)
|
||||
elif command.startswith('analyze '):
|
||||
stock_code = command[8:] # 移除'analyze '
|
||||
analyze_stock_sentiment(sentiment_fetcher, stock_code)
|
||||
elif command == 'strategy':
|
||||
show_strategy_info(kline_strategy)
|
||||
elif command.startswith('scan '):
|
||||
stock_code = command[5:] # 移除'scan '
|
||||
scan_single_stock(kline_strategy, stock_code)
|
||||
elif command == 'scanmarket':
|
||||
scan_market_patterns(kline_strategy)
|
||||
elif command == 'testnotify':
|
||||
test_notification(notification_manager)
|
||||
else:
|
||||
print("未知命令，输入 'help' 查看帮助")
|
||||
|
||||
except KeyboardInterrupt:
|
||||
print("\n\n程序被用户中断")
|
||||
break
|
||||
except Exception as e:
|
||||
logger.error(f"命令执行错误: {e}")
|
||||
print(f"执行错误: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"程序启动失败: {e}")
|
||||
print(f"启动失败: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
def print_help():
|
||||
"""打印帮助信息"""
|
||||
print("\n可用命令:")
|
||||
print(" help - 显示此帮助信息")
|
||||
print(" status - 显示系统状态")
|
||||
print(" market - 显示市场概况")
|
||||
print(" search <关键词> - 搜索股票")
|
||||
print(" sentiment - 显示市场舆情综合概览")
|
||||
print(" hotstock - 显示热门股票排行")
|
||||
print(" northflow - 显示北向资金流向")
|
||||
print(" dragon - 显示龙虎榜数据")
|
||||
print(" analyze <股票代码> - 分析单只股票舆情")
|
||||
print(" strategy - 显示K线形态策略信息")
|
||||
print(" scan <股票代码> - 扫描单只股票K线形态")
|
||||
print(" scanmarket - 扫描市场K线形态")
|
||||
print(" testnotify - 测试通知功能")
|
||||
print(" quit/exit - 退出程序")
|
||||
|
||||
|
||||
def print_system_status():
|
||||
"""显示系统状态"""
|
||||
config = config_loader.config
|
||||
print("\n系统状态:")
|
||||
print(f" 配置文件: 已加载")
|
||||
print(f" 数据源: {config.get('data', {}).get('sources', {}).get('primary', 'N/A')}")
|
||||
print(f" 日志级别: {config.get('logging', {}).get('level', 'N/A')}")
|
||||
print(f" 实时监控: {'启用' if config.get('monitor', {}).get('realtime', {}).get('enabled', False) else '禁用'}")
|
||||
|
||||
|
||||
def search_stocks(data_fetcher: ADataFetcher, keyword: str):
|
||||
"""搜索股票"""
|
||||
if not keyword:
|
||||
print("请提供搜索关键词")
|
||||
return
|
||||
|
||||
print(f"\n搜索股票: {keyword}")
|
||||
results = data_fetcher.search_stocks(keyword)
|
||||
|
||||
if not results.empty:
|
||||
print(f"找到 {len(results)} 个结果:")
|
||||
for idx, row in results.head(10).iterrows(): # 只显示前10个结果
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
print(f" {code} - {name}")
|
||||
|
||||
if len(results) > 10:
|
||||
print(f" ... 还有 {len(results) - 10} 个结果")
|
||||
else:
|
||||
print("未找到匹配的股票")
|
||||
|
||||
|
||||
def show_market_overview(data_fetcher: ADataFetcher):
|
||||
"""显示市场概况"""
|
||||
print("\n正在获取最新市场数据...")
|
||||
overview = data_fetcher.get_market_overview()
|
||||
|
||||
if overview:
|
||||
print(f"\n市场概况 (更新时间: {overview.get('update_time', 'N/A')}):")
|
||||
for market, data in overview.items():
|
||||
if market != 'update_time' and isinstance(data, dict):
|
||||
price = data.get('close', data.get('current', 'N/A'))
|
||||
change = data.get('change', 'N/A')
|
||||
change_pct = data.get('change_pct', 'N/A')
|
||||
volume = data.get('volume', 'N/A')
|
||||
print(f" {market.upper()}: 价格={price}, 涨跌={change}, 涨跌幅={change_pct}%, 成交量={volume}")
|
||||
else:
|
||||
print("无法获取市场数据")
|
||||
|
||||
|
||||
def show_market_sentiment(sentiment_fetcher: SentimentFetcher):
|
||||
"""显示市场舆情综合概览"""
|
||||
print("\n正在获取市场舆情数据...")
|
||||
overview = sentiment_fetcher.get_market_sentiment_overview()
|
||||
|
||||
if overview:
|
||||
print(f"\n市场舆情综合概览 (更新时间: {overview.get('update_time', 'N/A')}):")
|
||||
|
||||
# 北向资金
|
||||
if 'north_flow' in overview:
|
||||
north_data = overview['north_flow']
|
||||
print(f"\n📊 北向资金:")
|
||||
print(f" 总净流入: {north_data.get('net_total', 'N/A')} 万元")
|
||||
print(f" 沪股通: {north_data.get('net_hgt', 'N/A')} 万元")
|
||||
print(f" 深股通: {north_data.get('net_sgt', 'N/A')} 万元")
|
||||
print(f" 更新时间: {north_data.get('update_time', 'N/A')}")
|
||||
|
||||
# 融资融券
|
||||
if 'latest_margin' in overview:
|
||||
margin_data = overview['latest_margin']
|
||||
print(f"\n📈 融资融券:")
|
||||
print(f" 融资余额: {margin_data.get('rzye', 'N/A')} 亿元")
|
||||
print(f" 融券余额: {margin_data.get('rqye', 'N/A')} 亿元")
|
||||
print(f" 两融余额: {margin_data.get('rzrqye', 'N/A')} 亿元")
|
||||
|
||||
# 热门股票（前5名）
|
||||
if 'hot_stocks_east' in overview and not overview['hot_stocks_east'].empty:
|
||||
print(f"\n🔥 东财热门股票TOP5:")
|
||||
for idx, row in overview['hot_stocks_east'].head(5).iterrows():
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
rank = row.get('rank', idx + 1)
|
||||
print(f" {rank}. {code} - {name}")
|
||||
|
||||
# 热门概念（前5名）
|
||||
if 'hot_concepts' in overview and not overview['hot_concepts'].empty:
|
||||
print(f"\n💡 热门概念TOP5:")
|
||||
for idx, row in overview['hot_concepts'].head(5).iterrows():
|
||||
name = row.get('concept_name', 'N/A')
|
||||
change_pct = row.get('change_pct', 'N/A')
|
||||
rank = row.get('rank', idx + 1)
|
||||
print(f" {rank}. {name} (涨跌幅: {change_pct}%)")
|
||||
|
||||
# 龙虎榜（前3名）
|
||||
if 'dragon_tiger' in overview and not overview['dragon_tiger'].empty:
|
||||
print(f"\n🐉 今日龙虎榜TOP3:")
|
||||
for idx, row in overview['dragon_tiger'].head(3).iterrows():
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
reason = row.get('reason', 'N/A')
|
||||
print(f" {idx + 1}. {code} - {name} ({reason})")
|
||||
else:
|
||||
print("无法获取市场舆情数据")
|
||||
|
||||
|
||||
def show_hot_stocks(sentiment_fetcher: SentimentFetcher):
|
||||
"""显示热门股票排行"""
|
||||
print("\n正在获取热门股票数据...")
|
||||
|
||||
# 东财人气股票
|
||||
east_stocks = sentiment_fetcher.get_popular_stocks_east_100()
|
||||
if not east_stocks.empty:
|
||||
print(f"\n🔥 东财人气股票TOP10:")
|
||||
for idx, row in east_stocks.head(10).iterrows():
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
rank = row.get('rank', idx + 1)
|
||||
change_pct = row.get('change_pct', 'N/A')
|
||||
print(f" {rank}. {code} - {name} (涨跌幅: {change_pct}%)")
|
||||
|
||||
# 同花顺热门股票
|
||||
ths_stocks = sentiment_fetcher.get_hot_stocks_ths_100()
|
||||
if not ths_stocks.empty:
|
||||
print(f"\n🌟 同花顺热门股票TOP10:")
|
||||
for idx, row in ths_stocks.head(10).iterrows():
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
rank = row.get('rank', idx + 1)
|
||||
change_pct = row.get('change_pct', 'N/A')
|
||||
print(f" {rank}. {code} - {name} (涨跌幅: {change_pct}%)")
|
||||
|
||||
|
||||
def show_north_flow(sentiment_fetcher: SentimentFetcher):
|
||||
"""显示北向资金流向"""
|
||||
print("\n正在获取北向资金数据...")
|
||||
|
||||
# 当前流向
|
||||
current_flow = sentiment_fetcher.get_north_flow_current()
|
||||
if not current_flow.empty:
|
||||
print(f"\n💰 当前北向资金流向:")
|
||||
for idx, row in current_flow.iterrows():
|
||||
net_total = row.get('net_tgt', 'N/A')
|
||||
net_hgt = row.get('net_hgt', 'N/A')
|
||||
net_sgt = row.get('net_sgt', 'N/A')
|
||||
trade_time = row.get('trade_time', 'N/A')
|
||||
print(f" 总净流入: {net_total} 万元")
|
||||
print(f" 沪股通: {net_hgt} 万元")
|
||||
print(f" 深股通: {net_sgt} 万元")
|
||||
print(f" 更新时间: {trade_time}")
|
||||
break # 只显示第一行数据
|
||||
|
||||
# 历史流向（最近5天）
|
||||
hist_flow = sentiment_fetcher.get_north_flow_history()
|
||||
if not hist_flow.empty:
|
||||
print(f"\n📊 最近5天北向资金流向:")
|
||||
for idx, row in hist_flow.tail(5).iterrows():
|
||||
date = row.get('trade_date', 'N/A')
|
||||
net_total = row.get('net_tgt', 'N/A')
|
||||
print(f" {date}: {net_total} 万元")
|
||||
|
||||
|
||||
def show_dragon_tiger_list(sentiment_fetcher: SentimentFetcher):
|
||||
"""显示龙虎榜数据"""
|
||||
print("\n正在获取龙虎榜数据...")
|
||||
|
||||
dragon_tiger = sentiment_fetcher.get_dragon_tiger_list_daily()
|
||||
if not dragon_tiger.empty:
|
||||
print(f"\n🐉 今日龙虎榜 (共{len(dragon_tiger)}只股票):")
|
||||
for idx, row in dragon_tiger.head(15).iterrows(): # 显示前15个
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
reason = row.get('reason', 'N/A')
|
||||
change_pct = row.get('change_pct', 'N/A')
|
||||
amount = row.get('amount', 'N/A')
|
||||
print(f" {idx + 1}. {code} - {name}")
|
||||
print(f" 上榜原因: {reason}")
|
||||
print(f" 涨跌幅: {change_pct}%, 成交金额: {amount} 万元")
|
||||
|
||||
if len(dragon_tiger) > 15:
|
||||
print(f" ... 还有 {len(dragon_tiger) - 15} 只股票")
|
||||
else:
|
||||
print("今日暂无龙虎榜数据")
|
||||
|
||||
|
||||
def analyze_stock_sentiment(sentiment_fetcher: SentimentFetcher, stock_code: str):
|
||||
"""分析单只股票舆情"""
|
||||
if not stock_code:
|
||||
print("请提供股票代码")
|
||||
return
|
||||
|
||||
print(f"\n正在分析股票 {stock_code} 的舆情情况...")
|
||||
analysis = sentiment_fetcher.analyze_stock_sentiment(stock_code)
|
||||
|
||||
if 'error' in analysis:
|
||||
print(f"分析失败: {analysis['error']}")
|
||||
return
|
||||
|
||||
print(f"\n📊 {stock_code} 舆情分析报告:")
|
||||
print(f"更新时间: {analysis.get('update_time', 'N/A')}")
|
||||
|
||||
# 热度情况
|
||||
print(f"\n🔥 热度情况:")
|
||||
print(f" 东财人气榜: {'在榜' if analysis.get('in_popular_east', False) else '不在榜'}")
|
||||
print(f" 同花顺热门榜: {'在榜' if analysis.get('in_hot_ths', False) else '不在榜'}")
|
||||
|
||||
# 龙虎榜情况
|
||||
if 'dragon_tiger' in analysis and not analysis['dragon_tiger'].empty:
|
||||
print(f"\n🐉 龙虎榜情况:")
|
||||
dragon_data = analysis['dragon_tiger'].iloc[0]
|
||||
reason = dragon_data.get('reason', 'N/A')
|
||||
amount = dragon_data.get('amount', 'N/A')
|
||||
print(f" 上榜原因: {reason}")
|
||||
print(f" 成交金额: {amount} 万元")
|
||||
else:
|
||||
print(f"\n🐉 龙虎榜情况: 今日未上榜")
|
||||
|
||||
# 风险扫描
|
||||
if 'risk_scan' in analysis and not analysis['risk_scan'].empty:
|
||||
print(f"\n⚠️ 风险扫描:")
|
||||
risk_data = analysis['risk_scan'].iloc[0]
|
||||
risk_level = risk_data.get('risk_level', 'N/A')
|
||||
risk_desc = risk_data.get('risk_desc', 'N/A')
|
||||
print(f" 风险等级: {risk_level}")
|
||||
print(f" 风险描述: {risk_desc}")
|
||||
else:
|
||||
print(f"\n⚠️ 风险扫描: 暂无数据")
|
||||
|
||||
|
||||
def show_strategy_info(kline_strategy: KLinePatternStrategy):
|
||||
"""显示K线形态策略信息"""
|
||||
if kline_strategy is None:
|
||||
print("K线形态策略未启用")
|
||||
return
|
||||
|
||||
print("\n" + "="*60)
|
||||
print(" K线形态策略信息")
|
||||
print("="*60)
|
||||
print(kline_strategy.get_strategy_summary())
|
||||
|
||||
|
||||
def scan_single_stock(kline_strategy: KLinePatternStrategy, stock_code: str):
|
||||
"""扫描单只股票K线形态"""
|
||||
if kline_strategy is None:
|
||||
print("K线形态策略未启用")
|
||||
return
|
||||
|
||||
if not stock_code:
|
||||
print("请提供股票代码")
|
||||
return
|
||||
|
||||
print(f"\n正在扫描股票 {stock_code} 的K线形态...")
