Remove files
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# 分型强度显示修复总结
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## 问题描述
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用户反映图表上没有显示分型强度信息。
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## 问题诊断
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1. **后端数据传递问题**: 虽然计算了分型强度，但在返回给前端的JSON数据中遗漏了强度相关字段
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2. **JSON序列化问题**: NumPy的`bool_`类型无法被JSON序列化
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## 修复内容
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### 1. 后端数据修复 (web/app.py)
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- **主周期分型信息**: 在`klc_fx_info`中添加了遗漏的强度字段
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- **小周期分型信息**: 在`element_klc_fx_info`中添加了遗漏的强度字段
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**修复前**:
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```python
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'klc_fx_info': [{
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'time': format_time_safely(point['time'], client_tz),
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'price': point['price'],
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'fx_type': point['fx_type'],
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'is_bottom': point['is_bottom']
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} for point in analysis_result['klc_fx_info']]
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```
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**修复后**:
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```python
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'klc_fx_info': [{
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'time': format_time_safely(point['time'], client_tz),
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'price': float(point['price']),
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'fx_type': point['fx_type'],
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'is_bottom': bool(point['is_bottom']),
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'fx_strength': float(point['fx_strength']), # 分型强度分数
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'fx_strength_level': str(point['fx_strength_level']), # 分型强度等级
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'is_strong_fx': bool(point['is_strong_fx']) # 是否为强分型
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} for point in analysis_result['klc_fx_info']]
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```
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### 2. 数据类型转换
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- 将`numpy.bool_`转换为Python `bool`
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- 将强度分数转换为`float`
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- 将强度等级转换为`str`
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## 验证结果
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### 后端API测试 ✅
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```
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=== 测试Web API分型强度数据 ===
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找到 154 个分型
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分型 #1:
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强度分数: 8.79
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强度等级: 极弱
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是否强分型: False
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✅ 所有 154 个分型都包含完整的强度数据
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```
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### 前端显示格式
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- **分型标记**: `分型类型(强度等级分数)`，如`TOP1(极弱8.79)`
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- **视觉区分**: 强分型使用亮色+方形+大尺寸，普通分型使用圆形
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- **Tooltip详情**: 鼠标悬停显示完整强度信息
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## 使用说明
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1. **启动服务**:
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```bash
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cd user_data/Chan/web
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python app.py
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```
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2. **访问界面**: http://localhost:8123
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3. **查看分型强度**:
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- 确保勾选"显示K线分型类型"选项
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- 图表上会显示带强度信息的分型标记
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- 鼠标悬停可查看详细强度信息
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## 技术细节
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### 强度评估维度 (总分100分)
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- 价格差异强度: 40分
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- 突破历史点位: 20分
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- 成交量确认: 15分
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- RSI背离确认: 15分
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- MACD背离确认: 10分
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### 强度等级划分
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- 极强: 80-100分
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- 强: 60-79分
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- 中等: 40-59分
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- 弱: 20-39分
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- 极弱: 0-19分
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### 显示特色
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- 强分型阈值: ≥60分
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- 颜色编码: 强分型使用更亮颜色
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- 形状区分: 强分型用方形，普通分型用圆形
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- 尺寸差异: 强分型显示更大
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## 测试工具
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`test_web_data.py` - 验证API返回的分型强度数据完整性
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@@ -1,139 +0,0 @@
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# 分型强度检测功能完成总结
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## ✅ 已完成的功能
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### 1. **后端功能实现 (ChanKLC.py)**
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#### 核心方法添加：
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- `calculate_fx_strength()`: 计算0-100分的分型强度分数
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- `get_fx_strength_level()`: 获取强度等级描述（极强/强/中等/弱/极弱）
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- `is_strong_fx(threshold)`: 判断是否为强分型
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#### 多维度强度评估体系：
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- **价格差异强度 (40分)**: 分型点与相邻K线的价格差异
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- **突破历史点位 (20分)**: 是否突破前期重要高低点
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- **成交量确认 (15分)**: 分型形成时的成交量放大程度
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- **RSI背离确认 (15分)**: 价格与RSI指标的背离情况
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- **MACD背离确认 (10分)**: 价格与MACD指标的背离情况
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#### 特征数据集成：
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分型强度已自动集成到`get_feature_data()`方法中，新增8个特征：
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- `klc_fx_strength`: 强度分数 (0-100)
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- `klc_fx_strength_level`: 强度等级描述
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- `klc_is_strong_fx`: 是否为强分型 (1/0)
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- `klc_fx_strength_extreme`: 是否为极强分型 (1/0)
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- `klc_fx_strength_strong`: 是否为强分型 (1/0)
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- `klc_fx_strength_medium`: 是否为中等分型 (1/0)
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- `klc_fx_strength_weak`: 是否为弱分型 (1/0)
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- `klc_fx_strength_very_weak`: 是否为极弱分型 (1/0)
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### 2. **前端Web显示功能 (app.py + index.html)**
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#### 后端数据传输：
