更新
This commit is contained in:
aaron
parent
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10
.env.example
10
.env.example
@ -7,4 +7,12 @@ MYSQL_DATABASE=tradingai
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# Flask应用配置
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FLASK_ENV=production
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PYTHONPATH=/app
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PYTHONPATH=/app
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# 市场扫描服务配置
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MARKET_SCAN_STOCKS=200
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LOG_LEVEL=INFO
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# 钉钉通知配置（已启用）
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DINGTALK_WEBHOOK_URL=https://oapi.dingtalk.com/robot/send?access_token=50ad2c14e3c8bf7e262ba837dc2a35cb420228ee4165abd69a9e678c901e120e
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DINGTALK_SECRET=SEC6e9dbd71d4addd2c4e673fb72d686293b342da5ae48da2f8ec788a68de99f981
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4
.gitignore
vendored
4
.gitignore
vendored
@ -9,8 +9,8 @@ __pycache__/
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.env
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.venv
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# Runtime data and databases
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data/
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# Runtime data and databases (exclude source code)
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/data/
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logs/
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*.log
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*.db
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@ -11,12 +11,13 @@ ENV FLASK_APP=web/mysql_app.py
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ENV FLASK_ENV=production
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ENV TZ=Asia/Shanghai
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# 安装系统依赖，包括MySQL客户端库
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# 安装系统依赖，包括MySQL客户端库和cron
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RUN apt-get update && apt-get install -y \
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gcc \
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g++ \
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curl \
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tzdata \
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cron \
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default-libmysqlclient-dev \
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pkg-config \
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&& rm -rf /var/lib/apt/lists/*
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12
README.md
12
README.md
@ -1,10 +1,10 @@
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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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一个基于Python的A股市场监控和选股量化程序，使用TuShare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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- 📈 **实时数据获取**: 使用TuShare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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@ -22,7 +22,7 @@ TradingAI/
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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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│ │ ├── tushare_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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@ -79,9 +79,9 @@ python main.py
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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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from src.data.tushare_fetcher import TushareFetcher
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fetcher = ADataFetcher()
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fetcher = TushareFetcher()
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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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@ -167,7 +167,7 @@ hist_data = fetcher.get_historical_data(
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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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1. 首次使用需要确保网络连接正常，TuShare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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@ -91,12 +91,12 @@ python main.py
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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.data.tushare_fetcher import TushareFetcher
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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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data_fetcher = TushareFetcher()
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notification_manager = NotificationManager(notification_config)
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strategy_config = {
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@ -153,7 +153,7 @@ market_results = strategy.scan_market(max_stocks=20)
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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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- 优先使用TuShare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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@ -228,7 +228,7 @@ market_results = strategy.scan_market(max_stocks=20)
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### 常见问题
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1. **无法获取数据**: 检查网络连接和adata配置
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1. **无法获取数据**: 检查网络连接和TuShare配置
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2. **钉钉通知失败**: 验证webhook地址和安全设置
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3. **策略未启用**: 检查配置文件中的 `enabled` 设置
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4. **内存占用过高**: 减少 `scan_stocks_count` 和 `analysis_days`
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174
USAGE.md
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174
USAGE.md
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@ -0,0 +1,174 @@
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# A股量化交易策略系统
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## 项目简介
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本项目是一个模块化的A股量化交易策略执行系统，支持定时执行多种策略任务，主要包括：
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- 📊 **数据获取**: 基于TuShare的股票数据获取
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- 🎯 **股票池管理**: 多种股票筛选规则(热榜、龙头股等)
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- 📈 **策略分析**: K线形态识别等技术分析策略
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- ⏰ **任务调度**: 灵活的定时任务执行
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- 📱 **结果通知**: 多种通知方式支持
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## 快速开始
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### 启动策略测试系统
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```bash
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python main.py
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```
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### 基本命令
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```bash
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# 分析单只股票
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scan 000001.SZ
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# 扫描热门股票(默认20只)
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market 30
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# 查看可用股票池规则
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pools
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# 执行策略任务
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task tushare_hot 15
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# 查看定时任务示例
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schedule
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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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## 系统架构
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```
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数据层: TushareFetcher + StockPoolManager
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策略层: BaseStrategy + KLinePatternStrategy
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执行层: TaskScheduler + StrategyExecutor
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通知层: NotificationManager
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```
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### 核心组件
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- **TushareFetcher**: TuShare API数据获取器
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- **StockPoolManager**: 股票池管理器，支持多种筛选规则
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- **KLinePatternStrategy**: K线形态识别策略
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- **StrategyExecutor**: 策略执行协调器
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- **TaskScheduler**: 定时任务调度器
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## 股票池规则
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- `tushare_hot`: 同花顺热榜
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- `combined_hot`: 合并热门(同花顺+东财)
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- `leading_stocks`: 龙头牛股
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- 支持自定义股票列表
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## 策略功能
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### K线形态策略
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- 识别"两阳线+阴线+阳线"突破形态
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- 创新高回踩确认机制
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- 多时间周期分析支持
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- 回踩监控功能
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## 配置文件
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主要配置文件位于 `config/config.yaml`：
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```yaml
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# TuShare配置
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tushare:
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token: "your_token_here"
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# 策略配置
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strategy:
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kline_pattern:
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min_entity_ratio: 0.55
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final_yang_min_ratio: 0.40
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max_turnover_ratio: 40.0
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timeframes: ["daily"]
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# 通知配置
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notification:
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dingtalk:
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enabled: true
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webhook_url: "your_webhook_url"
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```
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## 定时任务示例
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查看 `examples/` 目录下的配置示例：
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- `examples/new_architecture_example.py`: 完整架构演示
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- `examples/task_config_examples.py`: 常见任务配置
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## 使用示例
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### 1. 分析单只股票
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```bash
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> scan 000001.SZ
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🔍 分析股票: 000001.SZ
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----------------------------------------
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📊 DAILY: 发现 1 个信号
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1. 2024-01-15 | 两阳+阴+阳突破 | 价格: 12.50元
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📈 总计: 1 个信号
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```
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### 2. 扫描市场热门股票
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```bash
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> market 20
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🌍 扫描市场热门股票 (前20只)
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--------------------------------------------------
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📊 扫描结果:
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股票池: 同花顺热榜
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总扫描: 20 只
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有信号: 3 只
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信号数: 5 个
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耗时: 15.23 秒
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```
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### 3. 执行策略任务
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```bash
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> task leading_stocks 10
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⚡ 执行策略任务
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股票池规则: leading_stocks
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最大股票数: 10
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----------------------------------------
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✅ 任务完成:
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任务ID: manual_leading_stocks_10
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成功: 是
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耗时: 8.45 秒
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信号数: 2 个
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```
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## 开发指南
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### 添加新策略
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1. 继承 `BaseStrategy` 基类
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2. 实现 `analyze_stock()` 方法
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3. 注册到 `StrategyExecutor`
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### 添加新股票池规则
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1. 继承 `StockPoolRule` 基类
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2. 实现 `get_stocks()` 方法
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3. 注册到 `StockPoolManager`
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## 注意事项
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- 确保配置文件中的TuShare token有效
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- 策略分析需要足够的历史数据
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- 定时任务需要稳定的网络连接
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- 建议在交易时间外进行大量数据扫描
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## 许可证
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MIT License
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20
crontab/market-scanner
Normal file
20
crontab/market-scanner
Normal file
@ -0,0 +1,20 @@
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# 市场扫描定时任务配置
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# 格式: 分钟 小时 日 月 星期 命令
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# 时区: Asia/Shanghai
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# 每个工作日开盘前扫描 (09:00)
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#0 9 * * 1-5 cd /app && python market_scanner.py 200 >> /app/logs/cron.log 2>&1
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# 每个工作日午休时间扫描 (12:30)
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#30 12 * * 1-5 cd /app && python market_scanner.py 100 >> /app/logs/cron.log 2>&1
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# 每个工作日收盘后扫描 (15:30)
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30 15 * * 1-5 cd /app && python market_scanner.py 300 >> /app/logs/cron.log 2>&1
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# 每周末进行一次深度扫描 (周六 10:00)
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#0 10 * * 6 cd /app && python market_scanner.py 500 >> /app/logs/cron.log 2>&1
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# 高频监控 - 每30分钟扫描一次热门股票 (交易时间内: 9:30-15:00)
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# 注释掉避免过于频繁，需要时可以开启
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# 30 9-14 * * 1-5 cd /app && python market_scanner.py 50 >> /app/logs/cron.log 2>&1
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# 0 10-14 * * 1-5 cd /app && python market_scanner.py 50 >> /app/logs/cron.log 2>&1
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@ -27,23 +27,26 @@ services:
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retries: 3
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start_period: 40s
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# 数据采集服务（可选）
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trading-collector:
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# 市场扫描定时任务服务
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trading-market-scanner:
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build: .
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container_name: trading-ai-collector-mysql
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container_name: trading-ai-market-scanner
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volumes:
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- ./config:/app/config
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- ./logs:/app/logs
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- ./crontab:/app/crontab
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environment:
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- PYTHONPATH=/app
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- LOG_LEVEL=${LOG_LEVEL:-INFO}
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- MARKET_SCAN_STOCKS=${MARKET_SCAN_STOCKS:-200}
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# MySQL连接配置
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- MYSQL_HOST=${MYSQL_HOST:-cd-cynosdbmysql-grp-7kdd8qe4.sql.tencentcdb.com}
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- MYSQL_PORT=${MYSQL_PORT:-26558}
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- MYSQL_USER=${MYSQL_USER:-root}
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- MYSQL_PASSWORD=${MYSQL_PASSWORD:-gUjjmQpu6c7V0hMF}
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- MYSQL_DATABASE=${MYSQL_DATABASE:-tradingai}
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# 运行数据采集脚本
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command: ["python", "main.py", "scanmarket"]
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# 运行定时市场扫描
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command: ["bash", "/app/start_market_scanner.sh"]
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restart: unless-stopped
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depends_on:
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- trading-web
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@ -58,7 +58,7 @@ database:
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path: "data/trading.db"
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data_source:
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provider: "adata"
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provider: "tushare"
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strategy:
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kline_pattern:
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206
docs/MARKET_SCANNER_DOCKER.md
Normal file
206
docs/MARKET_SCANNER_DOCKER.md
Normal file
@ -0,0 +1,206 @@
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# 市场扫描Docker定时任务配置
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## 概述
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本文档描述如何使用Docker容器定时执行`main.py market 200`命令进行股票市场扫描分析。
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## 服务组件
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### 1. 市场扫描服务 (trading-market-scanner)
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- **容器名称**: `trading-ai-market-scanner`
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- **功能**: 定时执行股票市场扫描和K线形态分析
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- **扫描对象**: 同花顺热榜前N只股票
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- **通知**: 支持钉钉webhook通知
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### 2. 主要文件
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- `market_scanner.py` - 市场扫描脚本
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- `start_market_scanner.sh` - 容器启动脚本
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- `crontab/market-scanner` - 定时任务配置
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- `docker-compose.yml` - Docker编排配置
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## 定时任务配置
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### 默认调度计划
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```bash
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# 每个工作日开盘前扫描 (09:00) - 200只股票
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0 9 * * 1-5 cd /app && python market_scanner.py 200
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# 每个工作日午休时间扫描 (12:30) - 100只股票
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30 12 * * 1-5 cd /app && python market_scanner.py 100
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# 每个工作日收盘后扫描 (15:30) - 200只股票
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30 15 * * 1-5 cd /app && python market_scanner.py 200
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# 每周末进行一次深度扫描 (周六 10:00) - 500只股票
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0 10 * * 6 cd /app && python market_scanner.py 500
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```
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### 自定义调度
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可通过修改 `crontab/market-scanner` 文件来自定义定时任务：
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```bash
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# 格式: 分钟 小时 日 月 星期 命令
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# 每15分钟扫描一次 (交易时间内)
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*/15 9-15 * * 1-5 cd /app && python market_scanner.py 50
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# 每小时扫描一次
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0 * * * * cd /app && python market_scanner.py 100
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```
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|
||||
## 环境变量配置
|
||||
|
||||
### 必要配置
|
||||
|
||||
| 变量名 | 默认值 | 说明 |
|
||||
|--------|--------|------|
|
||||
| `MARKET_SCAN_STOCKS` | 200 | 扫描的股票数量 |
|
||||
| `LOG_LEVEL` | INFO | 日志级别 |
|
||||
| `MYSQL_HOST` | - | MySQL主机地址 |
|
||||
| `MYSQL_PORT` | 26558 | MySQL端口 |
|
||||
| `MYSQL_USER` | root | MySQL用户名 |
|
||||
| `MYSQL_PASSWORD` | - | MySQL密码 |
|
||||
| `MYSQL_DATABASE` | tradingai | 数据库名 |
|
||||
|
||||
### 可选配置
|
||||
|
||||
| 变量名 | 说明 |
|
||||
|--------|------|
|
||||
| `DINGTALK_WEBHOOK_URL` | 钉钉通知webhook地址 |
|
||||
|
||||
## 部署步骤
|
||||
|
||||
### 1. 环境配置
|
||||
|
||||
复制并编辑环境变量文件：
|
||||
|
||||
```bash
|
||||
cp .env.example .env
|
||||
# 编辑 .env 文件，配置数据库连接和扫描参数
|
||||
```
|
||||
|
||||
### 2. 启动服务
|
||||
|
||||
```bash
|
||||
# 启动市场扫描服务
|
||||
docker-compose up -d trading-market-scanner
|
||||
|
||||
# 查看服务状态
|
||||
docker-compose ps
|
||||
|
||||
# 查看日志
|
||||
docker-compose logs -f trading-market-scanner
|
||||
```
|
||||
|
||||
### 3. 验证部署
|
||||
|
||||
```bash
|
||||
# 查看容器状态
|
||||
docker ps | grep trading-ai-market-scanner
|
||||
|
||||
# 查看定时任务配置
|
||||
docker exec trading-ai-market-scanner cat /etc/cron.d/market-scanner
|
||||
|
||||
# 查看扫描日志
|
||||
docker exec trading-ai-market-scanner tail -f /app/logs/market_scanner.log
|
||||
```
|
||||
|
||||
## 日志管理
|
||||
|
||||
### 日志文件位置
|
||||
|
||||
- `/app/logs/market_scanner.log` - 市场扫描日志
|
||||
- `/app/logs/cron.log` - Cron执行日志
|
||||
- `/app/logs/scanner_startup.log` - 服务启动日志
|
||||
|
||||
### 日志轮转
|
||||
|
||||
- 日志文件按日轮转
|
||||
- 保留30天历史日志
|
||||
- 自动压缩旧日志文件
|
||||
|
||||
## 监控和运维
|
||||
|
||||
### 健康检查
|
||||
|
||||
服务包含自动重启机制，如果进程异常退出会自动重启。
|
||||
|
||||
### 手动执行扫描
|
||||
|
||||
```bash
|
||||
# 进入容器手动执行扫描
|
||||
docker exec -it trading-ai-market-scanner python market_scanner.py 100
|
||||
|
||||
# 查看扫描结果
|
||||
docker exec trading-ai-market-scanner tail -20 /app/logs/market_scanner.log
|
||||
```
|
||||
|
||||
### 修改定时任务
|
||||
|
||||
1. 编辑 `crontab/market-scanner` 文件
|
||||
2. 重启容器使配置生效：
|
||||
|
||||
```bash
|
||||
docker-compose restart trading-market-scanner
|
||||
```
|
||||
|
||||
### 调试模式
|
||||
|
||||
设置更详细的日志级别：
|
||||
|
||||
```bash
|
||||
# 在 .env 文件中设置
|
||||
LOG_LEVEL=DEBUG
|
||||
|
||||
# 重启服务
|
||||
docker-compose restart trading-market-scanner
|
||||
```
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **时区设置**: 容器使用 Asia/Shanghai 时区
|
||||
2. **资源消耗**: 扫描大量股票会消耗较多CPU和内存
|
||||
3. **网络依赖**: 需要稳定的网络连接访问股票数据API
|
||||
4. **数据库连接**: 确保MySQL数据库可正常连接
|
||||
5. **存储空间**: 定期清理日志文件避免磁盘空间不足
|
||||
|
||||
## 故障排除
|
||||
|
||||
### 常见问题
|
||||
|
||||
1. **容器启动失败**
|
||||
```bash
|
||||
# 查看启动日志
|
||||
docker-compose logs trading-market-scanner
|
||||
```
|
||||
|
||||
2. **定时任务不执行**
|
||||
```bash
|
||||
# 检查cron服务状态
|
||||
docker exec trading-ai-market-scanner service cron status
|
||||
|
||||
# 查看cron日志
|
||||
docker exec trading-ai-market-scanner tail -f /app/logs/cron.log
|
||||
```
|
||||
|
||||
3. **数据库连接失败**
|
||||
```bash
|
||||
# 检查环境变量配置
|
||||
docker exec trading-ai-market-scanner env | grep MYSQL
|
||||
```
|
||||
|
||||
4. **内存不足**
|
||||
```bash
|
||||
# 减少扫描股票数量
|
||||
MARKET_SCAN_STOCKS=50
|
||||
```
|
||||
|
||||
### 性能优化
|
||||
|
||||
1. 根据服务器性能调整扫描频率和股票数量
|
||||
2. 设置合理的日志级别避免过多日志输出
|
||||
3. 定期清理历史数据和日志文件
|
||||
4. 监控服务器资源使用情况
|
||||
215
docs/NEW_ARCHITECTURE.md
Normal file
215
docs/NEW_ARCHITECTURE.md
Normal file
@ -0,0 +1,215 @@
|
||||
# 新架构说明文档
|
||||
|
||||
## 概述
|
||||
|
||||
本项目已重构为模块化的策略执行系统，支持定时执行多个策略任务。新架构分为四个层次：
|
||||
|
||||
1. **数据层**: TushareFetcher + StockPoolManager
|
||||
2. **策略层**: BaseStrategy + 具体策略实现
|
||||
3. **执行层**: TaskScheduler + StrategyExecutor
|
||||
4. **通知层**: NotificationManager
|
||||
|
||||
## 核心工作流程
|
||||
|
||||
每个策略任务都遵循以下标准流程：
|
||||
|
||||
```
|
||||
1. 根据规则获取股票池 → 2. 传递给策略进行分析 → 3. 以多种方式呈现或通知结果
|
||||
```
|
||||
|
||||
## 模块详解
|
||||
|
||||
### 1. 数据层
|
||||
|
||||
#### TushareFetcher
|
||||
- 负责从TuShare API获取股票数据
|
||||
- 支持历史K线、热榜数据、基本信息等
|
||||
- 已集成token管理和缓存机制
|
||||
|
||||
#### StockPoolManager
|
||||
- 管理不同的股票池获取规则
|
||||
- 内置规则：
|
||||
- `tushare_hot`: 同花顺热榜
|
||||
- `combined_hot`: 合并热门(同花顺+东财)
|
||||
- `leading_stocks`: 龙头牛股
|
||||
- 支持自定义股票列表
|
||||
|
||||
### 2. 策略层
|
||||
|
||||
#### BaseStrategy (抽象基类)
|
||||
- 定义统一的策略接口
|
||||
- 标准化输入输出格式
|
||||
- 提供股票池批量分析能力
|
||||
|
||||
#### KLinePatternStrategy
|
||||
- 实现K线形态识别策略
|
||||
- 继承自BaseStrategy
|
||||
- 支持多时间周期分析
|
||||
- 集成回踩监控功能
|
||||
|
||||
### 3. 执行层
|
||||
|
||||
#### StrategyExecutor
|
||||
- 协调股票池获取和策略分析
|
||||
- 管理策略注册和执行
|
||||
- 统一的结果处理和通知
|
||||
|
||||
#### TaskScheduler
|
||||
- 支持多种调度规则
|
||||
- 任务执行历史和统计
|
||||
- 灵活的任务管理
|
||||
|
||||
### 4. 通知层
|
||||
|
||||
#### NotificationManager
|
||||
- 支持多种通知方式
|
||||
- 策略结果汇总推送
|
||||
- 特殊事件提醒
|
||||
|
||||
## 快速开始
|
||||
|
||||
### 基础使用
|
||||
|
||||
```python
|
||||
# 1. 初始化组件
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
from src.execution.strategy_executor import StrategyExecutor
|
||||
from src.utils.notification import NotificationManager
|
||||
|
||||
# 创建实例
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
notification_manager = NotificationManager(config)
|
||||
executor = StrategyExecutor(pool_manager, notification_manager)
|
||||
|
||||
# 2. 注册策略
|
||||
strategy = KLinePatternStrategy(fetcher, notification_manager, config)
|
||||
executor.register_strategy("kline_pattern", strategy)
|
||||
|
||||
# 3. 执行任务
|
||||
result = executor.execute_task(
|
||||
task_id="test_task",
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
max_stocks=20
|
||||
)
|
||||
```
|
||||
|
||||
### 定时任务
|
||||
|
||||
```python
|
||||
from src.execution.task_scheduler import TaskScheduler
|
||||
|
||||
# 创建调度器
|
||||
scheduler = TaskScheduler()
|
||||
|
||||
# 添加定时任务
|
||||
task_func = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
max_stocks=30
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="morning_scan",
|
||||
name="晨间扫描",
|
||||
func=task_func,
|
||||
schedule_rule="weekdays at 09:00"
|
||||
)
|
||||
|
||||
# 启动调度器
|
||||
scheduler.start()
|
||||
```
|
||||
|
||||
## 支持的调度规则
|
||||
|
||||
- `"every 30 minutes"` - 每30分钟
|
||||
- `"every 1 hour"` - 每1小时
|
||||
- `"daily at 09:30"` - 每日09:30
|
||||
- `"weekdays at 14:00"` - 工作日14:00
|
||||
- `"monday at 10:00"` - 每周一10:00
|
||||
|
||||
## 配置示例
|
||||
|
||||
### 策略配置
|
||||
```python
|
||||
strategy_config = {
|
||||
'min_entity_ratio': 0.55,
|
||||
'final_yang_min_ratio': 0.40,
|
||||
'max_turnover_ratio': 40.0,
|
||||
'timeframes': ['daily'],
|
||||
'pullback_tolerance': 0.02,
|
||||
'monitor_days': 30
|
||||
}
|
||||
```
|
||||
|
||||
### 股票池参数
|
||||
```python
|
||||
# 同花顺热榜
|
||||
{"limit": 50}
|
||||
|
||||
# 合并热门
|
||||
{"limit_per_source": 30, "final_limit": 50}
|
||||
|
||||
# 龙头股
|
||||
{"top_boards": 8, "stocks_per_board": 3, "min_score": 70.0}
|
||||
```
|
||||
|
||||
## 扩展指南
|
||||
|
||||
### 添加新的股票池规则
|
||||
|
||||
1. 继承 `StockPoolRule` 基类
|
||||
2. 实现 `get_stocks()` 和 `get_rule_name()` 方法
|
||||
3. 注册到 `StockPoolManager`
|
||||
|
||||
```python
|
||||
class MyCustomRule(StockPoolRule):
|
||||
def get_stocks(self, fetcher, **kwargs):
|
||||
# 自定义获取逻辑
|
||||
return stock_list
|
||||
|
||||
def get_rule_name(self):
|
||||
return "我的自定义规则"
|
||||
|
||||
# 注册
|
||||
pool_manager.register_rule("my_rule", MyCustomRule())
|
||||
```
|
||||
|
||||
### 添加新策略
|
||||
|
||||
1. 继承 `BaseStrategy` 基类
|
||||
2. 实现 `analyze_stock()` 和 `get_strategy_description()` 方法
|
||||
3. 注册到 `StrategyExecutor`
|
||||
|
||||
```python
|
||||
class MyStrategy(BaseStrategy):
|
||||
def analyze_stock(self, stock_code, timeframes=None):
|
||||
# 实现分析逻辑
|
||||
return {timeframe: StrategyResult(...)}
|
||||
|
||||
def get_strategy_description(self):
|
||||
return "我的策略描述"
|
||||
|
||||
# 注册
|
||||
executor.register_strategy("my_strategy", MyStrategy(...))
|
||||
```
|
||||
|
||||
## 示例文件
|
||||
|
||||
- `examples/new_architecture_example.py` - 完整架构演示
|
||||
- `examples/task_config_examples.py` - 常见任务配置示例
|
||||
|
||||
## 优势总结
|
||||
|
||||
✅ **模块化设计**: 职责清晰，易于维护扩展
|
||||
✅ **灵活配置**: 支持多种股票池和调度规则
|
||||
✅ **标准化接口**: 统一的策略和结果格式
|
||||
✅ **任务调度**: 强大的定时执行能力
|
||||
✅ **统一通知**: 完善的结果推送机制
|
||||
✅ **缓存优化**: 避免重复API调用
|
||||
✅ **错误处理**: 完整的异常处理和日志记录
|
||||
|
||||
新架构完全满足了原始需求：通过简单配置实现复杂的多策略定时执行和结果通知系统。
|
||||
288
examples/new_architecture_example.py
Normal file
288
examples/new_architecture_example.py
Normal file
@ -0,0 +1,288 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
新架构集成示例
|
||||
展示如何使用重构后的模块化架构进行策略执行和任务调度
|
||||
"""
|
||||
|
||||
import sys
|
||||
import time
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
# 添加src目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir.parent / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from loguru import logger
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
from src.execution.strategy_executor import StrategyExecutor
|
||||
from src.execution.task_scheduler import TaskScheduler
|
||||
from src.utils.notification import NotificationManager
|
||||
from src.utils.config_loader import config_loader
|
||||
|
||||
|
||||
def demo_new_architecture():
|
||||
"""演示新架构的完整工作流程"""
|
||||
|
||||
print("=" * 80)
|
||||
print("🚀 新架构演示 - 模块化策略执行系统")
|
||||
print("=" * 80)
|
||||
print("📋 系统架构:")
|
||||
print(" 数据层: TushareFetcher + StockPoolManager")
|
||||
print(" 策略层: BaseStrategy + KLinePatternStrategy")
|
||||
print(" 执行层: TaskScheduler + StrategyExecutor")
|
||||
print(" 通知层: NotificationManager")
|
||||
print()
|
||||
|
||||
# 1. 初始化所有组件
|
||||
print("📦 第1步: 初始化所有组件")
|
||||
print("-" * 60)
|
||||
|
||||
# 数据层
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
|
||||
# 通知层
|
||||
notification_config = config_loader.get('notification', {})
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
# 策略层
|
||||
strategy_config = {
|
||||
'min_entity_ratio': 0.55,
|
||||
'final_yang_min_ratio': 0.40,
|
||||
'max_turnover_ratio': 40.0,
|
||||
'timeframes': ['daily'],
|
||||
'pullback_tolerance': 0.02,
|
||||
'monitor_days': 30,
|
||||
'pullback_confirmation_days': 7
|
||||
}
|
||||
kline_strategy = KLinePatternStrategy(
|
||||
data_fetcher=fetcher,
|
||||
notification_manager=notification_manager,
|
||||
config=strategy_config
|
||||
)
|
||||
|
||||
# 执行层
|
||||
executor = StrategyExecutor(pool_manager, notification_manager)
|
||||
scheduler = TaskScheduler()
|
||||
|
||||
print("✅ 所有组件初始化完成")
|
||||
print()
|
||||
|
||||
# 2. 注册策略到执行器
|
||||
print("📋 第2步: 注册策略")
|
||||
print("-" * 60)
|
||||
|
||||
executor.register_strategy("kline_pattern", kline_strategy)
|
||||
|
||||
print("已注册策略:")
|
||||
for strategy_id, strategy_name in executor.get_registered_strategies().items():
|
||||
print(f" {strategy_id}: {strategy_name}")
|
||||
print()
|
||||
|
||||
# 3. 展示股票池规则
|
||||
print("🎯 第3步: 可用股票池规则")
|
||||
print("-" * 60)
|
||||
|
||||
available_rules = pool_manager.get_available_rules()
|
||||
print("可用规则:")
|
||||
for rule_id, rule_name in available_rules.items():
|
||||
print(f" {rule_id}: {rule_name}")
|
||||
print()
|
||||
|
||||
# 4. 手动执行单个任务
|
||||
print("⚡ 第4步: 手动执行策略任务")
|
||||
print("-" * 60)
|
||||
|
||||
task_id = f"manual_task_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
|
||||
print(f"执行任务: {task_id}")
|
||||
print("参数:")
|
||||
print(f" 策略: kline_pattern")
|
||||
print(f" 股票池规则: tushare_hot")
|
||||
print(f" 最大股票数: 5")
|
||||
print()
|
||||
|
||||
result = executor.execute_task(
|
||||
task_id=task_id,
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": 10},
|
||||
max_stocks=5,
|
||||
send_notification=False # 演示时不发送通知
|
||||
)
|
||||
|
||||
# 显示执行结果
|
||||
print("📊 执行结果摘要:")
|
||||
summary = result.get_summary()
|
||||
for key, value in summary.items():
|
||||
print(f" {key}: {value}")
|
||||
print()
|
||||
|
||||
# 5. 设置定时任务
|
||||
print("⏰ 第5步: 设置定时任务")
|
||||
print("-" * 60)
|
||||
|
||||
# 创建任务函数
|
||||
task_function_1 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": 20},
|
||||
max_stocks=10,
|
||||
send_notification=False
|
||||
)
|
||||
|
||||
task_function_2 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="combined_hot",
|
||||
stock_pool_params={"limit_per_source": 15, "final_limit": 25},
|
||||
max_stocks=15,
|
||||
send_notification=False
|
||||
)
|
||||
|
||||
# 添加定时任务
|
||||
scheduler.add_task(
|
||||
task_id="hot_stocks_scan",
|
||||
name="同花顺热榜K线形态扫描",
|
||||
func=task_function_1,
|
||||
schedule_rule="every 30 minutes",
|
||||
enabled=False # 演示时不启用
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="combined_hot_scan",
|
||||
name="合并热门股票K线形态扫描",
|
||||
func=task_function_2,
|
||||
schedule_rule="daily at 09:30",
|
||||
enabled=False # 演示时不启用
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="leading_stocks_scan",
|
||||
name="龙头股K线形态扫描",
|
||||
func=executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="leading_stocks",
|
||||
stock_pool_params={"top_boards": 5, "stocks_per_board": 2},
|
||||
max_stocks=10,
|
||||
send_notification=False
|
||||
),
|
||||
schedule_rule="weekdays at 14:30",
|
||||
enabled=False # 演示时不启用
|
||||
)
|
||||
|
||||
print("已添加定时任务:")
|
||||
task_status = scheduler.get_task_status()
|
||||
for task_id, status in task_status.items():
|
||||
print(f" {task_id}:")
|
||||
print(f" 名称: {status['name']}")
|
||||
print(f" 规则: {status['schedule_rule']}")
|
||||
print(f" 状态: {status['status']}")
|
||||
print(f" 启用: {status['enabled']}")
|
||||
print()
|
||||
|
||||
# 6. 演示立即执行定时任务
|
||||
print("🎬 第6步: 演示立即执行定时任务")
|
||||
print("-" * 60)
|
||||
|
||||
print("立即执行任务: hot_stocks_scan")
|
||||
success = scheduler.execute_task_now("hot_stocks_scan")
|
||||
print(f"执行结果: {'成功' if success else '失败'}")
|
||||
print()
|
||||
|
||||
# 7. 显示任务执行历史
|
||||
print("📈 第7步: 任务执行历史")
|
||||
print("-" * 60)
|
||||
|
||||
updated_status = scheduler.get_task_status()
|
||||
for task_id, status in updated_status.items():
|
||||
if status['total_executions'] > 0:
|
||||
print(f"任务: {status['name']}")
|
||||
print(f" 总执行次数: {status['total_executions']}")
|
||||
print(f" 成功率: {status['success_rate']:.1f}%")
|
||||
print(f" 最后执行: {status['last_execution_time']}")
|
||||
print()
|
||||
|
||||
# 8. 系统功能总结
|
||||
print("🎯 第8步: 新架构优势总结")
|
||||
print("-" * 60)
|
||||
|
||||
advantages = """
|
||||
✅ 模块化设计:
|
||||
• 数据获取、股票池管理、策略执行、任务调度完全分离
|
||||
• 每个模块职责单一，易于维护和扩展
|
||||
• 支持插件化添加新的股票池规则和策略
|
||||
|
||||
✅ 灵活的股票池管理:
|
||||
• 支持多种数据源：同花顺热榜、合并热门、龙头股
|
||||
• 可配置参数：股票数量、筛选条件等
|
||||
• 易于添加新的股票池规则
|
||||
|
||||
✅ 标准化的策略接口:
|
||||
• BaseStrategy抽象基类统一策略接口
|
||||
• StrategyResult标准化输出格式
|
||||
• 支持多时间周期分析
|
||||
|
||||
✅ 强大的任务调度:
|
||||
• 支持多种调度规则：间隔时间、每日定时、工作日定时
|
||||
• 任务执行历史和成功率统计
|
||||
• 支持立即执行和定时执行
|
||||
|
||||
✅ 统一的执行协调:
|
||||
• StrategyExecutor协调股票池获取→策略分析→结果通知
|
||||
• 完整的执行结果记录和统计
|
||||
• 支持并发执行多个策略
|
||||
|
||||
✅ 完善的通知系统:
|
||||
• 支持多种通知方式
|
||||
• 策略结果汇总推送
|
||||
• 回踩提醒等特殊通知
|
||||
"""
|
||||
|
||||
print(advantages)
|
||||
|
||||
print("🚀 架构使用示例:")
|
||||
example_usage = """
|
||||
# 基础用法 - 立即执行
|
||||
result = executor.execute_task(
|
||||
task_id="my_task",
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
max_stocks=20
|
||||
)
|
||||
|
||||
# 定时任务 - 每天9:30执行
|
||||
scheduler.add_task(
|
||||
task_id="morning_scan",
|
||||
name="晨间形态扫描",
|
||||
func=executor.create_task_function(...),
|
||||
schedule_rule="daily at 09:30"
|
||||
)
|
||||
|
||||
# 启动调度器
|
||||
scheduler.start()
|
||||
"""
|
||||
print(example_usage)
|
||||
|
||||
print("=" * 80)
|
||||
print("🎉 新架构演示完成!")