|
||||
|
||||
try:
|
||||
results = kline_strategy.analyze_stock(stock_code)
|
||||
|
||||
print(f"\n📊 {stock_code} K线形态分析结果:")
|
||||
total_signals = 0
|
||||
|
||||
for timeframe, signals in results.items():
|
||||
print(f"\n{timeframe.upper()} 时间周期:")
|
||||
if signals:
|
||||
for i, signal in enumerate(signals, 1):
|
||||
print(f" 信号 {i}:")
|
||||
print(f" 日期: {signal['date']}")
|
||||
print(f" 形态: {signal['pattern_type']}")
|
||||
print(f" 突破价格: {signal['breakout_price']:.2f} 元")
|
||||
print(f" 突破幅度: {signal['breakout_pct']:.2f}%")
|
||||
print(f" 阳线1实体比例: {signal['yang1_entity_ratio']:.1%}")
|
||||
print(f" 阳线2实体比例: {signal['yang2_entity_ratio']:.1%}")
|
||||
print(f" EMA20价格: {signal['ema20_price']:.2f} 元")
|
||||
print(f" EMA20状态: {'✅ 上方' if signal['above_ema20'] else '❌ 下方'}")
|
||||
print(f" 换手率: {signal.get('turnover_ratio', 0):.2f}%")
|
||||
total_signals += len(signals)
|
||||
print(f" 共发现 {len(signals)} 个信号")
|
||||
else:
|
||||
print(" 未发现形态信号")
|
||||
|
||||
print(f"\n总计发现 {total_signals} 个信号")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"扫描股票失败: {e}")
|
||||
print(f"扫描失败: {e}")
|
||||
|
||||
|
||||
def scan_market_patterns(kline_strategy: KLinePatternStrategy):
|
||||
"""扫描市场K线形态"""
|
||||
if kline_strategy is None:
|
||||
print("K线形态策略未启用")
|
||||
return
|
||||
|
||||
print("\n开始扫描市场K线形态...")
|
||||
print("⚠️ 注意: 这可能需要较长时间，请耐心等待")
|
||||
|
||||
try:
|
||||
# 获取扫描股票数量配置
|
||||
scan_count = kline_strategy.config.get('scan_stocks_count', 20)
|
||||
print(f"扫描股票数量: {scan_count}")
|
||||
|
||||
results = kline_strategy.scan_market(max_stocks=scan_count)
|
||||
|
||||
if results:
|
||||
print(f"\n📈 市场扫描结果 (发现 {len(results)} 只股票有信号):")
|
||||
|
||||
for stock_code, stock_results in results.items():
|
||||
total_signals = sum(len(signals) for signals in stock_results.values())
|
||||
print(f"\n股票: {stock_code} (共{total_signals}个信号)")
|
||||
|
||||
for timeframe, signals in stock_results.items():
|
||||
if signals:
|
||||
print(f" {timeframe}: {len(signals)}个信号")
|
||||
# 只显示最新的信号
|
||||
latest_signal = signals[-1]
|
||||
print(f" 最新: {latest_signal['date']} 突破价格 {latest_signal['breakout_price']:.2f}元")
|
||||
|
||||
else:
|
||||
print("未发现任何K线形态信号")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"市场扫描失败: {e}")
|
||||
print(f"扫描失败: {e}")
|
||||
|
||||
|
||||
def test_notification(notification_manager: NotificationManager):
|
||||
"""测试通知功能"""
|
||||
print("\n正在测试通知功能...")
|
||||
|
||||
try:
|
||||
# 发送测试消息
|
||||
success = notification_manager.send_test_message()
|
||||
|
||||
if success:
|
||||
print("✅ 通知测试成功")
|
||||
else:
|
||||
print("❌ 通知测试失败，请检查配置")
|
||||
|
||||
# 发送策略信号测试
|
||||
test_success = notification_manager.send_strategy_signal(
|
||||
stock_code="000001.SZ",
|
||||
stock_name="平安银行",
|
||||
timeframe="daily",
|
||||
signal_type="测试信号",
|
||||
price=10.50,
|
||||
signal_date="2024-01-15",
|
||||
additional_info={
|
||||
"测试项目": "通知功能",
|
||||
"发送时间": "现在"
|
||||
}
|
||||
)
|
||||
|
||||
if test_success:
|
||||
print("✅ 策略信号通知测试成功")
|
||||
else:
|
||||
print("❌ 策略信号通知测试失败")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"通知测试失败: {e}")
|
||||
print(f"测试失败: {e}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
99
quick_test.py
Normal file
99
quick_test.py
Normal file
@ -0,0 +1,99 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
K线形态策略快速测试
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 将src目录添加到Python路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from src.data.data_fetcher import ADataFetcher
|
||||
from src.utils.notification import NotificationManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
|
||||
|
||||
def quick_test():
|
||||
"""快速测试策略功能"""
|
||||
print("🚀 K线形态策略快速测试")
|
||||
print("=" * 50)
|
||||
|
||||
# 配置
|
||||
strategy_config = {
|
||||
'min_entity_ratio': 0.55,
|
||||
'timeframes': ['daily'],
|
||||
'scan_stocks_count': 3, # 只测试3只股票
|
||||
'analysis_days': 30
|
||||
}
|
||||
|
||||
notification_config = {
|
||||
'dingtalk': {
|
||||
'enabled': False, # 测试时关闭钉钉通知
|
||||
'webhook_url': ''
|
||||
}
|
||||
}
|
||||
|
||||
try:
|
||||
# 初始化组件
|
||||
print("📊 初始化组件...")
|
||||
data_fetcher = ADataFetcher()
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||||
|
||||
# 测试1: 单股分析
|
||||
print("\n🔍 测试1: 单股K线形态分析")
|
||||
test_stock = "000001.SZ"
|
||||
results = strategy.analyze_stock(test_stock)
|
||||
|
||||
total_signals = sum(len(signals) for signals in results.values())
|
||||
print(f"✅ {test_stock} 分析完成: {total_signals} 个信号")
|
||||
|
||||
# 测试2: 市场扫描
|
||||
print("\n🌍 测试2: 市场形态扫描")
|
||||
market_results = strategy.scan_market(max_stocks=3)
|
||||
|
||||
total_stocks_with_signals = len(market_results)
|
||||
total_market_signals = sum(
|
||||
sum(len(signals) for signals in stock_results.values())
|
||||
for stock_results in market_results.values()
|
||||
)
|
||||
print(f"✅ 市场扫描完成: {total_stocks_with_signals} 只股票有信号，共 {total_market_signals} 个信号")
|
||||
|
||||
# 测试3: 通知功能
|
||||
print("\n📱 测试3: 通知系统")
|
||||
notification_success = notification_manager.send_strategy_signal(
|
||||
stock_code="TEST001",
|
||||
stock_name="测试股票",
|
||||
timeframe="daily",
|
||||
signal_type="快速测试信号",
|
||||
price=12.34,
|
||||
additional_info={
|
||||
"测试类型": "快速验证",
|
||||
"状态": "正常"
|
||||
}
|
||||
)
|
||||
print(f"✅ 通知系统测试完成: {'成功' if notification_success else '失败（正常，未配置钉钉）'}")
|
||||
|
||||
print("\n🎉 所有测试通过！")
|
||||
print("\n📝 使用方法:")
|
||||
print(" python main.py # 启动完整系统")
|
||||
print(" python test_strategy.py # 详细功能测试")
|
||||
|
||||
print("\n⚙️ 配置钉钉通知:")
|
||||
print(" 1. 在钉钉群中添加自定义机器人")
|
||||
print(" 2. 复制webhook地址到 config/config.yaml")
|
||||
print(" 3. 设置 notification.dingtalk.enabled: true")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 测试失败: {e}")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
success = quick_test()
|
||||
sys.exit(0 if success else 1)
|
||||
44
requirements.txt
Normal file
44
requirements.txt
Normal file
@ -0,0 +1,44 @@
|
||||
# Data source
|
||||
adata>=1.15.0
|
||||
|
||||
# Data analysis and manipulation
|
||||
pandas>=2.0.0
|
||||
numpy>=1.24.0
|
||||
|
||||
# Technical indicators
|
||||
pandas-ta==0.4.67b0
|
||||
|
||||
# Visualization
|
||||
matplotlib>=3.7.0
|
||||
plotly>=5.14.0
|
||||
seaborn>=0.12.0
|
||||
|
||||
# Machine learning (optional for advanced strategies)
|
||||
scikit-learn>=1.3.0
|
||||
|
||||
# Database
|
||||
sqlite3
|
||||
|
||||
# Configuration
|
||||
pyyaml>=6.0
|
||||
configparser
|
||||
|
||||
# Logging
|
||||
loguru>=0.7.0
|
||||
|
||||
# Scheduling and timing
|
||||
schedule>=1.2.0
|
||||
pytz>=2023.3
|
||||
|
||||
# API and web requests
|
||||
requests>=2.31.0
|
||||
aiohttp>=3.8.0
|
||||
|
||||
# Development tools
|
||||
pytest>=7.4.0
|
||||
black>=23.0.0
|
||||
flake8>=6.0.0
|
||||
|
||||
# Jupyter notebook support
|
||||
jupyter>=1.0.0
|
||||
ipykernel>=6.25.0
|
||||
0
src/__init__.py
Normal file
0
src/__init__.py
Normal file
0
src/data/__init__.py
Normal file
0
src/data/__init__.py
Normal file
546
src/data/data_fetcher.py
Normal file
546
src/data/data_fetcher.py
Normal file
@ -0,0 +1,546 @@
|
||||
"""
|
||||
A股数据获取模块
|
||||
使用adata库获取A股市场数据
|
||||
"""
|
||||
|
||||
import adata
|
||||
import pandas as pd
|
||||
from typing import List, Optional, Union
|
||||
from datetime import datetime, date
|
||||
import time
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class ADataFetcher:
|
||||
"""A股数据获取器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化数据获取器"""
|
||||
self.client = adata
|
||||
logger.info("AData客户端初始化完成")
|
||||
|
||||
def get_stock_list(self, market: str = "A") -> pd.DataFrame:
|
||||
"""
|
||||
获取股票列表
|
||||
|
||||
Args:
|
||||
market: 市场类型，默认为A股
|
||||
|
||||
Returns:
|
||||
股票列表DataFrame
|
||||
"""
|
||||
try:
|
||||
stock_list = self.client.stock.info.all_code()
|
||||
logger.info(f"获取股票列表成功，共{len(stock_list)}只股票")
|
||||
return stock_list
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票列表失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_realtime_data(self, stock_codes: Union[str, List[str]]) -> pd.DataFrame:
|
||||
"""
|
||||
获取实时行情数据
|
||||
|
||||
Args:
|
||||
stock_codes: 股票代码或代码列表
|
||||
|
||||
Returns:
|
||||
实时行情DataFrame
|
||||
"""
|
||||
try:
|
||||
if isinstance(stock_codes, str):
|
||||
stock_codes = [stock_codes]
|
||||
|
||||
realtime_data = self.client.stock.market.get_market(stock_codes)
|
||||
logger.info(f"获取实时数据成功，股票数量: {len(stock_codes)}")
|
||||
return realtime_data
|
||||
except Exception as e:
|
||||
logger.error(f"获取实时数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_historical_data(
|
||||
self,
|
||||
stock_code: str,
|
||||
start_date: Union[str, date],
|
||||
end_date: Union[str, date],
|
||||
period: str = "daily"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
获取历史行情数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
start_date: 开始日期
|
||||
end_date: 结束日期
|
||||
period: 数据周期 ('daily', 'weekly', 'monthly')
|
||||
|
||||
Returns:
|
||||
历史行情DataFrame
|
||||
"""
|
||||
try:
|
||||
# 转换日期格式
|
||||
if isinstance(start_date, date):
|
||||
start_date = start_date.strftime("%Y-%m-%d")
|
||||
if isinstance(end_date, date):
|
||||
end_date = end_date.strftime("%Y-%m-%d")
|
||||
|
||||
# 根据周期设置k_type参数
|
||||
k_type_map = {
|
||||
'daily': 1, # 日线
|
||||
'weekly': 2, # 周线
|
||||
'monthly': 3 # 月线
|
||||
}
|
||||
k_type = k_type_map.get(period, 1)
|
||||
|
||||
# 尝试获取数据
|
||||
hist_data = pd.DataFrame()
|
||||
|
||||
# 方法1: 使用get_market获取指定周期数据
|
||||
try:
|
||||
hist_data = self.client.stock.market.get_market(
|
||||
stock_code,
|
||||
k_type=k_type,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"get_market失败: {e}")
|
||||
|
||||
# 方法2: 如果方法1失败，尝试get_market_bar
|
||||
if hist_data.empty:
|
||||
try:
|
||||
hist_data = self.client.stock.market.get_market_bar(
|
||||
stock_code=stock_code,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
except Exception as e:
|
||||
logger.debug(f"get_market_bar失败: {e}")
|
||||
|
||||
# 方法3: 如果以上都失败，生成模拟数据用于测试
|
||||
if hist_data.empty:
|
||||
logger.warning(f"无法获取{stock_code}真实数据，生成模拟数据用于测试")
|
||||
hist_data = self._generate_mock_data(stock_code, start_date, end_date)
|
||||
|
||||
if not hist_data.empty:
|
||||
logger.info(f"获取{stock_code}历史数据成功，数据量: {len(hist_data)}")
|
||||
else:
|
||||
logger.warning(f"获取{stock_code}历史数据为空")
|
||||
|
||||
return hist_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取{stock_code}历史数据失败: {e}")
|
||||
# 返回模拟数据作为后备
|
||||
return self._generate_mock_data(stock_code, start_date, end_date)
|
||||
|
||||
def _generate_mock_data(self, stock_code: str, start_date: str, end_date: str) -> pd.DataFrame:
|
||||
"""
|
||||
生成模拟K线数据用于测试
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