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- 修改`app.py`中的分型信息提取，添加强度相关数据
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- 新增字段：`fx_strength`、`fx_strength_level`、`is_strong_fx`
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#### 前端图表显示：
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- 分型标记文本显示强度信息：`分型类型(强度等级分数)`
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- 强分型使用更亮颜色和方形标记，普通分型使用圆形标记
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- 强分型标记尺寸更大，更容易识别
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#### 鼠标悬停提示：
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- 添加详细的tooltip显示：
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- 分型类型（顶分型/底分型）
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- 强度分数和等级
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- 是否为强分型
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- 价格和时间信息
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- 支持同时显示买卖点和分型信息的tooltip
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### 3. **配置系统 (fx_strength_config.py)**
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#### 预定义配置：
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- **DEFAULT_CONFIG**: 默认平衡配置
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- **CONSERVATIVE_CONFIG**: 保守配置，更严格识别
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- **AGGRESSIVE_CONFIG**: 激进配置，更宽松识别
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- **TECHNICAL_CONFIG**: 技术指标重点配置
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#### 可调参数：
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- 各维度权重配置
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- 强度等级阈值设置
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- 计算参数（回看期数、放大倍数等）
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- 配置验证功能
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### 4. **示例和文档**
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#### 使用示例 (fx_strength_example.py)：
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- 功能演示代码
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- 筛选强分型方法
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- 统计分析功能
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- 实际应用建议
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#### 配置示例：
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- 多种预定义配置展示
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- 自定义配置方法
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- 参数调优指导
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#### 完整文档 (README_FX_STRENGTH.md)：
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- 详细功能说明
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- 使用方法指导
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- 实际应用建议
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- 注意事项说明
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### 5. **测试验证 (test_fx_strength.py)**
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- 功能完整性测试
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- 特征数据验证
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- 运行状态检查
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## 🎯 功能特色
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### 视觉区分：
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- **强分型**: 亮色 + 方形标记 + 大尺寸
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- **普通分型**: 普通色 + 圆形标记 + 标准尺寸
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### 信息丰富：
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- 标记文本包含类型和强度信息
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- 悬停显示详细分型数据
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- 多层次强度分类
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### 高度可配置：
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- 支持自定义权重和阈值
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- 多种预设配置选择
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- 灵活参数调整
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## 🚀 使用效果
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### 交易信号筛选：
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- 只关注强度≥60的分型作为主要信号
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- 极强分型(≥80分)作为重要转折点
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- 根据强度调整仓位和止损
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### 可视化体验：
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- 图表上直观显示分型强度
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- 鼠标悬停获取详细信息
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- 强弱分型一目了然
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### 数据分析：
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- 强度特征可用于机器学习模型
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- 支持历史分型强度统计
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- 便于策略回测验证
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## 📝 文件清单
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1. **ChanKLC.py** - 核心实现（已修改）
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2. **web/app.py** - 后端数据接口（已修改）
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3. **web/templates/index.html** - 前端显示（已修改）
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4. **fx_strength_config.py** - 配置系统（新建）
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5. **fx_strength_example.py** - 使用示例（新建）
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6. **test_fx_strength.py** - 功能测试（新建）
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7. **README_FX_STRENGTH.md** - 详细文档（新建）
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## ✅ 验证结果
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- ✅ 后端强度计算功能正常
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- ✅ 特征数据集成成功
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- ✅ 前端显示逻辑正确
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- ✅ 配置系统可用
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- ✅ 文档完整齐全
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- ✅ 测试验证通过
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**分型强度检测功能已全面完成并可投入使用！** 🎉
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# 分型强度检测功能文档
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## 概述
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本功能为缠论中的顶底分型添加了强度检测机制，通过多维度分析来量化分型的可靠性和重要性。强度分数范围为0-100分，数值越高表示分型越强、越可靠。
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## 功能特性
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### 1. 多维度强度评估
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分型强度通过以下5个维度进行综合评估：
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- **价格差异强度 (40分)**：分型点与相邻K线的价格差异
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- **突破历史点位 (20分)**：是否突破前期重要高低点
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- **成交量确认 (15分)**：分型形成时的成交量放大程度
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- **RSI背离确认 (15分)**：价格与RSI指标的背离情况
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- **MACD背离确认 (10分)**：价格与MACD指标的背离情况
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### 2. 强度等级分类
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- **极强 (80-100分)**：高可靠性分型，通常是重要转折点
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- **强 (60-79分)**：较高可靠性分型，值得重点关注
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- **中等 (40-59分)**：一般可靠性分型
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- **弱 (20-39分)**：较低可靠性分型
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- **极弱 (0-19分)**：最低可靠性分型
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## 核心方法
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### ChanKLC类新增方法
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```python
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def calculate_fx_strength(self):
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"""计算顶底分型强度，返回0-100的强度分数"""
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def get_fx_strength_level(self):
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"""获取分型强度等级描述字符串"""
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def is_strong_fx(self, threshold=60):
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"""判断是否为强分型，可自定义阈值"""
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```
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### 特征数据集成
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分型强度自动集成到`get_feature_data()`方法中：
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```python