|
||||
print("💡 现在可以通过简单配置实现复杂的策略执行和调度")
|
||||
print("🔧 支持灵活的股票池规则和多策略并行执行")
|
||||
print("📱 统一的结果通知和监控体系")
|
||||
print("=" * 80)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
try:
|
||||
demo_new_architecture()
|
||||
except Exception as e:
|
||||
logger.error(f"演示过程中发生错误: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
304
examples/task_config_examples.py
Normal file
304
examples/task_config_examples.py
Normal file
@ -0,0 +1,304 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
任务配置示例
|
||||
展示如何配置不同类型的定时策略任务
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
# 添加src目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir.parent / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from loguru import logger
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
from src.execution.strategy_executor import StrategyExecutor
|
||||
from src.execution.task_scheduler import TaskScheduler
|
||||
from src.utils.notification import NotificationManager
|
||||
from src.utils.config_loader import config_loader
|
||||
|
||||
|
||||
def setup_common_tasks():
|
||||
"""设置常见的策略任务配置"""
|
||||
|
||||
print("=" * 80)
|
||||
print("📋 常见策略任务配置示例")
|
||||
print("=" * 80)
|
||||
|
||||
# 初始化组件
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
notification_manager = NotificationManager(config_loader.get('notification', {}))
|
||||
executor = StrategyExecutor(pool_manager, notification_manager)
|
||||
scheduler = TaskScheduler()
|
||||
|
||||
# 策略配置
|
||||
strategy_config = {
|
||||
'min_entity_ratio': 0.55,
|
||||
'final_yang_min_ratio': 0.40,
|
||||
'max_turnover_ratio': 40.0,
|
||||
'timeframes': ['daily'],
|
||||
'pullback_tolerance': 0.02,
|
||||
'monitor_days': 30,
|
||||
'pullback_confirmation_days': 7
|
||||
}
|
||||
|
||||
# 注册策略
|
||||
kline_strategy = KLinePatternStrategy(
|
||||
data_fetcher=fetcher,
|
||||
notification_manager=notification_manager,
|
||||
config=strategy_config
|
||||
)
|
||||
executor.register_strategy("kline_pattern", kline_strategy)
|
||||
|
||||
print("🎯 任务配置场景:")
|
||||
print()
|
||||
|
||||
# 场景1: 开盘前扫描热门股票
|
||||
print("📊 场景1: 开盘前热门股票扫描")
|
||||
print("-" * 60)
|
||||
|
||||
task_1 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": 50},
|
||||
max_stocks=30,
|
||||
send_notification=True
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="pre_market_hot_scan",
|
||||
name="开盘前热门股票K线扫描",
|
||||
func=task_1,
|
||||
schedule_rule="weekdays at 09:00",
|
||||
enabled=False
|
||||
)
|
||||
|
||||
print("✅ 配置完成:")
|
||||
print(" 任务: 开盘前热门股票K线扫描")
|
||||
print(" 时间: 每个工作日 09:00")
|
||||
print(" 股票池: 同花顺热榜前50只")
|
||||
print(" 分析: 最多30只股票")
|
||||
print(" 通知: 启用")
|
||||
print()
|
||||
|
||||
# 场景2: 午间龙头股扫描
|
||||
print("🐲 场景2: 午间龙头股扫描")
|
||||
print("-" * 60)
|
||||
|
||||
task_2 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="leading_stocks",
|
||||
stock_pool_params={
|
||||
"top_boards": 8,
|
||||
"stocks_per_board": 3,
|
||||
"min_score": 70.0
|
||||
},
|
||||
max_stocks=20,
|
||||
send_notification=True
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="midday_leading_scan",
|
||||
name="午间龙头股K线扫描",
|
||||
func=task_2,
|
||||
schedule_rule="weekdays at 12:30",
|
||||
enabled=False
|
||||
)
|
||||
|
||||
print("✅ 配置完成:")
|
||||
print(" 任务: 午间龙头股K线扫描")
|
||||
print(" 时间: 每个工作日 12:30")
|
||||
print(" 股票池: 热门板块前8个，每板块前3只，评分>70")
|
||||
print(" 分析: 最多20只龙头股")
|
||||
print(" 通知: 启用")
|
||||
print()
|
||||
|
||||
# 场景3: 收盘后综合扫描
|
||||
print("🌅 场景3: 收盘后综合扫描")
|
||||
print("-" * 60)
|
||||
|
||||
task_3 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="combined_hot",
|
||||
stock_pool_params={
|
||||
"limit_per_source": 30,
|
||||
"final_limit": 50
|
||||
},
|
||||
max_stocks=40,
|
||||
send_notification=True
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="after_market_comprehensive_scan",
|
||||
name="收盘后综合热门股扫描",
|
||||
func=task_3,
|
||||
schedule_rule="weekdays at 15:30",
|
||||
enabled=False
|
||||
)
|
||||
|
||||
print("✅ 配置完成:")
|
||||
print(" 任务: 收盘后综合热门股扫描")
|
||||
print(" 时间: 每个工作日 15:30")
|
||||
print(" 股票池: 合并热门(同花顺+东财)，各取30只，合并后50只")
|
||||
print(" 分析: 最多40只股票")
|
||||
print(" 通知: 启用")
|
||||
print()
|
||||
|
||||
# 场景4: 高频监控
|
||||
print("⚡ 场景4: 高频监控扫描")
|
||||
print("-" * 60)
|
||||
|
||||
task_4 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": 20},
|
||||
max_stocks=15,
|
||||
send_notification=False # 高频不通知，避免打扰
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="high_freq_monitor",
|
||||
name="高频热门股监控",
|
||||
func=task_4,
|
||||
schedule_rule="every 15 minutes",
|
||||
enabled=False
|
||||
)
|
||||
|
||||
print("✅ 配置完成:")
|
||||
print(" 任务: 高频热门股监控")
|
||||
print(" 时间: 每15分钟执行一次")
|
||||
print(" 股票池: 同花顺热榜前20只")
|
||||
print(" 分析: 最多15只股票")
|
||||
print(" 通知: 关闭(避免频繁打扰)")
|
||||
print()
|
||||
|
||||
# 场景5: 自定义股票池
|
||||
print("🎯 场景5: 自定义股票池扫描")
|
||||
print("-" * 60)
|
||||
|
||||
# 创建自定义股票池
|
||||
custom_stocks = [
|
||||
"000001.SZ", # 平安银行
|
||||
"000002.SZ", # 万科A
|
||||
"600000.SH", # 浦发银行
|
||||
"600036.SH", # 招商银行
|
||||
"000858.SZ", # 五粮液
|
||||
"600519.SH", # 贵州茅台
|
||||
"000725.SZ", # 京东方A
|
||||
"002415.SZ" # 海康威视
|
||||
]
|
||||
|
||||
pool_manager.create_custom_rule("my_watchlist", custom_stocks)
|
||||
|
||||
task_5 = executor.create_task_function(
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="my_watchlist",
|
||||
stock_pool_params={},
|
||||
max_stocks=len(custom_stocks),
|
||||
send_notification=True
|
||||
)
|
||||
|
||||
scheduler.add_task(
|
||||
task_id="custom_watchlist_scan",
|
||||
name="自选股K线形态扫描",
|
||||
func=task_5,
|
||||
schedule_rule="daily at 21:00",
|
||||
enabled=False
|
||||
)
|
||||
|
||||
print("✅ 配置完成:")
|
||||
print(" 任务: 自选股K线形态扫描")
|
||||
print(" 时间: 每日 21:00")
|
||||
print(f" 股票池: 自定义股票池({len(custom_stocks)}只)")
|
||||
print(" 分析: 全部自选股")
|
||||
print(" 通知: 启用")
|
||||
print()
|
||||
|
||||
# 显示所有任务状态
|
||||
print("📋 所有配置任务总览:")
|
||||
print("-" * 60)
|
||||
|
||||
task_status = scheduler.get_task_status()
|
||||
for task_id, status in task_status.items():
|
||||
print(f"🔹 {status['name']}")
|
||||
print(f" ID: {task_id}")
|
||||
print(f" 规则: {status['schedule_rule']}")
|
||||
print(f" 状态: {status['status']}")
|
||||
print(f" 启用: {'是' if status['enabled'] else '否'}")
|
||||
print()
|
||||
|
||||
# 使用指南
|
||||
print("📖 使用指南:")
|
||||
print("-" * 60)
|
||||
print("1. 根据实际需求选择合适的任务配置")
|
||||
print("2. 调整股票池参数和分析数量")
|
||||
print("3. 设置合适的执行时间")
|
||||
print("4. 启用需要的任务: scheduler.enable_task('task_id')")
|
||||
print("5. 启动调度器: scheduler.start()")
|
||||
print("6. 立即测试: scheduler.execute_task_now('task_id')")
|
||||
print()
|
||||
|
||||
print("⚙️ 高级配置技巧:")
|
||||
print("-" * 60)
|
||||
print("• 开盘前(09:00): 扫描热门股，发现隔夜机会")
|
||||
print("• 午间时段(12:30): 扫描龙头股，捕捉强势股")
|
||||
print("• 收盘后(15:30): 综合扫描，总结全天机会")
|
||||
print("• 高频监控(15分钟): 实时跟踪，但关闭通知")
|
||||
print("• 晚间复盘(21:00): 扫描自选股，制定明日策略")
|
||||
print()
|
||||
|
||||
return scheduler, executor
|
||||
|
||||
|
||||
def demo_task_execution(scheduler, executor):
|
||||
"""演示任务执行"""
|
||||
|
||||
print("🎬 任务执行演示:")
|
||||
print("-" * 60)
|
||||
|
||||
# 立即执行一个任务进行测试
|
||||
print("立即执行任务: pre_market_hot_scan")
|
||||
success = scheduler.execute_task_now("pre_market_hot_scan")
|
||||
print(f"执行结果: {'成功' if success else '失败'}")
|
||||
print()
|
||||
|
||||
# 显示执行统计
|
||||
task_status = scheduler.get_task_status()
|
||||
for task_id, status in task_status.items():
|
||||
if status['total_executions'] > 0:
|
||||
print(f"📊 任务: {status['name']}")
|
||||
print(f" 执行次数: {status['total_executions']}")
|
||||
print(f" 成功率: {status['success_rate']:.1f}%")
|
||||
if status['last_execution_time']:
|
||||
print(f" 最后执行: {status['last_execution_time']}")
|
||||
print()
|
||||
|
||||
print("💡 提示: 在生产环境中启用任务并启动调度器")
|
||||
print(" scheduler.enable_task('task_id')")
|
||||
print(" scheduler.start()")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
try:
|
||||
scheduler, executor = setup_common_tasks()
|
||||
demo_task_execution(scheduler, executor)
|
||||
|
||||
print("=" * 80)
|
||||
print("🎉 任务配置示例演示完成!")
|
||||
print("💼 可根据实际需求调整参数和时间规则")
|
||||
print("🚀 启用任务并启动调度器即可自动运行")
|
||||
print("=" * 80)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"演示过程中发生错误: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
741
main.py
741
main.py
@ -1,6 +1,7 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
A股量化交易主程序
|
||||
A股量化交易 - 策略测试入口
|
||||
简化版本，专注于策略测试和执行
|
||||
"""
|
||||
|
||||
import sys
|
||||
@ -13,533 +14,313 @@ 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.data.tushare_fetcher import TushareFetcher
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
from src.database.mysql_database_manager import MySQLDatabaseManager
|
||||
from src.execution.strategy_executor import StrategyExecutor
|
||||
from src.execution.task_scheduler import TaskScheduler
|
||||
from src.utils.notification import NotificationManager
|
||||
|
||||
|
||||
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
|
||||
"""设置简化的日志配置"""
|
||||
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}')
|
||||
level="INFO",
|
||||
format="{time:HH:mm:ss} | {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')
|
||||
def create_strategy_system():
|
||||
"""创建策略系统的所有组件"""
|
||||
# 数据层
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
|
||||
# 通知层
|
||||
notification_config = config_loader.get('notification', {})
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
# 策略层
|
||||
strategy_config = config_loader.get('strategy', {}).get('kline_pattern', {
|
||||
'min_entity_ratio': 0.55,
|
||||
'final_yang_min_ratio': 0.40,
|
||||
'max_turnover_ratio': 40.0,
|
||||
'timeframes': ['daily'],
|
||||
'pullback_tolerance': 0.02,
|
||||
'monitor_days': 30,
|
||||
'pullback_confirmation_days': 7
|
||||
})
|
||||
|
||||
# 数据库层
|
||||
from src.database.mysql_database_manager import MySQLDatabaseManager
|
||||
db_manager = MySQLDatabaseManager()
|
||||
|
||||
kline_strategy = KLinePatternStrategy(
|
||||
data_fetcher=fetcher,
|
||||
notification_manager=notification_manager,
|
||||
config=strategy_config,
|
||||
db_manager=db_manager
|
||||
)
|
||||
|
||||
logger.info("日志系统初始化完成")
|
||||
# 执行层
|
||||
executor = StrategyExecutor(pool_manager, notification_manager)
|
||||
scheduler = TaskScheduler()
|
||||
|
||||
# 注册策略
|
||||
executor.register_strategy("kline_pattern", kline_strategy)
|
||||
|
||||
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MySQL数据库管理器
|
||||
db_manager = MySQLDatabaseManager()
|
||||
logger.info("MySQL数据库管理器初始化完成")
|
||||
|
||||
# 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, db_manager)
|
||||
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)
|
||||
return {
|
||||
'fetcher': fetcher,
|
||||
'pool_manager': pool_manager,
|
||||
'notification_manager': notification_manager,
|
||||
'kline_strategy': kline_strategy,
|
||||
'executor': executor,
|
||||
'scheduler': scheduler
|
||||
}
|
||||
|
||||
|
||||
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")
|
||||
print("\n🚀 策略测试命令:")
|
||||
print("-" * 50)
|
||||
print(" scan <股票代码> - 分析单只股票")
|
||||
print(" market <数量> - 扫描热门股票(默认20只)")
|
||||
print(" pools - 查看可用股票池规则")
|
||||
print(" task <规则> <数量> - 执行策略任务")
|
||||
print(" schedule - 显示定时任务示例")
|
||||
print(" help - 显示帮助")
|
||||
print(" quit - 退出程序")
|
||||
print("-" * 50)
|
||||
|
||||
|
||||
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 main():
|
||||
"""主函数"""
|
||||
setup_logging()
|
||||
|
||||
|
||||
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...")
|
||||
print("=" * 60)
|
||||
print("🎯 A股量化交易 - 策略测试系统")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
results = kline_strategy.analyze_stock(stock_code)
|
||||
# 初始化系统
|
||||
logger.info("正在初始化策略系统...")
|
||||
system = create_strategy_system()
|
||||
logger.info("✅ 策略系统初始化完成")
|
||||
|
||||
print(f"\n📊 {stock_code} K线形态分析结果:")
|
||||
total_signals = 0
|
||||
print(f"\n📊 系统组件:")
|
||||
print(f" ✅ 数据获取器: TushareFetcher")
|
||||
print(f" ✅ 股票池管理: StockPoolManager")
|
||||
print(f" ✅ K线策略: KLinePatternStrategy")
|
||||
print(f" ✅ 执行器: StrategyExecutor")
|
||||
print(f" ✅ 调度器: TaskScheduler")
|
||||
|
||||
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_help()
|
||||
|
||||
print(f"\n总计发现 {total_signals} 个信号")
|
||||
# 命令行交互
|
||||
while True:
|
||||
try:
|
||||
command = input("\n> ").strip()
|
||||
|
||||
if not command:
|
||||
continue
|
||||
|
||||
if command.lower() in ['quit', 'exit']:
|
||||
print("👋 感谢使用策略测试系统!")
|
||||
break
|
||||
|
||||
elif command.lower() == 'help':
|
||||
print_help()
|
||||
|
||||
elif command.startswith('scan '):
|
||||
stock_code = command[5:].strip()
|
||||
if stock_code:
|
||||
scan_single_stock(system['kline_strategy'], stock_code)
|
||||
else:
|
||||
print("请提供股票代码，如: scan 000001.SZ")
|
||||
|
||||
elif command.startswith('market'):
|
||||
parts = command.split()
|
||||
max_stocks = int(parts[1]) if len(parts) > 1 and parts[1].isdigit() else 20
|
||||
scan_market(system['executor'], max_stocks)
|
||||
|
||||
elif command.lower() == 'pools':
|
||||
show_stock_pools(system['pool_manager'])
|
||||
|
||||
elif command.startswith('task '):
|
||||
parts = command.split()
|
||||
if len(parts) >= 2:
|
||||
rule = parts[1]
|
||||
max_stocks = int(parts[2]) if len(parts) > 2 and parts[2].isdigit() else 10
|
||||
execute_task(system['executor'], rule, max_stocks)
|
||||
else:
|
||||
print("请提供股票池规则，如: task tushare_hot 15")
|
||||
|
||||
elif command.lower() == 'schedule':
|
||||
show_schedule_examples(system['scheduler'], system['executor'])
|
||||
|
||||
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"扫描股票失败: {e}")
|
||||
print(f"扫描失败: {e}")
|
||||
logger.error(f"系统启动失败: {e}")
|
||||
print(f"❌ 启动失败: {e}")
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
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")
|
||||
def scan_single_stock(strategy, stock_code):
|
||||
"""扫描单只股票"""
|
||||
print(f"\n🔍 分析股票: {stock_code}")
|
||||
print("-" * 40)
|
||||
|
||||
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 = strategy.analyze_stock(stock_code)
|
||||
|
||||
results = kline_strategy.scan_market(max_stocks=scan_count)
|
||||
total_signals = 0
|
||||
for timeframe, result in results.items():
|
||||
signal_count = result.get_signal_count()
|
||||
total_signals += signal_count
|
||||
|
||||
if results:
|
||||
print(f"\n📈 市场扫描结果 (发现 {len(results)} 只股票有信号):")
|
||||
if signal_count > 0:
|
||||
print(f"📊 {timeframe.upper()}: 发现 {signal_count} 个信号")
|
||||
for i, signal in enumerate(result.signals, 1):
|
||||
print(f" {i}. {signal['date']} | {signal['signal_type']} | 价格: {signal['price']:.2f}元")
|
||||
else:
|
||||
print(f"📭 {timeframe.upper()}: 无信号")
|
||||
|
||||
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)")
|
||||
print(f"\n📈 总计: {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")
|
||||
except Exception as e:
|
||||
logger.error(f"分析失败: {e}")
|
||||
print(f"❌ 分析失败: {e}")
|
||||
|
||||
else:
|
||||
print("未发现任何K线形态信号")
|
||||
|
||||
def scan_market(executor, max_stocks):
|
||||
"""扫描市场热门股票"""
|
||||
print(f"\n🌍 扫描市场热门股票 (前{max_stocks}只)")
|
||||
print("-" * 50)
|
||||
|
||||
try:
|
||||
result = executor.execute_task(
|
||||
task_id=f"market_scan_{max_stocks}",
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": max_stocks * 2},
|
||||
max_stocks=max_stocks,
|
||||
send_notification=False
|
||||
)
|
||||
|
||||
summary = result.get_summary()
|
||||
print(f"📊 扫描结果:")
|
||||
print(f" 股票池: {summary['stock_pool_rule_display']}")
|
||||
print(f" 总扫描: {summary['total_stocks_analyzed']} 只")
|
||||
print(f" 有信号: {summary['stocks_with_signals']} 只")
|
||||
print(f" 信号数: {summary['total_signals_found']} 个")
|
||||
print(f" 耗时: {summary['execution_time']:.2f} 秒")
|
||||
|
||||
if result.strategy_results:
|
||||
print(f"\n🎯 信号详情:")
|
||||
for stock_code, timeframe_results in result.strategy_results.items():
|
||||
for timeframe, strategy_result in timeframe_results.items():
|
||||
if strategy_result.get_signal_count() > 0:
|
||||
stock_name = executor.stock_pool_manager.fetcher.get_stock_name(stock_code)
|
||||
print(f" 📈 {stock_code}({stock_name}): {strategy_result.get_signal_count()} 个信号")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"市场扫描失败: {e}")
|
||||
print(f"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...")
|
||||
def show_stock_pools(pool_manager):
|
||||
"""显示可用股票池规则"""
|
||||
print(f"\n📋 可用股票池规则:")
|
||||
print("-" * 40)
|
||||
|
||||
rules = pool_manager.get_available_rules()
|
||||
for rule_id, rule_name in rules.items():
|
||||
print(f" 🎯 {rule_id}: {rule_name}")
|
||||
|
||||
print(f"\n💡 使用方法: task <规则名> <股票数量>")
|
||||
print(f" 示例: task tushare_hot 20")
|
||||
|
||||
|
||||
def execute_task(executor, rule, max_stocks):
|
||||
"""执行策略任务"""
|
||||
print(f"\n⚡ 执行策略任务")
|
||||
print(f"股票池规则: {rule}")
|
||||
print(f"最大股票数: {max_stocks}")
|
||||
print("-" * 40)
|
||||
|
||||
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"
|
||||
}
|
||||
result = executor.execute_task(
|
||||
task_id=f"manual_{rule}_{max_stocks}",
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule=rule,
|
||||
max_stocks=max_stocks,
|
||||
send_notification=False
|
||||
)
|
||||
|
||||
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")
|
||||
summary = result.get_summary()
|
||||
print(f"✅ 任务完成:")
|
||||
print(f" 任务ID: {summary['task_id']}")
|
||||
print(f" 成功: {'是' if summary['success'] else '否'}")
|
||||
print(f" 耗时: {summary['execution_time']:.2f} 秒")
|
||||
print(f" 信号数: {summary['total_signals_found']} 个")
|
||||
|
||||
if summary['error']:
|
||||
print(f" 错误: {summary['error']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"通知测试失败: {e}")
|
||||
print(f"测试失败: {e}")
|
||||
logger.error(f"任务执行失败: {e}")
|
||||
print(f"❌ 任务失败: {e}")
|
||||
|
||||
|
||||
def show_schedule_examples(scheduler, executor):
|
||||
"""显示定时任务配置示例"""
|
||||
print(f"\n⏰ 定时任务配置示例:")
|
||||
print("-" * 50)
|
||||
|
||||
examples = [
|
||||
{
|
||||
"name": "开盘前热门股扫描",
|
||||
"rule": "weekdays at 09:00",
|
||||
"desc": "每个工作日9点扫描同花顺热榜"
|
||||
},
|
||||
{
|
||||
"name": "午间龙头股扫描",
|
||||
"rule": "weekdays at 12:30",
|
||||
"desc": "每个工作日12:30扫描龙头股"
|
||||
},
|
||||
{
|
||||
"name": "收盘后综合扫描",
|
||||
"rule": "weekdays at 15:30",
|
||||
"desc": "每个工作日15:30综合扫描"
|
||||
},
|
||||
{
|
||||
"name": "高频监控",
|
||||
"rule": "every 15 minutes",
|
||||
"desc": "每15分钟监控一次"
|
||||
}
|
||||
]
|
||||
|
||||
for i, example in enumerate(examples, 1):
|
||||
print(f" {i}. {example['name']}")
|
||||
print(f" 规则: {example['rule']}")
|
||||
print(f" 说明: {example['desc']}")
|
||||
print()
|
||||
|
||||
print("💡 要启用定时任务，请参考 examples/ 目录下的配置示例")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
sys.exit(main())
|
||||
157
market_scanner.py
Normal file
157
market_scanner.py
Normal file
@ -0,0 +1,157 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
市场扫描定时任务脚本
|
||||
专门用于Docker容器中定时执行市场扫描
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
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.tushare_fetcher import TushareFetcher
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
from src.execution.strategy_executor import StrategyExecutor
|
||||
from src.utils.notification import NotificationManager
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
def setup_logging():
|
||||
"""设置日志配置"""
|
||||
log_level = os.environ.get('LOG_LEVEL', 'INFO')
|
||||
logger.remove()
|
||||
logger.add(
|
||||
sys.stdout,
|
||||
level=log_level,
|
||||
format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}"
|
||||
)
|
||||
|
||||
# 确定日志目录 - Docker环境使用/app/logs，本地环境使用./logs
|
||||
if os.path.exists('/app'):
|
||||
log_dir = "/app/logs"
|
||||
else:
|
||||
log_dir = "./logs"
|
||||
|
||||
# 创建日志目录
|
||||
os.makedirs(log_dir, exist_ok=True)
|
||||
|
||||
# 添加文件日志
|
||||
log_file = os.path.join(log_dir, "market_scanner.log")
|
||||
logger.add(
|
||||
log_file,
|
||||
level=log_level,
|
||||
format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {message}",
|
||||
rotation="1 day",
|
||||
retention="30 days"
|
||||
)
|
||||
|
||||
|
||||
def create_strategy_system():
|
||||
"""创建策略系统的所有组件"""
|
||||
# 数据层
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
|
||||
# 通知层
|
||||
notification_config = config_loader.get('notification', {})
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
# 策略层
|
||||
strategy_config = config_loader.get('strategy', {}).get('kline_pattern', {
|
||||
'min_entity_ratio': 0.55,
|
||||
'final_yang_min_ratio': 0.40,
|
||||
'max_turnover_ratio': 40.0,
|
||||
'timeframes': ['daily'],
|
||||
'pullback_tolerance': 0.02,
|
||||
'monitor_days': 30,
|
||||
'pullback_confirmation_days': 7
|
||||
})
|
||||
|
||||
# 数据库层
|
||||
from src.database.mysql_database_manager import MySQLDatabaseManager
|
||||
db_manager = MySQLDatabaseManager()
|
||||
|
||||
kline_strategy = KLinePatternStrategy(
|
||||
data_fetcher=fetcher,
|
||||
notification_manager=notification_manager,
|
||||
config=strategy_config,
|
||||
db_manager=db_manager
|
||||
)
|
||||
|
||||
# 执行层
|
||||
executor = StrategyExecutor(pool_manager, notification_manager)
|
||||
|
||||
# 注册策略
|
||||
executor.register_strategy("kline_pattern", kline_strategy)
|
||||
|
||||
return executor
|
||||
|
||||
|
||||
def scan_market(max_stocks=200):
|
||||
"""执行市场扫描"""
|
||||
logger.info(f"🚀 开始市场扫描任务 - 扫描前{max_stocks}只热门股票")
|
||||
|
||||
try:
|
||||
# 初始化系统
|
||||
executor = create_strategy_system()
|
||||
|
||||
# 执行扫描任务
|
||||
task_id = f"market_scan_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
result = executor.execute_task(
|
||||
task_id=task_id,
|
||||
strategy_id="kline_pattern",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": max_stocks},
|
||||
max_stocks=max_stocks,
|
||||
send_notification=True # 启用通知
|
||||
)
|
||||
|
||||
summary = result.get_summary()
|
||||
logger.info(f"✅ 市场扫描完成:")
|
||||
logger.info(f" 任务ID: {summary['task_id']}")
|
||||
logger.info(f" 股票池: {summary['stock_pool_rule_display']}")
|
||||
logger.info(f" 总扫描: {summary['total_stocks_analyzed']} 只")
|
||||
logger.info(f" 有信号: {summary['stocks_with_signals']} 只")
|
||||
logger.info(f" 信号数: {summary['total_signals_found']} 个")
|
||||
logger.info(f" 耗时: {summary['execution_time']:.2f} 秒")
|
||||
|
||||
if summary['error']:
|
||||
logger.error(f" 错误: {summary['error']}")
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 市场扫描失败: {e}")
|
||||
return 1
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
setup_logging()
|
||||
|
||||
# 从环境变量或命令行参数获取扫描数量
|
||||
max_stocks = 200
|
||||
if len(sys.argv) > 1:
|
||||
try:
|
||||
max_stocks = int(sys.argv[1])
|
||||
except ValueError:
|
||||
logger.warning(f"无效的股票数量参数: {sys.argv[1]}，使用默认值: 200")
|
||||
|
||||
# 从环境变量获取
|
||||
max_stocks = int(os.environ.get('MARKET_SCAN_STOCKS', max_stocks))
|
||||
|
||||
logger.info(f"📊 市场扫描参数: 最大股票数={max_stocks}")
|
||||
|
||||
return scan_market(max_stocks)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@ -1,99 +0,0 @@
|
||||
#!/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)
|
||||
@ -40,7 +40,7 @@ trading:
|
||||
data:
|
||||
# 数据源配置
|
||||
sources:
|
||||
primary: "adata"
|
||||
primary: "tushare"
|
||||
|
||||
# 数据更新频率
|
||||
update_frequency:
|
||||
|
||||
@ -1,863 +0,0 @@
|
||||
"""
|
||||
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
|
||||
|
||||
# 股票名称缓存机制
|
||||
self._stock_name_cache = {}
|
||||
self._stock_list_cache = None
|
||||
self._hot_stocks_cache = None
|
||||
self._east_stocks_cache = None
|
||||
self._cache_timestamp = None
|
||||
self._cache_duration = 3600 # 缓存1小时
|
||||
|
||||
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_filtered_a_share_list(self, exclude_st: bool = True, exclude_bj: bool = True, min_market_cap: float = 2000000000) -> pd.DataFrame:
|
||||
"""
|
||||
获取过滤后的A股股票列表
|
||||
|
||||
Args:
|
||||
exclude_st: 是否排除ST股票
|
||||
exclude_bj: 是否排除北交所股票
|
||||
min_market_cap: 最小市值要求（元），默认20亿
|
||||
|
||||
Returns:
|
||||
过滤后的股票列表DataFrame
|
||||
"""
|
||||
try:
|
||||
# 获取完整股票列表
|
||||
all_stocks = self.get_stock_list()
|
||||
|
||||
if all_stocks.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
filtered_stocks = all_stocks.copy()
|
||||
original_count = len(filtered_stocks)
|
||||
|
||||
# 排除北交所股票
|
||||
if exclude_bj:
|
||||
before_count = len(filtered_stocks)
|
||||
filtered_stocks = filtered_stocks[filtered_stocks['exchange'] != 'BJ']
|
||||
bj_excluded = before_count - len(filtered_stocks)
|
||||
logger.info(f"排除北交所股票: {bj_excluded}只")
|
||||
|
||||
# 排除ST股票（包含ST、*ST、PT、退市等）
|
||||
if exclude_st:
|
||||
before_count = len(filtered_stocks)
|
||||
# 排除包含ST、*ST、PT、退等字符的股票
|
||||
st_pattern = r'(\*?ST|PT|退|暂停)'
|
||||
filtered_stocks = filtered_stocks[~filtered_stocks['short_name'].str.contains(st_pattern, na=False, case=False)]
|
||||
st_excluded = before_count - len(filtered_stocks)
|
||||
logger.info(f"排除ST等风险股票: {st_excluded}只")
|
||||
|
||||
# 基于实际市值的筛选
|
||||
if min_market_cap > 0:
|
||||
before_count = len(filtered_stocks)
|
||||
filtered_stocks = self._filter_by_real_market_cap(filtered_stocks, min_market_cap)
|
||||
cap_excluded = before_count - len(filtered_stocks)
|
||||
logger.info(f"排除小市值股票(基于实际市值): {cap_excluded}只")
|
||||
|
||||
# 统计最终结果
|
||||
final_count = len(filtered_stocks)
|
||||
excluded_count = original_count - final_count
|
||||
|
||||
# 添加完整股票代码（带交易所后缀）
|
||||
if not filtered_stocks.empty and 'exchange' in filtered_stocks.columns:
|
||||
filtered_stocks['full_stock_code'] = filtered_stocks.apply(
|
||||
lambda row: f"{row['stock_code']}.{row['exchange']}", axis=1
|
||||
)
|
||||
|
||||
exchange_counts = filtered_stocks['exchange'].value_counts().to_dict()
|
||||
exchange_detail = " | ".join([f"{k}: {v}只" for k, v in exchange_counts.items()])
|
||||
logger.info(f"✅ 获取过滤后A股列表成功")
|
||||
logger.info(f"📊 原始股票: {original_count}只 | 过滤后: {final_count}只 | 排除: {excluded_count}只")
|
||||
logger.info(f"📈 交易所分布: {exchange_detail}")
|
||||
|
||||
return filtered_stocks
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取过滤A股列表失败: {e}")
|
||||
return pd.DataFrame()
|
||||
|
||||
def _filter_by_real_market_cap(self, stock_df: pd.DataFrame, min_market_cap: float) -> pd.DataFrame:
|
||||
"""
|
||||
基于实际市值筛选股票
|
||||
由于API限制，先使用启发式规则预筛选，再对部分股票进行实际市值验证
|
||||
|
||||
Args:
|
||||
stock_df: 股票列表DataFrame
|
||||
min_market_cap: 最小市值要求（元）
|
||||
|
||||
Returns:
|
||||
过滤后的股票DataFrame
|
||||
"""
|
||||
if stock_df.empty:
|
||||
return stock_df
|
||||
|
||||
logger.info(f"开始基于市值筛选股票，阈值: {min_market_cap/100000000:.0f}亿元")
|
||||
|
||||
# 步骤1: 使用启发式规则进行预筛选，减少API调用量
|
||||
logger.info("步骤1: 使用启发式规则预筛选...")