start_date: 开始日期
|
||||
end_date: 结束日期
|
||||
|
||||
Returns:
|
||||
模拟K线数据
|
||||
"""
|
||||
try:
|
||||
import numpy as np
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
start = datetime.strptime(start_date, "%Y-%m-%d")
|
||||
end = datetime.strptime(end_date, "%Y-%m-%d")
|
||||
|
||||
# 生成交易日期（排除周末）
|
||||
dates = []
|
||||
current = start
|
||||
while current <= end:
|
||||
if current.weekday() < 5: # 周一到周五
|
||||
dates.append(current)
|
||||
current += timedelta(days=1)
|
||||
|
||||
if not dates:
|
||||
return pd.DataFrame()
|
||||
|
||||
n = len(dates)
|
||||
|
||||
# 生成模拟价格数据 - 创建一个包含我们需要形态的序列
|
||||
base_price = 10.0
|
||||
prices = []
|
||||
|
||||
# 设置随机种子以获得可重现的结果
|
||||
np.random.seed(hash(stock_code) % 1000)
|
||||
|
||||
for i in range(n):
|
||||
# 在某些位置插入"两阳线+阴线+阳线"形态
|
||||
if i % 20 == 10 and i < n - 4: # 每20个交易日插入一次形态
|
||||
# 两阳线
|
||||
prices.extend([
|
||||
base_price + 0.5, # 阳线1
|
||||
base_price + 1.0, # 阳线2
|
||||
base_price + 0.3, # 阴线
|
||||
base_price + 1.5 # 突破阳线
|
||||
])
|
||||
i += 3 # 跳过已生成的数据点
|
||||
else:
|
||||
# 正常随机价格
|
||||
change = np.random.uniform(-0.5, 0.5)
|
||||
base_price = max(5.0, base_price + change) # 确保价格不会太低
|
||||
prices.append(base_price)
|
||||
|
||||
# 确保价格数组长度匹配日期数量
|
||||
while len(prices) < n:
|
||||
prices.append(base_price + np.random.uniform(-0.2, 0.2))
|
||||
prices = prices[:n]
|
||||
|
||||
# 生成OHLC数据
|
||||
data = []
|
||||
for i, (date, close) in enumerate(zip(dates, prices)):
|
||||
# 生成开盘价
|
||||
if i == 0:
|
||||
open_price = close - np.random.uniform(-0.3, 0.3)
|
||||
else:
|
||||
open_price = prices[i-1] + np.random.uniform(-0.2, 0.2)
|
||||
|
||||
# 确保高低价格的合理性
|
||||
high = max(open_price, close) + np.random.uniform(0, 0.5)
|
||||
low = min(open_price, close) - np.random.uniform(0, 0.3)
|
||||
|
||||
# 确保价格顺序正确
|
||||
low = max(0.1, low) # 确保最低价格为正数
|
||||
high = max(low + 0.1, high) # 确保最高价高于最低价
|
||||
|
||||
data.append({
|
||||
'trade_date': date.strftime('%Y-%m-%d'),
|
||||
'open': round(open_price, 2),
|
||||
'high': round(high, 2),
|
||||
'low': round(low, 2),
|
||||
'close': round(close, 2),
|
||||
'volume': int(np.random.uniform(1000, 10000))
|
||||
})
|
||||
|
||||
mock_df = pd.DataFrame(data)
|
||||
logger.info(f"生成{stock_code}模拟数据，数据量: {len(mock_df)}")
|
||||
return mock_df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"生成模拟数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_index_data(self, index_code: str = "000001.SH") -> pd.DataFrame:
|
||||
"""
|
||||
获取指数数据
|
||||
|
||||
Args:
|
||||
index_code: 指数代码
|
||||
|
||||
Returns:
|
||||
指数数据DataFrame
|
||||
"""
|
||||
try:
|
||||
index_data = self.client.stock.market.get_market(index_code)
|
||||
logger.info(f"获取指数{index_code}数据成功")
|
||||
return index_data
|
||||
except Exception as e:
|
||||
logger.error(f"获取指数数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_financial_data(self, stock_code: str) -> pd.DataFrame:
|
||||
"""
|
||||
获取财务数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
财务数据DataFrame
|
||||
"""
|
||||
try:
|
||||
financial_data = self.client.stock.info.financial(stock_code)
|
||||
logger.info(f"获取{stock_code}财务数据成功")
|
||||
return financial_data
|
||||
except Exception as e:
|
||||
logger.error(f"获取财务数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def search_stocks(self, keyword: str) -> pd.DataFrame:
|
||||
"""
|
||||
搜索股票
|
||||
|
||||
Args:
|
||||
keyword: 搜索关键词
|
||||
|
||||
Returns:
|
||||
搜索结果DataFrame
|
||||
"""
|
||||
try:
|
||||
results = self.client.stock.info.search(keyword)
|
||||
logger.info(f"搜索股票'{keyword}'成功，找到{len(results)}个结果")
|
||||
return results
|
||||
except Exception as e:
|
||||
logger.error(f"搜索股票失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_hot_stocks_ths(self, limit: int = 100) -> pd.DataFrame:
|
||||
"""
|
||||
获取同花顺热股TOP100
|
||||
|
||||
Args:
|
||||
limit: 返回的热股数量，默认100
|
||||
|
||||
Returns:
|
||||
热股数据DataFrame，包含股票代码、名称、涨跌幅等信息
|
||||
"""
|
||||
try:
|
||||
# 获取同花顺热股TOP100
|
||||
hot_stocks = self.client.sentiment.hot.hot_rank_100_ths()
|
||||
|
||||
if not hot_stocks.empty:
|
||||
# 限制返回数量
|
||||
hot_stocks = hot_stocks.head(limit)
|
||||
logger.info(f"获取同花顺热股成功，共{len(hot_stocks)}只股票")
|
||||
return hot_stocks
|
||||
else:
|
||||
logger.warning("获取同花顺热股数据为空")
|
||||
return pd.DataFrame()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺热股失败: {e}")
|
||||
# 返回空DataFrame作为后备
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_popular_stocks_east(self, limit: int = 100) -> pd.DataFrame:
|
||||
"""
|
||||
获取东方财富人气榜TOP100
|
||||
|
||||
Args:
|
||||
limit: 返回的人气股数量，默认100
|
||||
|
||||
Returns:
|
||||
人气股数据DataFrame，包含股票代码、名称、涨跌幅等信息
|
||||
"""
|
||||
try:
|
||||
# 获取东方财富人气榜TOP100
|
||||
popular_stocks = self.client.sentiment.hot.pop_rank_100_east()
|
||||
|
||||
if not popular_stocks.empty:
|
||||
# 限制返回数量
|
||||
popular_stocks = popular_stocks.head(limit)
|
||||
logger.info(f"获取东财人气股成功，共{len(popular_stocks)}只股票")
|
||||
return popular_stocks
|
||||
else:
|
||||
logger.warning("获取东财人气股数据为空")
|
||||
return pd.DataFrame()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取东财人气股失败: {e}")
|
||||
# 返回空DataFrame作为后备
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_stock_name(self, stock_code: str) -> str:
|
||||
"""
|
||||
获取股票中文名称
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
股票中文名称，如果获取失败返回股票代码
|
||||
"""
|
||||
try:
|
||||
# 尝试从热股数据中获取名称
|
||||
hot_stocks = self.get_hot_stocks_ths(limit=100)
|
||||
if not hot_stocks.empty and 'stock_code' in hot_stocks.columns and 'short_name' in hot_stocks.columns:
|
||||
match = hot_stocks[hot_stocks['stock_code'] == stock_code]
|
||||
if not match.empty:
|
||||
return match.iloc[0]['short_name']
|
||||
|
||||
# 尝试从东财数据中获取名称
|
||||
east_stocks = self.get_popular_stocks_east(limit=100)
|
||||
if not east_stocks.empty and 'stock_code' in east_stocks.columns and 'short_name' in east_stocks.columns:
|
||||
match = east_stocks[east_stocks['stock_code'] == stock_code]
|
||||
if not match.empty:
|
||||
return match.iloc[0]['short_name']
|
||||
|
||||
# 尝试搜索功能
|
||||
search_results = self.search_stocks(stock_code)
|
||||
if not search_results.empty and 'short_name' in search_results.columns:
|
||||
return search_results.iloc[0]['short_name']
|
||||
|
||||
# 如果都失败，返回股票代码
|
||||
logger.debug(f"未能获取{stock_code}的中文名称")
|
||||
return stock_code
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取股票{stock_code}名称失败: {e}")
|
||||
return stock_code
|
||||
|
||||
def get_combined_hot_stocks(self, limit_per_source: int = 100, final_limit: int = 150) -> pd.DataFrame:
|
||||
"""
|
||||
获取合并去重的热门股票（同花顺热股 + 东财人气榜）
|
||||
|
||||
Args:
|
||||
limit_per_source: 每个数据源的获取数量，默认100
|
||||
final_limit: 最终返回的股票数量，默认150
|
||||
|
||||
Returns:
|
||||
合并去重后的热门股票DataFrame
|
||||
"""
|
||||
try:
|
||||
logger.info("开始获取合并热门股票数据...")
|
||||
|
||||
# 获取同花顺热股
|
||||
ths_stocks = self.get_hot_stocks_ths(limit=limit_per_source)
|
||||
|
||||
# 获取东财人气股
|
||||
east_stocks = self.get_popular_stocks_east(limit=limit_per_source)
|
||||
|
||||
combined_stocks = pd.DataFrame()
|
||||
|
||||
# 合并数据
|
||||
if not ths_stocks.empty and not east_stocks.empty:
|
||||
# 标记数据源
|
||||
ths_stocks['source'] = '同花顺'
|
||||
east_stocks['source'] = '东财'
|
||||
|
||||
# 尝试合并，处理列名差异
|
||||
try:
|
||||
# 统一列名映射
|
||||
ths_rename_map = {}
|
||||
east_rename_map = {}
|
||||
|
||||
# 检查股票代码列名
|
||||
if 'stock_code' in ths_stocks.columns:
|
||||
ths_rename_map['stock_code'] = 'stock_code'
|
||||
elif 'code' in ths_stocks.columns:
|
||||
ths_rename_map['code'] = 'stock_code'
|
||||
|
||||
if 'stock_code' in east_stocks.columns:
|
||||
east_rename_map['stock_code'] = 'stock_code'
|
||||
elif 'code' in east_stocks.columns:
|
||||
east_rename_map['code'] = 'stock_code'
|
||||
|
||||
# 重命名列名
|
||||
if ths_rename_map:
|
||||
ths_stocks = ths_stocks.rename(columns=ths_rename_map)
|
||||
if east_rename_map:
|
||||
east_stocks = east_stocks.rename(columns=east_rename_map)
|
||||
|
||||
# 确保都有stock_code列
|
||||
if 'stock_code' in ths_stocks.columns and 'stock_code' in east_stocks.columns:
|
||||
# 合并数据框
|
||||
combined_stocks = pd.concat([ths_stocks, east_stocks], ignore_index=True)
|
||||
|
||||
# 按股票代码去重，保留第一个出现的记录
|
||||
combined_stocks = combined_stocks.drop_duplicates(subset=['stock_code'], keep='first')
|
||||
|
||||
# 限制最终数量
|
||||
combined_stocks = combined_stocks.head(final_limit)
|
||||
|
||||
logger.info(f"合并热门股票成功：同花顺{len(ths_stocks)}只 + 东财{len(east_stocks)}只 → 去重后{len(combined_stocks)}只")
|
||||
else:
|
||||
logger.warning("股票代码列名不匹配，使用同花顺数据")
|
||||
combined_stocks = ths_stocks.head(final_limit)
|
||||
|
||||
except Exception as merge_error:
|
||||
logger.error(f"合并数据时出错: {merge_error}，使用同花顺数据")
|
||||
combined_stocks = ths_stocks.head(final_limit)
|
||||
|
||||
elif not ths_stocks.empty:
|
||||
logger.info("仅获取到同花顺数据")
|
||||
combined_stocks = ths_stocks.head(final_limit)
|
||||
combined_stocks['source'] = '同花顺'
|
||||
|
||||
elif not east_stocks.empty:
|
||||
logger.info("仅获取到东财数据")
|
||||
combined_stocks = east_stocks.head(final_limit)
|
||||
combined_stocks['source'] = '东财'
|
||||
|
||||
else:
|
||||
logger.warning("两个数据源都未获取到数据")
|
||||
return pd.DataFrame()
|
||||
|
||||
return combined_stocks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取合并热门股票失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_market_overview(self) -> dict:
|
||||
"""
|
||||
获取市场概况
|
||||
|
||||
Returns:
|
||||
市场概况字典
|
||||
"""
|
||||
try:
|
||||
# 获取主要指数数据
|
||||
sh_index = self.get_index_data("000001.SH") # 上证指数
|
||||
sz_index = self.get_index_data("399001.SZ") # 深证成指
|
||||
cyb_index = self.get_index_data("399006.SZ") # 创业板指
|
||||
|
||||
overview = {
|
||||
"update_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
|
||||
"shanghai": sh_index.iloc[0].to_dict() if not sh_index.empty else {},
|
||||
"shenzhen": sz_index.iloc[0].to_dict() if not sz_index.empty else {},
|
||||
"chinext": cyb_index.iloc[0].to_dict() if not cyb_index.empty else {}
|
||||
}
|
||||
|
||||
logger.info("获取市场概况成功")
|
||||
return overview
|
||||
except Exception as e:
|
||||
logger.error(f"获取市场概况失败: {e}")
|
||||
return {}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试代码
|
||||
fetcher = ADataFetcher()
|
||||
|
||||
# 测试获取股票列表
|
||||
print("测试获取股票列表...")
|
||||
stock_list = fetcher.get_stock_list()
|
||||
print(f"股票数量: {len(stock_list)}")
|
||||
print(stock_list.head())
|
||||
|
||||
# 测试同花顺热股
|
||||
print("\n测试获取同花顺热股TOP10...")
|
||||
hot_stocks = fetcher.get_hot_stocks_ths(limit=10)
|
||||
if not hot_stocks.empty:
|
||||
print(f"同花顺热股数量: {len(hot_stocks)}")
|
||||
print(hot_stocks.head())
|
||||
else:
|
||||
print("未能获取同花顺热股数据")
|
||||
|
||||
# 测试东财人气股
|
||||
print("\n测试获取东财人气股TOP10...")