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features = klc.get_feature_data()
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# 可获取以下分型强度相关特征：
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- klc_fx_strength # 强度分数 (0-100)
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- klc_fx_strength_level # 强度等级描述
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- klc_is_strong_fx # 是否为强分型 (1/0)
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- klc_fx_strength_extreme # 是否为极强分型 (1/0)
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- klc_fx_strength_strong # 是否为强分型 (1/0)
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- klc_fx_strength_medium # 是否为中等分型 (1/0)
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- klc_fx_strength_weak # 是否为弱分型 (1/0)
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- klc_fx_strength_very_weak # 是否为极弱分型 (1/0)
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```
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## 使用示例
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### 基本使用
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```python
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from ChanKLC import ChanKLC
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from ChanEnum import Chan_FX_TYPE
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# 假设klc是一个已识别的分型
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if klc.fx != Chan_FX_TYPE.UNKNOWN:
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strength = klc.calculate_fx_strength()
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level = klc.get_fx_strength_level()
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is_strong = klc.is_strong_fx()
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print(f"分型强度: {strength}分")
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print(f"强度等级: {level}")
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print(f"是否强分型: {is_strong}")
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```
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### 筛选强分型
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```python
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def filter_strong_fractals(klc_list, min_strength=60):
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"""筛选强分型"""
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strong_fractals = []
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for klc in klc_list:
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if klc.fx != Chan_FX_TYPE.UNKNOWN and klc.is_strong_fx(min_strength):
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strong_fractals.append(klc)
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return strong_fractals
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# 使用示例
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strong_fractals = filter_strong_fractals(klc_list, min_strength=70)
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```
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### 获取统计信息
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```python
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def get_fractal_statistics(klc_list):
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"""获取分型强度统计信息"""
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stats = {
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'total_fractals': 0,
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'extreme_strength': 0,
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'strong_strength': 0,
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'medium_strength': 0,
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'weak_strength': 0,
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'very_weak_strength': 0,
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'avg_strength': 0
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}
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strengths = []
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for klc in klc_list:
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if klc.fx != Chan_FX_TYPE.UNKNOWN:
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stats['total_fractals'] += 1
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strength = klc.calculate_fx_strength()
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strengths.append(strength)
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if strength >= 80:
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stats['extreme_strength'] += 1
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elif strength >= 60:
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stats['strong_strength'] += 1
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# ... 其他分类
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if strengths:
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stats['avg_strength'] = sum(strengths) / len(strengths)
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return stats
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```
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## 配置选项
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通过`fx_strength_config.py`可以自定义分型强度检测的各项参数：
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### 预定义配置
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||||
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- **DEFAULT_CONFIG**：默认配置，平衡各项权重
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- **CONSERVATIVE_CONFIG**：保守配置，更严格的分型识别
|
||||
- **AGGRESSIVE_CONFIG**：激进配置，更宽松的分型识别
|
||||
- **TECHNICAL_CONFIG**：技术指标配置，重视技术指标背离
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||||
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### 自定义配置
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||||
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```python
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from fx_strength_config import FxStrengthConfig
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config = FxStrengthConfig()
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config.price_difference_weight = 50 # 调整价格差异权重
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config.strong_threshold = 70 # 调整强分型阈值
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config.volume_lookback = 10 # 调整成交量回看期数
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```
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## 强度计算详情
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||||
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### 1. 价格差异强度
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||||
|
||||
- 对于顶分型：计算当前高点与左右相邻点的价格差异
|
||||
- 对于底分型：计算当前低点与左右相邻点的价格差异
|
||||
- 差异越大，强度分数越高
|
||||
|
||||
### 2. 突破历史点位强度
|
||||
|
||||
- 检查是否突破前N根K线的最高/最低价
|
||||
- 突破幅度越大，强度分数越高
|
||||
|
||||
### 3. 成交量确认强度
|
||||
|
||||
- 比较当前K线成交量与前N根K线平均成交量
|
||||
- 成交量放大越多，强度分数越高
|
||||
|
||||
### 4. RSI背离确认强度
|
||||
|
||||
- 检查价格新高/新低时RSI是否出现相反走势
|
||||
- 背离程度越大，强度分数越高
|
||||
|
||||
### 5. MACD背离确认强度
|
||||
|
||||
- 检查价格新高/新低时MACD柱状图是否出现相反走势
|
||||
- 背离程度越大，强度分数越高
|
||||
|
||||
## 实际应用建议
|
||||
|
||||
### 交易策略应用
|
||||
|
||||
1. **入场信号**：只关注强度>=60的分型作为入场信号
|
||||
2. **重要转折**：极强分型(>=80分)通常预示重要转折点
|
||||
3. **止损设置**：根据分型强度调整止损距离
|
||||
4. **仓位管理**：强分型可以加大仓位，弱分型减小仓位
|
||||
|
||||
### 风险控制
|
||||
|
||||
1. **避免弱分型**：强度<40的分型建议谨慎对待
|
||||
2. **确认机制**：结合其他技术指标确认分型有效性
|
||||
3. **时间过滤**：高时间周期的强分型更可靠
|
||||
4. **市场环境**：在震荡市中提高强度阈值
|
||||
|
||||
### 机器学习特征
|
||||
|
||||
分型强度可以作为机器学习模型的重要特征：
|
||||
- 直接使用强度分数作为数值特征
|
||||
- 使用强度等级分类作为分类特征
|
||||
- 结合其他技术指标构建更复杂的特征
|
||||
|
||||
## 注意事项
|
||||
|
||||
1. **数据完整性**：确保KLC对象包含完整的价格和技术指标数据
|
||||
2. **时间序列**：确保KLC之间的pre/next关系正确建立
|
||||
3. **参数调优**：根据不同市场和时间周期调整配置参数
|
||||
4. **回测验证**：在实际使用前进行充分的历史数据回测
|
||||
5. **实时更新**：分型强度会随着后续K线的变化而更新
|
||||
|
||||
## 文件说明
|
||||
|
||||
- `ChanKLC.py`：主要实现文件，包含分型强度计算逻辑
|
||||
- `fx_strength_example.py`：使用示例和功能演示
|
||||
- `fx_strength_config.py`：配置文件，支持自定义参数
|
||||
- `README_FX_STRENGTH.md`：本文档，详细说明功能特性
|
||||
|
||||
## 版本更新
|
||||
|
||||
- **v1.0**：基础分型强度检测功能
|
||||
- 支持多维度强度评估
|
||||
- 集成到特征数据系统
|
||||
- 提供配置化参数调整
|
||||
Binary file not shown.
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Binary file not shown.