|
||||
pre_filtered = self._filter_by_market_cap_proxy(stock_df, min_market_cap)
|
||||
|
||||
if pre_filtered.empty:
|
||||
logger.warning("启发式预筛选后无股票，返回空结果")
|
||||
return pd.DataFrame()
|
||||
|
||||
logger.info(f"启发式预筛选完成: {len(pre_filtered)}/{len(stock_df)} 只股票")
|
||||
|
||||
# 步骤2: 对预筛选的股票进行小批量实际市值验证
|
||||
logger.info("步骤2: 对预筛选股票进行实际市值验证...")
|
||||
|
||||
# 限制验证数量以避免API超时
|
||||
max_verify_count = min(500, len(pre_filtered)) # 最多验证500只
|
||||
stocks_to_verify = pre_filtered.head(max_verify_count)
|
||||
|
||||
logger.info(f"将验证 {len(stocks_to_verify)} 只股票的实际市值")
|
||||
|
||||
valid_stocks = []
|
||||
total_to_verify = len(stocks_to_verify)
|
||||
|
||||
for idx, (_, stock) in enumerate(stocks_to_verify.iterrows()):
|
||||
stock_code = stock['stock_code']
|
||||
exchange = stock['exchange']
|
||||
full_stock_code = f"{stock_code}.{exchange}"
|
||||
|
||||
try:
|
||||
# 获取股本信息
|
||||
shares_info = self.client.stock.info.get_stock_shares(stock_code=stock_code)
|
||||
|
||||
if not shares_info.empty and 'total_share' in shares_info.columns:
|
||||
total_shares = shares_info.iloc[0]['total_share']
|
||||
|
||||
# 获取当前股价
|
||||
current_price = None
|
||||
try:
|
||||
market_data = self.client.stock.market.get_market(full_stock_code)
|
||||
if not market_data.empty and 'close' in market_data.columns:
|
||||
current_price = market_data.iloc[0]['close']
|
||||
except:
|
||||
pass
|
||||
|
||||
# 计算市值
|
||||
if current_price is not None and total_shares > 0:
|
||||
market_cap = total_shares * 10000 * current_price # 万股转换为股
|
||||
|
||||
if market_cap >= min_market_cap:
|
||||
stock_with_cap = stock.copy()
|
||||
stock_with_cap['market_cap'] = market_cap
|
||||
stock_with_cap['total_shares'] = total_shares
|
||||
stock_with_cap['current_price'] = current_price
|
||||
valid_stocks.append(stock_with_cap)
|
||||
|
||||
logger.debug(f"{full_stock_code}: 市值{market_cap/100000000:.1f}亿元 {'✓' if market_cap >= min_market_cap else '✗'}")
|
||||
else:
|
||||
# 如果无法获取实际市值，且预筛选通过，则保留
|
||||
valid_stocks.append(stock)
|
||||
logger.debug(f"{full_stock_code}: 无市值数据，保留预筛选结果")
|
||||
|
||||
except Exception as e:
|
||||
# 如果API调用失败，且预筛选通过，则保留
|
||||
valid_stocks.append(stock)
|
||||
logger.debug(f"{full_stock_code}: API失败，保留预筛选结果: {e}")
|
||||
|
||||
# 显示进度
|
||||
if (idx + 1) % 50 == 0 or idx + 1 == total_to_verify:
|
||||
logger.info(f"市值验证进度: {idx + 1}/{total_to_verify} ({(idx + 1)/total_to_verify*100:.1f}%)")
|
||||
|
||||
# 添加延时以避免API限制
|
||||
if idx % 10 == 9: # 每10个请求休息0.1秒
|
||||
time.sleep(0.1)
|
||||
|
||||
# 步骤3: 对于剩余未验证的股票，直接使用预筛选结果
|
||||
if len(pre_filtered) > max_verify_count:
|
||||
remaining_stocks = pre_filtered.iloc[max_verify_count:].copy()
|
||||
for _, stock in remaining_stocks.iterrows():
|
||||
valid_stocks.append(stock)
|
||||
|
||||
logger.info(f"保留 {len(remaining_stocks)} 只未验证股票（基于预筛选结果）")
|
||||
|
||||
# 转换为DataFrame
|
||||
if valid_stocks:
|
||||
result_df = pd.DataFrame(valid_stocks)
|
||||
|
||||
# 确保没有重复
|
||||
if 'stock_code' in result_df.columns:
|
||||
result_df = result_df.drop_duplicates(subset=['stock_code'], keep='first')
|
||||
|
||||
logger.info(f"✅ 市值筛选完成: {len(result_df)}/{len(stock_df)} 只股票符合要求")
|
||||
|
||||
# 统计实际验证vs预筛选的结果
|
||||
verified_count = min(max_verify_count, len(stocks_to_verify))
|
||||
unverified_count = len(result_df) - verified_count
|
||||
logger.info(f"📊 验证详情: 实际验证{verified_count}只, 预筛选保留{unverified_count}只")
|
||||
|
||||
return result_df
|
||||
else:
|
||||
logger.warning("⚠️ 没有股票通过市值筛选")
|
||||
return pd.DataFrame()
|
||||
|
||||
def _filter_by_market_cap_proxy(self, stock_df: pd.DataFrame, min_market_cap: float) -> pd.DataFrame:
|
||||
"""
|
||||
基于股票代码的启发式规则筛选大市值股票
|
||||
|
||||
Args:
|
||||
stock_df: 股票列表DataFrame
|
||||
min_market_cap: 最小市值要求（元）
|
||||
|
||||
Returns:
|
||||
过滤后的股票DataFrame
|
||||
"""
|
||||
if stock_df.empty:
|
||||
return stock_df
|
||||
|
||||
# 由于无法直接获取市值数据，使用启发式规则进行筛选
|
||||
# 注意：这只是一个近似筛选，真实的市值筛选需要实际的市值数据
|
||||
|
||||
def is_likely_large_cap(stock_code: str, exchange: str) -> bool:
|
||||
"""判断股票是否可能是大市值股票"""
|
||||
code_num = stock_code
|
||||
|
||||
if exchange == 'SH': # 上交所
|
||||
# 主板: 600xxx, 601xxx, 603xxx, 605xxx (通常市值较大)
|
||||
if code_num.startswith(('600', '601', '603', '605')):
|
||||
return True
|
||||
# 科创板: 688xxx (新兴科技公司，市值相对较大)
|
||||
elif code_num.startswith('688'):
|
||||
return True
|
||||
# 其他上交所股票
|
||||
return False
|
||||
|
||||
elif exchange == 'SZ': # 深交所
|
||||
# 主板: 000xxx, 001xxx (老牌蓝筹，通常市值较大)
|
||||
if code_num.startswith(('000', '001')):
|
||||
return True
|
||||
# 中小板: 002xxx (部分有大市值公司)
|
||||
elif code_num.startswith('002'):
|
||||
# 002开头的前1000只股票(002000-002999)，上市较早，可能市值较大
|
||||
try:
|
||||
code_suffix = int(code_num[3:])
|
||||
return code_suffix <= 999 # 002000-002999
|
||||
except:
|
||||
return False
|
||||
# 创业板: 300xxx, 301xxx (部分成长为大市值)
|
||||
elif code_num.startswith(('300', '301')):
|
||||
# 300开头的前500只股票，上市较早，部分已成长为大市值
|
||||
try:
|
||||
if code_num.startswith('300'):
|
||||
code_suffix = int(code_num[3:])
|
||||
return code_suffix <= 499 # 300000-300499
|
||||
else: # 301xxx较新，市值相对较小
|
||||
return False
|
||||
except:
|
||||
return False
|
||||
return False
|
||||
|
||||
return False # 其他情况默认排除
|
||||
|
||||
# 应用筛选规则
|
||||
if min_market_cap >= 2000000000: # 20亿以上
|
||||
logger.info(f"应用大市值筛选规则(≥{min_market_cap/100000000:.0f}亿元)")
|
||||
mask = stock_df.apply(lambda row: is_likely_large_cap(row['stock_code'], row['exchange']), axis=1)
|
||||
return stock_df[mask]
|
||||
else:
|
||||
# 小于20亿的筛选条件暂时不实施严格筛选
|
||||
logger.info(f"市值筛选阈值较低({min_market_cap/100000000:.1f}亿元)，保留所有股票")
|
||||
return stock_df
|
||||
|
||||
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（基于本地股票列表）
|
||||
|
||||
Args:
|
||||
keyword: 搜索关键词
|
||||
|
||||
Returns:
|
||||
搜索结果DataFrame
|
||||
"""
|
||||
try:
|
||||
# 获取完整股票列表
|
||||
all_stocks = self.get_stock_list()
|
||||
if all_stocks.empty:
|
||||
return pd.DataFrame()
|
||||
|
||||
# 在股票代码和名称中搜索关键词
|
||||
keyword = str(keyword).strip()
|
||||
if not keyword:
|
||||
return pd.DataFrame()
|
||||
|
||||
# 支持按代码或名称模糊搜索
|
||||
mask = (
|
||||
all_stocks['stock_code'].str.contains(keyword, case=False, na=False) |
|
||||
all_stocks['short_name'].str.contains(keyword, case=False, na=False)
|
||||
)
|
||||
|
||||
results = all_stocks[mask].copy()
|
||||
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 _is_cache_valid(self) -> bool:
|
||||
"""检查缓存是否有效"""
|
||||
if self._cache_timestamp is None:
|
||||
return False
|
||||
|
||||
import time
|
||||
return (time.time() - self._cache_timestamp) < self._cache_duration
|
||||
|
||||
def _update_stock_name_cache(self):
|
||||
"""更新股票名称缓存"""
|
||||
try:
|
||||
import time
|
||||
|
||||
# 检查缓存是否有效
|
||||
if self._is_cache_valid():
|
||||
return
|
||||
|
||||
logger.info("🔄 更新股票名称缓存...")
|
||||
|
||||
# 获取热门股票数据并缓存
|
||||
self._hot_stocks_cache = self.get_hot_stocks_ths(limit=100)
|
||||
self._east_stocks_cache = self.get_popular_stocks_east(limit=100)
|
||||
|
||||
# 清空名称缓存并重新构建
|
||||
self._stock_name_cache.clear()
|
||||
|
||||
# 从热门股票数据中构建缓存
|
||||
for df, source in [(self._hot_stocks_cache, '同花顺'), (self._east_stocks_cache, '东财')]:
|
||||
if not df.empty and 'stock_code' in df.columns and 'short_name' in df.columns:
|
||||
for _, row in df.iterrows():
|
||||
stock_code = row['stock_code']
|
||||
stock_name = row['short_name']
|
||||
if stock_code not in self._stock_name_cache:
|
||||
self._stock_name_cache[stock_code] = stock_name
|
||||
|
||||
# 更新缓存时间戳
|
||||
self._cache_timestamp = time.time()
|
||||
|
||||
logger.info(f"✅ 股票名称缓存更新完成，共缓存 {len(self._stock_name_cache)} 只股票")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"更新股票名称缓存失败: {e}")
|
||||
|
||||
def get_stock_name(self, stock_code: str) -> str:
|
||||
"""
|
||||
获取股票中文名称（带缓存机制）
|
||||
|
||||
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:
|
||||
# 更新缓存（如果需要）
|
||||
self._update_stock_name_cache()
|
||||
|
||||
# 从缓存中查找
|
||||
if stock_code in self._stock_name_cache:
|
||||
return self._stock_name_cache[stock_code]
|
||||
|
||||
# 缓存中没有，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:
|
||||
stock_name = search_results.iloc[0]['short_name']
|
||||
# 添加到缓存
|
||||
self._stock_name_cache[stock_code] = stock_name
|
||||
return stock_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)
|
||||
263
src/data/stock_pool_manager.py
Normal file
263
src/data/stock_pool_manager.py
Normal file
@ -0,0 +1,263 @@
|
||||
"""
|
||||
股票池管理器
|
||||
负责根据不同规则获取和管理股票池
|
||||
"""
|
||||
|
||||
from typing import List, Dict, Any, Optional
|
||||
import pandas as pd
|
||||
from loguru import logger
|
||||
from abc import ABC, abstractmethod
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
|
||||
|
||||
class StockPoolRule(ABC):
|
||||
"""股票池规则抽象基类"""
|
||||
|
||||
@abstractmethod
|
||||
def get_stocks(self, fetcher: TushareFetcher, **kwargs) -> List[str]:
|
||||
"""
|
||||
获取股票列表
|
||||
|
||||
Args:
|
||||
fetcher: 数据获取器
|
||||
**kwargs: 规则参数
|
||||
|
||||
Returns:
|
||||
股票代码列表
|
||||
"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def get_rule_name(self) -> str:
|
||||
"""获取规则名称"""
|
||||
pass
|
||||
|
||||
|
||||
class TushareHotStocksRule(StockPoolRule):
|
||||
"""同花顺热榜股票池规则"""
|
||||
|
||||
def get_stocks(self, fetcher: TushareFetcher, limit: int = 50, **kwargs) -> List[str]:
|
||||
"""获取同花顺热榜股票"""
|
||||
try:
|
||||
hot_stocks = fetcher.get_hot_stocks_ths(limit=limit)
|
||||
if not hot_stocks.empty and 'stock_code' in hot_stocks.columns:
|
||||
stocks = hot_stocks['stock_code'].tolist()
|
||||
logger.info(f"✅ 同花顺热榜获取成功: {len(stocks)}只股票")
|
||||
return stocks
|
||||
else:
|
||||
logger.warning("同花顺热榜数据为空")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺热榜失败: {e}")
|
||||
return []
|
||||
|
||||
def get_rule_name(self) -> str:
|
||||
return "同花顺热榜"
|
||||
|
||||
|
||||
class CombinedHotStocksRule(StockPoolRule):
|
||||
"""合并热门股票池规则（同花顺+东财）"""
|
||||
|
||||
def get_stocks(self, fetcher: TushareFetcher, limit_per_source: int = 30, final_limit: int = 50, **kwargs) -> List[str]:
|
||||
"""获取合并热门股票"""
|
||||
try:
|
||||
combined_stocks = fetcher.get_combined_hot_stocks(
|
||||
limit_per_source=limit_per_source,
|
||||
final_limit=final_limit
|
||||
)
|
||||
if not combined_stocks.empty and 'stock_code' in combined_stocks.columns:
|
||||
stocks = combined_stocks['stock_code'].tolist()
|
||||
logger.info(f"✅ 合并热门股票获取成功: {len(stocks)}只股票")
|
||||
return stocks
|
||||
else:
|
||||
logger.warning("合并热门股票数据为空")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"获取合并热门股票失败: {e}")
|
||||
return []
|
||||
|
||||
def get_rule_name(self) -> str:
|
||||
return "合并热门股票"
|
||||
|
||||
|
||||
class LeadingStocksRule(StockPoolRule):
|
||||
"""龙头牛股股票池规则"""
|
||||
|
||||
def get_stocks(self, fetcher: TushareFetcher, top_boards: int = 8, stocks_per_board: int = 3, min_score: float = 60.0, **kwargs) -> List[str]:
|
||||
"""获取龙头牛股"""
|
||||
try:
|
||||
result = fetcher.get_leading_stocks_from_hot_boards(
|
||||
top_boards=top_boards,
|
||||
stocks_per_board=stocks_per_board,
|
||||
min_score=min_score
|
||||
)
|
||||
|
||||
if 'error' not in result and not result['top_leading_stocks'].empty:
|
||||
stocks = result['top_leading_stocks']['stock_code'].tolist()
|
||||
logger.info(f"✅ 龙头牛股获取成功: {len(stocks)}只股票")
|
||||
return stocks
|
||||
else:
|
||||
logger.warning("龙头牛股数据为空")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"获取龙头牛股失败: {e}")
|
||||
return []
|
||||
|
||||
def get_rule_name(self) -> str:
|
||||
return "龙头牛股"
|
||||
|
||||
|
||||
class CustomStockListRule(StockPoolRule):
|
||||
"""自定义股票列表规则"""
|
||||
|
||||
def __init__(self, stock_list: List[str]):
|
||||
self.stock_list = stock_list
|
||||
|
||||
def get_stocks(self, fetcher: TushareFetcher, **kwargs) -> List[str]:
|
||||
"""返回自定义股票列表"""
|
||||
logger.info(f"✅ 使用自定义股票列表: {len(self.stock_list)}只股票")
|
||||
return self.stock_list.copy()
|
||||
|
||||
def get_rule_name(self) -> str:
|
||||
return "自定义股票列表"
|
||||
|
||||
|
||||
class StockPoolManager:
|
||||
"""股票池管理器"""
|
||||
|
||||
def __init__(self, fetcher: TushareFetcher):
|
||||
"""
|
||||
初始化股票池管理器
|
||||
|
||||
Args:
|
||||
fetcher: TuShare数据获取器
|
||||
"""
|
||||
self.fetcher = fetcher
|
||||
self.rules: Dict[str, StockPoolRule] = {}
|
||||
self._register_default_rules()
|
||||
|
||||
def _register_default_rules(self):
|
||||
"""注册默认规则"""
|
||||
self.register_rule("tushare_hot", TushareHotStocksRule())
|
||||
self.register_rule("combined_hot", CombinedHotStocksRule())
|
||||
self.register_rule("leading_stocks", LeadingStocksRule())
|
||||
|
||||
def register_rule(self, rule_name: str, rule: StockPoolRule):
|
||||
"""
|
||||
注册股票池规则
|
||||
|
||||
Args:
|
||||
rule_name: 规则名称
|
||||
rule: 规则实例
|
||||
"""
|
||||
self.rules[rule_name] = rule
|
||||
logger.info(f"注册股票池规则: {rule_name} - {rule.get_rule_name()}")
|
||||
|
||||
def get_stock_pool(self, rule_name: str, **kwargs) -> Dict[str, Any]:
|
||||
"""
|
||||
根据规则获取股票池
|
||||
|
||||
Args:
|
||||
rule_name: 规则名称
|
||||
**kwargs: 规则参数
|
||||
|
||||
Returns:
|
||||
包含股票列表和元信息的字典
|
||||
"""
|
||||
if rule_name not in self.rules:
|
||||
logger.error(f"未找到股票池规则: {rule_name}")
|
||||
return {
|
||||
'stocks': [],
|
||||
'rule_name': rule_name,
|
||||
'rule_display_name': '未知规则',
|
||||
'total_count': 0,
|
||||
'success': False,
|
||||
'error': f'未找到规则: {rule_name}'
|
||||
}
|
||||
|
||||
rule = self.rules[rule_name]
|
||||
|
||||
try:
|
||||
logger.info(f"🔍 执行股票池规则: {rule.get_rule_name()}")
|
||||
stocks = rule.get_stocks(self.fetcher, **kwargs)
|
||||
|
||||
return {
|
||||
'stocks': stocks,
|
||||
'rule_name': rule_name,
|
||||
'rule_display_name': rule.get_rule_name(),
|
||||
'total_count': len(stocks),
|
||||
'success': True,
|
||||
'parameters': kwargs
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"执行股票池规则失败 {rule.get_rule_name()}: {e}")
|
||||
return {
|
||||
'stocks': [],
|
||||
'rule_name': rule_name,
|
||||
'rule_display_name': rule.get_rule_name(),
|
||||
'total_count': 0,
|
||||
'success': False,
|
||||
'error': str(e)
|
||||
}
|
||||
|
||||
def get_available_rules(self) -> Dict[str, str]:
|
||||
"""
|
||||
获取可用的规则列表
|
||||
|
||||
Returns:
|
||||
规则名称到显示名称的映射
|
||||
"""
|
||||
return {name: rule.get_rule_name() for name, rule in self.rules.items()}
|
||||
|
||||
def create_custom_rule(self, rule_name: str, stock_list: List[str]):
|
||||
"""
|
||||
创建自定义股票列表规则
|
||||
|
||||
Args:
|
||||
rule_name: 规则名称
|
||||
stock_list: 股票代码列表
|
||||
"""
|
||||
custom_rule = CustomStockListRule(stock_list)
|
||||
self.register_rule(rule_name, custom_rule)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试股票池管理器
|
||||
from loguru import logger
|
||||
import sys
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
# 初始化
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
|
||||
print("=" * 60)
|
||||
print("📊 股票池管理器测试")
|
||||
print("=" * 60)
|
||||
|
||||
# 显示可用规则
|
||||
print("可用规则:")
|
||||
for rule_id, rule_name in pool_manager.get_available_rules().items():
|
||||
print(f" {rule_id}: {rule_name}")
|
||||
|
||||
# 测试同花顺热榜
|
||||
print(f"\n测试同花顺热榜:")
|
||||
result = pool_manager.get_stock_pool("tushare_hot", limit=10)
|
||||
if result['success']:
|
||||
print(f"✅ 获取成功: {result['total_count']}只股票")
|
||||
print(f"前5只: {result['stocks'][:5]}")
|
||||
else:
|
||||
print(f"❌ 获取失败: {result['error']}")
|
||||
|
||||
# 测试自定义规则
|
||||
print(f"\n测试自定义规则:")
|
||||
custom_stocks = ["000001.SZ", "000002.SZ", "600000.SH"]
|
||||
pool_manager.create_custom_rule("my_custom", custom_stocks)
|
||||
|
||||
result = pool_manager.get_stock_pool("my_custom")
|
||||
if result['success']:
|
||||
print(f"✅ 自定义规则: {result['total_count']}只股票")
|
||||
print(f"股票: {result['stocks']}")
|
||||
1560
src/data/tushare_fetcher.py
Normal file
1560
src/data/tushare_fetcher.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -12,6 +12,8 @@ import json
|
||||
from pathlib import Path
|
||||
import sys
|
||||
from loguru import logger
|
||||
from sqlalchemy import create_engine
|
||||
import warnings
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent.parent.parent
|
||||
@ -33,6 +35,9 @@ class MySQLDatabaseManager:
|
||||
self.config = config or MYSQL_CONFIG
|
||||
self.connection_params = self.config.to_dict()
|
||||
|
||||
# 创建SQLAlchemy引擎用于pandas操作
|
||||
self._create_sqlalchemy_engine()
|
||||
|
||||
# 测试连接并初始化数据库
|
||||
try:
|
||||
self._test_connection()
|
||||
@ -42,6 +47,62 @@ class MySQLDatabaseManager:
|
||||
logger.error(f"MySQL数据库初始化失败: {e}")
|
||||
raise
|
||||
|
||||
def _create_sqlalchemy_engine(self):
|
||||
"""创建SQLAlchemy引擎"""
|
||||
try:
|
||||
# 构建连接字符串
|
||||
connection_string = (
|
||||
f"mysql+pymysql://{self.config.user}:{self.config.password}"
|
||||
f"@{self.config.host}:{self.config.port}/{self.config.database}"
|
||||
f"?charset={self.config.charset}"
|
||||
)
|
||||
|
||||
# 创建引擎
|
||||
self.engine = create_engine(
|
||||
connection_string,
|
||||
pool_pre_ping=True,
|
||||
pool_recycle=3600,
|
||||
echo=False
|
||||
)
|
||||
|
||||
# 抑制pandas SQLAlchemy警告
|
||||
warnings.filterwarnings('ignore',
|
||||
message='pandas only supports SQLAlchemy connectable.*',
|
||||
category=UserWarning)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"创建SQLAlchemy引擎失败: {e}")
|
||||
self.engine = None
|
||||
|
||||
def _execute_query_with_engine(self, sql: str, params: list = None) -> pd.DataFrame:
|
||||
"""使用适当的引擎执行查询"""
|
||||
try:
|
||||
if self.engine and params:
|
||||
# 对于SQLAlchemy，将%s替换为实际值（仅适用于简单参数）
|
||||
formatted_sql = sql
|
||||
for param in params:
|
||||
if isinstance(param, str):
|
||||
# 字符串参数需要加引号
|
||||
formatted_sql = formatted_sql.replace('%s', f"'{param}'", 1)
|
||||
elif isinstance(param, (date, datetime)):
|
||||
# 日期参数需要转换为正确格式并加引号
|
||||
formatted_sql = formatted_sql.replace('%s', f"'{param.strftime('%Y-%m-%d')}'", 1)
|
||||
else:
|
||||
# 数值参数直接替换
|
||||
formatted_sql = formatted_sql.replace('%s', str(param), 1)
|
||||
return pd.read_sql_query(formatted_sql, self.engine)
|
||||
elif self.engine:
|
||||
return pd.read_sql_query(sql, self.engine)
|
||||
else:
|
||||
# 回退到pymysql
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
return pd.read_sql_query(sql, conn, params=params)
|
||||
except Exception as e:
|
||||
# 如果SQLAlchemy失败，回退到pymysql
|
||||
logger.warning(f"SQLAlchemy查询失败，回退到pymysql: {e}")
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
return pd.read_sql_query(sql, conn, params=params)
|
||||
|
||||
def _test_connection(self):
|
||||
"""测试数据库连接"""
|
||||
try:
|
||||
@ -72,7 +133,13 @@ class MySQLDatabaseManager:
|
||||
try:
|
||||
cursor.execute(statement)
|
||||
except Exception as e:
|
||||
if "already exists" not in str(e):
|
||||
# 忽略常见的重复创建警告
|
||||
error_str = str(e)
|
||||
if "Duplicate key name" in error_str:
|
||||
# 特别处理重复索引键名，只记录debug级别
|
||||
logger.debug(f"索引已存在，跳过: {e}")
|
||||
elif ("already exists" not in error_str and
|
||||
"Table" not in error_str):
|
||||
logger.warning(f"执行SQL语句时警告: {e}")
|
||||
|
||||
conn.commit()
|
||||
@ -172,6 +239,22 @@ class MySQLDatabaseManager:
|
||||
logger.error(f"创建扫描会话失败: {e}")
|
||||
raise
|
||||
|
||||
def update_scan_session_stats(self, session_id: int, total_scanned: int, total_signals: int):
|
||||
"""更新扫描会话统计"""
|
||||
try:
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
cursor = conn.cursor()
|
||||
cursor.execute("""
|
||||
UPDATE scan_sessions
|
||||
SET total_scanned = %s, total_signals = %s
|
||||
WHERE id = %s
|
||||
""", (total_scanned, total_signals, session_id))
|
||||
conn.commit()
|
||||
logger.debug(f"更新扫描会话统计: session_id={session_id}, 扫描={total_scanned}, 信号={total_signals}")
|
||||
except Exception as e:
|
||||
logger.error(f"更新扫描会话统计失败: {e}")
|
||||
raise
|
||||
|
||||
def save_stock_signal(self, session_id: int, strategy_id: int, signal: Dict[str, Any]) -> int:
|
||||
"""保存股票信号"""
|
||||
try:
|
||||
@ -253,19 +336,17 @@ class MySQLDatabaseManager:
|
||||
def get_latest_signals(self, strategy_name: str = None, limit: int = 100) -> pd.DataFrame:
|
||||
"""获取最新信号"""
|
||||
try:
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
sql = "SELECT * FROM latest_signals_view"
|
||||
params = []
|
||||
sql = "SELECT * FROM latest_signals_view"
|
||||
params = []
|
||||
|
||||
if strategy_name:
|
||||
sql += " WHERE strategy_name = %s"
|
||||
params.append(strategy_name)
|
||||
if strategy_name:
|
||||
sql += " WHERE strategy_name = %s"
|
||||
params.append(strategy_name)
|
||||
|
||||
sql += " LIMIT %s"
|
||||
params.append(limit)
|
||||
sql += " LIMIT %s"
|
||||
params.append(limit)
|
||||
|
||||
df = pd.read_sql_query(sql, conn, params=params)
|
||||
return df
|
||||
return self._execute_query_with_engine(sql, params)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取最新信号失败: {e}")
|
||||
@ -275,28 +356,26 @@ class MySQLDatabaseManager:
|
||||
strategy_name: str = None, timeframe: str = None) -> pd.DataFrame:
|
||||
"""按日期范围获取信号"""
|
||||
try:
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
if end_date is None:
|
||||
end_date = date.today()
|
||||
if end_date is None:
|
||||
end_date = date.today()
|
||||
|
||||
sql = """
|
||||
SELECT * FROM latest_signals_view
|
||||
WHERE signal_date >= %s AND signal_date <= %s
|
||||
"""
|
||||
params = [start_date, end_date]
|
||||
sql = """
|
||||
SELECT * FROM latest_signals_view
|
||||
WHERE signal_date >= %s AND signal_date <= %s
|
||||
"""
|
||||
params = [start_date, end_date]
|
||||
|
||||
if strategy_name:
|
||||
sql += " AND strategy_name = %s"
|
||||
params.append(strategy_name)
|
||||
if strategy_name:
|
||||
sql += " AND strategy_name = %s"
|
||||
params.append(strategy_name)
|
||||
|
||||
if timeframe:
|
||||
sql += " AND timeframe = %s"
|
||||
params.append(timeframe)
|
||||
if timeframe:
|
||||
sql += " AND timeframe = %s"
|
||||
params.append(timeframe)
|
||||
|
||||
sql += " ORDER BY signal_date DESC, scan_time DESC"
|
||||
sql += " ORDER BY signal_date DESC, scan_time DESC"
|
||||
|
||||
df = pd.read_sql_query(sql, conn, params=params)
|
||||
return df
|
||||
return self._execute_query_with_engine(sql, params)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"按日期范围获取信号失败: {e}")
|
||||
@ -305,9 +384,8 @@ class MySQLDatabaseManager:
|
||||
def get_strategy_stats(self) -> pd.DataFrame:
|
||||
"""获取策略统计"""
|
||||
try:
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
df = pd.read_sql_query("SELECT * FROM strategy_stats_view", conn)
|
||||
return df
|
||||
sql = "SELECT * FROM strategy_stats_view"
|
||||
return self._execute_query_with_engine(sql)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取策略统计失败: {e}")
|
||||
@ -316,17 +394,15 @@ class MySQLDatabaseManager:
|
||||
def get_pullback_alerts(self, days: int = 30) -> pd.DataFrame:
|
||||
"""获取回踩提醒"""
|
||||
try:
|
||||
with pymysql.connect(**self.connection_params) as conn:
|
||||
cutoff_date = date.today() - timedelta(days=days)
|
||||
cutoff_date = date.today() - timedelta(days=days)
|
||||
|
||||
sql = """
|
||||
SELECT * FROM pullback_alerts
|
||||
WHERE pullback_date >= %s
|
||||
ORDER BY pullback_date DESC
|
||||
"""
|
||||
sql = """
|
||||
SELECT * FROM pullback_alerts
|
||||
WHERE pullback_date >= %s
|
||||
ORDER BY pullback_date DESC
|
||||
"""
|
||||
|
||||
df = pd.read_sql_query(sql, conn, params=[cutoff_date])
|
||||
return df
|
||||
return self._execute_query_with_engine(sql, [cutoff_date])
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取回踩提醒失败: {e}")
|
||||
|
||||
@ -108,8 +108,8 @@ CREATE TABLE IF NOT EXISTS pullback_alerts (
|
||||
INDEX idx_pullback_date (pullback_date)
|
||||
) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_unicode_ci;
|
||||
|
||||
-- 创建索引以提高查询性能
|
||||
CREATE INDEX idx_scan_sessions_scan_date ON scan_sessions (scan_date);
|
||||
-- 创建索引以提高查询性能（忽略重复索引错误）
|
||||
-- CREATE INDEX idx_scan_sessions_scan_date ON scan_sessions (scan_date);
|
||||
|
||||
-- 创建视图：最新信号概览
|
||||
CREATE OR REPLACE VIEW latest_signals_view AS
|
||||
|
||||
382
src/execution/strategy_executor.py
Normal file
382
src/execution/strategy_executor.py
Normal file
@ -0,0 +1,382 @@
|
||||
"""
|
||||
策略执行器
|
||||
负责协调股票池获取和策略分析的执行流程
|
||||
"""
|
||||
|
||||
from typing import Dict, List, Any, Optional
|
||||
import time
|
||||
from datetime import datetime
|
||||
from loguru import logger
|
||||
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.base_strategy import BaseStrategy, StrategyResult
|
||||
from src.utils.notification import NotificationManager
|
||||
|
||||
|
||||
class ExecutionResult:
|
||||
"""执行结果"""
|
||||
|
||||
def __init__(self,
|
||||
task_id: str,
|
||||
strategy_name: str,
|
||||
stock_pool_rule: str,
|
||||
start_time: datetime):
|
||||
"""
|
||||
初始化执行结果
|
||||
|
||||
Args:
|
||||
task_id: 任务ID
|
||||
strategy_name: 策略名称
|
||||
stock_pool_rule: 股票池规则
|
||||
start_time: 开始时间
|
||||
"""
|
||||
self.task_id = task_id
|
||||
self.strategy_name = strategy_name
|
||||
self.stock_pool_rule = stock_pool_rule
|
||||
self.start_time = start_time
|
||||
self.end_time: Optional[datetime] = None
|
||||
|
||||
# 股票池信息
|
||||
self.stock_pool_info: Dict[str, Any] = {}
|
||||
self.stock_list: List[str] = []
|
||||
|
||||
# 策略分析结果
|
||||
self.strategy_results: Dict[str, Dict[str, StrategyResult]] = {}
|
||||
|
||||
# 统计信息
|
||||
self.total_stocks_analyzed = 0
|
||||
self.total_signals_found = 0
|
||||
self.stocks_with_signals = 0
|
||||
self.execution_time = 0.0
|
||||
|
||||
# 执行状态
|
||||
self.success = False
|
||||
self.error: Optional[str] = None
|
||||
|
||||
def complete(self, success: bool = True, error: str = None):
|
||||
"""完成执行"""
|
||||
self.end_time = datetime.now()
|
||||
self.execution_time = (self.end_time - self.start_time).total_seconds()
|
||||
self.success = success
|
||||
self.error = error
|
||||
|
||||
if success:
|
||||
logger.info(f"✅ 执行完成: {self.task_id} (耗时: {self.execution_time:.2f}秒)")
|
||||
else:
|
||||
logger.error(f"❌ 执行失败: {self.task_id} - {error}")
|
||||
|
||||
def add_strategy_results(self, results: Dict[str, Dict[str, StrategyResult]], actual_analyzed_count: int = None):
|
||||
"""添加策略分析结果"""
|
||||
self.strategy_results = results
|
||||
# 使用实际分析的股票数量，如果没有提供则使用结果数量
|
||||
if actual_analyzed_count is not None:
|
||||
self.total_stocks_analyzed = actual_analyzed_count
|
||||
else:
|
||||
self.total_stocks_analyzed = len(self.stock_list)
|
||||
self.stocks_with_signals = len(results)
|
||||
|
||||
# 统计信号总数
|
||||
for stock_results in results.values():
|
||||
for timeframe_result in stock_results.values():
|
||||
self.total_signals_found += timeframe_result.get_signal_count()
|
||||
|
||||
def get_summary(self) -> Dict[str, Any]:
|
||||
"""获取执行摘要"""
|
||||
return {
|
||||
'task_id': self.task_id,
|
||||
'strategy_name': self.strategy_name,
|
||||
'stock_pool_rule': self.stock_pool_rule,
|
||||
'stock_pool_rule_display': self.stock_pool_info.get('rule_display_name', ''),
|
||||
'start_time': self.start_time.isoformat(),
|
||||
'end_time': self.end_time.isoformat() if self.end_time else None,
|
||||
'execution_time': self.execution_time,
|
||||
'success': self.success,
|
||||
'error': self.error,
|
||||
'total_stocks_in_pool': len(self.stock_list),
|
||||
'total_stocks_analyzed': self.total_stocks_analyzed,
|
||||
'stocks_with_signals': self.stocks_with_signals,
|
||||
'total_signals_found': self.total_signals_found
|
||||
}
|
||||
|
||||
|
||||
class StrategyExecutor:
|
||||
"""策略执行器"""
|
||||
|
||||
def __init__(self,
|
||||
stock_pool_manager: StockPoolManager,
|
||||
notification_manager: NotificationManager):
|
||||
"""
|
||||
初始化策略执行器
|
||||
|
||||
Args:
|
||||
stock_pool_manager: 股票池管理器
|
||||
notification_manager: 通知管理器
|
||||
"""
|
||||
self.stock_pool_manager = stock_pool_manager
|
||||
self.notification_manager = notification_manager
|
||||
self.registered_strategies: Dict[str, BaseStrategy] = {}
|
||||
|
||||
def register_strategy(self, strategy_id: str, strategy: BaseStrategy):
|
||||
"""
|
||||
注册策略
|
||||
|
||||
Args:
|
||||
strategy_id: 策略唯一标识
|
||||
strategy: 策略实例
|
||||
"""
|
||||
self.registered_strategies[strategy_id] = strategy
|
||||
logger.info(f"注册策略: {strategy_id} - {strategy.get_strategy_name()}")
|
||||
|
||||
def execute_task(self,
|
||||
task_id: str,
|
||||
strategy_id: str,
|
||||
stock_pool_rule: str,
|
||||
stock_pool_params: Dict[str, Any] = None,
|
||||
max_stocks: int = None,
|
||||
send_notification: bool = True) -> ExecutionResult:
|
||||
"""
|
||||
执行策略分析任务
|
||||
|
||||
Args:
|
||||
task_id: 任务唯一标识
|
||||
strategy_id: 策略ID
|
||||
stock_pool_rule: 股票池规则名称
|
||||
stock_pool_params: 股票池参数
|
||||
max_stocks: 最大分析股票数量
|
||||
send_notification: 是否发送通知
|
||||
|
||||
Returns:
|
||||
执行结果
|
||||
"""
|
||||
start_time = datetime.now()
|
||||
|
||||
# 检查策略是否已注册
|
||||
if strategy_id not in self.registered_strategies:
|
||||
error = f"策略未注册: {strategy_id}"
|
||||
logger.error(error)
|
||||
result = ExecutionResult(task_id, strategy_id, stock_pool_rule, start_time)
|
||||
result.complete(False, error)
|
||||
return result
|
||||
|
||||
strategy = self.registered_strategies[strategy_id]
|
||||
result = ExecutionResult(task_id, strategy.get_strategy_name(), stock_pool_rule, start_time)
|
||||
|
||||
logger.info(f"🚀 开始执行策略任务: {task_id}")
|
||||
logger.info(f" 📊 策略: {strategy.get_strategy_name()}")
|
||||
logger.info(f" 🎯 股票池规则: {stock_pool_rule}")
|
||||
|
||||
try:
|
||||
# 第1步: 获取股票池
|
||||
logger.info("📊 第1步: 获取股票池...")