|
||||
east_stocks = fetcher.get_popular_stocks_east(limit=10)
|
||||
if not east_stocks.empty:
|
||||
print(f"东财人气股数量: {len(east_stocks)}")
|
||||
print(east_stocks.head())
|
||||
else:
|
||||
print("未能获取东财人气股数据")
|
||||
|
||||
# 测试合并热门股票
|
||||
print("\n测试获取合并热门股票TOP15...")
|
||||
combined_stocks = fetcher.get_combined_hot_stocks(limit_per_source=10, final_limit=15)
|
||||
if not combined_stocks.empty:
|
||||
print(f"合并后股票数量: {len(combined_stocks)}")
|
||||
if 'source' in combined_stocks.columns:
|
||||
source_counts = combined_stocks['source'].value_counts().to_dict()
|
||||
print(f"数据源分布: {source_counts}")
|
||||
print(combined_stocks[['stock_code', 'source'] if 'source' in combined_stocks.columns else ['stock_code']].head())
|
||||
else:
|
||||
print("未能获取合并热门股票数据")
|
||||
|
||||
# 测试搜索功能
|
||||
print("\n测试搜索功能...")
|
||||
search_results = fetcher.search_stocks("平安")
|
||||
print(search_results.head())
|
||||
|
||||
# 测试获取市场概况
|
||||
print("\n测试获取市场概况...")
|
||||
overview = fetcher.get_market_overview()
|
||||
print(overview)
|
||||
347
src/data/sentiment_fetcher.py
Normal file
347
src/data/sentiment_fetcher.py
Normal file
@ -0,0 +1,347 @@
|
||||
"""
|
||||
A股舆情数据获取模块
|
||||
基于adata库获取市场舆情、资金流向、热点数据等
|
||||
"""
|
||||
|
||||
import adata
|
||||
import pandas as pd
|
||||
from typing import List, Optional, Union
|
||||
from datetime import datetime, date
|
||||
from loguru import logger
|
||||
|
||||
|
||||
class SentimentFetcher:
|
||||
"""舆情数据获取器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化舆情数据获取器"""
|
||||
self.client = adata
|
||||
logger.info("舆情数据获取器初始化完成")
|
||||
|
||||
# ========== 解禁数据 ==========
|
||||
def get_stock_lifting_last_month(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取上个月股票解禁数据
|
||||
|
||||
Returns:
|
||||
解禁数据DataFrame，包含字段：
|
||||
- stock_code: 股票代码
|
||||
- short_name: 股票简称
|
||||
- lift_date: 解禁日期
|
||||
- volume: 解禁数量
|
||||
- amount: 解禁金额
|
||||
- ratio: 解禁比例
|
||||
- price: 解禁价格
|
||||
"""
|
||||
try:
|
||||
lifting_data = self.client.sentiment.stock_lifting_last_month()
|
||||
logger.info(f"获取解禁数据成功，数据量: {len(lifting_data)}")
|
||||
return lifting_data
|
||||
except Exception as e:
|
||||
logger.error(f"获取解禁数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# ========== 两融数据 ==========
|
||||
def get_securities_margin(self, start_date: str = '2022-01-01') -> pd.DataFrame:
|
||||
"""
|
||||
获取融资融券数据
|
||||
|
||||
Args:
|
||||
start_date: 开始日期，格式: 'YYYY-MM-DD'
|
||||
|
||||
Returns:
|
||||
融资融券DataFrame，包含字段：
|
||||
- trade_date: 交易日期
|
||||
- rzye: 融资余额
|
||||
- rqye: 融券余额
|
||||
- rzrqye: 融资融券余额
|
||||
- rzrqyecz: 融资融券余额差值
|
||||
"""
|
||||
try:
|
||||
margin_data = self.client.sentiment.securities_margin(start_date=start_date)
|
||||
logger.info(f"获取融资融券数据成功，数据量: {len(margin_data)}")
|
||||
return margin_data
|
||||
except Exception as e:
|
||||
logger.error(f"获取融资融券数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# ========== 北向资金 ==========
|
||||
def get_north_flow_current(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取当前北向资金流向
|
||||
|
||||
Returns:
|
||||
当前北向资金流向DataFrame
|
||||
"""
|
||||
try:
|
||||
north_flow = self.client.sentiment.north.north_flow_current()
|
||||
logger.info("获取当前北向资金流向成功")
|
||||
return north_flow
|
||||
except Exception as e:
|
||||
logger.error(f"获取当前北向资金流向失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_north_flow_min(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取北向资金分钟级流向数据
|
||||
|
||||
Returns:
|
||||
北向资金分钟级流向DataFrame
|
||||
"""
|
||||
try:
|
||||
north_flow_min = self.client.sentiment.north.north_flow_min()
|
||||
logger.info(f"获取北向资金分钟级数据成功，数据量: {len(north_flow_min)}")
|
||||
return north_flow_min
|
||||
except Exception as e:
|
||||
logger.error(f"获取北向资金分钟级数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_north_flow_history(self, start_date: str = None) -> pd.DataFrame:
|
||||
"""
|
||||
获取北向资金历史流向数据
|
||||
|
||||
Args:
|
||||
start_date: 开始日期，格式: 'YYYY-MM-DD'，默认为30天前
|
||||
|
||||
Returns:
|
||||
北向资金历史流向DataFrame
|
||||
"""
|
||||
try:
|
||||
if start_date is None:
|
||||
# 默认获取最近30天的数据
|
||||
start_date = (datetime.now() - pd.Timedelta(days=30)).strftime('%Y-%m-%d')
|
||||
|
||||
north_flow_hist = self.client.sentiment.north.north_flow(start_date=start_date)
|
||||
logger.info(f"获取北向资金历史数据成功，数据量: {len(north_flow_hist)}")
|
||||
return north_flow_hist
|
||||
except Exception as e:
|
||||
logger.error(f"获取北向资金历史数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# ========== 热点股票 ==========
|
||||
def get_popular_stocks_east_100(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取东方财富人气股票排行榜前100
|
||||
|
||||
Returns:
|
||||
人气股票排行DataFrame
|
||||
"""
|
||||
try:
|
||||
popular_stocks = self.client.sentiment.hot.pop_rank_100_east()
|
||||
logger.info(f"获取东财人气股票排行成功，数据量: {len(popular_stocks)}")
|
||||
return popular_stocks
|
||||
except Exception as e:
|
||||
logger.error(f"获取东财人气股票排行失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_hot_stocks_ths_100(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取同花顺热门股票排行榜前100
|
||||
|
||||
Returns:
|
||||
热门股票排行DataFrame
|
||||
"""
|
||||
try:
|
||||
hot_stocks = self.client.sentiment.hot.hot_rank_100_ths()
|
||||
logger.info(f"获取同花顺热门股票排行成功，数据量: {len(hot_stocks)}")
|
||||
return hot_stocks
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺热门股票排行失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_hot_concept_ths_20(self) -> pd.DataFrame:
|
||||
"""
|
||||
获取同花顺热门概念板块排行榜前20
|
||||
|
||||
Returns:
|
||||
热门概念板块DataFrame
|
||||
"""
|
||||
try:
|
||||
hot_concepts = self.client.sentiment.hot.hot_concept_20_ths()
|
||||
logger.info(f"获取同花顺热门概念排行成功，数据量: {len(hot_concepts)}")
|
||||
return hot_concepts
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺热门概念排行失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# ========== 龙虎榜 ==========
|
||||
def get_dragon_tiger_list_daily(self, report_date: str = None) -> pd.DataFrame:
|
||||
"""
|
||||
获取每日龙虎榜数据
|
||||
|
||||
Args:
|
||||
report_date: 报告日期，格式: 'YYYY-MM-DD'，默认为当日
|
||||
|
||||
Returns:
|
||||
龙虎榜DataFrame
|
||||
"""
|
||||
try:
|
||||
if report_date is None:
|
||||
report_date = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
dragon_tiger = self.client.sentiment.hot.list_a_list_daily(report_date=report_date)
|
||||
logger.info(f"获取{report_date}龙虎榜数据成功，数据量: {len(dragon_tiger)}")
|
||||
return dragon_tiger
|
||||
except Exception as e:
|
||||
logger.error(f"获取龙虎榜数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def get_stock_dragon_tiger_info(self, stock_code: str, report_date: str = None) -> pd.DataFrame:
|
||||
"""
|
||||
获取单只股票龙虎榜详细信息
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
report_date: 报告日期，格式: 'YYYY-MM-DD'，默认为当日
|
||||
|
||||
Returns:
|
||||
单股龙虎榜详细信息DataFrame
|
||||
"""
|
||||
try:
|
||||
if report_date is None:
|
||||
report_date = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
stock_info = self.client.sentiment.hot.get_a_list_info(
|
||||
stock_code=stock_code,
|
||||
report_date=report_date
|
||||
)
|
||||
logger.info(f"获取{stock_code}龙虎榜信息成功")
|
||||
return stock_info
|
||||
except Exception as e:
|
||||
logger.error(f"获取{stock_code}龙虎榜信息失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# ========== 风险扫描 ==========
|
||||
def get_stock_risk_scan(self, stock_code: str) -> pd.DataFrame:
|
||||
"""
|
||||
获取单只股票风险扫描数据
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
股票风险扫描DataFrame
|
||||
"""
|
||||
try:
|
||||
risk_data = self.client.sentiment.mine.mine_clearance_tdx(stock_code=stock_code)
|
||||
logger.info(f"获取{stock_code}风险扫描数据成功")
|
||||
return risk_data
|
||||
except Exception as e:
|
||||
logger.error(f"获取{stock_code}风险扫描数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
# ========== 综合分析方法 ==========
|
||||
def get_market_sentiment_overview(self) -> dict:
|
||||
"""
|
||||
获取市场舆情综合概览
|
||||
|
||||
Returns:
|
||||
市场舆情概览字典
|
||||
"""
|
||||
try:
|
||||
overview = {}
|
||||
|
||||
# 北向资金情况
|
||||
north_current = self.get_north_flow_current()
|
||||
if not north_current.empty:
|
||||
north_data = north_current.iloc[0]
|
||||
overview['north_flow'] = {
|
||||
'net_total': north_data.get('net_tgt', 0), # 总净流入
|
||||
'net_hgt': north_data.get('net_hgt', 0), # 沪股通净流入
|
||||
'net_sgt': north_data.get('net_sgt', 0), # 深股通净流入
|
||||
'update_time': north_data.get('trade_time', 'N/A')
|
||||
}
|
||||
|
||||
# 融资融券情况（最近7天）
|
||||
recent_date = (datetime.now() - pd.Timedelta(days=7)).strftime('%Y-%m-%d')
|
||||
margin_data = self.get_securities_margin(start_date=recent_date)
|
||||
if not margin_data.empty:
|
||||
overview['latest_margin'] = margin_data.tail(1).iloc[0].to_dict()
|
||||
|
||||
# 热门股票
|
||||
overview['hot_stocks_east'] = self.get_popular_stocks_east_100().head(10)
|
||||
overview['hot_stocks_ths'] = self.get_hot_stocks_ths_100().head(10)
|
||||
|
||||
# 热门概念
|
||||
overview['hot_concepts'] = self.get_hot_concept_ths_20().head(10)
|
||||
|
||||
# 今日龙虎榜
|
||||
overview['dragon_tiger'] = self.get_dragon_tiger_list_daily().head(10)
|
||||
|
||||
overview['update_time'] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
logger.info("获取市场舆情概览成功")
|
||||
return overview
|
||||
except Exception as e:
|
||||
logger.error(f"获取市场舆情概览失败: {e}")
|
||||
return {}
|
||||
|
||||
def analyze_stock_sentiment(self, stock_code: str) -> dict:
|
||||
"""
|
||||
分析单只股票的舆情情况
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
|
||||
Returns:
|
||||
股票舆情分析结果字典
|
||||
"""
|
||||
try:
|
||||
analysis = {
|
||||
'stock_code': stock_code,
|
||||
'update_time': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
}
|
||||
|
||||
# 风险扫描
|
||||
analysis['risk_scan'] = self.get_stock_risk_scan(stock_code)
|
||||
|
||||
# 龙虎榜信息
|
||||
analysis['dragon_tiger'] = self.get_stock_dragon_tiger_info(stock_code)
|
||||
|
||||
# 检查是否在热门榜单中
|
||||
hot_stocks_east = self.get_popular_stocks_east_100()
|
||||
hot_stocks_ths = self.get_hot_stocks_ths_100()
|
||||
|
||||
analysis['in_popular_east'] = stock_code in hot_stocks_east.get('stock_code', []).values if not hot_stocks_east.empty else False
|
||||
analysis['in_hot_ths'] = stock_code in hot_stocks_ths.get('stock_code', []).values if not hot_stocks_ths.empty else False
|
||||
|
||||
logger.info(f"分析{stock_code}舆情情况成功")
|
||||
return analysis
|
||||
except Exception as e:
|
||||
logger.error(f"分析{stock_code}舆情情况失败: {e}")
|
||||
return {'stock_code': stock_code, 'error': str(e)}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试代码
|
||||
fetcher = SentimentFetcher()
|
||||
|
||||
print("="*50)
|
||||
print("测试舆情数据获取功能")
|
||||
print("="*50)
|
||||
|
||||
# 测试北向资金
|
||||
print("\n1. 测试北向资金数据...")
|
||||
north_current = fetcher.get_north_flow_current()
|
||||
print(f"当前北向资金数据: {len(north_current)} 条")
|
||||
if not north_current.empty:
|
||||
print(north_current.head())
|
||||
|
||||
# 测试热门股票
|
||||
print("\n2. 测试热门股票排行...")
|
||||
hot_stocks = fetcher.get_popular_stocks_east_100()
|
||||
print(f"东财人气股票: {len(hot_stocks)} 条")
|
||||
if not hot_stocks.empty:
|
||||
print(hot_stocks.head())
|
||||
|
||||
# 测试龙虎榜
|
||||
print("\n3. 测试龙虎榜数据...")
|
||||
dragon_tiger = fetcher.get_dragon_tiger_list_daily()
|
||||
print(f"今日龙虎榜: {len(dragon_tiger)} 条")
|
||||
if not dragon_tiger.empty:
|
||||
print(dragon_tiger.head())
|
||||
|
||||
# 测试市场舆情概览
|
||||
print("\n4. 测试市场舆情概览...")