@@ -5,7 +5,7 @@
|
||||
"stake_amount": "unlimited",
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "USD",
|
||||
"dry_run": false,
|
||||
"dry_run": true,
|
||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
||||
"dry_run_wallet": 1000,
|
||||
"cancel_open_orders_on_exit": true,
|
||||
@@ -58,12 +58,12 @@
|
||||
}
|
||||
],
|
||||
"telegram": {
|
||||
"enabled": true,
|
||||
"enabled": false,
|
||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
||||
"chat_id": "580807463"
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": false,
|
||||
"enabled": true,
|
||||
"listen_ip_address": "127.0.0.1",
|
||||
"listen_port": 8811,
|
||||
"verbosity": "error",
|
||||
|
||||
@@ -0,0 +1,83 @@
|
||||
{
|
||||
"$schema": "https://schema.freqtrade.io/schema.json",
|
||||
"max_open_trades": 1,
|
||||
"stake_currency": "USDT",
|
||||
"stake_amount": "unlimited",
|
||||
"tradable_balance_ratio": 0.99,
|
||||
"fiat_display_currency": "USD",
|
||||
"dry_run": true,
|
||||
"db_url": "sqlite:///tradesv3.chanlun_btc_15.sqlite",
|
||||
"dry_run_wallet": 1000,
|
||||
"cancel_open_orders_on_exit": true,
|
||||
"trading_mode": "futures",
|
||||
"margin_mode": "isolated",
|
||||
"can_short" : true,
|
||||
"timeframe" : "1m",
|
||||
"process_only_new_candles" : false,
|
||||
"unfilledtimeout": {
|
||||
"entry": 1,
|
||||
"exit": 1,
|
||||
"exit_timeout_count": 5,
|
||||
"unit": "minutes"
|
||||
},
|
||||
"entry_pricing": {
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1,
|
||||
"price_last_balance": 0.0,
|
||||
"check_depth_of_market": {
|
||||
"enabled": false,
|
||||
"bids_to_ask_delta": 1
|
||||
}
|
||||
},
|
||||
"exit_pricing":{
|
||||
"price_side": "same",
|
||||
"use_order_book": true,
|
||||
"order_book_top": 1
|
||||
},
|
||||
"exchange": {
|
||||
"name": "binance",
|
||||
"key": "hvoXanRExQvcN4tyGFvEnsSF4gqxXp6ZJnBu5lnhvlVuHaDbj2PhLBQGCLkkyeI8",
|
||||
"secret": "3UKA2oyDj7OoXrausmnaLwLlNfXmlNf2imBdmQqqKHArcJfk6X9xjaUF19wzu82l",
|
||||
"ccxt_config": {},
|
||||
"ccxt_async_config": {},
|
||||
"pair_whitelist": [
|
||||
"BTC/USDT:USDT"
|
||||
],
|
||||
"pair_blacklist": [
|
||||
"BNB/.*"
|
||||
]
|
||||
},
|
||||
"pairlists": [
|
||||
{
|
||||
"method": "StaticPairList",
|
||||
"number_assets": 1,
|
||||
"sort_key": "quoteVolume",
|
||||
"min_value": 0,
|
||||
"refresh_period": 1800
|
||||
}
|
||||
],
|
||||
"telegram": {
|
||||
"enabled": false,
|
||||
"token": "7677670958:AAFL_jgZvNUTPR3R3vWieREX_tDVi9w2C1Y",
|
||||
"chat_id": "580807463"
|
||||
},
|
||||
"api_server": {
|
||||
"enabled": true,
|
||||
"listen_ip_address": "127.0.0.1",
|
||||
"listen_port": 8812,
|
||||
"verbosity": "error",
|
||||
"enable_openapi": false,
|
||||
"jwt_secret_key": "14d3510740e2c39a973a8895f1aa2704d98d08b86170260085709fa5ea48251d",
|
||||
"ws_token": "dtKKDnafBrX4icq_ZCw7acJTahTK4h_yvg",
|
||||
"CORS_origins": [],
|
||||
"username": "freqtrader",
|
||||
"password": "FreqTrade007"
|
||||
},
|
||||
"bot_name": "freqtrade",
|
||||
"initial_state": "running",
|
||||
"force_entry_enable": false,
|
||||
"internals": {
|
||||
"process_throttle_secs": 2
|
||||
}
|
||||
}
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 268 KiB |
@@ -21,13 +21,13 @@ logger = logging.getLogger(__name__)
|
||||
# freqtrade plot-dataframe --strategy ChanLun_BTC_15 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_15.json --timerange=20250309-
|
||||
|
||||
# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250416-
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
|
||||
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
|
||||
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_15.json -e 200 --timerange=20250201-20250401
|
||||
|
||||
# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_SOL.json --strategy ChanLun_SOL --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
||||
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
|
||||
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
|
||||
# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies --timerange=20250525-
|
||||
# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_15.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
|
||||
# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_BTC_15.json --strategy ChanLun_BTC_15 --strategy-path ./user_data/Chan/strategies
|
||||
|
||||
class ChanLun_BTC_15(IStrategy):
|
||||
INTERFACE_VERSION: int = 3
|
||||
@@ -35,7 +35,7 @@ class ChanLun_BTC_15(IStrategy):
|
||||
# This attribute will be overridden if the config file contains "minimal_roi"
|
||||
# 30m and 1h
|
||||
minimal_roi = {
|
||||
"0": 0.30,
|
||||
"0": 0.60,
|
||||
"360": 0.2,
|
||||
"640": 0.1,
|
||||
"1200": 0
|
||||
@@ -61,7 +61,7 @@ class ChanLun_BTC_15(IStrategy):
|
||||
"3600": 0
|
||||
}
|
||||
can_short = True
|
||||
lev = 20.0
|
||||
lev = 50.0
|
||||
stoploss = -0.3
|
||||
trailing_stop = False
|
||||
trailing_stop_positive = 0.025
|
||||
@@ -147,14 +147,35 @@ class ChanLun_BTC_15(IStrategy):
|
||||
# 填充缺失值（前N根K线）
|
||||
df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
|
||||
return df['volume_ratio']
|
||||
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
|
||||
entry_tag: str | None, side: str, **kwargs) -> float:
|
||||
new_entryprice = proposed_rate
|
||||
if trade:
|
||||
if trade.is_short:
|
||||
new_entryprice = proposed_rate - 50
|
||||
else:
|
||||
new_entryprice = proposed_rate + 50
|
||||
return new_entryprice
|
||||
|
||||
def custom_exit_price(self, pair: str, trade: Trade,
|
||||
current_time: datetime, proposed_rate: float,
|
||||
current_profit: float, exit_tag: str | None, **kwargs) -> float:
|
||||
new_exitprice = proposed_rate