|
||||
stock_pool_info = self.stock_pool_manager.get_stock_pool(
|
||||
stock_pool_rule,
|
||||
**(stock_pool_params or {})
|
||||
)
|
||||
|
||||
if not stock_pool_info['success']:
|
||||
error = f"股票池获取失败: {stock_pool_info.get('error', '未知错误')}"
|
||||
result.complete(False, error)
|
||||
return result
|
||||
|
||||
stock_list = stock_pool_info['stocks']
|
||||
if not stock_list:
|
||||
error = "股票池为空"
|
||||
result.complete(False, error)
|
||||
return result
|
||||
|
||||
result.stock_pool_info = stock_pool_info
|
||||
result.stock_list = stock_list
|
||||
|
||||
logger.info(f"✅ 股票池获取成功: {stock_pool_info['rule_display_name']} - {len(stock_list)}只股票")
|
||||
|
||||
# 第2步: 执行策略分析
|
||||
logger.info("🔍 第2步: 执行策略分析...")
|
||||
strategy_results = strategy.analyze_stock_pool(stock_list, max_stocks)
|
||||
|
||||
# 计算实际分析的股票数量(限制后的数量)
|
||||
actual_analyzed = min(len(stock_list), max_stocks) if max_stocks else len(stock_list)
|
||||
result.add_strategy_results(strategy_results, actual_analyzed)
|
||||
|
||||
# 第3步: 发送通知
|
||||
if send_notification and result.total_signals_found > 0:
|
||||
logger.info("📱 第3步: 发送分析结果通知...")
|
||||
try:
|
||||
self._send_notification(result, strategy_results)
|
||||
except Exception as e:
|
||||
logger.warning(f"发送通知失败: {e}")
|
||||
|
||||
# 完成执行
|
||||
result.complete(True)
|
||||
|
||||
except Exception as e:
|
||||
result.complete(False, str(e))
|
||||
|
||||
return result
|
||||
|
||||
def _send_notification(self, result: ExecutionResult, strategy_results: Dict[str, Dict[str, StrategyResult]]):
|
||||
"""发送分析结果通知"""
|
||||
# 准备通知数据
|
||||
summary = result.get_summary()
|
||||
|
||||
# 构建策略结果摘要
|
||||
strategy_summary = {
|
||||
'strategy_name': result.strategy_name,
|
||||
'stock_pool_source': result.stock_pool_info.get('rule_display_name', ''),
|
||||
'total_stocks': result.total_stocks_analyzed,
|
||||
'stocks_with_signals': result.stocks_with_signals,
|
||||
'total_signals': result.total_signals_found,
|
||||
'execution_time': result.execution_time
|
||||
}
|
||||
|
||||
# 发送策略摘要通知
|
||||
try:
|
||||
# 转换StrategyResult格式为通知所需格式
|
||||
all_signals = {}
|
||||
for stock_code, timeframe_results in strategy_results.items():
|
||||
for timeframe, strategy_result in timeframe_results.items():
|
||||
if strategy_result.get_signal_count() > 0:
|
||||
if stock_code not in all_signals:
|
||||
all_signals[stock_code] = {}
|
||||
all_signals[stock_code][timeframe] = strategy_result.signals
|
||||
|
||||
success = self.notification_manager.send_strategy_summary(
|
||||
all_signals,
|
||||
strategy_summary
|
||||
)
|
||||
if success:
|
||||
logger.info("📱 策略结果通知发送成功")
|
||||
else:
|
||||
logger.warning("📱 策略结果通知发送失败")
|
||||
except Exception as e:
|
||||
logger.error(f"发送策略摘要通知失败: {e}")
|
||||
|
||||
def get_registered_strategies(self) -> Dict[str, str]:
|
||||
"""
|
||||
获取已注册的策略列表
|
||||
|
||||
Returns:
|
||||
策略ID到策略名称的映射
|
||||
"""
|
||||
return {
|
||||
strategy_id: strategy.get_strategy_name()
|
||||
for strategy_id, strategy in self.registered_strategies.items()
|
||||
}
|
||||
|
||||
def create_task_function(self,
|
||||
strategy_id: str,
|
||||
stock_pool_rule: str,
|
||||
stock_pool_params: Dict[str, Any] = None,
|
||||
max_stocks: int = None,
|
||||
send_notification: bool = True):
|
||||
"""
|
||||
创建用于任务调度的函数
|
||||
|
||||
Args:
|
||||
strategy_id: 策略ID
|
||||
stock_pool_rule: 股票池规则
|
||||
stock_pool_params: 股票池参数
|
||||
max_stocks: 最大股票数量
|
||||
send_notification: 是否发送通知
|
||||
|
||||
Returns:
|
||||
可调度的任务函数
|
||||
"""
|
||||
def task_function():
|
||||
task_id = f"{strategy_id}_{stock_pool_rule}_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
|
||||
return self.execute_task(
|
||||
task_id=task_id,
|
||||
strategy_id=strategy_id,
|
||||
stock_pool_rule=stock_pool_rule,
|
||||
stock_pool_params=stock_pool_params,
|
||||
max_stocks=max_stocks,
|
||||
send_notification=send_notification
|
||||
)
|
||||
|
||||
return task_function
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试策略执行器
|
||||
from loguru import logger
|
||||
import sys
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
# 创建模拟组件
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from src.data.stock_pool_manager import StockPoolManager
|
||||
from src.strategy.base_strategy import BaseStrategy, StrategyResult
|
||||
from src.utils.config_loader import config_loader
|
||||
|
||||
# 创建测试策略
|
||||
class TestStrategy(BaseStrategy):
|
||||
def __init__(self):
|
||||
super().__init__("测试K线策略", {"timeframes": ["daily"]})
|
||||
|
||||
def analyze_stock(self, stock_code: str, timeframes: List[str] = None) -> Dict[str, StrategyResult]:
|
||||
results = {}
|
||||
# 模拟分析，只有某些股票有信号
|
||||
signals = []
|
||||
if stock_code.endswith(".SH"): # 模拟上海股票有信号
|
||||
signals = [{
|
||||
'date': '2024-01-10',
|
||||
'signal_type': '突破信号',
|
||||
'price': 15.50
|
||||
}]
|
||||
|
||||
results['daily'] = StrategyResult(
|
||||
strategy_name=self.strategy_name,
|
||||
stock_code=stock_code,
|
||||
timeframe='daily',
|
||||
signals=signals,
|
||||
execution_time=0.1
|
||||
)
|
||||
return results
|
||||
|
||||
def get_strategy_description(self) -> str:
|
||||
return "测试策略，用于验证执行器功能"
|
||||
|
||||
print("=" * 80)
|
||||
print("🚀 策略执行器测试")
|
||||
print("=" * 80)
|
||||
|
||||
# 初始化组件
|
||||
fetcher = TushareFetcher()
|
||||
pool_manager = StockPoolManager(fetcher)
|
||||
|
||||
notification_config = config_loader.get('notification', {})
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
executor = StrategyExecutor(pool_manager, notification_manager)
|
||||
|
||||
# 注册测试策略
|
||||
test_strategy = TestStrategy()
|
||||
executor.register_strategy("test_kline", test_strategy)
|
||||
|
||||
print("已注册策略:")
|
||||
for strategy_id, strategy_name in executor.get_registered_strategies().items():
|
||||
print(f" {strategy_id}: {strategy_name}")
|
||||
|
||||
# 执行测试任务
|
||||
print(f"\n执行测试任务...")
|
||||
result = executor.execute_task(
|
||||
task_id="test_task_001",
|
||||
strategy_id="test_kline",
|
||||
stock_pool_rule="tushare_hot",
|
||||
stock_pool_params={"limit": 10},
|
||||
max_stocks=5,
|
||||
send_notification=False
|
||||
)
|
||||
|
||||
# 显示结果
|
||||
print(f"\n执行结果摘要:")
|
||||
summary = result.get_summary()
|
||||
for key, value in summary.items():
|
||||
print(f" {key}: {value}")
|
||||
|
||||
print(f"\n策略结果详情:")
|
||||
for stock_code, timeframe_results in result.strategy_results.items():
|
||||
for timeframe, strategy_result in timeframe_results.items():
|
||||
signals = strategy_result.get_signal_count()
|
||||
print(f" {stock_code} ({timeframe}): {signals} 个信号")
|
||||
396
src/execution/task_scheduler.py
Normal file
396
src/execution/task_scheduler.py
Normal file
@ -0,0 +1,396 @@
|
||||
"""
|
||||
任务调度器
|
||||
负责定时执行策略分析任务
|
||||
"""
|
||||
|
||||
import schedule
|
||||
import time
|
||||
import threading
|
||||
from typing import Dict, List, Callable, Any, Optional
|
||||
from datetime import datetime, timedelta
|
||||
from loguru import logger
|
||||
from dataclasses import dataclass
|
||||
from enum import Enum
|
||||
|
||||
|
||||
class TaskStatus(Enum):
|
||||
"""任务状态"""
|
||||
PENDING = "pending" # 等待中
|
||||
RUNNING = "running" # 运行中
|
||||
COMPLETED = "completed" # 已完成
|
||||
FAILED = "failed" # 失败
|
||||
DISABLED = "disabled" # 已禁用
|
||||
|
||||
|
||||
@dataclass
|
||||
class TaskExecution:
|
||||
"""任务执行记录"""
|
||||
task_id: str
|
||||
start_time: datetime
|
||||
end_time: Optional[datetime] = None
|
||||
status: TaskStatus = TaskStatus.RUNNING
|
||||
result: Any = None
|
||||
error: Optional[str] = None
|
||||
duration: Optional[float] = None
|
||||
|
||||
def complete(self, result: Any = None):
|
||||
"""标记任务完成"""
|
||||
self.end_time = datetime.now()
|
||||
self.status = TaskStatus.COMPLETED
|
||||
self.result = result
|
||||
if self.start_time:
|
||||
self.duration = (self.end_time - self.start_time).total_seconds()
|
||||
|
||||
def fail(self, error: str):
|
||||
"""标记任务失败"""
|
||||
self.end_time = datetime.now()
|
||||
self.status = TaskStatus.FAILED
|
||||
self.error = error
|
||||
if self.start_time:
|
||||
self.duration = (self.end_time - self.start_time).total_seconds()
|
||||
|
||||
|
||||
class ScheduledTask:
|
||||
"""调度任务"""
|
||||
|
||||
def __init__(self,
|
||||
task_id: str,
|
||||
name: str,
|
||||
func: Callable,
|
||||
schedule_rule: str,
|
||||
enabled: bool = True,
|
||||
max_history: int = 50):
|
||||
"""
|
||||
初始化调度任务
|
||||
|
||||
Args:
|
||||
task_id: 任务唯一标识
|
||||
name: 任务名称
|
||||
func: 执行函数
|
||||
schedule_rule: 调度规则 (如: "every 30 minutes", "daily at 09:30")
|
||||
enabled: 是否启用
|
||||
max_history: 最大历史记录数量
|
||||
"""
|
||||
self.task_id = task_id
|
||||
self.name = name
|
||||
self.func = func
|
||||
self.schedule_rule = schedule_rule
|
||||
self.enabled = enabled
|
||||
self.max_history = max_history
|
||||
|
||||
# 执行历史
|
||||
self.execution_history: List[TaskExecution] = []
|
||||
self.current_execution: Optional[TaskExecution] = None
|
||||
|
||||
# 统计信息
|
||||
self.total_executions = 0
|
||||
self.successful_executions = 0
|
||||
self.failed_executions = 0
|
||||
self.last_execution_time: Optional[datetime] = None
|
||||
self.next_execution_time: Optional[datetime] = None
|
||||
|
||||
def execute(self) -> TaskExecution:
|
||||
"""执行任务"""
|
||||
if not self.enabled:
|
||||
logger.warning(f"任务已禁用: {self.name}")
|
||||
return None
|
||||
|
||||
# 创建执行记录
|
||||
execution = TaskExecution(
|
||||
task_id=self.task_id,
|
||||
start_time=datetime.now()
|
||||
)
|
||||
self.current_execution = execution
|
||||
|
||||
logger.info(f"🚀 开始执行任务: {self.name}")
|
||||
|
||||
try:
|
||||
# 执行任务函数
|
||||
result = self.func()
|
||||
execution.complete(result)
|
||||
|
||||
logger.info(f"✅ 任务执行成功: {self.name} (耗时: {execution.duration:.2f}秒)")
|
||||
self.successful_executions += 1
|
||||
|
||||
except Exception as e:
|
||||
error_msg = str(e)
|
||||
execution.fail(error_msg)
|
||||
|
||||
logger.error(f"❌ 任务执行失败: {self.name} - {error_msg}")
|
||||
self.failed_executions += 1
|
||||
|
||||
finally:
|
||||
# 更新统计信息
|
||||
self.total_executions += 1
|
||||
self.last_execution_time = execution.start_time
|
||||
self.current_execution = None
|
||||
|
||||
# 添加到历史记录
|
||||
self.execution_history.append(execution)
|
||||
|
||||
# 限制历史记录数量
|
||||
if len(self.execution_history) > self.max_history:
|
||||
self.execution_history = self.execution_history[-self.max_history:]
|
||||
|
||||
return execution
|
||||
|
||||
def get_success_rate(self) -> float:
|
||||
"""获取成功率"""
|
||||
if self.total_executions == 0:
|
||||
return 0.0
|
||||
return self.successful_executions / self.total_executions * 100
|
||||
|
||||
def get_status(self) -> TaskStatus:
|
||||
"""获取任务状态"""
|
||||
if not self.enabled:
|
||||
return TaskStatus.DISABLED
|
||||
if self.current_execution:
|
||||
return TaskStatus.RUNNING
|
||||
return TaskStatus.PENDING
|
||||
|
||||
def get_last_execution(self) -> Optional[TaskExecution]:
|
||||
"""获取最后一次执行记录"""
|
||||
return self.execution_history[-1] if self.execution_history else None
|
||||
|
||||
|
||||
class TaskScheduler:
|
||||
"""任务调度器"""
|
||||
|
||||
def __init__(self):
|
||||
"""初始化调度器"""
|
||||
self.tasks: Dict[str, ScheduledTask] = {}
|
||||
self.running = False
|
||||
self.scheduler_thread: Optional[threading.Thread] = None
|
||||
|
||||
def add_task(self,
|
||||
task_id: str,
|
||||
name: str,
|
||||
func: Callable,
|
||||
schedule_rule: str,
|
||||
enabled: bool = True) -> bool:
|
||||
"""
|
||||
添加调度任务
|
||||
|
||||
Args:
|
||||
task_id: 任务唯一标识
|
||||
name: 任务名称
|
||||
func: 执行函数
|
||||
schedule_rule: 调度规则
|
||||
enabled: 是否启用
|
||||
|
||||
Returns:
|
||||
是否添加成功
|
||||
"""
|
||||
try:
|
||||
# 创建任务
|
||||
task = ScheduledTask(task_id, name, func, schedule_rule, enabled)
|
||||
|
||||
# 解析并设置调度规则
|
||||
self._parse_schedule_rule(task, schedule_rule)
|
||||
|
||||
# 添加到任务列表
|
||||
self.tasks[task_id] = task
|
||||
|
||||
logger.info(f"✅ 添加调度任务: {name} - {schedule_rule}")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 添加调度任务失败: {name} - {e}")
|
||||
return False
|
||||
|
||||
def _parse_schedule_rule(self, task: ScheduledTask, rule: str):
|
||||
"""
|
||||
解析调度规则并设置到schedule库
|
||||
|
||||
Args:
|
||||
task: 任务实例
|
||||
rule: 调度规则字符串
|
||||
"""
|
||||
rule = rule.lower().strip()
|
||||
|
||||
# 解析不同的调度规则格式
|
||||
if rule.startswith("every"):
|
||||
# 格式: "every 30 minutes", "every 1 hour", "every 2 days"
|
||||
parts = rule.split()
|
||||
if len(parts) >= 3:
|
||||
interval = int(parts[1])
|
||||
unit = parts[2].rstrip('s') # 移除复数s
|
||||
|
||||
if unit == "second":
|
||||
schedule.every(interval).seconds.do(task.execute)
|
||||
elif unit == "minute":
|
||||
schedule.every(interval).minutes.do(task.execute)
|
||||
elif unit == "hour":
|
||||
schedule.every(interval).hours.do(task.execute)
|
||||
elif unit == "day":
|
||||
schedule.every(interval).days.do(task.execute)
|
||||
elif unit == "week":
|
||||
schedule.every(interval).weeks.do(task.execute)
|
||||
|
||||
elif "daily at" in rule:
|
||||
# 格式: "daily at 09:30", "daily at 14:00"
|
||||
time_str = rule.split("at")[1].strip()
|
||||
schedule.every().day.at(time_str).do(task.execute)
|
||||
|
||||
elif "weekdays at" in rule:
|
||||
# 格式: "weekdays at 09:30"
|
||||
time_str = rule.split("at")[1].strip()
|
||||
schedule.every().monday.at(time_str).do(task.execute)
|
||||
schedule.every().tuesday.at(time_str).do(task.execute)
|
||||
schedule.every().wednesday.at(time_str).do(task.execute)
|
||||
schedule.every().thursday.at(time_str).do(task.execute)
|
||||
schedule.every().friday.at(time_str).do(task.execute)
|
||||
|
||||
elif "monday at" in rule:
|
||||
time_str = rule.split("at")[1].strip()
|
||||
schedule.every().monday.at(time_str).do(task.execute)
|
||||
|
||||
elif "tuesday at" in rule:
|
||||
time_str = rule.split("at")[1].strip()
|
||||
schedule.every().tuesday.at(time_str).do(task.execute)
|
||||
|
||||
# 可以继续添加更多规则...
|
||||
|
||||
else:
|
||||
raise ValueError(f"不支持的调度规则: {rule}")
|
||||
|
||||
def remove_task(self, task_id: str) -> bool:
|
||||
"""
|
||||
移除任务
|
||||
|
||||
Args:
|
||||
task_id: 任务ID
|
||||
|
||||
Returns:
|
||||
是否移除成功
|
||||
"""
|
||||
if task_id in self.tasks:
|
||||
# 清除schedule中的任务
|
||||
schedule.clear(tag=task_id)
|
||||
del self.tasks[task_id]
|
||||
logger.info(f"✅ 移除任务: {task_id}")
|
||||
return True
|
||||
else:
|
||||
logger.warning(f"⚠️ 任务不存在: {task_id}")
|
||||
return False
|
||||
|
||||
def enable_task(self, task_id: str) -> bool:
|
||||
"""启用任务"""
|
||||
if task_id in self.tasks:
|
||||
self.tasks[task_id].enabled = True
|
||||
logger.info(f"✅ 启用任务: {task_id}")
|
||||
return True
|
||||
return False
|
||||
|
||||
def disable_task(self, task_id: str) -> bool:
|
||||
"""禁用任务"""
|
||||
if task_id in self.tasks:
|
||||
self.tasks[task_id].enabled = False
|
||||
logger.info(f"⏸️ 禁用任务: {task_id}")
|
||||
return True
|
||||
return False
|
||||
|
||||
def start(self):
|
||||
"""启动调度器"""
|
||||
if self.running:
|
||||
logger.warning("调度器已在运行中")
|
||||
return
|
||||
|
||||
self.running = True
|
||||
self.scheduler_thread = threading.Thread(target=self._run_scheduler, daemon=True)
|
||||
self.scheduler_thread.start()
|
||||
|
||||
logger.info(f"🚀 任务调度器已启动，共 {len(self.tasks)} 个任务")
|
||||
|
||||
def stop(self):
|
||||
"""停止调度器"""
|
||||
self.running = False
|
||||
if self.scheduler_thread:
|
||||
self.scheduler_thread.join(timeout=5)
|
||||
|
||||
logger.info("⏹️ 任务调度器已停止")
|
||||
|
||||
def _run_scheduler(self):
|
||||
"""运行调度器主循环"""
|
||||
logger.info("📅 调度器主循环已启动")
|
||||
|
||||
while self.running:
|
||||
try:
|
||||
schedule.run_pending()
|
||||
time.sleep(1)
|
||||
except Exception as e:
|
||||
logger.error(f"调度器运行异常: {e}")
|
||||
time.sleep(5)
|
||||
|
||||
def get_task_status(self) -> Dict[str, Dict[str, Any]]:
|
||||
"""获取所有任务状态"""
|
||||
status = {}
|
||||
for task_id, task in self.tasks.items():
|
||||
last_execution = task.get_last_execution()
|
||||
status[task_id] = {
|
||||
'name': task.name,
|
||||
'schedule_rule': task.schedule_rule,
|
||||
'enabled': task.enabled,
|
||||
'status': task.get_status().value,
|
||||
'total_executions': task.total_executions,
|
||||
'success_rate': task.get_success_rate(),
|
||||
'last_execution_time': task.last_execution_time.isoformat() if task.last_execution_time else None,
|
||||
'last_execution_result': last_execution.status.value if last_execution else None
|
||||
}
|
||||
return status
|
||||
|
||||
def execute_task_now(self, task_id: str) -> bool:
|
||||
"""立即执行指定任务"""
|
||||
if task_id not in self.tasks:
|
||||
logger.error(f"任务不存在: {task_id}")
|
||||
return False
|
||||
|
||||
task = self.tasks[task_id]
|
||||
execution = task.execute()
|
||||
return execution.status == TaskStatus.COMPLETED if execution else False
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试任务调度器
|
||||
from loguru import logger
|
||||
import sys
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
def test_task_1():
|
||||
"""测试任务1"""
|
||||
logger.info("执行测试任务1")
|
||||
time.sleep(0.5)
|
||||
return {"result": "task1_completed"}
|
||||
|
||||
def test_task_2():
|
||||
"""测试任务2"""
|
||||
logger.info("执行测试任务2")
|
||||
return {"result": "task2_completed"}
|
||||
|
||||
# 创建调度器
|
||||
scheduler = TaskScheduler()
|
||||
|
||||
# 添加任务
|
||||
scheduler.add_task("task1", "测试任务1", test_task_1, "every 10 seconds")
|
||||
scheduler.add_task("task2", "测试任务2", test_task_2, "every 30 seconds")
|
||||
|
||||
print("=" * 60)
|
||||
print("📅 任务调度器测试")
|
||||
print("=" * 60)
|
||||
|
||||
# 显示任务状态
|
||||
print("任务状态:")
|
||||
for task_id, status in scheduler.get_task_status().items():
|
||||
print(f" {task_id}: {status['name']} - {status['schedule_rule']}")
|
||||
|
||||
# 立即执行任务测试
|
||||
print("\n立即执行任务测试:")
|
||||
scheduler.execute_task_now("task1")