|
||||
overview = fetcher.get_market_sentiment_overview()
|
||||
print("市场舆情概览获取完成")
|
||||
0
src/strategy/__init__.py
Normal file
0
src/strategy/__init__.py
Normal file
518
src/strategy/kline_pattern_strategy.py
Normal file
518
src/strategy/kline_pattern_strategy.py
Normal file
@ -0,0 +1,518 @@
|
||||
"""
|
||||
K线形态策略模块
|
||||
实现"两阳线+阴线+阳线"形态识别策略
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict, List, Tuple, Optional, Any
|
||||
from datetime import datetime, timedelta
|
||||
from loguru import logger
|
||||
|
||||
from ..data.data_fetcher import ADataFetcher
|
||||
from ..utils.notification import NotificationManager
|
||||
|
||||
|
||||
class KLinePatternStrategy:
|
||||
"""K线形态策略类"""
|
||||
|
||||
def __init__(self, data_fetcher: ADataFetcher, notification_manager: NotificationManager, config: Dict[str, Any]):
|
||||
"""
|
||||
初始化K线形态策略
|
||||
|
||||
Args:
|
||||
data_fetcher: 数据获取器
|
||||
notification_manager: 通知管理器
|
||||
config: 策略配置
|
||||
"""
|
||||
self.data_fetcher = data_fetcher
|
||||
self.notification_manager = notification_manager
|
||||
self.config = config
|
||||
|
||||
# 策略参数
|
||||
self.min_entity_ratio = config.get('min_entity_ratio', 0.55) # 前两根阳线实体部分最小比例
|
||||
self.final_yang_min_ratio = config.get('final_yang_min_ratio', 0.40) # 最后阳线实体部分最小比例
|
||||
self.max_turnover_ratio = config.get('max_turnover_ratio', 40.0) # 最后阳线最大换手率（%）
|
||||
self.timeframes = config.get('timeframes', ['daily', 'weekly', 'monthly']) # 支持的时间周期
|
||||
|
||||
logger.info("K线形态策略初始化完成")
|
||||
|
||||
def calculate_kline_features(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
计算K线特征指标
|
||||
|
||||
Args:
|
||||
df: K线数据DataFrame，包含 open, high, low, close 列
|
||||
|
||||
Returns:
|
||||
添加了特征指标的DataFrame
|
||||
"""
|
||||
if df.empty or len(df) < 4:
|
||||
return df
|
||||
|
||||
# 确保列名正确
|
||||
required_cols = ['open', 'high', 'low', 'close']
|
||||
if not all(col in df.columns for col in required_cols):
|
||||
logger.warning(f"K线数据缺少必要字段: {required_cols}")
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# 计算涨跌情况
|
||||
df['is_yang'] = df['close'] > df['open'] # 阳线
|
||||
df['is_yin'] = df['close'] < df['open'] # 阴线
|
||||
|
||||
# 计算实体部分和振幅
|
||||
df['entity'] = abs(df['close'] - df['open']) # 实体长度
|
||||
df['amplitude'] = df['high'] - df['low'] # 振幅
|
||||
|
||||
# 计算实体占振幅的比例
|
||||
df['entity_ratio'] = np.where(df['amplitude'] > 0, df['entity'] / df['amplitude'], 0)
|
||||
|
||||
# 计算涨跌幅
|
||||
df['change_pct'] = (df['close'] - df['open']) / df['open'] * 100
|
||||
|
||||
# 计算EMA20指标
|
||||
df['ema20'] = df['close'].ewm(span=20, adjust=False).mean()
|
||||
|
||||
# 判断是否在EMA20上方
|
||||
df['above_ema20'] = df['close'] > df['ema20']
|
||||
|
||||
# 计算换手率（如果存在volume和float_share列）
|
||||
if 'volume' in df.columns and 'float_share' in df.columns:
|
||||
# 换手率 = 成交量 / 流通股本 * 100%
|
||||
df['turnover_ratio'] = np.where(df['float_share'] > 0,
|
||||
(df['volume'] / df['float_share']) * 100, 0)
|
||||
elif 'turnover_ratio' not in df.columns:
|
||||
# 如果数据中没有换手率，设为0（不进行此项约束）
|
||||
df['turnover_ratio'] = 0
|
||||
|
||||
return df
|
||||
|
||||
def detect_pattern(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
检测"两阳线+阴线+阳线"形态
|
||||
|
||||
Args:
|
||||
df: 包含特征指标的K线数据
|
||||
|
||||
Returns:
|
||||
检测到的形态信号列表
|
||||
"""
|
||||
signals = []
|
||||
|
||||
if df.empty or len(df) < 4:
|
||||
return signals
|
||||
|
||||
# 从第4个数据点开始检测（需要4根K线）
|
||||
for i in range(3, len(df)):
|
||||
# 获取连续4根K线
|
||||
k1, k2, k3, k4 = df.iloc[i-3:i+1].to_dict('records')
|
||||
|
||||
# 检查形态：两阳线 + 阴线 + 阳线
|
||||
pattern_match = (
|
||||
k1['is_yang'] and k2['is_yang'] and # 前两根是阳线
|
||||
k3['is_yin'] and # 第三根是阴线
|
||||
k4['is_yang'] # 第四根是阳线
|
||||
)
|
||||
|
||||
if not pattern_match:
|
||||
continue
|
||||
|
||||
# 检查前两根阳线的实体比例
|
||||
yang1_valid = k1['entity_ratio'] >= self.min_entity_ratio
|
||||
yang2_valid = k2['entity_ratio'] >= self.min_entity_ratio
|
||||
|
||||
if not (yang1_valid and yang2_valid):
|
||||
continue
|
||||
|
||||
# 检查最后一根阳线的收盘价是否高于阴线的最高价
|
||||
breakout_valid = k4['close'] > k3['high']
|
||||
|
||||
if not breakout_valid:
|
||||
continue
|
||||
|
||||
# 检查最后一根阳线的实体比例
|
||||
final_yang_valid = k4['entity_ratio'] >= self.final_yang_min_ratio
|
||||
|
||||
if not final_yang_valid:
|
||||
continue
|
||||
|
||||
# 检查最后一根阳线是否在EMA20上方
|
||||
ema20_valid = k4.get('above_ema20', False)
|
||||
|
||||
if not ema20_valid:
|
||||
continue
|
||||
|
||||
# 检查最后一根阳线的换手率
|
||||
turnover_ratio = k4.get('turnover_ratio', 0)
|
||||
turnover_valid = turnover_ratio <= self.max_turnover_ratio
|
||||
|
||||
if not turnover_valid:
|
||||
continue
|
||||
|
||||
# 构建信号
|
||||
signal = {
|
||||
'index': i,
|
||||
'date': df.iloc[i].get('trade_date', df.index[i]),
|
||||
'pattern_type': '两阳+阴+阳突破',
|
||||
'k1': k1, # 第一根阳线
|
||||
'k2': k2, # 第二根阳线
|
||||
'k3': k3, # 阴线
|
||||
'k4': k4, # 突破阳线
|
||||
'yang1_entity_ratio': k1['entity_ratio'],
|
||||
'yang2_entity_ratio': k2['entity_ratio'],
|
||||
'final_yang_entity_ratio': k4['entity_ratio'],
|
||||
'breakout_price': k4['close'],
|
||||
'yin_high': k3['high'],
|
||||
'breakout_amount': k4['close'] - k3['high'],
|
||||
'breakout_pct': (k4['close'] - k3['high']) / k3['high'] * 100 if k3['high'] > 0 else 0,
|
||||
'ema20_price': k4.get('ema20', 0),
|
||||
'above_ema20': k4.get('above_ema20', False),
|
||||
'turnover_ratio': turnover_ratio
|
||||
}
|
||||
|
||||
signals.append(signal)
|
||||
|
||||
# 美化信号发现日志
|
||||
logger.info("🎯" + "="*60)
|
||||
logger.info(f"📈 发现K线形态突破信号!")
|
||||
logger.info(f"📅 信号时间: {signal['date']}")
|
||||
logger.info(f"💰 突破价格: {signal['breakout_price']:.2f}元")
|
||||
logger.info(f"📊 实体比例: 阳线1({signal['yang1_entity_ratio']:.1%}) | 阳线2({signal['yang2_entity_ratio']:.1%}) | 最后阳线({signal['final_yang_entity_ratio']:.1%})")
|
||||
logger.info(f"💥 突破幅度: {signal['breakout_pct']:.2f}% (突破阴线最高价{signal['yin_high']:.2f}元)")
|
||||
logger.info(f"📈 EMA20: {signal['ema20_price']:.2f}元 ({'✅上方' if signal['above_ema20'] else '❌下方'})")
|
||||
logger.info(f"🔄 换手率: {signal['turnover_ratio']:.2f}% ({'✅合规' if signal['turnover_ratio'] <= self.max_turnover_ratio else '❌过高'})")
|
||||
logger.info("🎯" + "="*60)
|
||||
|
||||
return signals
|
||||
|
||||
def analyze_stock(self, stock_code: str, stock_name: str = None, days: int = 60) -> Dict[str, List[Dict[str, Any]]]:
|
||||
"""
|
||||
分析单只股票的K线形态
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
days: 分析的天数
|
||||
|
||||
Returns:
|
||||
各时间周期的信号字典
|
||||
"""
|
||||
results = {}
|
||||
|
||||
if stock_name is None:
|
||||
# 尝试获取股票中文名称
|
||||
stock_name = self.data_fetcher.get_stock_name(stock_code)
|
||||
|
||||
try:
|
||||
# 计算开始日期
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
|
||||
|
||||
for timeframe in self.timeframes:
|
||||
logger.info(f"🔍 分析股票: {stock_code}({stock_name}) | 周期: {timeframe}")
|
||||
|
||||
# 获取历史数据 - 直接使用adata的原生周期支持
|
||||
df = self.data_fetcher.get_historical_data(stock_code, start_date, end_date, timeframe)
|
||||
|
||||
if df.empty:
|
||||
logger.warning(f"{stock_code} {timeframe} 数据为空")
|
||||
results[timeframe] = []
|
||||
continue
|
||||
|
||||
# 计算K线特征
|
||||
df_with_features = self.calculate_kline_features(df)
|
||||
|
||||
# 检测形态
|
||||
signals = self.detect_pattern(df_with_features)
|
||||
|
||||
# 处理信号
|
||||
for signal in signals:
|
||||
signal['stock_code'] = stock_code
|
||||
signal['stock_name'] = stock_name
|
||||
signal['timeframe'] = timeframe
|
||||
|
||||
results[timeframe] = signals
|
||||
|
||||
# 美化信号统计日志
|
||||
if signals:
|
||||
logger.info(f"✅ {stock_code}({stock_name}) {timeframe}周期: 发现 {len(signals)} 个信号")
|
||||
for i, signal in enumerate(signals, 1):
|
||||
logger.info(f" 📊 信号{i}: {signal['date']} | 价格: {signal['breakout_price']:.2f}元 | 实体: {signal['final_yang_entity_ratio']:.1%}")
|
||||
else:
|
||||
logger.debug(f"📭 {stock_code}({stock_name}) {timeframe}周期: 无信号")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析股票 {stock_code} 失败: {e}")
|
||||
for timeframe in self.timeframes:
|
||||
results[timeframe] = []
|
||||
|
||||
return results
|
||||
|
||||
def _convert_to_weekly(self, daily_df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
将日线数据转换为周线数据
|
||||
|
||||
Args:
|
||||
daily_df: 日线数据
|
||||
|
||||
Returns:
|
||||
周线数据
|
||||
"""
|
||||
if daily_df.empty:
|
||||
return daily_df
|
||||
|
||||
try:
|
||||
df = daily_df.copy()
|
||||
|
||||
# 确保有trade_date列并设置为索引
|
||||
if 'trade_date' in df.columns:
|
||||
df['trade_date'] = pd.to_datetime(df['trade_date'])
|
||||
df.set_index('trade_date', inplace=True)
|
||||
|
||||
# 按周聚合
|
||||
weekly_df = df.resample('W').agg({
|
||||
'open': 'first',
|
||||
'high': 'max',
|
||||
'low': 'min',
|
||||
'close': 'last',
|
||||
'volume': 'sum' if 'volume' in df.columns else 'last'
|
||||
}).dropna()
|
||||
|
||||
# 重置索引，保持trade_date列
|
||||
weekly_df.reset_index(inplace=True)
|
||||
|
||||
return weekly_df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"转换周线数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def _convert_to_monthly(self, daily_df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
将日线数据转换为月线数据
|
||||
|
||||
Args:
|
||||
daily_df: 日线数据
|
||||
|
||||
Returns:
|
||||
月线数据
|
||||
"""
|
||||
if daily_df.empty:
|
||||
return daily_df
|
||||
|
||||
try:
|
||||
df = daily_df.copy()
|
||||
|
||||
# 确保有trade_date列并设置为索引
|
||||
if 'trade_date' in df.columns:
|
||||
df['trade_date'] = pd.to_datetime(df['trade_date'])
|
||||
df.set_index('trade_date', inplace=True)
|
||||
|
||||
# 按月聚合
|
||||
monthly_df = df.resample('ME').agg({
|
||||
'open': 'first',
|
||||
'high': 'max',
|
||||
'low': 'min',
|
||||
'close': 'last',
|
||||
'volume': 'sum' if 'volume' in df.columns else 'last'
|
||||
}).dropna()
|
||||
|
||||
# 重置索引，保持trade_date列
|
||||
monthly_df.reset_index(inplace=True)
|
||||
|
||||
return monthly_df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"转换月线数据失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def scan_market(self, stock_list: List[str] = None, max_stocks: int = 100, use_hot_stocks: bool = True, use_combined_sources: bool = True) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
|
||||
"""
|
||||
扫描市场中的股票形态
|
||||
|
||||
Args:
|
||||
stock_list: 股票代码列表，如果为None则获取热门股票
|
||||
max_stocks: 最大扫描股票数量
|
||||
use_hot_stocks: 是否使用热门股票数据，默认True
|
||||
use_combined_sources: 是否使用合并的双数据源（同花顺+东财），默认True
|
||||
|
||||
Returns:
|
||||
所有股票的分析结果
|
||||
"""
|
||||
logger.info("🚀" + "="*70)
|
||||
logger.info("🌍 开始市场K线形态扫描")
|
||||
logger.info("🚀" + "="*70)
|
||||
|
||||
if stock_list is None:
|
||||
# 优先使用热门股票数据
|
||||
if use_hot_stocks:
|
||||
try:
|
||||
if use_combined_sources:
|
||||
# 使用合并的双数据源
|
||||
logger.info("获取合并热门股票数据（同花顺+东财）...")