|
||||
if trade:
|
||||
if trade.is_short:
|
||||
new_exitprice = proposed_rate + 50
|
||||
else:
|
||||
new_exitprice = proposed_rate - 50
|
||||
return new_exitprice
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
|
||||
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state'] == "-30")
|
||||
(dataframe[state_str].shift(self.time5*2) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5*2) == -1)
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == -1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
|
||||
@@ -164,8 +185,8 @@ class ChanLun_BTC_15(IStrategy):
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state'] == "-30")
|
||||
(dataframe[state_str].shift(self.time5*2) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5*2) == 1)
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == 1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
|
||||
@@ -179,8 +200,8 @@ class ChanLun_BTC_15(IStrategy):
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state']== "30")
|
||||
(dataframe[state_str].shift(self.time5*2) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5*2) == 1)
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == 1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
|
||||
),
|
||||
@@ -188,8 +209,8 @@ class ChanLun_BTC_15(IStrategy):
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state']== "30")
|
||||
(dataframe[state_str].shift(self.time5*2) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5*2) == -1)
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == -1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
|
||||
),
|
||||
|
||||
@@ -0,0 +1,225 @@
|
||||
# --- Do not remove these libs ---
|
||||
from statistics import median
|
||||
from freqtrade.strategy import IStrategy
|
||||
import sys
|
||||
import os
|
||||
# 添加父目录到系统路径
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
from ChanLun import ChanLun
|
||||
from ChanLun_Classifier import ChanLunClassifier
|
||||
from ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
|
||||
# --------------------------------
|
||||
from technical.util import resample_to_interval, resampled_merge
|
||||
import talib.abstract as ta
|
||||
from pandas import DataFrame
|
||||
from datetime import datetime, timedelta
|
||||
from freqtrade.persistence import Trade
|
||||
from typing import Optional
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
### Now you can use logger.info('asfd') to log
|
||||
# freqtrade plot-dataframe --strategy ChanLun_BTC_30 --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-
|
||||
|
||||
# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies
|
||||
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies --timerange=20250520-
|
||||
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m --pairs BTC/USDT:USDT --timerange=20250405-
|
||||
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi stoploss --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250401
|
||||
|
||||
# sudo docker compose run --rm chan_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies --timerange=20250101-
|
||||
# sudo docker compose run --rm chan_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
|
||||
# sudo docker compose run --rm chan_btc trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC_30 --strategy-path ./user_data/Chan/strategies
|
||||
|
||||
class ChanLun_BTC_30(IStrategy):
|
||||
INTERFACE_VERSION: int = 3
|
||||
# Minimal ROI designed for the strategy.
|
||||
# This attribute will be overridden if the config file contains "minimal_roi"
|
||||
# 30m and 1h
|
||||
minimal_roi = {
|
||||
"0": 0.60,
|
||||
"360": 0.2,
|
||||
"640": 0.1,
|
||||
"1200": 0
|
||||
}
|
||||
# 5m and 15m
|
||||
minimal_roi_1 = {
|
||||
"0": 0.1,
|
||||
"60": 0.05,
|
||||
"120": 0.02,
|
||||
"240": 0
|
||||
}
|
||||
# 15m and 30m
|
||||
minimal_roi_1 = {
|
||||
"0": 0.1,
|
||||
"240": 0.05,
|
||||
"480": 0.03,
|
||||
"600": 0
|
||||
}
|
||||
minimal_roi_2 = {
|
||||
"0": 0.10,
|
||||
"1200": 0.05,
|
||||
"2400": 0.025,
|
||||
"3600": 0
|
||||
}
|
||||
can_short = True
|
||||
lev = 50.0
|
||||
stoploss = -0.3
|
||||
trailing_stop = False
|
||||
trailing_stop_positive = 0.025
|
||||
trailing_stop_positive_offset = 0.045
|
||||
trailing_only_offset_is_reached = False
|
||||
|
||||
position_adjustment_enable = True
|
||||
startup_candle_count = 600
|
||||
|
||||
time5 = 5
|
||||
time15 = 15
|
||||
time30 = 30
|
||||
time60 = 60
|
||||
time4h = 240
|
||||
time5 = 30
|
||||
last_time = datetime.now()
|
||||
chan = ChanLun()
|
||||
classifier = ChanLunClassifier(None)
|
||||
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
|
||||
# resample our dataframes
|
||||
dataframe_5 = resample_to_interval(dataframe, self.get_ticker_indicator() * 5)
|
||||
dataframe_15 = resample_to_interval(dataframe, self.get_ticker_indicator() * 15)
|
||||
dataframe_30 = resample_to_interval(dataframe, self.get_ticker_indicator() * 30)
|
||||
dataframe_60 = resample_to_interval(dataframe, self.get_ticker_indicator() * 60)
|
||||
dataframe_4h = resample_to_interval(dataframe, self.get_ticker_indicator() * 240)
|
||||
|
||||
#dataframe_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe='1d')
|
||||
#dataframe_1w = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 10080)
|
||||
#dataframe_1m = resample_to_interval(dataframe_1d, self.get_ticker_indicator() * 43200)
|
||||
|
||||
dataframe_1d = resample_to_interval(dataframe, self.get_ticker_indicator() * 1440)
|
||||
#dataframe_1w = resample_to_interval(dataframe, self.get_ticker_indicator() * 10080)
|
||||
#dataframe_1m = resample_to_interval(dataframe, self.get_ticker_indicator() * 43200)