|
||||
|
||||
# 可以取消下面的注释来测试定时执行
|
||||
# print("\n启动调度器...")
|
||||
# scheduler.start()
|
||||
# time.sleep(60) # 运行1分钟
|
||||
# scheduler.stop()
|
||||
257
src/strategy/base_strategy.py
Normal file
257
src/strategy/base_strategy.py
Normal file
@ -0,0 +1,257 @@
|
||||
"""
|
||||
策略基类
|
||||
定义所有技术分析策略的通用接口
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List, Dict, Any, Optional
|
||||
import pandas as pd
|
||||
from loguru import logger
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class StrategyResult:
|
||||
"""策略执行结果"""
|
||||
|
||||
def __init__(self,
|
||||
strategy_name: str,
|
||||
stock_code: str,
|
||||
timeframe: str,
|
||||
signals: List[Dict[str, Any]] = None,
|
||||
success: bool = True,
|
||||
error: str = None,
|
||||
execution_time: float = 0.0):
|
||||
"""
|
||||
初始化策略结果
|
||||
|
||||
Args:
|
||||
strategy_name: 策略名称
|
||||
stock_code: 股票代码
|
||||
timeframe: 时间周期
|
||||
signals: 信号列表
|
||||
success: 是否成功
|
||||
error: 错误信息
|
||||
execution_time: 执行时间
|
||||
"""
|
||||
self.strategy_name = strategy_name
|
||||
self.stock_code = stock_code
|
||||
self.timeframe = timeframe
|
||||
self.signals = signals or []
|
||||
self.success = success
|
||||
self.error = error
|
||||
self.execution_time = execution_time
|
||||
self.timestamp = datetime.now()
|
||||
|
||||
def has_signals(self) -> bool:
|
||||
"""是否有信号"""
|
||||
return len(self.signals) > 0
|
||||
|
||||
def get_signal_count(self) -> int:
|
||||
"""获取信号数量"""
|
||||
return len(self.signals)
|
||||
|
||||
def to_dict(self) -> Dict[str, Any]:
|
||||
"""转换为字典"""
|
||||
return {
|
||||
'strategy_name': self.strategy_name,
|
||||
'stock_code': self.stock_code,
|
||||
'timeframe': self.timeframe,
|
||||
'signals': self.signals,
|
||||
'signal_count': self.get_signal_count(),
|
||||
'success': self.success,
|
||||
'error': self.error,
|
||||
'execution_time': self.execution_time,
|
||||
'timestamp': self.timestamp.isoformat()
|
||||
}
|
||||
|
||||
|
||||
class BaseStrategy(ABC):
|
||||
"""策略基类"""
|
||||
|
||||
def __init__(self, strategy_name: str, config: Dict[str, Any]):
|
||||
"""
|
||||
初始化策略
|
||||
|
||||
Args:
|
||||
strategy_name: 策略名称
|
||||
config: 策略配置
|
||||
"""
|
||||
self.strategy_name = strategy_name
|
||||
self.config = config
|
||||
self.timeframes = config.get('timeframes', ['daily'])
|
||||
|
||||
@abstractmethod
|
||||
def analyze_stock(self, stock_code: str, timeframes: List[str] = None) -> Dict[str, StrategyResult]:
|
||||
"""
|
||||
分析单只股票
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
timeframes: 时间周期列表，如果为None则使用策略默认周期
|
||||
|
||||
Returns:
|
||||
时间周期到策略结果的映射
|
||||
"""
|
||||
pass
|
||||
|
||||
def analyze_stock_pool(self, stock_list: List[str], max_stocks: int = None) -> Dict[str, Dict[str, StrategyResult]]:
|
||||
"""
|
||||
分析股票池
|
||||
|
||||
Args:
|
||||
stock_list: 股票代码列表
|
||||
max_stocks: 最大分析股票数量
|
||||
|
||||
Returns:
|
||||
股票代码到时间周期结果映射的字典
|
||||
"""
|
||||
if max_stocks and len(stock_list) > max_stocks:
|
||||
stock_list = stock_list[:max_stocks]
|
||||
logger.info(f"限制分析股票数量为: {max_stocks}")
|
||||
|
||||
results = {}
|
||||
total_signals = 0
|
||||
|
||||
logger.info(f"🔍 开始分析股票池，共 {len(stock_list)} 只股票")
|
||||
|
||||
for i, stock_code in enumerate(stock_list, 1):
|
||||
try:
|
||||
logger.info(f"⏳ 分析进度: [{i:3d}/{len(stock_list):3d}] 🔍 {stock_code}")
|
||||
|
||||
# 分析单只股票
|
||||
stock_results = self.analyze_stock(stock_code)
|
||||
|
||||
# 统计信号数量
|
||||
stock_signal_count = sum(
|
||||
result.get_signal_count() for result in stock_results.values()
|
||||
)
|
||||
|
||||
if stock_signal_count > 0:
|
||||
results[stock_code] = stock_results
|
||||
total_signals += stock_signal_count
|
||||
logger.info(f"✅ {stock_code} 发现 {stock_signal_count} 个信号")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 分析股票 {stock_code} 失败: {e}")
|
||||
continue
|
||||
|
||||
logger.info(f"🎉 股票池分析完成: 扫描 {len(stock_list)} 只，发现 {total_signals} 个信号，涉及 {len(results)} 只股票")
|
||||
|
||||
return results
|
||||
|
||||
def get_strategy_name(self) -> str:
|
||||
"""获取策略名称"""
|
||||
return self.strategy_name
|
||||
|
||||
def get_timeframes(self) -> List[str]:
|
||||
"""获取支持的时间周期"""
|
||||
return self.timeframes
|
||||
|
||||
def get_config(self) -> Dict[str, Any]:
|
||||
"""获取策略配置"""
|
||||
return self.config
|
||||
|
||||
@abstractmethod
|
||||
def get_strategy_description(self) -> str:
|
||||
"""获取策略描述"""
|
||||
pass
|
||||
|
||||
def format_results_summary(self, results: Dict[str, Dict[str, StrategyResult]]) -> str:
|
||||
"""
|
||||
格式化结果摘要
|
||||
|
||||
Args:
|
||||
results: 分析结果
|
||||
|
||||
Returns:
|
||||
格式化的摘要字符串
|
||||
"""
|
||||
if not results:
|
||||
return f"📊 {self.strategy_name}: 未发现任何信号"
|
||||
|
||||
total_stocks = len(results)
|
||||
total_signals = sum(
|
||||
sum(result.get_signal_count() for result in stock_results.values())
|
||||
for stock_results in results.values()
|
||||
)
|
||||
|
||||
summary = f"📊 {self.strategy_name} 分析结果:\n"
|
||||
summary += f" 🎯 发现信号: {total_signals} 个\n"
|
||||
summary += f" 📈 涉及股票: {total_stocks} 只\n"
|
||||
|
||||
# 按信号数量排序显示前5只股票
|
||||
stock_signal_counts = []
|
||||
for stock_code, stock_results in results.items():
|
||||
signal_count = sum(result.get_signal_count() for result in stock_results.values())
|
||||
stock_signal_counts.append((stock_code, signal_count))
|
||||
|
||||
stock_signal_counts.sort(key=lambda x: x[1], reverse=True)
|
||||
|
||||
summary += f"\n 🔥 信号最多的股票:\n"
|
||||
for i, (stock_code, signal_count) in enumerate(stock_signal_counts[:5], 1):
|
||||
summary += f" {i}. {stock_code}: {signal_count} 个信号\n"
|
||||
|
||||
return summary
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试策略基类功能
|
||||
from loguru import logger
|
||||
import sys
|
||||
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
# 创建一个测试策略
|
||||
class TestStrategy(BaseStrategy):
|
||||
def __init__(self):
|
||||
super().__init__("测试策略", {"timeframes": ["daily", "weekly"]})
|
||||
|
||||
def analyze_stock(self, stock_code: str, timeframes: List[str] = None) -> Dict[str, StrategyResult]:
|
||||
# 模拟分析结果
|
||||
results = {}
|
||||
for timeframe in self.timeframes:
|
||||
# 模拟生成一些信号
|
||||
signals = []
|
||||
if stock_code.startswith("000001"): # 模拟只有某些股票有信号
|
||||
signals = [
|
||||
{
|
||||
'date': '2024-01-10',
|
||||
'signal_type': '买入信号',
|
||||
'price': 10.50,
|
||||
'confidence': 0.85
|
||||
}
|
||||
]
|
||||
|
||||
results[timeframe] = StrategyResult(
|
||||
strategy_name=self.strategy_name,
|
||||
stock_code=stock_code,
|
||||
timeframe=timeframe,
|
||||
signals=signals,
|
||||
execution_time=0.1
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
def get_strategy_description(self) -> str:
|
||||
return "这是一个测试策略，用于验证基类功能"
|
||||
|
||||
# 测试
|
||||
print("=" * 60)
|
||||
print("📊 策略基类测试")
|
||||
print("=" * 60)
|
||||
|
||||
strategy = TestStrategy()
|
||||
test_stocks = ["000001.SZ", "000002.SZ", "600000.SH"]
|
||||
|
||||
print(f"策略名称: {strategy.get_strategy_name()}")
|
||||
print(f"支持周期: {strategy.get_timeframes()}")
|
||||
print(f"策略描述: {strategy.get_strategy_description()}")
|
||||
|
||||
# 测试股票池分析
|
||||
results = strategy.analyze_stock_pool(test_stocks)
|
||||
print(f"\n{strategy.format_results_summary(results)}")
|
||||
|
||||
# 测试单股分析
|
||||
single_result = strategy.analyze_stock("000001.SZ")
|
||||
print(f"\n单股分析结果: {single_result['daily'].to_dict()}")
|
||||
@ -9,15 +9,16 @@ from typing import Dict, List, Tuple, Optional, Any
|
||||
from datetime import datetime, timedelta
|
||||
from loguru import logger
|
||||
|
||||
from ..data.tushare_fetcher import TushareFetcher as ADataFetcher
|
||||
from ..data.tushare_fetcher import TushareFetcher
|
||||
from ..utils.notification import NotificationManager
|
||||
from ..database.mysql_database_manager import MySQLDatabaseManager
|
||||
from .base_strategy import BaseStrategy, StrategyResult
|
||||
|
||||
|
||||
class KLinePatternStrategy:
|
||||
class KLinePatternStrategy(BaseStrategy):
|
||||
"""K线形态策略类"""
|
||||
|
||||
def __init__(self, data_fetcher: ADataFetcher, notification_manager: NotificationManager,
|
||||
def __init__(self, data_fetcher: TushareFetcher, notification_manager: NotificationManager,
|
||||
config: Dict[str, Any], db_manager: MySQLDatabaseManager = None):
|
||||
"""
|
||||
初始化K线形态策略
|
||||
@ -28,17 +29,22 @@ class KLinePatternStrategy:
|
||||
config: 策略配置
|
||||
db_manager: 数据库管理器
|
||||
"""
|
||||
# 初始化基类
|
||||
super().__init__("K线形态策略", config)
|
||||
|
||||
self.data_fetcher = data_fetcher
|
||||
self.notification_manager = notification_manager
|
||||
self.config = config
|
||||
self.db_manager = db_manager or MySQLDatabaseManager()
|
||||
|
||||
# 策略参数
|
||||
self.strategy_name = "K线形态策略"
|
||||
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']) # 支持的时间周期
|
||||
|
||||
# 热榜缓存机制
|
||||
self._hot_stocks_cache = None
|
||||
self._cache_timestamp = None
|
||||
self._cache_duration = 300 # 缓存5分钟
|
||||
|
||||
# 回踩监控参数
|
||||
self.pullback_tolerance = config.get('pullback_tolerance', 0.02) # 回踩容忍度（2%）
|
||||
@ -51,10 +57,6 @@ class KLinePatternStrategy:
|
||||
# 格式: {stock_code: {'signals': [signal_dict], 'last_check_date': date}}
|
||||
self.triggered_signals = {}
|
||||
|
||||
# 新增：存储待确认的模式（等待回踩确认）
|
||||
# 格式: {stock_code: {'pending_patterns': [pattern_dict], 'last_check_date': date}}
|
||||
self.pending_patterns = {}
|
||||
|
||||
# 确保策略在数据库中存在
|
||||
self.strategy_id = self.db_manager.create_or_update_strategy(
|
||||
strategy_name=self.strategy_name,
|
||||
@ -65,6 +67,47 @@ class KLinePatternStrategy:
|
||||
|
||||
logger.info(f"K线形态策略初始化完成 (策略ID: {self.strategy_id})")
|
||||
|
||||
def _get_cached_hot_stocks(self, max_stocks: int) -> List[str]:
|
||||
"""
|
||||
获取缓存的热榜股票列表
|
||||
|
||||
Args:
|
||||
max_stocks: 最大股票数量
|
||||
|
||||
Returns:
|
||||
股票代码列表
|
||||
"""
|
||||
import time
|
||||
current_time = time.time()
|
||||
|
||||
# 检查缓存是否有效
|
||||
if (self._hot_stocks_cache is not None and
|
||||
self._cache_timestamp is not None and
|
||||
current_time - self._cache_timestamp < self._cache_duration):
|
||||
|
||||
logger.info(f"🔄 使用缓存的同花顺热榜数据 ({len(self._hot_stocks_cache)} 只股票)")
|
||||
return self._hot_stocks_cache[:max_stocks]
|
||||
|
||||
# 缓存失效或不存在，重新获取
|
||||
logger.info(f"🔥 获取同花顺热榜股票 (前{max_stocks}只)...")
|
||||
hot_stocks = self.data_fetcher.get_hot_stocks_ths(limit=max_stocks * 2) # 多获取一些以备不同max_stocks需求
|
||||
|
||||
if not hot_stocks.empty and 'stock_code' in hot_stocks.columns:
|
||||
self._hot_stocks_cache = hot_stocks['stock_code'].tolist()
|
||||
self._cache_timestamp = current_time
|
||||
|
||||
logger.info(f"✅ 同花顺热榜获取成功，已缓存 {len(self._hot_stocks_cache)} 只股票")
|
||||
return self._hot_stocks_cache[:max_stocks]
|
||||
else:
|
||||
logger.error("❌ 同花顺热榜数据为空")
|
||||
return []
|
||||
|
||||
def clear_hot_stocks_cache(self):
|
||||
"""清除热榜缓存，强制下次重新获取"""
|
||||
self._hot_stocks_cache = None
|
||||
self._cache_timestamp = None
|
||||
logger.info("🔄 热榜缓存已清除")
|
||||
|
||||
def calculate_kline_features(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
计算K线特征指标
|
||||
@ -117,47 +160,6 @@ class KLinePatternStrategy:
|
||||
|
||||
return df
|
||||
|
||||
def check_follow_up_strength(self, df: pd.DataFrame, pattern_index: int, yin_high: float, ema20_price: float) -> bool:
|
||||
"""
|
||||
检查形态发生后的后续强势条件
|
||||
|
||||
Args:
|
||||
df: K线数据DataFrame
|
||||
pattern_index: 形态完成的索引位置（第4根阳线的位置）
|
||||
yin_high: 阴线的最高价
|
||||
ema20_price: 形态完成时的EMA20价格
|
||||
|
||||
Returns:
|
||||
bool: 是否满足后续强势条件
|
||||
"""
|
||||
# 获取配置的后续验证天数，默认为3天
|
||||
follow_up_days = self.config.get('follow_up_days', 3)
|
||||
|
||||
# 检查是否有足够的后续数据
|
||||
if pattern_index + follow_up_days >= len(df):
|
||||
# 如果没有足够的后续数据，说明是最新的形态，暂时通过验证
|
||||
return True
|
||||
|
||||
# 检查后续N天的K线
|
||||
for j in range(1, follow_up_days + 1):
|
||||
if pattern_index + j >= len(df):
|
||||
break
|
||||
|
||||
next_kline = df.iloc[pattern_index + j]
|
||||
|
||||
# 检查1: 最低价不能回踩阴线最高价
|
||||
if next_kline['low'] <= yin_high:
|
||||
logger.debug(f"后续第{j}天最低价{next_kline['low']:.2f}回踩阴线最高价{yin_high:.2f}，不符合强势条件")
|
||||
return False
|
||||
|
||||
# 检查2: 收盘价不能跌破EMA20
|
||||
next_ema20 = next_kline.get('ema20', ema20_price)
|
||||
if next_kline['close'] <= next_ema20:
|
||||
logger.debug(f"后续第{j}天收盘价{next_kline['close']:.2f}跌破EMA20价格{next_ema20:.2f}，不符合强势条件")
|
||||
return False
|
||||
|
||||
logger.debug(f"后续{follow_up_days}天强势验证通过")
|
||||
return True
|
||||
|
||||
def detect_potential_pattern(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
@ -382,6 +384,52 @@ class KLinePatternStrategy:
|
||||
|
||||
return None
|
||||
|
||||
def _filter_recent_signals(self, signals: List[Dict[str, Any]], days: int = 7) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
过滤最近N天内产生的信号
|
||||
|
||||
Args:
|
||||
signals: 信号列表
|
||||
days: 最近天数，默认7天
|
||||
|
||||
Returns:
|
||||
过滤后的信号列表
|
||||
"""
|
||||
if not signals:
|
||||
return signals
|
||||
|
||||
current_date = datetime.now().date()
|
||||
recent_signals = []
|
||||
|
||||
for signal in signals:
|
||||
signal_date = signal.get('confirmation_date') or signal.get('date')
|
||||
|
||||
# 处理不同的日期格式
|
||||
if isinstance(signal_date, str):
|
||||
try:
|
||||
signal_date = pd.to_datetime(signal_date).date()
|
||||
except:
|
||||
continue
|
||||
elif hasattr(signal_date, 'date'):
|
||||
signal_date = signal_date.date()
|
||||
elif not isinstance(signal_date, datetime.date):
|
||||
continue
|
||||
|
||||
# 计算信号距今天数
|
||||
days_ago = (current_date - signal_date).days
|
||||
|
||||
# 只保留最近N天内的信号
|
||||
if days_ago <= days:
|
||||
recent_signals.append(signal)
|
||||
logger.debug(f"✅ 保留近期信号: {signal_date} (距今{days_ago}天)")
|
||||
else:
|
||||
logger.debug(f"🗓️ 过滤历史信号: {signal_date} (距今{days_ago}天)")
|
||||
|
||||
if len(recent_signals) != len(signals):
|
||||
logger.info(f"📅 信号过滤: 总共{len(signals)}个 → 近{days}天内{len(recent_signals)}个")
|
||||
|
||||
return recent_signals
|
||||
|
||||
def detect_pattern(self, df: pd.DataFrame) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
检测"两阳线+阴线+阳线"形态（创新高回踩确认逻辑）
|
||||
@ -410,40 +458,45 @@ class KLinePatternStrategy:
|
||||
|
||||
return confirmed_signals
|
||||
|
||||
def analyze_stock(self, stock_code: str, stock_name: str = None, days: int = 60,
|
||||
session_id: Optional[int] = None) -> Dict[str, List[Dict[str, Any]]]:
|
||||
def analyze_stock(self, stock_code: str, timeframes: List[str] = None, session_id: int = None) -> Dict[str, StrategyResult]:
|
||||
"""
|
||||
分析单只股票的K线形态
|
||||
分析单只股票的K线形态 - 继承自BaseStrategy
|
||||
|
||||
Args:
|
||||
stock_code: 股票代码
|
||||
stock_name: 股票名称
|
||||
days: 分析的天数
|
||||
timeframes: 时间周期列表，如果为None则使用策略默认周期
|
||||
|
||||
Returns:
|
||||
各时间周期的信号字典
|
||||
时间周期到策略结果的映射
|
||||
"""
|
||||
if timeframes is None:
|
||||
timeframes = self.timeframes
|
||||
|
||||
results = {}
|
||||
stock_name = self.data_fetcher.get_stock_name(stock_code)
|
||||
|
||||
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，针对不同周期调整时间范围
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
|
||||
for timeframe in self.timeframes:
|
||||
analysis_days = days
|
||||
start_date = (datetime.now() - timedelta(days=analysis_days)).strftime('%Y-%m-%d')
|
||||
for timeframe in timeframes:
|
||||
start_time = datetime.now()
|
||||
try:
|
||||
# 计算开始日期
|
||||
end_date = datetime.now().strftime('%Y-%m-%d')
|
||||
start_date = (datetime.now() - timedelta(days=60)).strftime('%Y-%m-%d')
|
||||
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
|
||||
# 获取历史数据
|
||||
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] = []
|
||||
results[timeframe] = StrategyResult(
|
||||
strategy_name=self.strategy_name,
|
||||
stock_code=stock_code,
|
||||
timeframe=timeframe,
|
||||
signals=[],
|
||||
success=False,
|
||||
error="数据为空",
|
||||
execution_time=(datetime.now() - start_time).total_seconds()
|
||||
)
|
||||
continue
|
||||
|
||||
# 计算K线特征
|
||||
@ -452,16 +505,46 @@ class KLinePatternStrategy:
|
||||
# 检测形态
|
||||
signals = self.detect_pattern(df_with_features)
|
||||
|
||||
# 处理信号
|
||||
for signal in signals:
|
||||
# 过滤一周内的信号
|
||||
recent_signals = self._filter_recent_signals(signals, days=7)
|
||||
|
||||
# 处理信号格式
|
||||
formatted_signals = []
|
||||
for signal in recent_signals:
|
||||
formatted_signal = {
|
||||
'date': signal['date'],
|
||||
'signal_type': signal['pattern_type'],
|
||||
'price': signal['breakout_price'],
|
||||
'confidence': signal['final_yang_entity_ratio'],
|
||||
'stock_name': stock_name, # 添加股票名称
|
||||
'details': {
|
||||
'yin_high': signal['yin_high'],
|
||||
'breakout_amount': signal['breakout_amount'],
|
||||
'breakout_pct': signal['breakout_pct'],
|
||||
'ema20_price': signal['ema20_price'],
|
||||
'turnover_ratio': signal['turnover_ratio']
|
||||
}
|
||||
}
|
||||
|
||||
# 如果是确认信号，添加确认信息
|
||||
if not signal.get('confirmation_pending', True):
|
||||
formatted_signal['details'].update({
|
||||
'new_high_price': signal.get('new_high_price'),
|
||||
'new_high_date': signal.get('new_high_date'),
|
||||
'confirmation_date': signal.get('confirmation_date'),
|
||||
'confirmation_days': signal.get('confirmation_days'),
|
||||
'pullback_distance': signal.get('pullback_distance')
|
||||
})
|
||||
|
||||
formatted_signals.append(formatted_signal)
|
||||
|
||||
# 将信号添加到监控列表
|
||||
signal['stock_code'] = stock_code
|
||||
signal['stock_name'] = stock_name
|
||||
signal['timeframe'] = timeframe
|
||||
|
||||
# 将信号添加到监控列表
|
||||
self.add_triggered_signal(signal)
|
||||
|
||||
# 保存信号到数据库（如果提供了session_id）
|
||||
# 保存信号到数据库
|
||||
if session_id is not None:
|
||||
try:
|
||||
signal_id = self.db_manager.save_stock_signal(
|
||||
@ -469,28 +552,59 @@ class KLinePatternStrategy:
|
||||
strategy_id=self.strategy_id,
|
||||
signal=signal
|
||||
)
|
||||
signal['signal_id'] = signal_id
|
||||
logger.debug(f"信号已保存到数据库: {stock_code} (ID: {signal_id})")
|
||||
logger.debug(f"信号已保存到数据库: signal_id={signal_id}")
|
||||
except Exception as e:
|
||||
logger.error(f"保存信号到数据库失败: {e}")
|
||||
|
||||
results[timeframe] = signals
|
||||
execution_time = (datetime.now() - start_time).total_seconds()
|
||||
results[timeframe] = StrategyResult(
|
||||
strategy_name=self.strategy_name,
|
||||
stock_code=stock_code,
|
||||
timeframe=timeframe,
|
||||
signals=formatted_signals,
|
||||
success=True,
|
||||
execution_time=execution_time
|
||||
)
|
||||
|
||||
# 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%}")
|
||||
if formatted_signals:
|
||||
logger.info(f"✅ {stock_code}({stock_name}) {timeframe}周期: 发现 {len(formatted_signals)} 个信号")
|
||||
for i, signal in enumerate(formatted_signals, 1):
|
||||
logger.info(f" 📊 信号{i}: {signal['date']} | 价格: {signal['price']:.2f}元 | 置信度: {signal['confidence']:.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] = []
|
||||
except Exception as e:
|
||||
logger.error(f"分析股票 {stock_code} {timeframe}周期失败: {e}")
|
||||
execution_time = (datetime.now() - start_time).total_seconds()
|
||||
results[timeframe] = StrategyResult(
|
||||
strategy_name=self.strategy_name,
|
||||
stock_code=stock_code,
|
||||
timeframe=timeframe,
|
||||
signals=[],
|
||||
success=False,
|
||||
error=str(e),
|
||||
execution_time=execution_time
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
def get_strategy_description(self) -> str:
|
||||
"""获取策略描述"""
|
||||
return f"""K线形态策略 - 两阳线+阴线+阳线突破（创新高回踩确认）
|
||||
|
||||
该策略通过识别特定的K线形态来发现股票突破机会：
|
||||
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. 价格必须创新高后回踩到阴线最高点附近才产生正式信号
|
||||
|
||||
支持时间周期：{', '.join(self.timeframes)}
|
||||
"""
|
||||
|
||||
def check_pullback_signals(self, stock_code: str, current_data: pd.DataFrame) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
检查已触发信号的价格回踩情况
|
||||
@ -618,109 +732,7 @@ class KLinePatternStrategy:
|
||||
self.triggered_signals[stock_code]['signals'] = \
|
||||
self.triggered_signals[stock_code]['signals'][:max_signals_per_stock]
|
||||
|
||||
def add_pending_pattern(self, pattern: Dict[str, Any]):
|
||||
"""
|
||||
添加待确认的模式到监控列表
|
||||
|
||||
Args:
|
||||
pattern: 潜在模式字典
|
||||
"""
|
||||
stock_code = pattern.get('stock_code')
|
||||
if not stock_code:
|
||||
return
|
||||
|
||||
if stock_code not in self.pending_patterns:
|
||||
self.pending_patterns[stock_code] = {
|
||||
'pending_patterns': [],
|
||||
'last_check_date': datetime.now().date()
|
||||
}
|
||||
|
||||
# 添加模式到监控列表
|
||||
self.pending_patterns[stock_code]['pending_patterns'].append(pattern)
|
||||
|
||||
# 只保留最近的模式（避免内存占用过多）
|
||||
max_patterns_per_stock = 5
|
||||
if len(self.pending_patterns[stock_code]['pending_patterns']) > max_patterns_per_stock:
|
||||
# 按日期排序，保留最新的模式
|
||||
self.pending_patterns[stock_code]['pending_patterns'].sort(
|
||||
key=lambda x: pd.to_datetime(x['date']) if isinstance(x['date'], str) else x['date'],
|
||||
reverse=True
|
||||
)
|
||||
self.pending_patterns[stock_code]['pending_patterns'] = \
|
||||
self.pending_patterns[stock_code]['pending_patterns'][:max_patterns_per_stock]
|
||||
|
||||
def monitor_pending_pattern_confirmations(self) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
监控待确认模式的回踩确认情况
|
||||
|
||||
Returns:
|
||||
新确认的信号列表
|
||||
"""
|
||||
newly_confirmed_signals = []
|
||||
current_date = datetime.now().date()
|
||||
|
||||
# 清理过期的模式
|
||||
stocks_to_remove = []
|
||||
for stock_code, pattern_info in self.pending_patterns.items():
|
||||
# 过滤掉过期的模式
|
||||
valid_patterns = []
|
||||
for pattern in pattern_info['pending_patterns']:
|
||||
pattern_date = pattern['date']
|
||||
if isinstance(pattern_date, str):
|
||||
pattern_date = pd.to_datetime(pattern_date).date()
|
||||
elif hasattr(pattern_date, 'date'):
|
||||
pattern_date = pattern_date.date()
|
||||
|
||||
days_since_pattern = (current_date - pattern_date).days
|
||||
if days_since_pattern <= self.pullback_confirmation_days:
|
||||
valid_patterns.append(pattern)
|
||||
|
||||
if valid_patterns:
|
||||
self.pending_patterns[stock_code]['pending_patterns'] = valid_patterns
|
||||
else:
|
||||
stocks_to_remove.append(stock_code)
|
||||
|
||||
# 移除没有有效模式的股票
|
||||
for stock_code in stocks_to_remove:
|
||||
del self.pending_patterns[stock_code]
|
||||
|
||||
logger.info(f"🔍 当前监控待确认模式的股票数量: {len(self.pending_patterns)}")
|
||||
|
||||
# 检查每只股票的回踩确认情况
|
||||
for stock_code in self.pending_patterns.keys():
|
||||
try:
|
||||
# 获取最近的数据（包含确认窗口）
|
||||
end_date = current_date.strftime('%Y-%m-%d')
|
||||
start_date = (current_date - timedelta(days=self.pullback_confirmation_days + 5)).strftime('%Y-%m-%d')
|
||||
|
||||
current_data = self.data_fetcher.get_historical_data(
|
||||
stock_code, start_date, end_date, 'daily'
|
||||
)
|
||||
|
||||
if not current_data.empty:
|
||||
# 计算K线特征
|
||||
df_with_features = self.calculate_kline_features(current_data)
|
||||
|
||||
# 检查每个待确认模式
|
||||
for pattern in self.pending_patterns[stock_code]['pending_patterns']:
|
||||
has_confirmation = self.check_pullback_confirmation(df_with_features, pattern)
|
||||
|
||||
if has_confirmation:
|
||||
# 创建确认信号
|
||||
confirmed_signal = pattern.copy()
|
||||
confirmed_signal['pattern_type'] = '两阳+阴+阳突破(已确认)'
|
||||
confirmed_signal['confirmation_pending'] = False
|
||||
confirmed_signal['pullback_confirmed'] = True
|
||||
confirmed_signal['stock_code'] = stock_code
|
||||
|
||||
newly_confirmed_signals.append(confirmed_signal)
|
||||
|
||||
logger.info(f"✅ 股票 {stock_code} 的待确认模式已通过回踩确认")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"监控股票 {stock_code} 待确认模式失败: {e}")
|
||||
|
||||
return newly_confirmed_signals
|
||||
|
||||
def monitor_pullback_for_triggered_signals(self) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
@ -790,108 +802,27 @@ class KLinePatternStrategy:
|
||||
|
||||
return all_pullback_alerts
|
||||
|
||||
def _convert_to_weekly(self, daily_df: pd.DataFrame) -> pd.DataFrame:
|
||||
|
||||
|
||||
def scan_market(self, stock_list: List[str] = None, max_stocks: int = 100) -> Dict[str, Dict[str, List[Dict[str, Any]]]]:
|
||||
"""
|
||||
将日线数据转换为周线数据
|
||||
扫描市场中的股票形态 - 只使用同花顺热榜股票
|
||||
|
||||
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, use_all_a_shares: bool = False) -> 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则自动选择股票池
|
||||
stock_list: 股票代码列表，如果为None则使用同花顺热榜
|
||||
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
|
||||
use_all_a_shares: 是否使用所有A股股票(排除北交所和ST)，优先级最高
|
||||
|
||||
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("🌍 开始市场K线形态扫描 - 只使用同花顺热榜")
|
||||
logger.info("🚀" + "="*70)
|
||||
|
||||
# 创建扫描会话
|
||||
scan_config = {
|
||||
'max_stocks': max_stocks,
|
||||
'use_hot_stocks': use_hot_stocks,
|
||||
'use_combined_sources': use_combined_sources,
|
||||
'use_all_a_shares': use_all_a_shares,
|
||||
'data_source': '同花顺热榜',
|
||||
'timeframes': self.timeframes
|
||||
}
|
||||
session_id = self.db_manager.create_scan_session(
|
||||
@ -900,81 +831,14 @@ class KLinePatternStrategy:
|
||||
)
|
||||
|
||||
if stock_list is None:
|
||||
# 优先级1: 使用所有A股股票
|
||||
if use_all_a_shares:
|
||||
try:
|
||||
logger.info("📊 获取所有A股股票数据（排除北交所和ST股票）...")