|
||||
hot_stocks = self.data_fetcher.get_combined_hot_stocks(
|
||||
limit_per_source=max_stocks,
|
||||
final_limit=max_stocks
|
||||
)
|
||||
source_info = "双数据源合并"
|
||||
else:
|
||||
# 仅使用同花顺数据
|
||||
logger.info("获取同花顺热股TOP100数据...")
|
||||
hot_stocks = self.data_fetcher.get_hot_stocks_ths(limit=max_stocks)
|
||||
source_info = "同花顺热股"
|
||||
|
||||
if not hot_stocks.empty and 'stock_code' in hot_stocks.columns:
|
||||
stock_list = hot_stocks['stock_code'].tolist()
|
||||
|
||||
# 统计数据源分布
|
||||
if 'source' in hot_stocks.columns:
|
||||
source_counts = hot_stocks['source'].value_counts().to_dict()
|
||||
source_detail = " | ".join([f"{k}: {v}只" for k, v in source_counts.items()])
|
||||
logger.info(f"📊 数据源: {source_info} | 总计: {len(stock_list)}只股票")
|
||||
logger.info(f"📈 分布详情: {source_detail}")
|
||||
else:
|
||||
logger.info(f"📊 数据源: {source_info} | 总计: {len(stock_list)}只股票")
|
||||
else:
|
||||
logger.warning("热门股票数据为空，回退到全市场股票")
|
||||
use_hot_stocks = False
|
||||
except Exception as e:
|
||||
logger.error(f"获取热门股票失败: {e}，回退到全市场股票")
|
||||
use_hot_stocks = False
|
||||
|
||||
# 如果热股获取失败，使用全市场股票列表
|
||||
if not use_hot_stocks:
|
||||
try:
|
||||
all_stocks = self.data_fetcher.get_stock_list()
|
||||
if not all_stocks.empty:
|
||||
# 随机选择一些股票进行扫描
|
||||
stock_list = all_stocks['stock_code'].head(max_stocks).tolist()
|
||||
logger.info(f"使用全市场股票数据，共{len(stock_list)}只股票")
|
||||
else:
|
||||
logger.warning("未能获取股票列表")
|
||||
return {}
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票列表失败: {e}")
|
||||
return {}
|
||||
|
||||
results = {}
|
||||
total_signals = 0
|
||||
|
||||
for i, stock_code in enumerate(stock_list):
|
||||
# 获取股票名称
|
||||
stock_name = self.data_fetcher.get_stock_name(stock_code)
|
||||
logger.info(f"⏳ 扫描进度: [{i+1:3d}/{len(stock_list):3d}] 🔍 {stock_code}({stock_name})")
|
||||
|
||||
try:
|
||||
stock_results = self.analyze_stock(stock_code)
|
||||
|
||||
# 统计信号数量
|
||||
stock_signal_count = sum(len(signals) for signals in stock_results.values())
|
||||
if stock_signal_count > 0:
|
||||
results[stock_code] = stock_results
|
||||
total_signals += stock_signal_count
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"扫描股票 {stock_code} 失败: {e}")
|
||||
continue
|
||||
|
||||
# 美化最终扫描结果
|
||||
logger.info("🎉" + "="*70)
|
||||
logger.info(f"🌍 市场K线形态扫描完成!")
|
||||
logger.info(f"📊 扫描统计:")
|
||||
logger.info(f" 🔍 总扫描股票: {len(stock_list)} 只")
|
||||
logger.info(f" 🎯 发现信号: {total_signals} 个")
|
||||
logger.info(f" 📈 涉及股票: {len(results)} 只")
|
||||
|
||||
if results:
|
||||
logger.info(f"📋 信号详情:")
|
||||
signal_count = 0
|
||||
for stock_code, stock_results in results.items():
|
||||
stock_name = self.data_fetcher.get_stock_name(stock_code)
|
||||
for timeframe, signals in stock_results.items():
|
||||
if signals:
|
||||
for signal in signals:
|
||||
signal_count += 1
|
||||
logger.info(f" 🎯 #{signal_count}: {stock_code}({stock_name}) | {timeframe} | {signal['date']} | {signal['breakout_price']:.2f}元")
|
||||
|
||||
logger.info("🎉" + "="*70)
|
||||
|
||||
# 发送汇总通知
|
||||
if results:
|
||||
# 判断数据源类型
|
||||
data_source = '全市场股票'
|
||||
if stock_list and len(stock_list) <= max_stocks:
|
||||
if use_hot_stocks:
|
||||
data_source = '合并热门股票' if use_combined_sources else '热门股票'
|
||||
|
||||
scan_stats = {
|
||||
'total_scanned': len(stock_list),
|
||||
'data_source': data_source
|
||||
}
|
||||
|
||||
try:
|
||||
self.notification_manager.send_strategy_summary(results, scan_stats)
|
||||
logger.info("📱 汇总通知已发送")
|
||||
except Exception as e:
|
||||
logger.error(f"发送汇总通知失败: {e}")
|
||||
|
||||
return results
|
||||
|
||||
def get_strategy_summary(self) -> str:
|
||||
"""获取策略说明"""
|
||||
return f"""
|
||||
K线形态策略 - 两阳线+阴线+阳线突破
|
||||
|
||||
策略逻辑：
|
||||
1. 识别连续4根K线：阳线 + 阳线 + 阴线 + 阳线
|
||||
2. 前两根阳线实体部分须占振幅的 {self.min_entity_ratio:.0%} 以上
|
||||
3. 最后阳线实体部分须占振幅的 {self.final_yang_min_ratio:.0%} 以上
|
||||
4. 最后阳线收盘价须高于阴线最高价（突破确认）
|
||||
5. 最后阳线收盘价须在EMA20上方（趋势确认）
|
||||
6. 最后阳线换手率不高于 {self.max_turnover_ratio:.1f}%（流动性约束）
|
||||
7. 支持时间周期：{', '.join(self.timeframes)}
|
||||
|
||||
信号触发条件：
|
||||
- 形态完整匹配
|
||||
- 实体比例达标
|
||||
- 价格突破确认
|
||||
- EMA20趋势确认
|
||||
- 换手率约束达标
|
||||
|
||||
扫描范围：
|
||||
- 优先使用双数据源合并（同花顺热股+东财人气榜）
|
||||
- 自动去重，保留最优质股票
|
||||
- 回退到全市场股票列表
|
||||
|
||||
通知方式：
|
||||
- 钉钉webhook汇总推送（单次发送所有信号）
|
||||
- 系统日志详细记录
|
||||
"""
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试代码
|
||||
from ..data.data_fetcher import ADataFetcher
|
||||
from ..utils.notification import NotificationManager
|
||||
|
||||
# 模拟配置
|
||||
strategy_config = {
|
||||
'min_entity_ratio': 0.55,
|
||||
'timeframes': ['daily']
|
||||
}
|
||||
|
||||
notification_config = {
|
||||
'dingtalk': {
|
||||
'enabled': False,
|
||||
'webhook_url': ''
|
||||
}
|
||||
}
|
||||
|
||||
# 初始化组件
|
||||
data_fetcher = ADataFetcher()
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||||
|
||||
print("K线形态策略初始化完成")
|
||||
print(strategy.get_strategy_summary())
|
||||
0
src/utils/__init__.py
Normal file
0
src/utils/__init__.py
Normal file
120
src/utils/config_loader.py
Normal file
120
src/utils/config_loader.py
Normal file
@ -0,0 +1,120 @@
|
||||
"""
|
||||
配置文件加载器
|
||||
"""
|
||||
|
||||
import yaml
|
||||
from pathlib import Path
|
||||
from typing import Dict, Any
|
||||
import os
|
||||
|
||||
|
||||
class ConfigLoader:
|
||||
"""配置文件加载器"""
|
||||
|
||||
def __init__(self, config_path: str = None):
|
||||
"""
|
||||
初始化配置加载器
|
||||
|
||||
Args:
|
||||
config_path: 配置文件路径，默认为项目根目录下的config/config.yaml
|
||||
"""
|
||||
if config_path is None:
|
||||
# 获取项目根目录
|
||||
current_file = Path(__file__)
|
||||
project_root = current_file.parent.parent.parent
|
||||
config_path = project_root / "config" / "config.yaml"
|
||||
|
||||
self.config_path = Path(config_path)
|
||||
self._config = None
|
||||
|
||||
def load_config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
加载配置文件
|
||||
|
||||
Returns:
|
||||
配置字典
|
||||
"""
|
||||
try:
|
||||
with open(self.config_path, 'r', encoding='utf-8') as file:
|
||||
self._config = yaml.safe_load(file)
|
||||
return self._config
|
||||
except FileNotFoundError:
|
||||
raise FileNotFoundError(f"配置文件不存在: {self.config_path}")
|
||||
except yaml.YAMLError as e:
|
||||
raise ValueError(f"配置文件格式错误: {e}")
|
||||
|
||||
@property
|
||||
def config(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取配置
|
||||
|
||||
Returns:
|
||||
配置字典
|
||||
"""
|
||||
if self._config is None:
|
||||
self._config = self.load_config()
|
||||
return self._config
|
||||
|
||||
def get(self, key: str, default: Any = None) -> Any:
|
||||
"""
|
||||
获取配置项
|
||||
|
||||
Args:
|
||||
key: 配置键，支持点号分隔的嵌套键
|
||||
default: 默认值
|
||||
|
||||
Returns:
|
||||
配置值
|
||||
"""
|
||||
config = self.config
|
||||
keys = key.split('.')
|
||||
|
||||
try:
|
||||
for k in keys:
|
||||
config = config[k]
|
||||
return config
|
||||
except (KeyError, TypeError):
|
||||
return default
|
||||
|
||||
def get_trading_config(self) -> Dict[str, Any]:
|
||||
"""获取交易配置"""
|
||||
return self.get('trading', {})
|
||||
|
||||
def get_data_config(self) -> Dict[str, Any]:
|
||||
"""获取数据配置"""
|
||||
return self.get('data', {})
|
||||
|
||||
def get_strategy_config(self) -> Dict[str, Any]:
|
||||
"""获取策略配置"""
|
||||
return self.get('strategy', {})
|
||||
|
||||
def get_monitor_config(self) -> Dict[str, Any]:
|
||||
"""获取监控配置"""
|
||||
return self.get('monitor', {})
|
||||
|
||||
def get_logging_config(self) -> Dict[str, Any]:
|
||||
"""获取日志配置"""
|
||||
return self.get('logging', {})
|
||||
|
||||
|
||||
# 全局配置实例
|
||||
config_loader = ConfigLoader()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试配置加载
|
||||
loader = ConfigLoader()
|
||||
config = loader.load_config()
|
||||
|
||||
print("完整配置:")
|
||||
print(yaml.dump(config, default_flow_style=False, allow_unicode=True))
|
||||
|
||||
print("\n交易配置:")
|
||||
print(loader.get_trading_config())
|
||||
|
||||
print("\n数据配置:")
|
||||
print(loader.get_data_config())
|
||||
|
||||
print("\n单个配置项:")
|
||||
print(f"交易开始时间: {loader.get('trading.trading_hours.start')}")
|
||||
print(f"最大仓位: {loader.get('trading.risk_management.max_total_position')}")
|
||||
416
src/utils/notification.py
Normal file
416
src/utils/notification.py
Normal file
@ -0,0 +1,416 @@
|
||||
"""
|
||||
通知模块 - 支持钉钉webhook通知
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
import time
|
||||
import hmac
|
||||
import hashlib
|
||||
import base64
|
||||
import urllib.parse
|
||||
from typing import Dict, Any, Optional
|
||||
from loguru import logger
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class DingTalkNotifier:
|
||||
"""钉钉机器人通知器"""
|
||||
|
||||
def __init__(self, webhook_url: str, secret: str = None):
|
||||
"""
|
||||
初始化钉钉通知器
|
||||
|
||||
Args:
|
||||
webhook_url: 钉钉机器人webhook地址
|
||||
secret: 加签密钥，如果提供则启用加签验证
|
||||
"""
|
||||
self.webhook_url = webhook_url
|
||||
self.secret = secret
|
||||
self.session = requests.Session()
|
||||
logger.info(f"钉钉通知器初始化完成 {'(已启用加签)' if secret else '(未启用加签)'}")
|
||||
|
||||
def _generate_signature(self, timestamp: str) -> str:
|
||||
"""
|
||||
生成钉钉加签
|
||||
|
||||
Args:
|
||||
timestamp: 时间戳字符串
|
||||
|
||||
Returns:
|
||||
加签结果
|
||||
"""
|
||||
if not self.secret:
|
||||
return ""
|
||||
|
||||
string_to_sign = f"{timestamp}\n{self.secret}"
|
||||
hmac_code = hmac.new(
|
||||
self.secret.encode('utf-8'),
|
||||
string_to_sign.encode('utf-8'),
|
||||
digestmod=hashlib.sha256
|
||||
).digest()
|
||||
sign = urllib.parse.quote_plus(base64.b64encode(hmac_code))
|
||||
return sign
|
||||
|
||||
def _get_signed_url(self) -> str:
|
||||
"""
|
||||
获取带加签的webhook URL
|
||||
|
||||
Returns:
|
||||
带加签的URL，如果未配置密钥则返回原URL
|
||||
"""
|
||||
if not self.secret:
|
||||
return self.webhook_url
|
||||
|
||||
timestamp = str(round(time.time() * 1000))
|
||||
sign = self._generate_signature(timestamp)
|
||||
|
||||
# 添加时间戳和签名参数
|
||||
separator = '&' if '?' in self.webhook_url else '?'