|
||||
dataframe = self.add_indicators(dataframe)
|
||||
dataframe_5 = self.add_indicators(dataframe_5)
|
||||
dataframe_30 = self.add_indicators(dataframe_30)
|
||||
dataframe_60 = self.add_indicators(dataframe_60)
|
||||
dataframe_4h = self.add_indicators(dataframe_4h)
|
||||
dataframe_1d = self.add_indicators(dataframe_1d)
|
||||
#self.chan.plot_dual(dataframe_5, dataframe_30)
|
||||
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
|
||||
|
||||
state_list, fx_list = self.chan.get_klc_strength_list(dataframe_30)
|
||||
dataframe_30['state'] = state_list
|
||||
dataframe_30['fx'] = fx_list
|
||||
klc_list = self.chan.get_klc_list(dataframe_30)
|
||||
bi_list = self.chan.cal_bi_list(klc_list)
|
||||
if self.last_time + timedelta(minutes=1) < datetime.now():
|
||||
print(state_list[-1], state_list[-2], state_list[-3], state_list[-4], state_list[-5])
|
||||
print(fx_list[-1], fx_list[-2], fx_list[-3], fx_list[-4], fx_list[-5])
|
||||
print(klc_list[-1].klc_fx_type, klc_list[-2].klc_fx_type, klc_list[-3].klc_fx_type, klc_list[-4].klc_fx_type, klc_list[-5].klc_fx_type)
|
||||
print("-------------------------------------------------------------------------------")
|
||||
self.last_time = datetime.now()
|
||||
#dataframe = resampled_merge(dataframe, dataframe_5)
|
||||
dataframe = resampled_merge(dataframe, dataframe_30)
|
||||
#dataframe = resampled_merge(dataframe, dataframe_30)
|
||||
#dataframe = resampled_merge(dataframe, dataframe_60)
|
||||
#dataframe = resampled_merge(dataframe, dataframe_4h)
|
||||
return dataframe
|
||||
|
||||
def add_indicators(self, df):
|
||||
fast = 8
|
||||
slow = 16
|
||||
period = 6
|
||||
macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
|
||||
df['macd'] = macd['macd']
|
||||
df['macdsignal'] = macd['macdsignal']
|
||||
df['macdhist'] = macd['macdhist']
|
||||
df['ma5'] = ta.MA(df, timeperiod=5)
|
||||
df['ma10'] = ta.MA(df, timeperiod=10)
|
||||
df['ma30'] = ta.EMA(df, timeperiod=30)
|
||||
df['ma250'] = ta.MA(df, timeperiod=250)
|
||||
df['rsi'] = ta.RSI(df, timeperiod=14)
|
||||
df['volume_ratio'] = self.cal_volume_ratio(df)
|
||||
return df
|
||||
def cal_volume_ratio(self, dataframe, window=10):
|
||||
df = dataframe.copy()
|
||||
# 计算过去N根K线的平均成交量
|
||||
df['avg_volume'] = df['volume'].rolling(window=window).mean()
|
||||
# 计算量比
|
||||
df['volume_ratio'] = df['volume'] / df['avg_volume']
|
||||
# 填充缺失值（前N根K线）
|
||||
df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
|
||||
return df['volume_ratio']
|
||||
def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
|
||||
entry_tag: str | None, side: str, **kwargs) -> float:
|
||||
new_entryprice = proposed_rate
|
||||
if trade:
|
||||
if trade.is_short:
|
||||
new_entryprice = proposed_rate - 50
|
||||
else:
|
||||
new_entryprice = proposed_rate + 50
|
||||
return new_entryprice
|
||||
|
||||
def custom_exit_price(self, pair: str, trade: Trade,
|
||||
current_time: datetime, proposed_rate: float,
|
||||
current_profit: float, exit_tag: str | None, **kwargs) -> float:
|
||||
new_exitprice = proposed_rate
|
||||
if trade:
|
||||
if trade.is_short:
|
||||
new_exitprice = proposed_rate + 50
|
||||
else:
|
||||
new_exitprice = proposed_rate - 50
|
||||
return new_exitprice
|
||||
|
||||
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
|
||||
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state'] == "-30")
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == -1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
|
||||
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
|
||||
),
|
||||
['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state'] == "-30")
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == 1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "-10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time5)].shift(self.time5) == "-10")
|
||||
#(qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']))
|
||||
),
|
||||
['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
|
||||
return dataframe
|
||||
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||||
state_str = 'resample_{}_state'.format(self.get_ticker_indicator()*self.time5)
|
||||
fx_str = 'resample_{}_fx'.format(self.get_ticker_indicator()*self.time5)
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state']== "30")
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == 1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
|
||||
),
|
||||
['exit_long', 'exit_tag']] = (1, 'long_close_signal_chan')
|
||||
dataframe.loc[
|
||||
(
|
||||
#(dataframe['state']== "30")
|
||||
(dataframe[state_str].shift(self.time5) > 1.0) &
|
||||
(dataframe[fx_str].shift(self.time5) == -1)
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time30)] == "10") &
|
||||
#(dataframe['resample_{}_state'.format(self.get_ticker_indicator()*self.time60)] == "10")
|
||||
),
|
||||
['exit_short', 'exit_tag']] = (1, 'short_close_signal_chan')
|
||||
return dataframe
|
||||
def leverage(self, pair: str, current_time: datetime, current_rate: float,
|
||||
proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
|
||||
**kwargs) -> float:
|
||||
return self.lev
|
||||
|
||||
def get_ticker_indicator(self):
|
||||
return int(self.timeframe[:-1])
|
||||
@@ -1,78 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
A股数据获取测试脚本
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.append(os.path.join(os.path.dirname(__file__), 'web'))
|
||||
|
||||
from web.cn_stock_data import ChinaStockData
|
||||
import pandas as pd
|
||||
|
||||
def test_a_stock_data():
|
||||
"""测试A股数据获取功能"""
|
||||
print("开始测试A股数据获取功能...")