|
||||
filtered_stocks = self.data_fetcher.get_filtered_a_share_list()
|
||||
# 使用缓存的同花顺热榜数据
|
||||
stock_list = self._get_cached_hot_stocks(max_stocks)
|
||||
|
||||
if not filtered_stocks.empty:
|
||||
# 如果max_stocks小于总股票数，随机采样
|
||||
if max_stocks > 0 and max_stocks < len(filtered_stocks):
|
||||
# 按市值排序或随机选择，这里先随机选择
|
||||
selected_stocks = filtered_stocks.sample(max_stocks)
|
||||
stock_list = selected_stocks['full_stock_code'].tolist()
|
||||
logger.info(f"📈 从{len(filtered_stocks)}只A股中随机选择{len(stock_list)}只进行分析")
|
||||
else:
|
||||
stock_list = filtered_stocks['full_stock_code'].tolist()
|
||||
logger.info(f"📈 分析全部{len(stock_list)}只A股股票")
|
||||
|
||||
source_info = "全A股(排除北交所和ST)"
|
||||
else:
|
||||
logger.warning("获取A股列表失败，回退到热门股票")
|
||||
use_all_a_shares = False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取A股列表失败: {e}，回退到热门股票")
|
||||
use_all_a_shares = False
|
||||
|
||||
# 优先级2: 使用热门股票数据
|
||||
if not use_all_a_shares and 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
|
||||
|
||||
# 优先级3: 如果热股获取失败，使用全市场股票列表
|
||||
if not use_all_a_shares and 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 {}
|
||||
if stock_list:
|
||||
logger.info(f"📊 数据源: 同花顺热榜 | 扫描股票: {len(stock_list)} 只")
|
||||
else:
|
||||
logger.error("❌ 同花顺热榜数据为空，无法进行扫描")
|
||||
return {}
|
||||
|
||||
results = {}
|
||||
total_signals = 0
|
||||
@ -1038,15 +902,9 @@ class KLinePatternStrategy:
|
||||
|
||||
# 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
|
||||
'data_source': '同花顺热榜'
|
||||
}
|
||||
|
||||
try:
|
||||
@ -1119,10 +977,77 @@ K线形态策略 - 两阳线+阴线+阳线突破（优化版：创新高回踩
|
||||
- 系统日志详细记录
|
||||
"""
|
||||
|
||||
def analyze_stock_pool(self, stock_list: List[str], max_stocks: int = None) -> Dict[str, Dict[str, StrategyResult]]:
|
||||
"""
|
||||
分析股票池（带数据库会话管理）
|
||||
|
||||
Args:
|
||||
stock_list: 股票代码列表
|
||||
max_stocks: 最大分析股票数量
|
||||
|
||||
Returns:
|
||||
股票代码到时间周期结果映射的字典
|
||||
"""
|
||||
if max_stocks and len(stock_list) > max_stocks:
|
||||
stock_list = stock_list[:max_stocks]
|
||||
logger.info(f"限制分析股票数量为: {max_stocks}")
|
||||
|
||||
# 创建扫描会话
|
||||
scan_config = {
|
||||
'max_stocks': max_stocks,
|
||||
'timeframes': self.timeframes
|
||||
}
|
||||
session_id = self.db_manager.create_scan_session(
|
||||
strategy_id=self.strategy_id,
|
||||
scan_config=scan_config
|
||||
)
|
||||
logger.debug(f"创建扫描会话: session_id={session_id}")
|
||||
|
||||
results = {}
|
||||
total_signals = 0
|
||||
|
||||
logger.info(f"🔍 开始分析股票池，共 {len(stock_list)} 只股票")
|
||||
|
||||
for i, stock_code in enumerate(stock_list, 1):
|
||||
try:
|
||||
logger.info(f"⏳ 分析进度: [{i:3d}/{len(stock_list):3d}] 🔍 {stock_code}")
|
||||
|
||||
# 分析单只股票，传递session_id
|
||||
stock_results = self.analyze_stock(stock_code, session_id=session_id)
|
||||
|
||||
# 统计信号数量
|
||||
stock_signal_count = sum(
|
||||
result.get_signal_count() for result in stock_results.values()
|
||||
)
|
||||
|
||||
if stock_signal_count > 0:
|
||||
results[stock_code] = stock_results
|
||||
total_signals += stock_signal_count
|
||||
logger.info(f"✅ {stock_code} 发现 {stock_signal_count} 个信号")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 分析股票 {stock_code} 失败: {e}")
|
||||
continue
|
||||
|
||||
# 更新扫描会话统计
|
||||
try:
|
||||
self.db_manager.update_scan_session_stats(
|
||||
session_id=session_id,
|
||||
total_scanned=len(stock_list),
|
||||
total_signals=total_signals
|
||||
)
|
||||
logger.debug(f"更新扫描会话统计: session_id={session_id}, 扫描={len(stock_list)}, 信号={total_signals}")
|
||||
except Exception as e:
|
||||
logger.error(f"更新扫描会话统计失败: {e}")
|
||||
|
||||
logger.info(f"🎉 股票池分析完成: 扫描 {len(stock_list)} 只，发现 {total_signals} 个信号，涉及 {len(results)} 只股票")
|
||||
|
||||
return results
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 测试代码
|
||||
from ..data.tushare_fetcher import TushareFetcher as ADataFetcher
|
||||
from ..data.tushare_fetcher import TushareFetcher
|
||||
from ..utils.notification import NotificationManager
|
||||
|
||||
# 模拟配置
|
||||
@ -1139,7 +1064,7 @@ if __name__ == "__main__":
|
||||
}
|
||||
|
||||
# 初始化组件
|
||||
data_fetcher = ADataFetcher()
|
||||
data_fetcher = TushareFetcher()
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
strategy = KLinePatternStrategy(data_fetcher, notification_manager, strategy_config)
|
||||
|
||||
|
||||
@ -96,6 +96,10 @@ class ConfigLoader:
|
||||
"""获取日志配置"""
|
||||
return self.get('logging', {})
|
||||
|
||||
def get_tushare_token(self) -> str:
|
||||
"""获取TuShare token"""
|
||||
return self.get('data_source.tushare_token', '')
|
||||
|
||||
|
||||
# 全局配置实例
|
||||
config_loader = ConfigLoader()
|
||||
|
||||
@ -357,8 +357,9 @@ class NotificationManager:
|
||||
return False
|
||||
|
||||
try:
|
||||
from datetime import datetime
|
||||
from datetime import datetime, date
|
||||
import math
|
||||
import pandas as pd
|
||||
current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||
|
||||
# 收集所有信号详情
|
||||
@ -366,15 +367,39 @@ class NotificationManager:
|
||||
total_signals = 0
|
||||
total_stocks = len(all_signals)
|
||||
|
||||
# 计算一周前的日期
|
||||
current_date = date.today()
|
||||
one_week_ago = current_date - pd.Timedelta(days=7)
|
||||
|
||||
for stock_code, stock_results in all_signals.items():
|
||||
for timeframe, signals in stock_results.items():
|
||||
for signal in signals:
|
||||
# 添加一周内信号过滤
|
||||
signal_date = signal.get('details', {}).get('confirmation_date') or signal['date']
|
||||
|
||||
# 处理不同的日期格式
|
||||
if isinstance(signal_date, str):
|
||||
try:
|
||||
signal_date = pd.to_datetime(signal_date).date()
|
||||
except:
|
||||
continue
|
||||
elif hasattr(signal_date, 'date'):
|
||||
signal_date = signal_date.date()
|
||||
elif not isinstance(signal_date, date):
|
||||
continue
|
||||
|
||||
# 只处理一周内的信号
|
||||
if signal_date < one_week_ago:
|
||||
logger.debug(f"🗓️ 通知过滤历史信号: {stock_code} {signal_date} (距今{(current_date - signal_date).days}天)")
|
||||
continue
|
||||
|
||||
total_signals += 1
|
||||
|
||||
# 根据新的信号格式提取信息
|
||||
confirmation_date = signal.get('confirmation_date', signal['date'])
|
||||
new_high_price = signal.get('new_high_price', signal['breakout_price'])
|
||||
confirmation_days = signal.get('confirmation_days', 0)
|
||||
details = signal.get('details', {})
|
||||
confirmation_date = details.get('confirmation_date', signal['date'])
|
||||
new_high_price = details.get('new_high_price', signal['price'])
|
||||
confirmation_days = details.get('confirmation_days', 0)
|
||||
|
||||
all_signal_details.append({
|
||||
'stock_code': stock_code,
|
||||
@ -383,20 +408,26 @@ class NotificationManager:
|
||||
'pattern_date': signal['date'], # 模式形成日期
|
||||
'confirmation_date': confirmation_date, # 回踩确认日期
|
||||
'price': new_high_price, # 创新高价格
|
||||
'original_breakout_price': signal['breakout_price'], # 原突破价
|
||||
'yin_high': signal.get('yin_high', 0), # 阴线最高价
|
||||
'turnover': signal.get('turnover_ratio', 0),
|
||||
'breakout_pct': signal.get('breakout_pct', 0),
|
||||
'ema20_status': '✅上方' if signal.get('above_ema20', False) else '❌下方',
|
||||
'original_breakout_price': signal['price'], # 原突破价
|
||||
'yin_high': details.get('yin_high', 0), # 阴线最高价
|
||||
'turnover': details.get('turnover_ratio', 0),
|
||||
'breakout_pct': details.get('breakout_pct', 0),
|
||||
'ema20_status': '✅上方' if details.get('above_ema20', False) else '❌下方',
|
||||
'confirmation_days': confirmation_days,
|
||||
'pullback_distance': signal.get('pullback_distance', 0),
|
||||
'is_new_format': signal.get('new_high_confirmed', False) # 是否为新格式信号
|
||||
'pullback_distance': details.get('pullback_distance', 0),
|
||||
'is_new_format': details.get('new_high_confirmed', False) # 是否为新格式信号
|
||||
})
|
||||
|
||||
# 如果没有信号，直接返回
|
||||
if total_signals == 0:
|
||||
logger.info("📱 通知过滤: 没有一周内的新信号，不发送通知")
|
||||
return True
|
||||
|
||||
# 记录过滤后的信号统计
|
||||
original_signals = sum(len(signals) for stock_results in all_signals.values() for signals in stock_results.values())
|
||||
if original_signals > total_signals:
|
||||
logger.info(f"📅 通知信号过滤: 原始{original_signals}个 → 一周内{total_signals}个")
|
||||
|
||||
# 按10个信号为一组分批发送
|
||||
signals_per_group = 10
|
||||
total_groups = math.ceil(total_signals / signals_per_group)
|
||||
|
||||
43
start_market_scanner.sh
Executable file
43
start_market_scanner.sh
Executable file
@ -0,0 +1,43 @@
|
||||
#!/bin/bash
|
||||
# 市场扫描服务启动脚本
|
||||
|
||||
echo "🚀 启动市场扫描定时任务服务..."
|
||||
|
||||
# 创建日志目录
|
||||
mkdir -p /app/logs
|
||||
|
||||
# 安装cron
|
||||
echo "📦 安装cron服务..."
|
||||
apt-get update && apt-get install -y cron
|
||||
|
||||
# 复制crontab配置
|
||||
echo "⏰ 配置定时任务..."
|
||||
cp /app/crontab/market-scanner /etc/cron.d/market-scanner
|
||||
|
||||
# 设置权限
|
||||
chmod 0644 /etc/cron.d/market-scanner
|
||||
|
||||
# 启动cron服务
|
||||
echo "🔄 启动cron守护进程..."
|
||||
service cron start
|
||||
|
||||
# 显示已配置的任务
|
||||
echo "📋 已配置的定时任务:"
|
||||
crontab -l 2>/dev/null || echo "使用系统cron配置: /etc/cron.d/market-scanner"
|
||||
cat /etc/cron.d/market-scanner
|
||||
|
||||
# 记录启动信息
|
||||
echo "$(date): 市场扫描服务启动完成" >> /app/logs/scanner_startup.log
|
||||
|
||||
echo "✅ 市场扫描定时任务服务启动完成"
|
||||
echo "📊 扫描参数: MARKET_SCAN_STOCKS=${MARKET_SCAN_STOCKS:-200}"
|
||||
echo "📝 日志文件: /app/logs/market_scanner.log"
|
||||
echo "⏰ Cron日志: /app/logs/cron.log"
|
||||
|
||||
# 执行一次初始扫描
|
||||
echo "🔍 执行初始市场扫描..."
|
||||
python /app/market_scanner.py ${MARKET_SCAN_STOCKS:-200}
|
||||
|
||||
# 保持容器运行并显示日志
|
||||
echo "👁️ 监控日志输出..."
|
||||
tail -f /app/logs/market_scanner.log /app/logs/cron.log
|
||||
@ -1,97 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试股票名称获取的缓存优化
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
# 添加src目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
src_dir = current_dir / "src"
|
||||
sys.path.insert(0, str(src_dir))
|
||||
|
||||
from loguru import logger
|
||||
from src.data.data_fetcher import ADataFetcher
|
||||
|
||||
def test_stock_name_cache():
|
||||
"""测试股票名称缓存机制"""
|
||||
logger.info("🧪 开始测试股票名称缓存优化")
|
||||
|
||||
# 初始化数据获取器
|
||||
data_fetcher = ADataFetcher()
|
||||
|
||||
# 测试股票列表
|
||||
test_stocks = ['000001.SZ', '000002.SZ', '600000.SH', '600036.SH', '000858.SZ']
|
||||
|
||||
# 第一次获取股票名称（会触发缓存构建）
|
||||
logger.info("📊 第一次获取股票名称（构建缓存）...")
|
||||
start_time = time.time()
|
||||
|
||||
names_first = {}
|
||||
for stock_code in test_stocks:
|
||||
name = data_fetcher.get_stock_name(stock_code)
|
||||
names_first[stock_code] = name
|
||||
logger.info(f" {stock_code}: {name}")
|
||||
|
||||
first_duration = time.time() - start_time
|
||||
logger.info(f"⏱️ 第一次获取耗时: {first_duration:.2f}秒")
|
||||
|
||||
# 等待一秒
|
||||
time.sleep(1)
|
||||
|
||||
# 第二次获取股票名称（应该从缓存读取）
|
||||
logger.info("📊 第二次获取股票名称（从缓存读取）...")
|
||||
start_time = time.time()
|
||||
|
||||
names_second = {}
|
||||
for stock_code in test_stocks:
|
||||
name = data_fetcher.get_stock_name(stock_code)
|
||||
names_second[stock_code] = name
|
||||
logger.info(f" {stock_code}: {name}")
|
||||
|
||||
second_duration = time.time() - start_time
|
||||
logger.info(f"⏱️ 第二次获取耗时: {second_duration:.2f}秒")
|
||||
|
||||
# 比较结果
|
||||
logger.info("📈 性能对比:")
|
||||
if second_duration < first_duration:
|
||||
speedup = first_duration / second_duration
|
||||
logger.info(f"✅ 缓存优化成功! 第二次比第一次快 {speedup:.1f}x")
|
||||
else:
|
||||
logger.warning("❌ 缓存优化效果不明显")
|
||||
|
||||
# 验证数据一致性
|
||||
consistent = names_first == names_second
|
||||
logger.info(f"🔍 数据一致性: {'✅ 一致' if consistent else '❌ 不一致'}")
|
||||
|
||||
# 显示缓存状态
|
||||
logger.info(f"📦 当前缓存中的股票数量: {len(data_fetcher._stock_name_cache)}")
|
||||
|
||||
return first_duration, second_duration, consistent
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
print("=" * 60)
|
||||
print("🧪 股票名称缓存优化测试")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
first_time, second_time, is_consistent = test_stock_name_cache()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("📊 测试结果总结:")
|
||||
print(f" 第一次获取耗时: {first_time:.2f}秒")
|
||||
print(f" 第二次获取耗时: {second_time:.2f}秒")
|
||||
print(f" 性能提升倍数: {first_time/second_time:.1f}x")
|
||||
print(f" 数据一致性: {'✅ 通过' if is_consistent else '❌ 失败'}")
|
||||
print("=" * 60)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"测试过程中发生错误: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
@ -1,96 +0,0 @@
|
||||
#!/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()
|
||||
@ -1,268 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
使用东财概念板块数据分析本周强势板块
|
||||
需要5000积分
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_recent_trading_dates(days_back=5):
|
||||
"""获取最近的交易日期"""
|
||||
dates = []
|
||||
current = datetime.now()
|
||||
|
||||
while len(dates) < days_back:
|
||||
# 排除周末
|
||||
if current.weekday() < 5: # 0-4是周一到周五
|
||||
dates.append(current.strftime('%Y%m%d'))
|
||||
current -= timedelta(days=1)
|
||||
|
||||
return sorted(dates) # 升序返回
|
||||
|
||||
|
||||
def analyze_eastmoney_concepts(fetcher: TushareFetcher):
|
||||
"""使用东财概念板块数据分析"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("🚀 使用东财概念板块数据分析...")
|
||||
|
||||
# 获取最近5个交易日
|
||||
trading_dates = get_recent_trading_dates(5)
|
||||
logger.info(f"分析时间范围: {trading_dates[0]} 到 {trading_dates[-1]}")
|
||||
|
||||
# 获取最新交易日的概念板块数据
|
||||
latest_date = trading_dates[-1]
|
||||
|
||||
try:
|
||||
# 使用东财概念板块接口
|
||||
dc_concepts = fetcher.pro.dc_index(trade_date=latest_date)
|
||||
logger.info(f"获取到 {len(dc_concepts)} 个东财概念板块")
|
||||
|
||||
if dc_concepts.empty:
|
||||
logger.warning("未获取到东财概念板块数据")
|
||||
return
|
||||
|
||||
# 打印数据结构以便调试
|
||||
logger.info(f"数据列名: {list(dc_concepts.columns)}")
|
||||
if not dc_concepts.empty:
|
||||
logger.info(f"样本数据:\n{dc_concepts.head(2)}")
|
||||
|
||||
# 检查涨跌幅字段名
|
||||
change_col = None
|
||||
for col in ['pct_chg', 'pct_change', 'change_pct', 'chg_pct']:
|
||||
if col in dc_concepts.columns:
|
||||
change_col = col
|
||||
break
|
||||
|
||||
if change_col:
|
||||
# 按涨跌幅排序
|
||||
dc_concepts = dc_concepts.sort_values(change_col, ascending=False)
|
||||
else:
|
||||
logger.warning("未找到涨跌幅字段，使用原始顺序")
|
||||
change_col = 'code' # 使用code作为默认排序
|
||||
|
||||
print("\n" + "="*80)
|
||||
print("📈 东财概念板块实时排行榜")
|
||||
print("="*80)
|
||||
# 显示表头
|
||||
if change_col != 'code':
|
||||
print(f"{'排名':<4} {'概念名称':<25} {'涨跌幅':<10} {'概念代码':<15}")
|
||||
else:
|
||||
print(f"{'排名':<4} {'概念名称':<25} {'概念代码':<15}")
|
||||
print("-" * 80)
|
||||
|
||||
for i, (_, concept) in enumerate(dc_concepts.head(20).iterrows()):
|
||||
rank = i + 1
|
||||
name = concept.get('name', 'N/A')[:23] + '..' if len(str(concept.get('name', 'N/A'))) > 23 else concept.get('name', 'N/A')
|
||||
code = concept.get('ts_code', 'N/A')
|
||||
|
||||
if change_col != 'code':
|
||||
change_pct = f"{concept[change_col]:+.2f}%" if not pd.isna(concept.get(change_col, 0)) else "N/A"
|
||||
print(f"{rank:<4} {name:<25} {change_pct:<10} {code:<15}")
|
||||
else:
|
||||
print(f"{rank:<4} {name:<25} {code:<15}")
|
||||
|
||||
# 强势概念TOP10
|
||||
if change_col != 'code':
|
||||
print(f"\n🚀 强势概念板块TOP10:")
|
||||
for i, (_, concept) in enumerate(dc_concepts.head(10).iterrows()):
|
||||
change_val = concept.get(change_col, 0)
|
||||
if not pd.isna(change_val):
|
||||
print(f" {i+1:2d}. {concept.get('name', 'N/A')}: {change_val:+.2f}%")
|
||||
|
||||
# 弱势概念TOP10
|
||||
print(f"\n📉 弱势概念板块TOP10:")
|
||||
weak_concepts = dc_concepts.tail(10).iloc[::-1] # 反转顺序
|
||||
for i, (_, concept) in enumerate(weak_concepts.iterrows()):
|
||||
change_val = concept.get(change_col, 0)
|
||||
if not pd.isna(change_val):
|
||||
print(f" {i+1:2d}. {concept.get('name', 'N/A')}: {change_val:+.2f}%")
|
||||
else:
|
||||
print(f"\n📋 概念板块列表（前10个）:")
|
||||
for i, (_, concept) in enumerate(dc_concepts.head(10).iterrows()):
|
||||
print(f" {i+1:2d}. {concept.get('name', 'N/A')} ({concept.get('ts_code', 'N/A')})")
|
||||
|
||||
return dc_concepts
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取东财概念板块数据失败: {e}")
|
||||
return None
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析东财概念板块失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def analyze_concept_trend(fetcher: TushareFetcher, concept_codes=None):
|
||||
"""分析概念板块的趋势（多日对比）"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("📊 分析概念板块趋势...")
|
||||
|
||||
# 获取最近5个交易日
|
||||
trading_dates = get_recent_trading_dates(5)
|
||||
|
||||
# 如果没有指定概念代码，获取当日表现最好的前10个
|
||||
if concept_codes is None:
|
||||
latest_concepts = analyze_eastmoney_concepts(fetcher)
|
||||
if latest_concepts is not None and not latest_concepts.empty:
|
||||
concept_codes = latest_concepts.head(5)['code'].tolist()
|
||||
else:
|
||||
logger.warning("无法获取概念代码")
|
||||
return
|
||||
|
||||
print(f"\n" + "="*80)
|
||||
print("📈 热门概念板块多日趋势分析")
|
||||
print("="*80)
|
||||
|
||||
for concept_code in concept_codes:
|
||||
concept_trend = []
|
||||
|
||||
for date in trading_dates:
|
||||
try:
|
||||
# 获取特定日期的概念数据
|
||||
daily_data = fetcher.pro.dc_index(
|
||||
trade_date=date,
|
||||
ts_code=concept_code
|
||||
)
|
||||
|
||||
if not daily_data.empty:
|
||||
# 检查数据结构
|
||||
logger.debug(f"概念 {concept_code} 在 {date} 的数据字段: {list(daily_data.columns)}")
|
||||
|
||||
# 东财概念数据可能没有close字段，使用其他字段替代
|
||||
close_value = daily_data.iloc[0].get('total_mv', 1) # 使用总市值代替
|
||||
if close_value == 0:
|
||||
close_value = 1 # 避免除零
|
||||
|
||||
concept_trend.append({
|
||||
'date': date,
|
||||
'name': daily_data.iloc[0]['name'],
|
||||
'close': close_value,
|
||||
'pct_chg': daily_data.iloc[0]['pct_change']
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取概念 {concept_code} 在 {date} 的数据失败: {e}")
|
||||
continue
|
||||
|
||||
# 输出趋势
|
||||
if concept_trend:
|
||||
concept_name = concept_trend[0]['name']
|
||||
print(f"\n📊 {concept_name} ({concept_code}) 近5日走势:")
|
||||
|
||||
# 计算总涨跌幅
|
||||
if len(concept_trend) >= 2:
|
||||
start_close = concept_trend[0]['close']
|
||||
end_close = concept_trend[-1]['close']
|
||||
|
||||
if start_close != 0 and start_close is not None:
|
||||
total_change = (end_close - start_close) / start_close * 100
|
||||
print(f" 总涨跌幅: {total_change:+.2f}%")
|
||||
else:
|
||||
print(f" 总涨跌幅: 无法计算（起始值为0）")
|
||||
|
||||
# 显示每日数据
|
||||
for data in concept_trend:
|
||||
print(f" {data['date']}: {data['pct_chg']:+6.2f}% (指数: {data['close']:8.2f})")
|
||||
|
||||
print("\n" + "="*80)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析概念趋势失败: {e}")
|
||||
|
||||
|
||||
def get_concept_constituents(fetcher: TushareFetcher, concept_code: str):
|
||||
"""获取概念板块成分股"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info(f"获取概念 {concept_code} 的成分股...")
|
||||
|
||||
# 尝试通过概念板块获取成分股
|
||||
try:
|
||||
# 使用concept_detail接口（如果可用）
|
||||
constituents = fetcher.pro.concept_detail(id=concept_code)
|
||||
|
||||
if not constituents.empty:
|
||||
print(f"\n📋 概念成分股 ({len(constituents)}只):")
|
||||
for _, stock in constituents.head(10).iterrows():
|
||||
print(f" {stock['ts_code']}: {stock.get('name', 'N/A')}")
|
||||
else:
|
||||
logger.warning(f"概念 {concept_code} 无成分股数据")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取概念成分股失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取概念成分股失败: {e}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("🚀 开始使用东财概念板块数据分析...")
|
||||
|
||||
# 初始化Tushare数据获取器
|
||||
token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
|
||||
fetcher = TushareFetcher(token=token)
|
||||
|
||||
# 1. 分析当日概念板块表现
|
||||
concepts_data = analyze_eastmoney_concepts(fetcher)
|
||||
|
||||
# 2. 分析热门概念的多日趋势
|
||||
if concepts_data is not None and not concepts_data.empty:
|
||||
print("\n" + "="*80 + "\n")
|
||||
|
||||
# 获取表现最好的前3个概念进行趋势分析
|
||||
top_concepts = concepts_data.head(3)['ts_code'].tolist()
|
||||
analyze_concept_trend(fetcher, top_concepts)
|
||||
|
||||
# 3. 获取第一个概念的成分股示例
|
||||
# top_concept_code = top_concepts[0] if top_concepts else None
|
||||
# if top_concept_code:
|
||||
# get_concept_constituents(fetcher, top_concept_code)
|
||||
|
||||
logger.info("✅ 分析完成!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,191 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试优化后的钉钉通知格式（创新高回踩确认版）
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.utils.notification import NotificationManager
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def test_optimized_notification():
|
||||
"""
|
||||
测试优化后的钉钉通知格式
|
||||
"""
|
||||
logger.info("🚀 开始测试优化后的钉钉通知格式...")
|
||||
|
||||
# 配置通知管理器（测试模式，不实际发送）
|
||||
notification_config = {
|
||||
'dingtalk': {
|
||||
'enabled': False, # 设置为True并提供真实webhook进行实际测试
|
||||
'webhook_url': 'https://oapi.dingtalk.com/robot/send?access_token=TEST_TOKEN'
|
||||
}
|
||||
}
|
||||
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
# 测试1: 单个策略信号通知
|
||||
logger.info("\n📊 测试1: 单个策略信号通知")
|
||||
test_signal_data = {
|
||||
'stock_code': '000001.SZ',
|
||||
'stock_name': '平安银行',
|
||||
'timeframe': 'daily',
|
||||
'signal_type': '两阳+阴+阳突破(创新高回踩确认)',
|
||||
'price': 14.50,
|
||||
'signal_date': '2024-01-11',
|
||||
'additional_info': {
|
||||
'pattern_date': '2024-01-04',
|
||||
'breakout_price': 11.60,
|
||||
'new_high_price': 14.50,
|
||||
'new_high_date': '2024-01-10',
|
||||
'yin_high': 11.20,
|
||||
'confirmation_date': '2024-01-11',
|
||||
'confirmation_days': 7,
|
||||
'pullback_distance': -0.89,
|
||||
'yang1_entity_ratio': 0.60,
|
||||
'yang2_entity_ratio': 0.67,
|
||||
'final_yang_entity_ratio': 0.89,
|
||||
'breakout_pct': 3.57,
|
||||
'turnover_ratio': 2.50,
|
||||
'above_ema20': True
|
||||
}
|
||||
}
|
||||
|
||||
try:
|
||||
# 模拟发送单个信号通知
|
||||
if notification_config['dingtalk']['enabled']:
|
||||
success = notification_manager.send_strategy_signal(**test_signal_data)
|
||||
logger.info(f"单个信号通知发送: {'✅成功' if success else '❌失败'}")
|
||||
else:
|
||||
logger.info("单个信号通知格式测试完成（未实际发送）")
|
||||
except Exception as e:
|
||||
logger.error(f"单个信号通知测试失败: {e}")
|
||||
|
||||
# 测试2: 策略汇总通知
|
||||
logger.info("\n📊 测试2: 策略汇总通知")
|
||||
test_summary_data = {
|
||||
'000001.SZ': {
|
||||
'daily': [
|
||||
{
|
||||
'stock_name': '平安银行',
|
||||
'date': '2024-01-04',
|
||||
'breakout_price': 11.60,
|
||||
'new_high_price': 14.50,
|
||||
'new_high_date': '2024-01-10',
|
||||
'confirmation_date': '2024-01-11',
|
||||
'confirmation_days': 7,
|
||||
'pullback_distance': -0.89,
|
||||
'yin_high': 11.20,
|
||||
'turnover_ratio': 2.5,
|
||||
'breakout_pct': 3.57,
|
||||
'above_ema20': True,
|
||||
'new_high_confirmed': True # 标记为新格式
|
||||
}
|
||||
]
|
||||
},
|
||||
'000002.SZ': {
|
||||
'daily': [
|
||||
{
|
||||
'stock_name': '万科A',
|
||||
'date': '2024-01-05',
|
||||
'breakout_price': 9.80,
|
||||
'new_high_price': 12.30,
|
||||
'new_high_date': '2024-01-09',
|
||||
'confirmation_date': '2024-01-12',
|
||||
'confirmation_days': 7,
|
||||
'pullback_distance': -1.2,
|
||||
'yin_high': 9.60,
|
||||
'turnover_ratio': 3.2,
|
||||
'breakout_pct': 2.08,
|
||||
'above_ema20': True,
|
||||
'new_high_confirmed': True # 标记为新格式
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
|
||||
scan_stats = {
|
||||
'total_scanned': 100,
|
||||
'data_source': '双数据源合并'
|
||||
}
|
||||
|
||||
try:
|
||||
# 模拟发送汇总通知
|
||||
if notification_config['dingtalk']['enabled']:
|
||||
success = notification_manager.send_strategy_summary(test_summary_data, scan_stats)
|
||||
logger.info(f"汇总通知发送: {'✅成功' if success else '❌失败'}")
|
||||
else:
|
||||
logger.info("汇总通知格式测试完成（未实际发送）")
|
||||
except Exception as e:
|
||||
logger.error(f"汇总通知测试失败: {e}")
|
||||
|
||||
# 测试3: 回踩提醒通知
|
||||
logger.info("\n📊 测试3: 回踩提醒通知")
|
||||
test_pullback_alerts = [
|
||||
{
|
||||
'stock_code': '000001.SZ',
|
||||
'stock_name': '平安银行',
|
||||
'signal_date': '2024-01-11',
|
||||
'current_date': '2024-01-18',
|
||||
'timeframe': 'daily',
|
||||
'yin_high': 11.20,
|
||||
'breakout_price': 11.60,
|
||||
'current_price': 11.15,
|
||||
'current_low': 11.10,
|
||||
'pullback_pct': -4.5,
|
||||
'distance_to_yin_high': -0.45,
|
||||
'days_since_signal': 7,
|
||||
'alert_type': 'pullback_to_yin_high'
|
||||
}
|
||||
]
|
||||
|
||||
try:
|
||||
# 模拟发送回踩提醒
|
||||
if notification_config['dingtalk']['enabled']:
|
||||
success = notification_manager.send_pullback_alerts(test_pullback_alerts)
|
||||
logger.info(f"回踩提醒发送: {'✅成功' if success else '❌失败'}")
|
||||
else:
|
||||
logger.info("回踩提醒格式测试完成（未实际发送）")
|
||||
except Exception as e:
|
||||
logger.error(f"回踩提醒测试失败: {e}")
|
||||
|
||||
# 显示消息格式预览
|
||||
print("\n" + "="*80)
|
||||
print("📱 优化后的钉钉消息格式预览")
|
||||
print("="*80)
|
||||
|
||||
print("\n🎯 单个信号通知示例:")
|
||||
print("标题: 🎯 两阳+阴+阳突破(创新高回踩确认)信号确认")
|
||||
print("内容包含: 股票信息、创新高回踩确认详情、技术指标、操作建议等")
|
||||
|
||||
print("\n📊 汇总通知示例:")
|
||||
print("标题: 🎯 K线形态策略信号汇总")
|
||||
print("内容包含: 扫描统计、确认信号详情（模式日期+确认日期+创新高价等）")
|
||||
|
||||
print("\n⚠️ 回踩提醒示例:")
|
||||
print("标题: ⚠️ 已确认信号二次回踩提醒")
|
||||
print("内容包含: 已确认信号的二次回踩情况、支撑分析建议等")
|
||||
|
||||
print("\n✅ 钉钉消息优化完成!")
|
||||
print("主要改进:")
|
||||
print("- 突出创新高回踩确认逻辑")
|
||||
print("- 详细展示时间线（模式日期→创新高日期→确认日期）")
|
||||
print("- 增加操作建议和风险提示")
|
||||
print("- 区分新旧格式信号，向下兼容")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("🚀 开始钉钉通知优化测试...")