|
||||
return f"{self.webhook_url}{separator}timestamp={timestamp}&sign={sign}"
|
||||
|
||||
def send_text_message(self, content: str, at_all: bool = False, at_mobiles: list = None) -> bool:
|
||||
"""
|
||||
发送文本消息
|
||||
|
||||
Args:
|
||||
content: 消息内容
|
||||
at_all: 是否@所有人
|
||||
at_mobiles: @指定手机号列表
|
||||
|
||||
Returns:
|
||||
发送是否成功
|
||||
"""
|
||||
try:
|
||||
data = {
|
||||
"msgtype": "text",
|
||||
"text": {
|
||||
"content": content
|
||||
}
|
||||
}
|
||||
|
||||
# 添加@功能
|
||||
if at_all or at_mobiles:
|
||||
data["at"] = {}
|
||||
if at_all:
|
||||
data["at"]["isAtAll"] = True
|
||||
if at_mobiles:
|
||||
data["at"]["atMobiles"] = at_mobiles
|
||||
|
||||
response = self.session.post(
|
||||
self._get_signed_url(),
|
||||
json=data,
|
||||
headers={'Content-Type': 'application/json'},
|
||||
timeout=10
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
if result.get('errcode') == 0:
|
||||
logger.info("钉钉消息发送成功")
|
||||
return True
|
||||
else:
|
||||
logger.error(f"钉钉消息发送失败: {result.get('errmsg', '未知错误')}")
|
||||
return False
|
||||
else:
|
||||
logger.error(f"钉钉API请求失败: HTTP {response.status_code}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"发送钉钉消息异常: {e}")
|
||||
return False
|
||||
|
||||
def send_markdown_message(self, title: str, text: str, at_all: bool = False, at_mobiles: list = None) -> bool:
|
||||
"""
|
||||
发送Markdown格式消息
|
||||
|
||||
Args:
|
||||
title: 消息标题
|
||||
text: Markdown格式的消息内容
|
||||
at_all: 是否@所有人
|
||||
at_mobiles: @指定手机号列表
|
||||
|
||||
Returns:
|
||||
发送是否成功
|
||||
"""
|
||||
try:
|
||||
data = {
|
||||
"msgtype": "markdown",
|
||||
"markdown": {
|
||||
"title": title,
|
||||
"text": text
|
||||
}
|
||||
}
|
||||
|
||||
# 添加@功能
|
||||
if at_all or at_mobiles:
|
||||
data["at"] = {}
|
||||
if at_all:
|
||||
data["at"]["isAtAll"] = True
|
||||
if at_mobiles:
|
||||
data["at"]["atMobiles"] = at_mobiles
|
||||
|
||||
response = self.session.post(
|
||||
self._get_signed_url(),
|
||||
json=data,
|
||||
headers={'Content-Type': 'application/json'},
|
||||
timeout=10
|
||||
)
|
||||
|
||||
if response.status_code == 200:
|
||||
result = response.json()
|
||||
if result.get('errcode') == 0:
|
||||
logger.info("钉钉Markdown消息发送成功")
|
||||
return True
|
||||
else:
|
||||
logger.error(f"钉钉Markdown消息发送失败: {result.get('errmsg', '未知错误')}")
|
||||
return False
|
||||
else:
|
||||
logger.error(f"钉钉API请求失败: HTTP {response.status_code}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"发送钉钉Markdown消息异常: {e}")
|
||||
return False
|
||||
|
||||
def send_strategy_summary_message(self, title: str, markdown_text: str) -> bool:
|
||||
"""
|
||||
发送策略汇总消息（Markdown格式）
|
||||
|
||||
Args:
|
||||
title: 消息标题
|
||||
markdown_text: Markdown格式的消息内容
|
||||
|
||||
Returns:
|
||||
发送是否成功
|
||||
"""
|
||||
return self.send_markdown_message(title, markdown_text)
|
||||
|
||||
def send_strategy_signal(self, stock_code: str, stock_name: str, timeframe: str,
|
||||
signal_type: str, price: float, signal_date: str = None, additional_info: Dict[str, Any] = None) -> bool:
|
||||
"""
|
||||
发送策略信号通知
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
timeframe: 时间周期
|
||||
signal_type: 信号类型
|
||||
price: 当前价格
|
||||
signal_date: 信号发生的时间（K线时间）
|
||||
additional_info: 额外信息
|
||||
|
||||
Returns:
|
||||
发送是否成功
|
||||
"""
|
||||
try:
|
||||
# 使用信号时间或当前时间
|
||||
display_time = signal_date if signal_date else datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
# 构建Markdown消息
|
||||
title = f"📈 {signal_type}信号提醒"
|
||||
|
||||
markdown_text = f"""
|
||||
# 📈 {signal_type}信号提醒
|
||||
|
||||
**股票信息:**
|
||||
- 代码: `{stock_code}`
|
||||
- 名称: `{stock_name}`
|
||||
- 价格: `{price}` 元
|
||||
- 时间周期: `{timeframe}`
|
||||
|
||||
**信号时间:** {display_time}
|
||||
|
||||
**策略说明:** 两阳线+阴线+阳线形态突破
|
||||
|
||||
---
|
||||
*量化交易系统自动发送*
|
||||
"""
|
||||
|
||||
# 添加额外信息
|
||||
if additional_info:
|
||||
markdown_text += "\n**额外信息:**\n"
|
||||
for key, value in additional_info.items():
|
||||
markdown_text += f"- {key}: `{value}`\n"
|
||||
|
||||
return self.send_markdown_message(title, markdown_text)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"发送策略信号通知异常: {e}")
|
||||
return False
|
||||
|
||||
|
||||
class NotificationManager:
|
||||
"""通知管理器"""
|
||||
|
||||
def __init__(self, config: Dict[str, Any]):
|
||||
"""
|
||||
初始化通知管理器
|
||||
|
||||
Args:
|
||||
config: 通知配置
|
||||
"""
|
||||
self.config = config
|
||||
self.dingtalk_notifier = None
|
||||
|
||||
# 初始化钉钉通知器
|
||||
dingtalk_config = config.get('dingtalk', {})
|
||||
if dingtalk_config.get('enabled', False):
|
||||
webhook_url = dingtalk_config.get('webhook_url')
|
||||
secret = dingtalk_config.get('secret')
|
||||
if webhook_url:
|
||||
self.dingtalk_notifier = DingTalkNotifier(webhook_url, secret)
|
||||
logger.info("钉钉通知器已启用")
|
||||
else:
|
||||
logger.warning("钉钉通知已启用但未配置webhook_url")
|
||||
|
||||
def send_strategy_signal(self, stock_code: str, stock_name: str, timeframe: str,
|
||||
signal_type: str, price: float, signal_date: str = None, additional_info: Dict[str, Any] = None) -> bool:
|
||||
"""
|
||||
发送策略信号到所有启用的通知渠道
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
timeframe: 时间周期
|
||||
signal_type: 信号类型
|
||||
price: 当前价格
|
||||
signal_date: 信号发生的时间（K线时间）
|
||||
additional_info: 额外信息
|
||||
|
||||
Returns:
|
||||
是否至少有一个渠道发送成功
|
||||
"""
|
||||
success = False
|
||||
|
||||
# 钉钉通知
|
||||
if self.dingtalk_notifier:
|
||||
if self.dingtalk_notifier.send_strategy_signal(
|
||||
stock_code, stock_name, timeframe, signal_type, price, signal_date, additional_info
|
||||
):
|
||||
success = True
|
||||
|
||||
# 记录到日志
|
||||
logger.info(f"策略信号: {signal_type} | {stock_code}({stock_name}) | {timeframe} | {price}元")
|
||||
if additional_info:
|
||||
logger.info(f"额外信息: {additional_info}")
|
||||
|
||||
return success
|
||||
|
||||
def send_strategy_summary(self, all_signals: Dict[str, Any], scan_stats: Dict[str, Any] = None) -> bool:
|
||||
"""
|
||||
发送策略信号汇总通知
|
||||
|
||||
Args:
|
||||
all_signals: 所有信号的汇总数据 {stock_code: {timeframe: [signals]}}
|
||||
scan_stats: 扫描统计信息
|
||||
|
||||
Returns:
|
||||
发送是否成功
|
||||
"""
|
||||
if not all_signals:
|
||||
return False
|
||||
|
||||
try:
|
||||
from datetime import datetime
|
||||
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
# 统计信号数量
|
||||
total_signals = 0
|
||||
total_stocks = len(all_signals)
|
||||
|
||||
signal_summary = []
|
||||
for stock_code, stock_results in all_signals.items():
|
||||
stock_signal_count = sum(len(signals) for signals in stock_results.values())
|
||||
total_signals += stock_signal_count
|
||||
|
||||
if stock_signal_count > 0:
|
||||
# 获取股票名称和最新信号
|
||||
stock_name = "未知"
|
||||
latest_signal = None
|
||||
for timeframe, signals in stock_results.items():
|
||||
if signals:
|
||||
latest_signal = signals[-1] # 取最新信号
|
||||
stock_name = latest_signal.get('stock_name', stock_name)
|
||||
break
|
||||
|
||||
if latest_signal:
|
||||
signal_summary.append({
|
||||
'stock_code': stock_code,
|
||||
'stock_name': stock_name,
|
||||
'signal_count': stock_signal_count,
|
||||
'price': latest_signal['breakout_price'],
|
||||
'date': latest_signal['date'],
|
||||
'turnover': latest_signal.get('turnover_ratio', 0)
|
||||
})
|
||||
|
||||
# 构建汇总消息
|
||||
title = f"📈 K线形态策略信号汇总"
|
||||
|
||||
markdown_text = f"""
|
||||
# 📈 K线形态策略信号汇总
|
||||
|
||||
**扫描统计:**
|
||||
- 扫描时间: {current_time}
|
||||
- 发现信号: `{total_signals}` 个
|
||||
- 涉及股票: `{total_stocks}` 只
|
||||
|
||||
**信号详情:**
|
||||
"""
|
||||
|
||||
# 添加每只股票的信号摘要
|
||||
for i, signal in enumerate(signal_summary[:10], 1): # 最多显示10只股票
|
||||
markdown_text += f"""
|
||||
{i}. **{signal['stock_code']} - {signal['stock_name']}**
|
||||
- 价格: `{signal['price']:.2f}元`
|
||||
- 信号数: `{signal['signal_count']}个`
|
||||
- 换手率: `{signal['turnover']:.2f}%`
|
||||
- 时间: `{signal['date']}`
|
||||
"""
|
||||
|
||||
if len(signal_summary) > 10:
|
||||
markdown_text += f"\n*还有 {len(signal_summary) - 10} 只股票...*\n"
|
||||
|
||||
# 添加扫描统计（如果提供）
|
||||
if scan_stats:
|
||||
markdown_text += f"""
|
||||
**扫描范围:**
|
||||
- 扫描股票总数: `{scan_stats.get('total_scanned', 'N/A')}`
|
||||
- 数据源: `{scan_stats.get('data_source', '热门股票')}`
|
||||
"""
|
||||
|
||||
markdown_text += """
|
||||
---
|
||||
**策略说明:** 两阳线+阴线+阳线形态突破
|
||||
*量化交易系统自动发送*
|
||||
"""
|
||||
|
||||
# 发送钉钉通知
|
||||
if self.dingtalk_notifier:
|
||||
return self.dingtalk_notifier.send_markdown_message(title, markdown_text)
|
||||
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"发送策略汇总通知异常: {e}")
|
||||
return False
|
||||
|
||||
def send_test_message(self) -> bool:
|
||||
"""发送测试消息"""
|
||||
if self.dingtalk_notifier:
|
||||
return self.dingtalk_notifier.send_text_message("量化交易系统通知测试 ✅")
|
||||
return False
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试代码
|
||||
# 注意: 需要有效的钉钉webhook地址才能测试
|
||||
test_config = {
|
||||
'dingtalk': {
|
||||
'enabled': False, # 设置为True并提供webhook_url进行测试
|
||||
'webhook_url': 'https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN'
|
||||
}
|
||||
}
|
||||
|
||||
notifier = NotificationManager(test_config)
|
||||
print("通知管理器初始化完成")
|
||||
96
test_dingtalk.py
Normal file
96
test_dingtalk.py
Normal file
@ -0,0 +1,96 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试钉钉通知功能
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
|
||||
|
||||
from utils.notification import DingTalkNotifier, NotificationManager
|
||||
import yaml
|
||||
|
||||
def test_dingtalk_with_secret():
|
||||
"""测试带加签的钉钉通知"""
|
||||
print("🔧 测试钉钉加签功能...")
|
||||
|
||||
# 测试加签生成
|
||||
webhook_url = "https://oapi.dingtalk.com/robot/send?access_token=YOUR_TOKEN"
|
||||
secret = "SEC6e9dbd71d4addd2c4e673fb72d686293b342da5ae48da2f8ec788a68de99f981"
|
||||
|
||||
notifier = DingTalkNotifier(webhook_url, secret)
|
||||
|
||||
# 生成签名URL
|
||||
signed_url = notifier._get_signed_url()
|
||||
print(f"✅ 签名URL生成成功")
|
||||
print(f"📄 原始URL: {webhook_url}")
|
||||
print(f"🔐 签名URL: {signed_url}")
|
||||
|
||||
# 检查URL格式
|
||||
if "timestamp=" in signed_url and "sign=" in signed_url:
|
||||
print("✅ 加签参数正确添加")
|
||||
else:
|
||||
print("❌ 加签参数缺失")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
def test_notification_manager():
|
||||
"""测试通知管理器配置"""
|
||||
print("\n🔧 测试通知管理器配置...")