|
||||
|
||||
# 初始化A股数据获取器
|
||||
china_stock = ChinaStockData()
|
||||
|
||||
# 测试获取热门股票列表
|
||||
print("\n1. 测试获取热门股票列表:")
|
||||
popular_stocks = china_stock.get_popular_stocks()
|
||||
print(f"热门股票数量: {len(popular_stocks)}")
|
||||
for i, stock in enumerate(popular_stocks[:5]):
|
||||
print(f" {i+1}. {stock['symbol']} - {stock['name']}")
|
||||
|
||||
# 测试获取股票K线数据
|
||||
print("\n2. 测试获取股票K线数据:")
|
||||
test_symbols = ['000001', '600519', '000858'] # 平安银行、贵州茅台、五粮液
|
||||
|
||||
for symbol in test_symbols:
|
||||
print(f"\n测试股票: {symbol}")
|
||||
|
||||
# 测试日线数据
|
||||
print(" 获取日线数据...")
|
||||
try:
|
||||
df_daily = china_stock.get_kl_data(symbol, '1d', limit=100)
|
||||
if df_daily is not None:
|
||||
print(f" 成功获取 {len(df_daily)} 条日线数据")
|
||||
print(f" 时间范围: {df_daily['date'].min()} 到 {df_daily['date'].max()}")
|
||||
print(f" 最新价格: {df_daily['close'].iloc[-1]:.2f}")
|
||||
else:
|
||||
print(" 获取日线数据失败")
|
||||
except Exception as e:
|
||||
print(f" 获取日线数据出错: {e}")
|
||||
|
||||
# 测试分钟数据
|
||||
print(" 获取5分钟数据...")
|
||||
try:
|
||||
df_5m = china_stock.get_kl_data(symbol, '5m', limit=50)
|
||||
if df_5m is not None:
|
||||
print(f" 成功获取 {len(df_5m)} 条5分钟数据")
|
||||
print(f" 时间范围: {df_5m['date'].min()} 到 {df_5m['date'].max()}")
|
||||
else:
|
||||
print(" 获取5分钟数据失败")
|
||||
except Exception as e:
|
||||
print(f" 获取5分钟数据出错: {e}")
|
||||
|
||||
# 测试获取股票列表
|
||||
print("\n3. 测试获取股票列表:")
|
||||
try:
|
||||
stock_list = china_stock.get_stock_list()
|
||||
if stock_list:
|
||||
print(f"成功获取 {len(stock_list)} 只股票")
|
||||
print("前5只股票:")
|
||||
for i, stock in enumerate(stock_list[:5]):
|
||||
print(f" {i+1}. {stock['symbol']} - {stock['name']} - 价格: {stock['price']} - 涨跌幅: {stock['change_pct']}%")
|
||||
else:
|
||||
print("获取股票列表失败")
|
||||
except Exception as e:
|
||||
print(f"获取股票列表出错: {e}")
|
||||
|
||||
print("\nA股数据获取测试完成!")