|
||||
test_optimized_notification()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,179 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试价格回踩阴线最高点提醒功能
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加src目录到路径
|
||||
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.utils.notification import NotificationManager
|
||||
from src.strategy.kline_pattern_strategy import KLinePatternStrategy
|
||||
|
||||
|
||||
def create_test_signal():
|
||||
"""创建一个测试用的K线形态信号"""
|
||||
test_signal = {
|
||||
'stock_code': '000001.SZ',
|
||||
'stock_name': '平安银行',
|
||||
'date': datetime.now() - timedelta(days=5), # 5天前的信号
|
||||
'timeframe': 'daily',
|
||||
'breakout_price': 15.50, # 突破价格
|
||||
'yin_high': 15.20, # 阴线最高点
|
||||
'pattern_type': '两阳+阴+阳突破'
|
||||
}
|
||||
return test_signal
|
||||
|
||||
|
||||
def create_test_pullback_data():
|
||||
"""创建模拟回踩数据 - 模拟价格回踩到阴线最高点附近"""
|
||||
dates = pd.date_range(start=datetime.now() - timedelta(days=3), end=datetime.now(), freq='D')
|
||||
|
||||
# 模拟价格回踩阴线最高点的情况
|
||||
test_data = []
|
||||
yin_high = 15.20 # 阴线最高点价格
|
||||
initial_price = 15.50 # 突破价格
|
||||
|
||||
# 设计价格走势：从突破价格逐步回调到阴线最高点附近
|
||||
price_path = [15.45, 15.35, 15.25, 15.18] # 最后一个价格接近阴线最高点
|
||||
|
||||
for i, date in enumerate(dates):
|
||||
if i < len(price_path):
|
||||
close_price = price_path[i]
|
||||
else:
|
||||
close_price = yin_high - 0.02 # 稍低于阴线最高点
|
||||
|
||||
low_price = close_price - 0.03 # 最低价更接近阴线最高点
|
||||
|
||||
test_data.append({
|
||||
'trade_date': date,
|
||||
'open': close_price + 0.02,
|
||||
'high': close_price + 0.05,
|
||||
'low': low_price,
|
||||
'close': close_price,
|
||||
'volume': 1000000
|
||||
})
|
||||
|
||||
return pd.DataFrame(test_data)
|
||||
|
||||
|
||||
def test_pullback_detection():
|
||||
"""测试回踩检测功能"""
|
||||
logger.info("🧪 开始测试价格回踩阴线最高点提醒功能")
|
||||
|
||||
# 初始化配置
|
||||
config = config_loader.load_config()
|
||||
|
||||
# 初始化组件
|
||||
data_fetcher = ADataFetcher()
|
||||
notification_config = config.get('notification', {})
|
||||
notification_manager = NotificationManager(notification_config)
|
||||
|
||||
# 获取K线形态策略配置
|
||||
kline_config = config.get('strategy', {}).get('kline_pattern', {})
|
||||
strategy = KLinePatternStrategy(data_fetcher, notification_manager, kline_config)
|
||||
|
||||
# 创建测试信号
|
||||
test_signal = create_test_signal()
|
||||
logger.info(f"📊 创建测试信号: {test_signal['stock_code']}({test_signal['stock_name']})")
|
||||
logger.info(f" - 信号日期: {test_signal['date']}")
|
||||
logger.info(f" - 突破价格: {test_signal['breakout_price']}")
|
||||
logger.info(f" - 阴线最高点: {test_signal['yin_high']}")
|
||||
|
||||
# 添加到策略的监控列表
|
||||
strategy.add_triggered_signal(test_signal)
|
||||
logger.info("✅ 测试信号已添加到监控列表")
|
||||
|
||||
# 创建模拟回踩数据
|
||||
test_pullback_data = create_test_pullback_data()
|
||||
logger.info(f"📈 创建模拟K线数据，共{len(test_pullback_data)}条记录")
|
||||
|
||||
print("\n模拟K线数据:")
|
||||
for _, row in test_pullback_data.iterrows():
|
||||
print(f" {row['trade_date'].strftime('%Y-%m-%d')}: "
|
||||
f"开{row['open']:.2f} 高{row['high']:.2f} 低{row['low']:.2f} 收{row['close']:.2f}")
|
||||
|
||||
# 检测回踩情况
|
||||
logger.info("🔍 开始检测回踩情况...")
|
||||
pullback_alerts = strategy.check_pullback_signals(test_signal['stock_code'], test_pullback_data)
|
||||
|
||||
if pullback_alerts:
|
||||
logger.info(f"⚠️ 检测到 {len(pullback_alerts)} 个回踩提醒")
|
||||
for i, alert in enumerate(pullback_alerts, 1):
|
||||
logger.info(f" 提醒{i}: {alert['stock_code']} - 当前价格{alert['current_price']:.2f}，"
|
||||
f"回调{alert['pullback_pct']:.2f}%，距阴线高点{alert['distance_to_yin_high']:.2f}%")
|
||||
|
||||
# 测试通知发送（如果启用了钉钉通知）
|
||||
if notification_config.get('dingtalk', {}).get('enabled', False):
|
||||
logger.info("📱 测试发送回踩提醒通知...")
|
||||
success = notification_manager.send_pullback_alerts(pullback_alerts)
|
||||
if success:
|
||||
logger.info("✅ 回踩提醒通知发送成功")
|
||||
else:
|
||||
logger.warning("❌ 回踩提醒通知发送失败")
|
||||
else:
|
||||
logger.info("ℹ️ 钉钉通知未启用，跳过通知发送测试")
|
||||
else:
|
||||
logger.info("ℹ️ 未检测到回踩情况")
|
||||
|
||||
# 测试完整的监控流程
|
||||
logger.info("\n🔍 测试完整的回踩监控流程...")
|
||||
all_pullback_alerts = strategy.monitor_pullback_for_triggered_signals()
|
||||
|
||||
logger.info("🎯 测试完成!")
|
||||
if all_pullback_alerts:
|
||||
logger.info(f"✅ 成功检测到 {len(all_pullback_alerts)} 个回踩提醒")
|
||||
else:
|
||||
logger.info("ℹ️ 当前监控中无回踩情况")
|
||||
|
||||
|
||||
def test_strategy_config():
|
||||
"""测试策略配置是否正确加载"""
|
||||
logger.info("🔧 测试策略配置加载...")
|
||||
|
||||
config = config_loader.load_config()
|
||||
kline_config = config.get('strategy', {}).get('kline_pattern', {})
|
||||
|
||||
logger.info("📋 当前K线形态策略配置:")
|
||||
logger.info(f" - 启用状态: {kline_config.get('enabled', False)}")
|
||||
logger.info(f" - 前两阳线实体比例: {kline_config.get('min_entity_ratio', 0.55)}")
|
||||
logger.info(f" - 最后阳线实体比例: {kline_config.get('final_yang_min_ratio', 0.40)}")
|
||||
logger.info(f" - 回踩容忍度: {kline_config.get('pullback_tolerance', 0.02)}")
|
||||
logger.info(f" - 监控天数: {kline_config.get('monitor_days', 30)}")
|
||||
logger.info(f" - 支持时间周期: {kline_config.get('timeframes', ['daily'])}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 设置日志
|
||||
logger.remove()
|
||||
logger.add(sys.stdout, level="INFO", format="{time:HH:mm:ss} | {level} | {message}")
|
||||
|
||||
print("=" * 60)
|
||||
print("🧪 价格回踩阴线最高点提醒功能测试")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
# 测试配置加载
|
||||
test_strategy_config()
|
||||
print()
|
||||
|
||||
# 测试回踩检测功能
|
||||
test_pullback_detection()
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"测试过程中发生错误: {e}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
|
||||
print("\n" + "=" * 60)
|
||||
print("🏁 测试结束")
|
||||
print("=" * 60)
|
||||
@ -1,135 +0,0 @@
|
||||
#!/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
185
test_strategy.py
@ -1,185 +0,0 @@
|
||||
#!/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()
|
||||
@ -1,313 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
高级强势板块筛选器
|
||||
筛选条件：
|
||||
1. 本周收阳（周涨幅>0）
|
||||
2. 周线级别创阶段新高（20周新高）
|
||||
3. 成交额巨大（超过1000亿）
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_trading_dates(days_back=100):
|
||||
"""获取过去N个交易日"""
|
||||
dates = []
|
||||
current = datetime.now()
|
||||
|
||||
while len(dates) < days_back:
|
||||
if current.weekday() < 5: # 周一到周五
|
||||
dates.append(current.strftime('%Y%m%d'))
|
||||
current -= timedelta(days=1)
|
||||
|
||||
return sorted(dates) # 升序返回
|
||||
|
||||
|
||||
def filter_index_concepts(ths_concepts):
|
||||
"""过滤掉指数型板块"""
|
||||
index_keywords = [
|
||||
'成份股', '样本股', '成分股', '50', '100', '300', '500', '1000',
|
||||
'上证', '深证', '中证', '创业板', '科创板', '北证',
|
||||
'ETF', '指数', 'Index', '基准'
|
||||
]
|
||||
|
||||
def is_concept_plate(name: str) -> bool:
|
||||
name_lower = name.lower()
|
||||
for keyword in index_keywords:
|
||||
if keyword.lower() in name_lower:
|
||||
return False
|
||||
return True
|
||||
|
||||
original_count = len(ths_concepts)
|
||||
filtered_concepts = ths_concepts[ths_concepts['name'].apply(is_concept_plate)]
|
||||
filtered_count = len(filtered_concepts)
|
||||
|
||||
logger.info(f"过滤指数型板块: {original_count} -> {filtered_count} 个")
|
||||
return filtered_concepts
|
||||
|
||||
|
||||
def analyze_concept_strength(fetcher: TushareFetcher, concept_info, trading_dates):
|
||||
"""分析单个概念的强势特征"""
|
||||
ts_code = concept_info['ts_code']
|
||||
name = concept_info['name']
|
||||
|
||||
try:
|
||||
# 获取过去20周的数据（约100个交易日）
|
||||
start_date = trading_dates[0]
|
||||
end_date = trading_dates[-1]
|
||||
|
||||
daily_data = fetcher.pro.ths_daily(
|
||||
ts_code=ts_code,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
|
||||
if daily_data.empty or len(daily_data) < 20:
|
||||
return None
|
||||
|
||||
# 按日期排序
|
||||
daily_data = daily_data.sort_values('trade_date')
|
||||
daily_data.reset_index(drop=True, inplace=True)
|
||||
|
||||
# 1. 计算本周涨幅（最近5个交易日）
|
||||
recent_data = daily_data.tail(5)
|
||||
if len(recent_data) < 2:
|
||||
return None
|
||||
|
||||
week_start_close = recent_data.iloc[0]['close']
|
||||
week_end_close = recent_data.iloc[-1]['close']
|
||||
week_change = (week_end_close - week_start_close) / week_start_close * 100
|
||||
|
||||
# 2. 检查是否本周收阳
|
||||
is_weekly_positive = week_change > 0
|
||||
|
||||
# 3. 计算20周新高（约100个交易日）
|
||||
current_close = daily_data.iloc[-1]['close']
|
||||
past_20weeks_high = daily_data['high'].max()
|
||||
is_20week_high = current_close >= past_20weeks_high * 0.99 # 允许1%的误差
|
||||
|
||||
# 4. 计算成交额（最近5日平均）
|
||||
recent_turnover = recent_data['vol'].mean() * recent_data['close'].mean() # 简化计算
|
||||
turnover_100yi = recent_turnover / 100000000 # 转换为亿元
|
||||
|
||||
# 5. 计算技术指标
|
||||
# RSI相对强弱指数
|
||||
rsi = calculate_rsi(daily_data['close'].values)
|
||||
|
||||
# 20日均线趋势
|
||||
ma20 = daily_data['close'].rolling(20).mean()
|
||||
ma20_trend = (ma20.iloc[-1] - ma20.iloc[-10]) / ma20.iloc[-10] * 100 if len(ma20) >= 20 else 0
|
||||
|
||||
# 波动率
|
||||
volatility = daily_data['pct_change'].std() * np.sqrt(250) # 年化波动率
|
||||
|
||||
return {
|
||||
'ts_code': ts_code,
|
||||
'name': name,
|
||||
'week_change': week_change,
|
||||
'is_weekly_positive': is_weekly_positive,
|
||||
'is_20week_high': is_20week_high,
|
||||
'avg_turnover_yi': turnover_100yi,
|
||||
'current_close': current_close,
|
||||
'rsi': rsi,
|
||||
'ma20_trend': ma20_trend,
|
||||
'volatility': volatility,
|
||||
'data_length': len(daily_data)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"分析 {name} 失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def calculate_rsi(prices, period=14):
|
||||
"""计算RSI指标"""
|
||||
try:
|
||||
delta = np.diff(prices)
|
||||
gain = np.where(delta > 0, delta, 0)
|
||||
loss = np.where(delta < 0, -delta, 0)
|
||||
|
||||
avg_gain = np.mean(gain[-period:]) if len(gain) >= period else 0
|
||||
avg_loss = np.mean(loss[-period:]) if len(loss) >= period else 0
|
||||
|
||||
if avg_loss == 0:
|
||||
return 100
|
||||
|
||||
rs = avg_gain / avg_loss
|
||||
rsi = 100 - (100 / (1 + rs))
|
||||
return rsi
|
||||
except:
|
||||
return 50 # 默认值
|
||||
|
||||
|
||||
def find_strong_sectors(fetcher: TushareFetcher):
|
||||
"""寻找强势板块"""
|
||||
try:
|
||||
logger.info("🔍 开始寻找强势板块...")
|
||||
|
||||
# 获取交易日期
|
||||
trading_dates = get_trading_dates(100)
|
||||
logger.info(f"分析周期: {trading_dates[0]} 到 {trading_dates[-1]} (100个交易日)")
|
||||
|
||||
# 获取同花顺概念列表
|
||||
ths_concepts = fetcher.pro.ths_index(exchange='A', type='N')
|
||||
if ths_concepts.empty:
|
||||
logger.error("未获取到概念数据")
|
||||
return
|
||||
|
||||
# 过滤指数型概念
|
||||
ths_concepts = filter_index_concepts(ths_concepts)
|
||||
|
||||
# 分析概念强度
|
||||
strong_concepts = []
|
||||
total_concepts = min(80, len(ths_concepts)) # 分析前80个概念
|
||||
logger.info(f"分析 {total_concepts} 个概念...")
|
||||
|
||||
for i, (_, concept) in enumerate(ths_concepts.head(total_concepts).iterrows()):
|
||||
if i % 10 == 0:
|
||||
logger.info(f"进度: {i+1}/{total_concepts}")
|
||||
|
||||
result = analyze_concept_strength(fetcher, concept, trading_dates)
|
||||
if result:
|
||||
strong_concepts.append(result)
|
||||
|
||||
if not strong_concepts:
|
||||
logger.warning("未找到符合条件的强势板块")
|
||||
return
|
||||
|
||||
# 转换为DataFrame
|
||||
df = pd.DataFrame(strong_concepts)
|
||||
|
||||
# 强势板块筛选
|
||||
logger.info("🚀 应用强势板块筛选条件...")
|
||||
|
||||
# 条件1：本周收阳
|
||||
weekly_positive = df[df['is_weekly_positive']]
|
||||
logger.info(f"本周收阳概念: {len(weekly_positive)} 个")
|
||||
|
||||
# 条件2：20周新高
|
||||
new_high_concepts = df[df['is_20week_high']]
|
||||
logger.info(f"20周新高概念: {len(new_high_concepts)} 个")
|
||||
|
||||
# 条件3：成交额超过1000亿（这里设置为10亿，因为单个概念1000亿太高）
|
||||
high_turnover = df[df['avg_turnover_yi'] >= 10]
|
||||
logger.info(f"成交额超过10亿概念: {len(high_turnover)} 个")
|
||||
|
||||
# 综合强势板块（满足至少2个条件）
|
||||
df['strength_score'] = (
|
||||
df['is_weekly_positive'].astype(int) +
|
||||
df['is_20week_high'].astype(int) +
|
||||
(df['avg_turnover_yi'] >= 10).astype(int)
|
||||
)
|
||||
|
||||
# 按强势得分和周涨幅排序
|
||||
strong_sectors = df[df['strength_score'] >= 2].sort_values(['strength_score', 'week_change'], ascending=[False, False])
|
||||
|
||||
# 显示结果
|
||||
display_strong_sectors(df, strong_sectors, weekly_positive, new_high_concepts, high_turnover)
|
||||
|
||||
return df
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"寻找强势板块失败: {e}")
|
||||
|
||||
|
||||
def display_strong_sectors(df, strong_sectors, weekly_positive, new_high_concepts, high_turnover):
|
||||
"""显示强势板块分析结果"""
|
||||
|
||||
print("\n" + "="*100)
|
||||
print("🔍 强势板块综合分析报告")
|
||||
print("="*100)
|
||||
|
||||
# 1. 综合强势板块（满足多个条件）
|
||||
if not strong_sectors.empty:
|
||||
print(f"\n🚀 综合强势板块TOP10（满足2+条件）:")
|
||||
print(f"{'排名':<4} {'概念名称':<25} {'周涨幅':<10} {'强势分':<8} {'RSI':<8} {'成交额(亿)':<12} {'条件':<20}")
|
||||
print("-" * 100)
|
||||
|
||||
for i, (_, concept) in enumerate(strong_sectors.head(10).iterrows()):
|
||||
rank = i + 1
|
||||
name = concept['name'][:23] + '..' if len(concept['name']) > 23 else concept['name']
|
||||
week_chg = f"{concept['week_change']:+.2f}%"
|
||||
score = f"{concept['strength_score']}/3"
|
||||
rsi = f"{concept['rsi']:.1f}"
|
||||
turnover = f"{concept['avg_turnover_yi']:.1f}"
|
||||
|
||||
conditions = []
|
||||
if concept['is_weekly_positive']:
|
||||
conditions.append("周阳")
|
||||
if concept['is_20week_high']:
|
||||
conditions.append("新高")
|
||||
if concept['avg_turnover_yi'] >= 10:
|
||||
conditions.append("大额")
|
||||
|
||||
condition_str = "+".join(conditions)
|
||||
|
||||
print(f"{rank:<4} {name:<25} {week_chg:<10} {score:<8} {rsi:<8} {turnover:<12} {condition_str:<20}")
|
||||
|
||||
# 2. 分类展示
|
||||
print(f"\n📊 分类统计:")
|
||||
print(f" 本周收阳: {len(weekly_positive)} 个")
|
||||
print(f" 20周新高: {len(new_high_concepts)} 个")
|
||||
print(f" 大成交额: {len(high_turnover)} 个")
|
||||
print(f" 综合强势: {len(strong_sectors)} 个")
|
||||
|
||||
# 3. 本周收阳TOP10
|
||||
if not weekly_positive.empty:
|
||||
top_weekly = weekly_positive.sort_values('week_change', ascending=False)
|
||||
print(f"\n📈 本周收阳TOP10:")
|
||||
for i, (_, concept) in enumerate(top_weekly.head(10).iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['week_change']:+.2f}%")
|
||||
|
||||
# 4. 20周新高概念
|
||||
if not new_high_concepts.empty:
|
||||
print(f"\n🎯 20周新高概念TOP10:")
|
||||
new_high_sorted = new_high_concepts.sort_values('week_change', ascending=False)
|
||||
for i, (_, concept) in enumerate(new_high_sorted.head(10).iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['week_change']:+.2f}% (RSI: {concept['rsi']:.1f})")
|
||||
|
||||
# 5. 大成交额概念
|
||||
if not high_turnover.empty:
|
||||
print(f"\n💰 大成交额概念TOP10:")
|
||||
turnover_sorted = high_turnover.sort_values('avg_turnover_yi', ascending=False)
|
||||
for i, (_, concept) in enumerate(turnover_sorted.head(10).iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['avg_turnover_yi']:.1f}亿 ({concept['week_change']:+.2f}%)")
|
||||
|
||||
# 6. 技术面强势
|
||||
strong_tech = df[(df['rsi'] > 60) & (df['ma20_trend'] > 0)].sort_values('week_change', ascending=False)
|
||||
if not strong_tech.empty:
|
||||
print(f"\n📊 技术面强势概念TOP10:")
|
||||
for i, (_, concept) in enumerate(strong_tech.head(10).iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: RSI {concept['rsi']:.1f}, MA20趋势 {concept['ma20_trend']:+.2f}%")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("🚀 开始高级强势板块分析...")
|
||||
|
||||
# 初始化Tushare数据获取器
|
||||
token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
|
||||
fetcher = TushareFetcher(token=token)
|
||||
|
||||
# 寻找强势板块
|
||||
result_df = find_strong_sectors(fetcher)
|
||||
|
||||
if result_df is not None:
|
||||
logger.info("✅ 强势板块分析完成!")
|
||||
else:
|
||||
logger.error("❌ 强势板块分析失败!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,318 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试同花顺概念板块数据
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def explore_ths_interfaces(fetcher: TushareFetcher):
|
||||
"""探索同花顺相关接口"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("🔍 探索同花顺概念板块相关接口...")
|
||||
|
||||
# 1. 获取同花顺概念指数列表
|
||||
try:
|
||||
logger.info("1. 获取同花顺概念指数列表...")
|
||||
ths_index = fetcher.pro.ths_index(exchange='A', type='N')
|
||||
logger.info(f"获取到 {len(ths_index)} 个同花顺概念指数")
|
||||
|
||||
if not ths_index.empty:
|
||||
print("\n📋 同花顺概念指数（前20个）：")
|
||||
print(f"{'代码':<15} {'名称':<30} {'发布日期':<12}")
|
||||
print("-" * 60)
|
||||
for _, index in ths_index.head(20).iterrows():
|
||||
code = index['ts_code']
|
||||
name = index['name'][:28] + '..' if len(index['name']) > 28 else index['name']
|
||||
pub_date = index.get('list_date', 'N/A')
|
||||
print(f"{code:<15} {name:<30} {pub_date:<12}")
|
||||
|
||||
return ths_index
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺概念指数失败: {e}")
|
||||
|
||||
# 2. 尝试获取同花顺概念成分股
|
||||
try:
|
||||
logger.info("\n2. 测试获取同花顺概念成分股...")
|
||||
# 尝试获取一个概念的成分股
|
||||
sample_concept = "885311.TI" # 智能电网
|
||||
ths_member = fetcher.pro.ths_member(ts_code=sample_concept)
|
||||
logger.info(f"获取智能电网概念成分股: {len(ths_member)} 只")
|
||||
|
||||
if not ths_member.empty:
|
||||
print(f"\n📊 智能电网概念成分股（前10只）：")
|
||||
for _, stock in ths_member.head(10).iterrows():
|
||||
print(f" {stock['code']}: {stock.get('name', 'N/A')}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺概念成分股失败: {e}")
|
||||
|
||||
# 3. 尝试获取同花顺概念日行情
|
||||
try:
|
||||
logger.info("\n3. 测试获取同花顺概念日行情...")
|
||||
today = datetime.now().strftime('%Y%m%d')
|
||||
yesterday = (datetime.now() - timedelta(days=1)).strftime('%Y%m%d')
|
||||
|
||||
ths_daily = fetcher.pro.ths_daily(
|
||||
ts_code="885311.TI", # 智能电网
|
||||
start_date=yesterday,
|
||||
end_date=today
|
||||
)
|
||||
logger.info(f"获取智能电网概念日行情: {len(ths_daily)} 条记录")
|
||||
|
||||
if not ths_daily.empty:
|
||||
print(f"\n📈 智能电网概念近期行情：")
|
||||
print(ths_daily[['trade_date', 'close', 'pct_chg', 'vol']].head())
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺概念日行情失败: {e}")
|
||||
|
||||
# 4. 探索其他可能的同花顺接口
|
||||
try:
|
||||
logger.info("\n4. 探索同花顺行业分类...")
|
||||
ths_industry = fetcher.pro.ths_index(exchange='A', type='I')
|
||||
logger.info(f"获取到 {len(ths_industry)} 个同花顺行业指数")
|
||||
|
||||
if not ths_industry.empty:
|
||||
print(f"\n📊 同花顺行业指数（前10个）：")
|
||||
for _, index in ths_industry.head(10).iterrows():
|
||||
print(f" {index['ts_code']}: {index['name']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺行业分类失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"探索同花顺接口失败: {e}")
|
||||
|
||||
|
||||
def get_ths_concept_7day_ranking(fetcher: TushareFetcher):
|
||||
"""获取同花顺概念板块过去7个交易日排名"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("📊 计算同花顺概念板块过去7个交易日涨幅...")
|
||||
|
||||
# 获取同花顺概念指数列表
|
||||
ths_concepts = fetcher.pro.ths_index(exchange='A', type='N')
|
||||
if ths_concepts.empty:
|
||||
logger.error("未获取到同花顺概念指数")
|
||||
return
|
||||
|
||||
# 过滤掉指数型板块，只保留真正的概念板块
|
||||
logger.info("过滤指数型板块...")
|
||||
index_keywords = [
|
||||
'成份股', '样本股', '成分股', '50', '100', '300', '500', '1000',
|
||||
'上证', '深证', '中证', '创业板', '科创板', '北证',
|
||||
'ETF', '指数', 'Index', '基准'
|
||||
]
|
||||
|
||||
def is_concept_plate(name: str) -> bool:
|
||||
"""判断是否为真正的概念板块"""
|
||||
name_lower = name.lower()
|
||||
for keyword in index_keywords:
|
||||
if keyword.lower() in name_lower:
|
||||
return False
|
||||
return True
|
||||
|
||||
# 过滤数据
|
||||
original_count = len(ths_concepts)
|
||||
ths_concepts = ths_concepts[ths_concepts['name'].apply(is_concept_plate)]
|
||||
filtered_count = len(ths_concepts)
|
||||
|
||||
logger.info(f"过滤结果: {original_count} -> {filtered_count} 个概念板块（剔除{original_count - filtered_count}个指数型板块）")
|
||||
|
||||
# 获取过去7个交易日
|
||||
trading_dates = []
|
||||
current = datetime.now()
|
||||
|
||||
while len(trading_dates) < 7:
|
||||
if current.weekday() < 5: # 周一到周五
|
||||
trading_dates.append(current.strftime('%Y%m%d'))
|
||||
current -= timedelta(days=1)
|
||||
|
||||
trading_dates.reverse() # 升序排列，最早的日期在前
|
||||
|
||||
if len(trading_dates) < 2:
|
||||
logger.warning("交易日不足")
|
||||
return
|
||||
|
||||
start_date = trading_dates[0] # 7个交易日前
|
||||
end_date = trading_dates[-1] # 最新交易日
|
||||
|
||||
logger.info(f"分析周期: {start_date} 到 {end_date} (过去7个交易日)")
|
||||
|
||||
# 计算各概念的7日涨幅
|
||||
concept_performance = []
|
||||
|
||||
# 限制分析数量，避免API调用过多
|
||||
sample_concepts = ths_concepts.head(50) # 分析前50个概念（过滤后数量减少）
|
||||
logger.info(f"分析前 {len(sample_concepts)} 个同花顺概念...")
|
||||
|
||||
for _, concept in sample_concepts.iterrows():
|
||||
ts_code = concept['ts_code']
|
||||
name = concept['name']
|
||||
|
||||
try:
|
||||
# 获取过去7个交易日行情数据
|
||||
daily_data = fetcher.pro.ths_daily(
|
||||
ts_code=ts_code,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
|
||||
if not daily_data.empty:
|
||||
# 检查数据结构
|
||||
logger.debug(f"{name} 数据字段: {list(daily_data.columns)}")
|
||||
|
||||
if len(daily_data) >= 2:
|
||||
# 按日期排序
|
||||
daily_data = daily_data.sort_values('trade_date')
|
||||
|
||||
start_close = daily_data.iloc[0]['close']
|
||||
end_close = daily_data.iloc[-1]['close']
|
||||
|
||||
if start_close > 0:
|
||||
period_change = (end_close - start_close) / start_close * 100
|
||||
|
||||
# 检查涨跌幅字段名
|
||||
pct_change_col = None
|
||||
for col in ['pct_chg', 'pct_change', 'change']:
|
||||
if col in daily_data.columns:
|
||||
pct_change_col = col
|
||||
break
|
||||
|
||||
latest_daily_change = daily_data.iloc[-1][pct_change_col] if pct_change_col else 0
|
||||
|
||||
concept_performance.append({
|
||||
'ts_code': ts_code,
|
||||
'name': name,
|
||||
'period_change': period_change,
|
||||
'start_close': start_close,
|
||||
'end_close': end_close,
|
||||
'latest_daily_change': latest_daily_change,
|
||||
'trading_days': len(daily_data)
|
||||
})
|
||||
|
||||
logger.debug(f"{name}: 过去7日{period_change:+.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取 {name} 数据失败: {e}")
|
||||
continue
|
||||
|
||||
# 显示结果
|
||||
if concept_performance:
|
||||
df_ths = pd.DataFrame(concept_performance)
|
||||
df_ths = df_ths.sort_values('period_change', ascending=False)
|
||||
|
||||
print(f"\n" + "="*80)
|
||||
print("📈 同花顺概念板块过去7个交易日涨幅排行榜")
|
||||
print("="*80)
|
||||
print(f"{'排名':<4} {'概念名称':<30} {'7日涨幅':<12} {'今日涨幅':<12} {'指数代码':<15}")
|
||||
print("-" * 80)
|
||||
|
||||
for i, (_, concept) in enumerate(df_ths.iterrows()):
|
||||
rank = i + 1
|
||||
name = concept['name'][:28] + '..' if len(concept['name']) > 28 else concept['name']
|
||||
period_chg = f"{concept['period_change']:+.2f}%"
|
||||
daily_chg = f"{concept['latest_daily_change']:+.2f}%"
|
||||
ts_code = concept['ts_code']
|
||||
|
||||
print(f"{rank:<4} {name:<30} {period_chg:<12} {daily_chg:<12} {ts_code:<15}")
|
||||
|
||||
# 强势概念TOP15
|
||||
print(f"\n🚀 同花顺强势概念TOP15:")
|
||||
for i, (_, concept) in enumerate(df_ths.head(15).iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['period_change']:+.2f}%")
|
||||
|
||||
# 弱势概念TOP10
|
||||
print(f"\n📉 同花顺弱势概念TOP10:")
|
||||
weak_concepts = df_ths.tail(10).iloc[::-1] # 反转顺序
|
||||
for i, (_, concept) in enumerate(weak_concepts.iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['period_change']:+.2f}%")
|
||||
|
||||
return df_ths
|
||||
|
||||
else:
|
||||
logger.warning("未能计算同花顺概念涨幅")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺概念排名失败: {e}")
|
||||
|
||||
|
||||
def compare_concept_sources(fetcher: TushareFetcher):
|
||||
"""对比东财和同花顺概念数据"""
|
||||
try:
|
||||
logger.info("📊 对比东财 vs 同花顺概念数据...")
|
||||
|
||||
# 获取东财概念数量
|
||||
today = datetime.now().strftime('%Y%m%d')
|
||||
dc_concepts = fetcher.pro.dc_index(trade_date=today)
|
||||
dc_count = len(dc_concepts) if not dc_concepts.empty else 0
|
||||
|
||||
# 获取同花顺概念数量
|
||||
ths_concepts = fetcher.pro.ths_index(exchange='A', type='N')
|
||||
ths_count = len(ths_concepts) if not ths_concepts.empty else 0
|
||||
|
||||
print(f"\n📊 概念板块数据源对比:")
|
||||
print(f" 东财概念板块: {dc_count} 个")
|
||||
print(f" 同花顺概念: {ths_count} 个")
|
||||
|
||||
# 数据特点对比
|
||||
print(f"\n📈 数据特点对比:")
|
||||
print(f" 东财概念:")
|
||||
print(f" - 更新频率: 每日更新")
|
||||
print(f" - 数据字段: 涨跌幅、市值、上涨下跌股数等")
|
||||
print(f" - 适用场景: 实时概念轮动分析")
|
||||
|
||||
print(f" 同花顺概念:")
|
||||
print(f" - 更新频率: 每日更新")
|
||||
print(f" - 数据字段: 指数价格、涨跌幅、成交量等")
|
||||
print(f" - 适用场景: 概念指数走势分析")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"对比概念数据源失败: {e}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("🚀 开始探索同花顺概念板块数据...")
|
||||
|
||||
# 初始化Tushare数据获取器
|
||||
token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
|
||||
fetcher = TushareFetcher(token=token)
|
||||
|
||||
# 1. 探索同花顺接口
|
||||
ths_index = explore_ths_interfaces(fetcher)
|
||||
|
||||
print("\n" + "="*80 + "\n")
|
||||
|
||||
# 2. 计算同花顺概念过去7个交易日排名
|
||||
get_ths_concept_7day_ranking(fetcher)
|
||||
|
||||
print("\n" + "="*80 + "\n")
|
||||
|
||||
# 3. 对比数据源
|
||||
compare_concept_sources(fetcher)
|
||||
|
||||
logger.info("✅ 探索完成!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,238 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
使用Tushare直接获取板块数据的测试
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_concept_sectors(fetcher: TushareFetcher):
|
||||
"""获取概念板块数据"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("尝试获取概念板块数据...")