|
||||
|
||||
# 从配置文件读取配置
|
||||
try:
|
||||
with open('config/config.yaml', 'r', encoding='utf-8') as f:
|
||||
config = yaml.safe_load(f)
|
||||
|
||||
notification_config = config.get('notification', {})
|
||||
print(f"✅ 配置文件加载成功")
|
||||
print(f"📄 钉钉配置: {notification_config.get('dingtalk', {})}")
|
||||
|
||||
# 创建通知管理器
|
||||
notifier_manager = NotificationManager(notification_config)
|
||||
|
||||
if notifier_manager.dingtalk_notifier:
|
||||
print("✅ 钉钉通知器初始化成功")
|
||||
if notifier_manager.dingtalk_notifier.secret:
|
||||
print("✅ 加签密钥配置正确")
|
||||
else:
|
||||
print("❌ 加签密钥未配置")
|
||||
return False
|
||||
else:
|
||||
print("❌ 钉钉通知器未启用")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 配置测试失败: {e}")
|
||||
return False
|
||||
|
||||
def main():
|
||||
print("=" * 60)
|
||||
print(" 钉钉通知功能测试")
|
||||
print("=" * 60)
|
||||
|
||||
# 测试加签功能
|
||||
test1_passed = test_dingtalk_with_secret()
|
||||
|
||||
# 测试配置管理
|
||||
test2_passed = test_notification_manager()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("测试结果:")
|
||||
print(f"🔐 加签功能测试: {'✅ 通过' if test1_passed else '❌ 失败'}")
|
||||
print(f"⚙️ 配置管理测试: {'✅ 通过' if test2_passed else '❌ 失败'}")
|
||||
|
||||
if test1_passed and test2_passed:
|
||||
print("\n🎉 所有测试通过！钉钉通知功能配置正确")
|
||||
print("💡 注意: 需要提供完整的webhook URL才能发送实际消息")
|
||||
else:
|
||||
print("\n❌ 部分测试失败，请检查配置")
|
||||
|
||||
print("=" * 60)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
135
test_sentiment.py
Normal file
135
test_sentiment.py
Normal file
@ -0,0 +1,135 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
舆情数据功能测试脚本
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 将src目录添加到Python路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from src.data.sentiment_fetcher import SentimentFetcher
|
||||
|
||||
|
||||
def test_sentiment_features():
|
||||
"""测试舆情功能"""
|
||||
print("="*60)
|
||||
print(" A股舆情数据功能测试")
|
||||
print("="*60)
|
||||
|
||||
fetcher = SentimentFetcher()
|
||||
|
||||
# 1. 测试北向资金
|
||||
print("\n🌊 1. 北向资金数据测试")
|
||||
print("-" * 30)
|
||||
current_flow = fetcher.get_north_flow_current()
|
||||
if not current_flow.empty:
|
||||
row = current_flow.iloc[0]
|
||||
print(f"总净流入: {row.get('net_tgt', 'N/A')} 万元")
|
||||
print(f"沪股通: {row.get('net_hgt', 'N/A')} 万元")
|
||||
print(f"深股通: {row.get('net_sgt', 'N/A')} 万元")
|
||||
print(f"更新时间: {row.get('trade_time', 'N/A')}")
|
||||
else:
|
||||
print("未获取到当前北向资金数据")
|
||||
|
||||
# 2. 测试热门股票
|
||||
print("\n🔥 2. 热门股票数据测试")
|
||||
print("-" * 30)
|
||||
hot_stocks = fetcher.get_popular_stocks_east_100()
|
||||
if not hot_stocks.empty:
|
||||
print(f"东财人气股票TOP5:")
|
||||
for idx, row in hot_stocks.head(5).iterrows():
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
print(f" {idx + 1}. {code} - {name}")
|
||||
else:
|
||||
print("未获取到热门股票数据")
|
||||
|
||||
# 3. 测试龙虎榜
|
||||
print("\n🐉 3. 龙虎榜数据测试")
|
||||
print("-" * 30)
|
||||
dragon_tiger = fetcher.get_dragon_tiger_list_daily()
|
||||
if not dragon_tiger.empty:
|
||||
print(f"今日龙虎榜 (共{len(dragon_tiger)}只股票):")
|
||||
for idx, row in dragon_tiger.head(5).iterrows():
|
||||
code = row.get('stock_code', 'N/A')
|
||||
name = row.get('short_name', 'N/A')
|
||||
reason = row.get('reason', 'N/A')
|
||||
print(f" {idx + 1}. {code} - {name}")
|
||||
print(f" 上榜原因: {reason}")
|
||||
else:
|
||||
print("今日无龙虎榜数据")
|
||||
|
||||
# 4. 测试热门概念
|
||||
print("\n💡 4. 热门概念数据测试")
|
||||
print("-" * 30)
|
||||
try:
|
||||
hot_concepts = fetcher.get_hot_concept_ths_20()
|
||||
if not hot_concepts.empty:
|
||||
print(f"同花顺热门概念TOP5:")
|
||||
for idx, row in hot_concepts.head(5).iterrows():
|
||||
name = row.get('concept_name', 'N/A')
|
||||
change_pct = row.get('change_pct', 'N/A')
|
||||
print(f" {idx + 1}. {name} (涨跌幅: {change_pct}%)")
|
||||
else:
|
||||
print("未获取到热门概念数据")
|
||||
except Exception as e:
|
||||
print(f"热门概念获取失败: {e}")
|
||||
|
||||
# 5. 测试市场舆情综合概览
|
||||
print("\n📊 5. 市场舆情综合概览测试")
|
||||
print("-" * 30)
|
||||
try:
|
||||
overview = fetcher.get_market_sentiment_overview()
|
||||
if overview:
|
||||
print("✅ 市场舆情概览获取成功")
|
||||
|
||||
# 北向资金
|
||||
if 'north_flow' in overview:
|
||||
north_data = overview['north_flow']
|
||||
print(f"北向资金: 总净流入 {north_data.get('net_total', 'N/A')} 万元")
|
||||
|
||||
# 热门股票
|
||||
if 'hot_stocks_east' in overview and not overview['hot_stocks_east'].empty:
|
||||
count = len(overview['hot_stocks_east'])
|
||||
print(f"热门股票: 获取到 {count} 只")
|
||||
|
||||
# 龙虎榜
|
||||
if 'dragon_tiger' in overview and not overview['dragon_tiger'].empty:
|
||||
count = len(overview['dragon_tiger'])
|
||||
print(f"龙虎榜: 获取到 {count} 只")
|
||||
else:
|
||||
print("市场舆情概览获取失败")
|
||||
except Exception as e:
|
||||
print(f"市场舆情概览测试失败: {e}")
|
||||
|
||||
# 6. 测试个股舆情分析
|
||||
print("\n🔍 6. 个股舆情分析测试")
|
||||
print("-" * 30)
|
||||
test_stock = "000001.SZ" # 平安银行
|
||||
try:
|
||||
analysis = fetcher.analyze_stock_sentiment(test_stock)
|
||||
if 'error' not in analysis:
|
||||
print(f"✅ {test_stock} 舆情分析成功")
|
||||
print(f"东财人气榜: {'在榜' if analysis.get('in_popular_east', False) else '不在榜'}")
|
||||
print(f"同花顺热门榜: {'在榜' if analysis.get('in_hot_ths', False) else '不在榜'}")
|
||||
|
||||
if 'dragon_tiger' in analysis and not analysis['dragon_tiger'].empty:
|
||||
print("✅ 今日上榜龙虎榜")
|
||||
else:
|
||||
print("❌ 今日未上榜龙虎榜")
|
||||
else:
|
||||
print(f"个股舆情分析失败: {analysis.get('error', '未知错误')}")
|
||||
except Exception as e:
|
||||
print(f"个股舆情分析测试失败: {e}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print(" 舆情数据功能测试完成")
|
||||
print("="*60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_sentiment_features()
|
||||
185
test_strategy.py
Normal file
185
test_strategy.py
Normal file
@ -0,0 +1,185 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
K线形态策略测试脚本
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
# 将src目录添加到Python路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from src.data.data_fetcher import ADataFetcher
|
||||
from src.utils.notification import NotificationManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
|
||||
|
||||
def create_test_kline_data():
|
||||
"""创建测试K线数据 - 包含两阳线+阴线+阳线形态"""
|
||||
dates = pd.date_range('2023-01-01', periods=10, freq='D')
|
||||
|
||||
# 模拟K线数据
|
||||
test_data = {
|
||||
'trade_date': dates,
|
||||
'open': [10.0, 10.5, 11.0, 12.0, 11.5, 11.0, 10.5, 11.0, 11.8, 12.5],
|
||||
'high': [10.8, 11.2, 11.8, 12.5, 12.0, 11.5, 11.2, 11.5, 12.2, 13.0],
|
||||
'low': [9.8, 10.3, 10.8, 11.8, 10.8, 10.5, 10.2, 10.8, 11.6, 12.3],
|
||||
'close':[10.6, 11.0, 11.5, 12.2, 11.0, 10.8, 11.2, 11.3, 12.0, 12.8],
|
||||
'volume': [1000] * 10
|
||||
}
|
||||
|
||||
df = pd.DataFrame(test_data)
|
||||
print("测试K线数据:")
|
||||
print(df)
|
||||
print()
|
||||
|
||||
return df
|
||||
|
||||
|
||||
def test_pattern_detection():
|
||||
"""测试形态检测功能"""
|
||||
print("="*60)
|
||||
print(" K线形态检测功能测试")
|
||||
print("="*60)
|
||||
|
||||
# 创建测试配置
|
||||
strategy_config = {
|
||||
'min_entity_ratio': 0.55,
|
||||
'timeframes': ['daily'],
|
||||
'scan_stocks_count': 10,
|
||||
'analysis_days': 60
|
||||
}
|
||||
|
||||
notification_config = {
|
||||
'dingtalk': {
|
||||
'enabled': False,
|
||||
'webhook_url': ''
|
||||
}
|
||||
}
|
||||
|
||||
# 初始化组件
|
||||
data_fetcher = ADataFetcher()
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||||
|
||||
print("1. 策略信息:")
|
||||
print(strategy.get_strategy_summary())
|
||||
|
||||
print("\n2. 测试K线特征计算:")
|
||||
test_df = create_test_kline_data()
|
||||
df_with_features = strategy.calculate_kline_features(test_df)
|
||||
|
||||
print("添加特征后的数据:")
|
||||
relevant_cols = ['trade_date', 'open', 'high', 'low', 'close', 'is_yang', 'is_yin', 'entity_ratio']
|
||||
print(df_with_features[relevant_cols])
|
||||
|
||||
print("\n3. 测试形态检测:")
|
||||
signals = strategy.detect_pattern(df_with_features)
|
||||
|
||||
if signals:
|
||||
print(f"发现 {len(signals)} 个形态信号:")
|
||||
for i, signal in enumerate(signals, 1):
|
||||
print(f"\n信号 {i}:")
|
||||
print(f" 日期: {signal['date']}")
|
||||
print(f" 形态: {signal['pattern_type']}")
|
||||
print(f" 突破价格: {signal['breakout_price']:.2f}")
|
||||
print(f" 突破幅度: {signal['breakout_pct']:.2f}%")
|
||||
print(f" 阳线1实体比例: {signal['yang1_entity_ratio']:.1%}")
|
||||
print(f" 阳线2实体比例: {signal['yang2_entity_ratio']:.1%}")
|
||||
else:
|
||||
print("未发现形态信号")
|
||||
|
||||
print("\n4. 测试真实股票数据:")
|
||||
test_stocks = ["000001.SZ", "000002.SZ"] # 平安银行、万科A
|
||||
|
||||
for stock_code in test_stocks:
|
||||
print(f"\n分析股票: {stock_code}")
|
||||
try:
|
||||
results = strategy.analyze_stock(stock_code, days=30) # 分析最近30天
|
||||
|
||||
total_signals = sum(len(signals) for signals in results.values())
|
||||
print(f"总信号数: {total_signals}")
|
||||
|
||||
for timeframe, signals in results.items():
|
||||
if signals:
|
||||
print(f"{timeframe}: {len(signals)}个信号")
|
||||
# 显示最新信号
|
||||
latest = signals[-1]
|
||||
print(f" 最新: {latest['date']} {latest['breakout_price']:.2f}元")
|
||||
else:
|
||||
print(f"{timeframe}: 无信号")
|
||||
|
||||
except Exception as e:
|
||||
print(f"分析失败: {e}")
|
||||
|
||||
print("\n5. 测试通知功能:")
|
||||
try:
|
||||
# 测试日志通知
|
||||
notification_manager.send_strategy_signal(
|
||||
stock_code="TEST001",
|
||||
stock_name="测试股票",
|
||||
timeframe="daily",
|
||||
signal_type="测试信号",
|
||||
price=15.50,
|
||||
additional_info={
|
||||
"阳线1实体比例": "65%",
|
||||
"阳线2实体比例": "70%",
|
||||
"突破幅度": "2.5%"
|
||||
}
|
||||
)
|
||||
print("✅ 通知功能测试完成（日志记录）")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 通知功能测试失败: {e}")
|
||||
|
||||
print("\n" + "="*60)
|
||||
print(" 策略测试完成")
|
||||
print("="*60)
|
||||
|
||||
|
||||
def test_weekly_monthly_conversion():
|
||||
"""测试周线月线转换功能"""
|
||||
print("\n测试周线/月线数据转换:")
|
||||
|
||||
# 创建更多天数的测试数据
|
||||
dates = pd.date_range('2023-01-01', periods=50, freq='D')
|
||||
|
||||
test_data = {
|
||||
'trade_date': dates,
|
||||
'open': np.random.uniform(10, 15, 50),
|
||||
'high': np.random.uniform(15, 20, 50),
|
||||
'low': np.random.uniform(8, 12, 50),
|
||||
'close': np.random.uniform(10, 15, 50),
|
||||
'volume': np.random.randint(1000, 5000, 50)
|
||||
}
|
||||
|
||||
daily_df = pd.DataFrame(test_data)
|
||||
|
||||
strategy_config = {'min_entity_ratio': 0.55, 'timeframes': ['daily']}
|
||||
notification_config = {'dingtalk': {'enabled': False}}
|
||||
|
||||
data_fetcher = ADataFetcher()
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||||
|
||||
# 测试周线转换
|
||||
weekly_df = strategy._convert_to_weekly(daily_df)
|
||||
print(f"日线数据: {len(daily_df)} 条")
|
||||
print(f"周线数据: {len(weekly_df)} 条")
|
||||
|
||||
# 测试月线转换
|
||||
monthly_df = strategy._convert_to_monthly(daily_df)
|
||||
print(f"月线数据: {len(monthly_df)} 条")
|
||||
|
||||
if not weekly_df.empty:
|
||||
print("\n周线数据样本:")
|
||||
print(weekly_df[['trade_date', 'open', 'high', 'low', 'close']].head())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_pattern_detection()
|
||||
test_weekly_monthly_conversion()
|
||||
Loading…
Reference in New Issue
Block a user