|
||||
|
||||
# 1. 尝试获取概念板块列表
|
||||
try:
|
||||
concept_list = fetcher.pro.concept()
|
||||
logger.info(f"获取到 {len(concept_list)} 个概念板块")
|
||||
|
||||
if not concept_list.empty:
|
||||
print("概念板块列表（前10个）：")
|
||||
for _, concept in concept_list.head(10).iterrows():
|
||||
print(f" {concept['code']}: {concept['name']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取概念板块列表失败: {e}")
|
||||
|
||||
# 2. 尝试获取同花顺概念指数
|
||||
try:
|
||||
ths_concept = fetcher.pro.ths_index(exchange='A', type='N')
|
||||
logger.info(f"获取同花顺概念指数: {len(ths_concept)} 个")
|
||||
|
||||
if not ths_concept.empty:
|
||||
print("\n同花顺概念指数（前10个）：")
|
||||
for _, index in ths_concept.head(10).iterrows():
|
||||
print(f" {index['ts_code']}: {index['name']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取同花顺概念指数失败: {e}")
|
||||
|
||||
# 3. 尝试获取行业指数
|
||||
try:
|
||||
industry_index = fetcher.pro.index_basic(market='SW')
|
||||
logger.info(f"获取申万行业指数: {len(industry_index)} 个")
|
||||
|
||||
if not industry_index.empty:
|
||||
print("\n申万行业指数（前10个）：")
|
||||
for _, index in industry_index.head(10).iterrows():
|
||||
print(f" {index['ts_code']}: {index['name']}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取申万行业指数失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取板块数据失败: {e}")
|
||||
|
||||
|
||||
def analyze_hot_concepts(fetcher: TushareFetcher):
|
||||
"""分析热门概念板块"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("分析热门概念板块...")
|
||||
|
||||
# 获取今日涨跌停统计
|
||||
today = datetime.now().strftime('%Y%m%d')
|
||||
|
||||
try:
|
||||
# 获取涨停股票
|
||||
limit_up = fetcher.pro.limit_list(trade_date=today, limit_type='U')
|
||||
logger.info(f"今日涨停股票: {len(limit_up)} 只")
|
||||
|
||||
if not limit_up.empty:
|
||||
print(f"\n今日涨停股票（前10只）：")
|
||||
for _, stock in limit_up.head(10).iterrows():
|
||||
print(f" {stock['ts_code']}: {stock['name']} (+{stock['pct_chg']:.2f}%)")
|
||||
|
||||
# 分析涨停股票的行业分布
|
||||
if 'industry' in limit_up.columns:
|
||||
industry_counts = limit_up['industry'].value_counts()
|
||||
print(f"\n涨停股票行业分布：")
|
||||
for industry, count in industry_counts.head(5).items():
|
||||
print(f" {industry}: {count}只")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取涨停数据失败: {e}")
|
||||
|
||||
# 获取龙虎榜数据
|
||||
try:
|
||||
top_list = fetcher.pro.top_list(trade_date=today)
|
||||
logger.info(f"今日龙虎榜: {len(top_list)} 只股票")
|
||||
|
||||
if not top_list.empty:
|
||||
print(f"\n今日龙虎榜股票（前5只）：")
|
||||
for _, stock in top_list.head(5).iterrows():
|
||||
print(f" {stock['ts_code']}: {stock['name']} 净买入: {stock['amount']:.0f}万元")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取龙虎榜数据失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析热门概念失败: {e}")
|
||||
|
||||
|
||||
def get_sector_performance_direct(fetcher: TushareFetcher):
|
||||
"""直接通过指数数据获取板块表现"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return
|
||||
|
||||
logger.info("通过指数数据分析板块表现...")
|
||||
|
||||
# 获取申万一级行业指数
|
||||
try:
|
||||
sw_index = fetcher.pro.index_basic(market='SW', level='L1')
|
||||
logger.info(f"获取申万一级行业指数: {len(sw_index)} 个")
|
||||
|
||||
if sw_index.empty:
|
||||
logger.warning("未获取到申万行业指数")
|
||||
return
|
||||
|
||||
# 获取最近两个交易日的指数行情
|
||||
end_date = datetime.now().strftime('%Y%m%d')
|
||||
start_date = (datetime.now() - timedelta(days=7)).strftime('%Y%m%d')
|
||||
|
||||
sector_performance = []
|
||||
|
||||
for _, index in sw_index.head(15).iterrows(): # 分析前15个行业
|
||||
ts_code = index['ts_code']
|
||||
name = index['name']
|
||||
|
||||
try:
|
||||
# 获取指数行情
|
||||
index_data = fetcher.pro.index_daily(
|
||||
ts_code=ts_code,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
|
||||
if not index_data.empty and len(index_data) >= 2:
|
||||
# 计算涨跌幅
|
||||
latest = index_data.iloc[0]
|
||||
previous = index_data.iloc[1]
|
||||
|
||||
change_pct = (latest['close'] - previous['close']) / previous['close'] * 100
|
||||
|
||||
sector_performance.append({
|
||||
'name': name,
|
||||
'code': ts_code,
|
||||
'change_pct': change_pct,
|
||||
'latest_close': latest['close'],
|
||||
'volume': latest['vol']
|
||||
})
|
||||
|
||||
logger.debug(f"{name}: {change_pct:+.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取 {name} 指数数据失败: {e}")
|
||||
continue
|
||||
|
||||
# 输出结果
|
||||
if sector_performance:
|
||||
df = pd.DataFrame(sector_performance)
|
||||
df = df.sort_values('change_pct', ascending=False)
|
||||
|
||||
print("\n" + "="*60)
|
||||
print("📈 申万行业指数表现排行")
|
||||
print("="*60)
|
||||
print(f"{'排名':<4} {'行业名称':<20} {'涨跌幅':<10} {'最新点位':<10}")
|
||||
print("-" * 60)
|
||||
|
||||
for i, (_, row) in enumerate(df.iterrows()):
|
||||
rank = i + 1
|
||||
name = row['name'][:18] + '..' if len(row['name']) > 18 else row['name']
|
||||
change = f"{row['change_pct']:+.2f}%"
|
||||
close = f"{row['latest_close']:.2f}"
|
||||
|
||||
print(f"{rank:<4} {name:<20} {change:<10} {close:<10}")
|
||||
|
||||
# 强势行业
|
||||
print(f"\n🚀 强势行业TOP5:")
|
||||
for _, row in df.head(5).iterrows():
|
||||
print(f" {row['name']}: {row['change_pct']:+.2f}%")
|
||||
|
||||
# 弱势行业
|
||||
print(f"\n📉 弱势行业TOP5:")
|
||||
for _, row in df.tail(5).iterrows():
|
||||
print(f" {row['name']}: {row['change_pct']:+.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取申万指数失败: {e}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析板块表现失败: {e}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("测试Tushare板块数据接口...")
|
||||
|
||||
# 初始化Tushare数据获取器
|
||||
token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
|
||||
fetcher = TushareFetcher(token=token)
|
||||
|
||||
# 1. 获取板块分类数据
|
||||
get_concept_sectors(fetcher)
|
||||
|
||||
print("\n" + "="*80 + "\n")
|
||||
|
||||
# 2. 分析热门概念
|
||||
analyze_hot_concepts(fetcher)
|
||||
|
||||
print("\n" + "="*80 + "\n")
|
||||
|
||||
# 3. 通过指数直接获取板块表现
|
||||
get_sector_performance_direct(fetcher)
|
||||
|
||||
logger.info("测试完成!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,244 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
按本周总涨幅排名东财概念板块
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_this_week_dates():
|
||||
"""获取本周的交易日期（周一到周五）"""
|
||||
today = datetime.now()
|
||||
# 获取本周一
|
||||
monday = today - timedelta(days=today.weekday())
|
||||
# 获取本周五（或今天如果还没到周五）
|
||||
friday = monday + timedelta(days=4)
|
||||
if friday > today:
|
||||
friday = today
|
||||
|
||||
# 生成本周所有交易日
|
||||
dates = []
|
||||
current = monday
|
||||
while current <= friday:
|
||||
if current.weekday() < 5: # 周一到周五
|
||||
dates.append(current.strftime('%Y%m%d'))
|
||||
current += timedelta(days=1)
|
||||
|
||||
return dates
|
||||
|
||||
|
||||
def calculate_weekly_concept_performance(fetcher: TushareFetcher):
|
||||
"""计算概念板块本周总涨幅"""
|
||||
try:
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限")
|
||||
return None
|
||||
|
||||
logger.info("🚀 计算概念板块本周总涨幅排名...")
|
||||
|
||||
# 获取本周交易日
|
||||
week_dates = get_this_week_dates()
|
||||
logger.info(f"本周交易日: {week_dates}")
|
||||
|
||||
if len(week_dates) < 2:
|
||||
logger.warning("本周交易日不足，无法计算周涨幅")
|
||||
return None
|
||||
|
||||
start_date = week_dates[0] # 周一
|
||||
end_date = week_dates[-1] # 最新交易日
|
||||
|
||||
logger.info(f"分析周期: {start_date} 到 {end_date}")
|
||||
|
||||
# 获取周一的概念板块数据（基准）
|
||||
logger.info(f"获取 {start_date} 的概念数据作为基准...")
|
||||
start_concepts = fetcher.pro.dc_index(trade_date=start_date)
|
||||
|
||||
# 获取最新交易日的概念板块数据
|
||||
logger.info(f"获取 {end_date} 的概念数据...")
|
||||
end_concepts = fetcher.pro.dc_index(trade_date=end_date)
|
||||
|
||||
if start_concepts.empty or end_concepts.empty:
|
||||
logger.error("无法获取概念板块数据")
|
||||
return None
|
||||
|
||||
logger.info(f"周一概念数据: {len(start_concepts)} 个")
|
||||
logger.info(f"最新概念数据: {len(end_concepts)} 个")
|
||||
|
||||
# 计算本周涨幅
|
||||
weekly_performance = []
|
||||
|
||||
# 以最新数据为准，匹配周一数据
|
||||
for _, end_concept in end_concepts.iterrows():
|
||||
ts_code = end_concept['ts_code']
|
||||
name = end_concept['name']
|
||||
end_mv = end_concept['total_mv']
|
||||
|
||||
# 查找对应的周一数据
|
||||
start_data = start_concepts[start_concepts['ts_code'] == ts_code]
|
||||
|
||||
if not start_data.empty:
|
||||
start_mv = start_data.iloc[0]['total_mv']
|
||||
|
||||
# 计算本周总涨幅
|
||||
if start_mv > 0:
|
||||
weekly_change = (end_mv - start_mv) / start_mv * 100
|
||||
|
||||
weekly_performance.append({
|
||||
'ts_code': ts_code,
|
||||
'name': name,
|
||||
'weekly_change': weekly_change,
|
||||
'start_mv': start_mv,
|
||||
'end_mv': end_mv,
|
||||
'latest_daily_change': end_concept['pct_change'],
|
||||
'up_num': end_concept.get('up_num', 0),
|
||||
'down_num': end_concept.get('down_num', 0)
|
||||
})
|
||||
|
||||
if not weekly_performance:
|
||||
logger.error("无法计算概念板块周涨幅")
|
||||
return None
|
||||
|
||||
# 转换为DataFrame并按周涨幅排序
|
||||
df_weekly = pd.DataFrame(weekly_performance)
|
||||
df_weekly = df_weekly.sort_values('weekly_change', ascending=False)
|
||||
|
||||
logger.info(f"成功计算 {len(df_weekly)} 个概念板块的本周涨幅")
|
||||
|
||||
return df_weekly
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算概念板块周涨幅失败: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def display_weekly_ranking(df_weekly: pd.DataFrame):
|
||||
"""显示本周涨幅排名"""
|
||||
if df_weekly is None or df_weekly.empty:
|
||||
logger.error("无数据可显示")
|
||||
return
|
||||
|
||||
print("\n" + "="*100)
|
||||
print("📈 东财概念板块本周涨幅排行榜")
|
||||
print("="*100)
|
||||
print(f"{'排名':<4} {'概念名称':<25} {'本周涨幅':<12} {'今日涨幅':<12} {'上涨股数':<8} {'下跌股数':<8} {'概念代码':<15}")
|
||||
print("-" * 100)
|
||||
|
||||
for i, (_, concept) in enumerate(df_weekly.head(30).iterrows()):
|
||||
rank = i + 1
|
||||
name = concept['name'][:23] + '..' if len(concept['name']) > 23 else concept['name']
|
||||
weekly_chg = f"{concept['weekly_change']:+.2f}%"
|
||||
daily_chg = f"{concept['latest_daily_change']:+.2f}%"
|
||||
up_num = f"{concept['up_num']:.0f}"
|
||||
down_num = f"{concept['down_num']:.0f}"
|
||||
ts_code = concept['ts_code']
|
||||
|
||||
print(f"{rank:<4} {name:<25} {weekly_chg:<12} {daily_chg:<12} {up_num:<8} {down_num:<8} {ts_code:<15}")
|
||||
|
||||
# 强势概念TOP15
|
||||
print(f"\n🚀 本周强势概念板块TOP15:")
|
||||
for i, (_, concept) in enumerate(df_weekly.head(15).iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['weekly_change']:+.2f}% (今日{concept['latest_daily_change']:+.2f}%)")
|
||||
|
||||
# 弱势概念TOP10
|
||||
print(f"\n📉 本周弱势概念板块TOP10:")
|
||||
weak_concepts = df_weekly.tail(10).iloc[::-1] # 反转顺序
|
||||
for i, (_, concept) in enumerate(weak_concepts.iterrows()):
|
||||
print(f" {i+1:2d}. {concept['name']}: {concept['weekly_change']:+.2f}% (今日{concept['latest_daily_change']:+.2f}%)")
|
||||
|
||||
# 统计分析
|
||||
print(f"\n📊 本周概念板块统计:")
|
||||
total_concepts = len(df_weekly)
|
||||
positive_concepts = len(df_weekly[df_weekly['weekly_change'] > 0])
|
||||
negative_concepts = len(df_weekly[df_weekly['weekly_change'] < 0])
|
||||
|
||||
print(f" 总概念数量: {total_concepts}")
|
||||
print(f" 上涨概念: {positive_concepts} ({positive_concepts/total_concepts*100:.1f}%)")
|
||||
print(f" 下跌概念: {negative_concepts} ({negative_concepts/total_concepts*100:.1f}%)")
|
||||
print(f" 平均涨幅: {df_weekly['weekly_change'].mean():+.2f}%")
|
||||
print(f" 涨幅中位数: {df_weekly['weekly_change'].median():+.2f}%")
|
||||
|
||||
return df_weekly
|
||||
|
||||
|
||||
def analyze_top_concepts_detail(fetcher: TushareFetcher, df_weekly: pd.DataFrame, top_n=5):
|
||||
"""分析TOP概念的详细趋势"""
|
||||
if df_weekly is None or df_weekly.empty:
|
||||
return
|
||||
|
||||
logger.info(f"详细分析TOP{top_n}强势概念...")
|
||||
|
||||
print(f"\n" + "="*80)
|
||||
print(f"📊 TOP{top_n}强势概念详细分析")
|
||||
print("="*80)
|
||||
|
||||
week_dates = get_this_week_dates()
|
||||
|
||||
for i, (_, concept) in enumerate(df_weekly.head(top_n).iterrows()):
|
||||
concept_code = concept['ts_code']
|
||||
concept_name = concept['name']
|
||||
|
||||
print(f"\n📈 {i+1}. {concept_name} ({concept_code})")
|
||||
print(f" 本周总涨幅: {concept['weekly_change']:+.2f}%")
|
||||
|
||||
# 获取每日详细数据
|
||||
daily_data = []
|
||||
for date in week_dates:
|
||||
try:
|
||||
daily_concept = fetcher.pro.dc_index(
|
||||
trade_date=date,
|
||||
ts_code=concept_code
|
||||
)
|
||||
|
||||
if not daily_concept.empty:
|
||||
daily_data.append({
|
||||
'date': date,
|
||||
'pct_change': daily_concept.iloc[0]['pct_change'],
|
||||
'total_mv': daily_concept.iloc[0]['total_mv'],
|
||||
'up_num': daily_concept.iloc[0].get('up_num', 0),
|
||||
'down_num': daily_concept.iloc[0].get('down_num', 0)
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"获取 {concept_code} 在 {date} 的数据失败: {e}")
|
||||
continue
|
||||
|
||||
# 显示每日走势
|
||||
if daily_data:
|
||||
print(f" 每日走势:")
|
||||
for data in daily_data:
|
||||
print(f" {data['date']}: {data['pct_change']:+6.2f}% (上涨{data['up_num']:.0f}只/下跌{data['down_num']:.0f}只)")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("🚀 开始计算东财概念板块本周涨幅排名...")
|
||||
|
||||
# 初始化Tushare数据获取器
|
||||
token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
|
||||
fetcher = TushareFetcher(token=token)
|
||||
|
||||
# 1. 计算本周涨幅
|
||||
df_weekly = calculate_weekly_concept_performance(fetcher)
|
||||
|
||||
# 2. 显示排名
|
||||
if df_weekly is not None:
|
||||
display_weekly_ranking(df_weekly)
|
||||
|
||||
# 3. 详细分析TOP5概念
|
||||
analyze_top_concepts_detail(fetcher, df_weekly, top_n=5)
|
||||
|
||||
logger.info("✅ 分析完成!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -1,258 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
测试使用Tushare找出本周强势板块
|
||||
"""
|
||||
|
||||
import sys
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
# 添加项目根目录到路径
|
||||
current_dir = Path(__file__).parent
|
||||
sys.path.insert(0, str(current_dir))
|
||||
|
||||
from src.data.tushare_fetcher import TushareFetcher
|
||||
from loguru import logger
|
||||
|
||||
|
||||
def get_this_week_dates():
|
||||
"""获取本周的开始和结束日期"""
|
||||
today = datetime.now()
|
||||
# 获取本周一
|
||||
monday = today - timedelta(days=today.weekday())
|
||||
# 获取本周五（或今天如果还没到周五）
|
||||
friday = monday + timedelta(days=4)
|
||||
if friday > today:
|
||||
friday = today
|
||||
|
||||
return monday.strftime('%Y%m%d'), friday.strftime('%Y%m%d')
|
||||
|
||||
|
||||
def analyze_sector_performance(fetcher: TushareFetcher):
|
||||
"""分析行业板块表现"""
|
||||
try:
|
||||
logger.info("开始分析本周强势板块...")
|
||||
|
||||
# 获取本周日期范围
|
||||
start_date, end_date = get_this_week_dates()
|
||||
logger.info(f"分析时间范围: {start_date} 到 {end_date}")
|
||||
|
||||
if not fetcher.pro:
|
||||
logger.error("需要Tushare Pro权限才能获取行业数据")
|
||||
return
|
||||
|
||||
# 直接使用概念和行业数据分析
|
||||
logger.info("通过个股数据分析板块表现...")
|
||||
|
||||
# 获取股票基本信息（包含行业分类）
|
||||
try:
|
||||
stock_basic = fetcher.pro.stock_basic(
|
||||
exchange='',
|
||||
list_status='L',
|
||||
fields='ts_code,symbol,name,area,industry,market'
|
||||
)
|
||||
logger.info(f"获取到 {len(stock_basic)} 只股票基本信息")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取股票基本信息失败: {e}")
|
||||
return
|
||||
|
||||
# 按行业分组分析
|
||||
industry_performance = {}
|
||||
|
||||
# 取样分析（避免API请求过多）
|
||||
sample_stocks = stock_basic.sample(min(200, len(stock_basic))) # 随机取200只股票
|
||||
logger.info(f"随机抽样 {len(sample_stocks)} 只股票进行分析...")
|
||||
|
||||
for _, stock in sample_stocks.iterrows():
|
||||
ts_code = stock['ts_code']
|
||||
industry = stock['industry']
|
||||
stock_name = stock['name']
|
||||
|
||||
if pd.isna(industry) or industry == '':
|
||||
continue
|
||||
|
||||
try:
|
||||
# 获取个股本周数据
|
||||
stock_data = fetcher.pro.daily(
|
||||
ts_code=ts_code,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
|
||||
if not stock_data.empty and len(stock_data) >= 1:
|
||||
# 获取最新价格和前一个交易日价格
|
||||
if len(stock_data) >= 2:
|
||||
latest_close = stock_data.iloc[0]['close']
|
||||
prev_close = stock_data.iloc[1]['close']
|
||||
else:
|
||||
# 如果只有一天数据，获取开盘价作为对比
|
||||
latest_close = stock_data.iloc[0]['close']
|
||||
prev_close = stock_data.iloc[0]['open']
|
||||
|
||||
if prev_close > 0:
|
||||
change_pct = (latest_close - prev_close) / prev_close * 100
|
||||
|
||||
# 按行业归类
|
||||
if industry not in industry_performance:
|
||||
industry_performance[industry] = {
|
||||
'stock_changes': [],
|
||||
'stock_count': 0,
|
||||
'stock_names': []
|
||||
}
|
||||
|
||||
industry_performance[industry]['stock_changes'].append(change_pct)
|
||||
industry_performance[industry]['stock_count'] += 1
|
||||
industry_performance[industry]['stock_names'].append(f"{stock_name}({change_pct:+.2f}%)")
|
||||
|
||||
logger.debug(f"{stock_name} ({industry}): {change_pct:+.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"分析个股 {ts_code} 失败: {e}")
|
||||
continue
|
||||
|
||||
# 计算各行业平均表现
|
||||
industry_results = []
|
||||
for industry, data in industry_performance.items():
|
||||
if data['stock_count'] >= 3: # 至少要有3只股票才参与排名
|
||||
avg_change = sum(data['stock_changes']) / len(data['stock_changes'])
|
||||
industry_results.append({
|
||||
'industry_name': industry,
|
||||
'avg_change_pct': avg_change,
|
||||
'stock_count': data['stock_count'],
|
||||
'best_stocks': sorted(data['stock_names'], key=lambda x: float(x.split('(')[1].split('%')[0]), reverse=True)[:3]
|
||||
})
|
||||
|
||||
# 3. 分析结果
|
||||
if industry_results:
|
||||
df_performance = pd.DataFrame(industry_results)
|
||||
|
||||
# 按平均涨跌幅排序
|
||||
df_performance = df_performance.sort_values('avg_change_pct', ascending=False)
|
||||
|
||||
logger.info("\n" + "="*80)
|
||||
logger.info("📈 本周强势板块排行榜（基于抽样股票分析）")
|
||||
logger.info("="*80)
|
||||
|
||||
print(f"{'排名':<4} {'行业名称':<20} {'平均涨跌幅':<12} {'样本数量':<8} {'代表个股':<30}")
|
||||
print("-" * 80)
|
||||
|
||||
for i, (_, row) in enumerate(df_performance.head(15).iterrows()):
|
||||
rank = i + 1
|
||||
industry_name = row['industry_name'][:18] + '..' if len(row['industry_name']) > 18 else row['industry_name']
|
||||
change_pct = f"{row['avg_change_pct']:+.2f}%"
|
||||
stock_count = f"{row['stock_count']}只"
|
||||
best_stock = row['best_stocks'][0] if row['best_stocks'] else "无数据"
|
||||
|
||||
print(f"{rank:<4} {industry_name:<20} {change_pct:<12} {stock_count:<8} {best_stock:<30}")
|
||||
|
||||
# 输出强势板块（涨幅前5）
|
||||
top_sectors = df_performance.head(5)
|
||||
logger.info(f"\n🚀 本周TOP5强势板块:")
|
||||
for i, (_, sector) in enumerate(top_sectors.iterrows()):
|
||||
logger.info(f" {i+1}. {sector['industry_name']}: {sector['avg_change_pct']:+.2f}% (样本{sector['stock_count']}只)")
|
||||
for j, stock in enumerate(sector['best_stocks'][:3]):
|
||||
logger.info(f" └─ {stock}")
|
||||
|
||||
# 输出弱势板块（跌幅前5）
|
||||
weak_sectors = df_performance.tail(5)
|
||||
logger.info(f"\n📉 本周TOP5弱势板块:")
|
||||
for i, (_, sector) in enumerate(weak_sectors.iterrows()):
|
||||
logger.info(f" {i+1}. {sector['industry_name']}: {sector['avg_change_pct']:+.2f}% (样本{sector['stock_count']}只)")
|
||||
|
||||
else:
|
||||
logger.warning("未获取到有效的行业表现数据")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"分析行业表现失败: {e}")
|
||||
|
||||
|
||||
def get_sector_top_stocks(fetcher: TushareFetcher, industry_code: str, industry_name: str, limit: int = 5):
|
||||
"""获取指定板块的强势个股"""
|
||||
try:
|
||||
logger.info(f"获取 {industry_name} 板块的强势个股...")
|
||||
|
||||
if not fetcher.pro:
|
||||
return
|
||||
|
||||
# 获取该行业的成分股
|
||||
try:
|
||||
constituents = fetcher.pro.index_member(index_code=industry_code)
|
||||
if constituents.empty:
|
||||
logger.warning(f"{industry_name} 行业无成分股数据")
|
||||
return
|
||||
|
||||
stock_codes = constituents['con_code'].tolist()[:20] # 取前20只股票测试
|
||||
logger.info(f"{industry_name} 行业共 {len(constituents)} 只成分股，分析前 {len(stock_codes)} 只")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取 {industry_name} 成分股失败: {e}")
|
||||
return
|
||||
|
||||
# 获取本周日期
|
||||
start_date, end_date = get_this_week_dates()
|
||||
|
||||
# 分析各股票本周表现
|
||||
stock_performance = []
|
||||
|
||||
for stock_code in stock_codes[:10]: # 限制分析数量
|
||||
try:
|
||||
# 获取个股本周数据
|
||||
stock_data = fetcher.pro.daily(
|
||||
ts_code=stock_code,
|
||||
start_date=start_date,
|
||||
end_date=end_date
|
||||
)
|
||||
|
||||
if not stock_data.empty and len(stock_data) >= 2:
|
||||
latest_close = stock_data.iloc[0]['close']
|
||||
week_start_close = stock_data.iloc[-1]['close']
|
||||
week_change = (latest_close - week_start_close) / week_start_close * 100
|
||||
|
||||
# 获取股票名称
|
||||
stock_name = fetcher.get_stock_name(stock_code.split('.')[0])
|
||||
|
||||
stock_performance.append({
|
||||
'stock_code': stock_code,
|
||||
'stock_name': stock_name,
|
||||
'week_change_pct': week_change,
|
||||
'latest_close': latest_close
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.debug(f"分析个股 {stock_code} 失败: {e}")
|
||||
continue
|
||||
|
||||
# 输出该板块强势个股
|
||||
if stock_performance:
|
||||
df_stocks = pd.DataFrame(stock_performance)
|
||||
df_stocks = df_stocks.sort_values('week_change_pct', ascending=False)
|
||||
|
||||
logger.info(f"\n📊 {industry_name} 板块强势个股 TOP{limit}:")
|
||||
for _, stock in df_stocks.head(limit).iterrows():
|
||||
logger.info(f" {stock['stock_name']} ({stock['stock_code']}): {stock['week_change_pct']:+.2f}%")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取 {industry_name} 板块个股失败: {e}")
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数"""
|
||||
logger.info("开始测试Tushare获取本周强势板块...")
|
||||
|
||||
# 初始化Tushare数据获取器
|
||||
token = "0ed6419a00d8923dc19c0b58fc92d94c9a0696949ab91a13aa58a0cc"
|
||||
fetcher = TushareFetcher(token=token)
|
||||
|
||||
# 分析板块表现
|
||||
analyze_sector_performance(fetcher)
|
||||
|
||||
# 分析某个强势板块的个股（示例）
|
||||
# get_sector_top_stocks(fetcher, "801010.SI", "农林牧渔")
|
||||
|
||||
logger.info("测试完成!")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@ -29,28 +29,9 @@ config_loader = ConfigLoader()
|
||||
|
||||
@app.route('/')
|
||||
def index():
|
||||
"""首页 - 显示信号概览和统计"""
|
||||
try:
|
||||
# 获取策略统计
|
||||
strategy_stats = db_manager.get_strategy_stats()
|
||||
|
||||
# 获取最新信号（前10条）
|
||||
signals_df = db_manager.get_latest_signals(limit=10)
|
||||
|
||||
# 获取回踩提醒（前5条）
|
||||
pullback_alerts = db_manager.get_pullback_alerts(days=7)
|
||||
|
||||
# 当前时间
|
||||
current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
||||
|
||||
return render_template('index.html',
|
||||
strategy_stats=strategy_stats.to_dict('records') if not strategy_stats.empty else [],
|
||||
signals=signals_df.to_dict('records') if not signals_df.empty else [],
|
||||
pullback_alerts=pullback_alerts.to_dict('records') if not pullback_alerts.empty else [],
|
||||
current_time=current_time)
|
||||
except Exception as e:
|
||||
logger.error(f"首页数据加载失败: {e}")
|
||||
return render_template('error.html', error=str(e))
|
||||
"""首页 - 重定向到信号页面"""
|
||||
from flask import redirect, url_for
|
||||
return redirect(url_for('signals'))
|
||||
|
||||
|
||||
@app.route('/signals')
|
||||
@ -60,7 +41,7 @@ def signals():
|
||||
# 获取查询参数
|
||||
strategy_name = request.args.get('strategy', '')
|
||||
timeframe = request.args.get('timeframe', '')
|
||||
days = int(request.args.get('days', 30))
|
||||
days = int(request.args.get('days', 7)) # 默认显示7天内的信号
|
||||
page = int(request.args.get('page', 1))
|
||||
per_page = int(request.args.get('per_page', 20))
|
||||
|
||||
@ -154,20 +135,6 @@ def signals():
|
||||
return render_template('error.html', error=str(e))
|
||||
|
||||
|
||||
@app.route('/pullbacks')
|
||||
def pullbacks():
|
||||
"""回踩监控页面"""
|
||||
try:
|
||||
days = int(request.args.get('days', 30))
|
||||
pullback_alerts = db_manager.get_pullback_alerts(days=days)
|
||||
|
||||
return render_template('pullbacks.html',
|
||||
pullback_alerts=pullback_alerts.to_dict('records') if not pullback_alerts.empty else [],
|
||||
days=days)
|
||||
except Exception as e:
|
||||
logger.error(f"回踩监控页面数据加载失败: {e}")
|
||||
return render_template('error.html', error=str(e))
|
||||
|
||||
|
||||
@app.route('/api/signals')
|
||||
def api_signals():
|
||||
@ -206,25 +173,11 @@ def api_stats():
|
||||
return jsonify({'success': False, 'error': str(e)})
|
||||
|
||||
|
||||
@app.route('/api/pullbacks')
|
||||
def api_pullbacks():
|
||||
"""API接口 - 获取回踩提醒"""
|
||||
try:
|
||||
days = int(request.args.get('days', 7))
|
||||
pullback_alerts = db_manager.get_pullback_alerts(days=days)
|
||||
|
||||
return jsonify({
|
||||
'success': True,
|
||||
'data': pullback_alerts.to_dict('records') if not pullback_alerts.empty else []
|
||||
})
|
||||
except Exception as e:
|
||||
logger.error(f"API获取回踩提醒失败: {e}")
|
||||
return jsonify({'success': False, 'error': str(e)})
|
||||
|
||||
|
||||
@app.template_filter('datetime_format')
|
||||
def datetime_format(value, format='%Y-%m-%d %H:%M'):
|
||||
"""日期时间格式化过滤器 - 转换为东八区时间"""
|
||||
"""日期时间格式化过滤器 - 智能处理时区转换"""
|
||||
if value is None:
|
||||
return ''
|
||||
|
||||
@ -245,10 +198,17 @@ def datetime_format(value, format='%Y-%m-%d %H:%M'):
|
||||
if isinstance(value, datetime) and value.tzinfo is None:
|
||||
value = value.replace(tzinfo=timezone.utc)
|
||||
|
||||
# 转换为东八区时间 (UTC+8)
|
||||
# 智能时区转换：检查是否已经是东八区时间
|
||||
if isinstance(value, datetime) and value.tzinfo is not None:
|
||||
china_tz = timezone(timedelta(hours=8))
|
||||
value = value.astimezone(china_tz)
|
||||
|
||||
# 如果已经是东八区时间，直接使用；否则转换
|
||||
if value.utcoffset() == timedelta(hours=8):
|
||||
# 已经是东八区时间，无需转换
|
||||
pass
|
||||
else:
|
||||
# 转换为东八区时间
|
||||
value = value.astimezone(china_tz)
|
||||
|
||||
return value.strftime(format)
|
||||
|
||||
@ -284,4 +244,4 @@ if __name__ == '__main__':
|
||||
print(f"📋 数据库: {db_manager.config.database}")
|
||||
print("=" * 60)
|
||||
|
||||
app.run(host='0.0.0.0', port=8080, debug=True)
|
||||
app.run(host='0.0.0.0', port=8081, debug=True)
|
||||
Loading…
Reference in New Issue
Block a user