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_a_stock_data()
|
||||
@@ -1,192 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
"""
|
||||
测试分批次数据获取功能
|
||||
验证A股和加密货币数据的大时间范围获取
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
sys.path.append('web')
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
from cn_stock_data import ChinaStockData
|
||||
import ccxt
|
||||
|
||||
def test_a_stock_batch_data():
|
||||
"""测试A股分批次数据获取"""
|
||||
print("=== 测试A股分批次数据获取 ===")
|
||||
|
||||
china_stock = ChinaStockData()
|
||||
|
||||
# 测试获取更长时间范围的数据
|
||||
end_date = datetime.now()
|
||||
start_date = end_date - timedelta(days=180) # 6个月数据
|
||||
|
||||
print(f"测试时间范围: {start_date.strftime('%Y-%m-%d')} 到 {end_date.strftime('%Y-%m-%d')}")
|
||||
|
||||
# 测试不同时间周期
|
||||
test_cases = [
|
||||
('600519', '1d', '日线数据'),
|
||||
('600519', '1h', '1小时数据'),
|
||||
('600519', '15m', '15分钟数据'),
|
||||
]
|
||||
|
||||
for symbol, timeframe, description in test_cases:
|
||||
print(f"\n测试 {description}: {symbol} {timeframe}")
|
||||
|
||||
try:
|
||||
df = china_stock.get_kl_data(
|
||||
symbol=symbol,
|
||||
timeframe=timeframe,
|
||||
start_date=start_date.strftime('%Y-%m-%d'),
|
||||
end_date=end_date.strftime('%Y-%m-%d'),
|
||||
limit=5000
|
||||
)
|
||||
|
||||
if df is not None:
|
||||
print(f"✅ 成功获取 {len(df)} 条记录")
|
||||
print(f" 时间范围: {df['date'].min()} 到 {df['date'].max()}")
|
||||
print(f" 数据列: {list(df.columns)}")
|
||||
else:
|
||||
print(f"❌ 获取失败")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 错误: {e}")
|
||||
|
||||
def test_crypto_batch_data():
|
||||
"""测试加密货币分批次数据获取"""
|
||||
print("\n=== 测试加密货币分批次数据获取 ===")
|
||||
|
||||
# 初始化交易所
|
||||
exchange = ccxt.binance({
|
||||
'enableRateLimit': True,
|
||||
})
|
||||
|
||||
# 测试获取更长时间范围的数据
|
||||
end_time = datetime.now()
|
||||
start_time = end_time - timedelta(days=30) # 30天数据
|
||||
|
||||
print(f"测试时间范围: {start_time} 到 {end_time}")
|
||||
|
||||
# 转换为时间戳
|
||||
start_timestamp = int(start_time.timestamp() * 1000)
|
||||
end_timestamp = int(end_time.timestamp() * 1000)
|
||||
|
||||
# 测试不同时间周期
|
||||
test_cases = [
|
||||
('BTC/USDT:USDT', '1d', '日线数据'),
|
||||
('BTC/USDT:USDT', '1h', '1小时数据'),
|
||||
('BTC/USDT:USDT', '5m', '5分钟数据'),
|
||||
]
|
||||
|
||||
for symbol, timeframe, description in test_cases:
|
||||
print(f"\n测试 {description}: {symbol} {timeframe}")
|
||||
|
||||
try:
|
||||
# 模拟分批次获取逻辑
|
||||
all_ohlcv = []
|
||||
current_since = start_timestamp
|
||||
request_count = 0
|
||||
max_requests = 10
|
||||
|
||||
batch_size = 500 if timeframe in ['1m', '5m'] else 1000
|
||||
|
||||
while request_count < max_requests and current_since < end_timestamp:
|
||||
request_count += 1
|
||||
print(f" 批次 {request_count}: 获取数据...")
|
||||
|
||||
ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=current_since, limit=batch_size)
|
||||
|
||||
if not ohlcv or len(ohlcv) == 0:
|
||||
break
|
||||
|
||||
all_ohlcv.extend(ohlcv)
|
||||
last_timestamp = ohlcv[-1][0]
|
||||
|
||||
if last_timestamp >= end_timestamp:
|
||||
break
|
||||
|
||||
if len(ohlcv) < batch_size:
|
||||
break
|
||||
|
||||
current_since = last_timestamp + 1
|
||||
|
||||
# 防止请求过频
|
||||
import time
|
||||
time.sleep(0.3)
|
||||
|
||||
if all_ohlcv:
|
||||
print(f"✅ 成功获取 {len(all_ohlcv)} 条记录 (共 {request_count} 个批次)")
|
||||
|
||||
# 时间范围检查
|
||||
first_time = datetime.fromtimestamp(all_ohlcv[0][0] / 1000)
|
||||
last_time = datetime.fromtimestamp(all_ohlcv[-1][0] / 1000)
|
||||
print(f" 时间范围: {first_time} 到 {last_time}")
|
||||
else:
|
||||
print(f"❌ 获取失败")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 错误: {e}")
|
||||
|
||||
def test_data_quality():
|
||||
"""测试数据质量"""
|
||||
print("\n=== 测试数据质量 ===")
|
||||
|
||||
china_stock = ChinaStockData()
|
||||
|
||||
# 获取一小段数据进行质量检查
|
||||
df = china_stock.get_kl_data(
|
||||
symbol='600519',
|
||||
timeframe='1d',
|
||||
limit=100
|
||||
)
|
||||
|
||||
if df is not None:
|
||||
print(f"数据行数: {len(df)}")
|
||||
print(f"数据列: {list(df.columns)}")
|
||||
|
||||
# 检查缺失值
|
||||
missing_values = df.isnull().sum()
|
||||
print(f"缺失值统计:")
|
||||
for col, count in missing_values.items():
|
||||
if count > 0:
|
||||
print(f" {col}: {count}")
|
||||
|
||||
# 检查数据类型
|
||||
print(f"数据类型:")
|
||||
for col, dtype in df.dtypes.items():
|
||||
print(f" {col}: {dtype}")
|
||||
|
||||
# 检查时间连续性
|
||||
if len(df) > 1:
|
||||
time_diffs = df['date'].diff().dropna()
|
||||
print(f"时间间隔统计:")
|
||||
print(f" 最小间隔: {time_diffs.min()}")
|
||||
print(f" 最大间隔: {time_diffs.max()}")
|
||||
print(f" 平均间隔: {time_diffs.mean()}")
|
||||
|
||||
# 检查价格合理性
|
||||
price_cols = ['open', 'high', 'low', 'close']
|
||||
for col in price_cols:
|
||||
if col in df.columns:
|
||||
print(f"{col} 价格范围: {df[col].min():.2f} - {df[col].max():.2f}")
|
||||
|
||||
print("✅ 数据质量检查完成")
|
||||
else:
|
||||
print("❌ 无法获取数据进行质量检查")
|
||||
|
||||
if __name__ == '__main__':
|
||||
print("开始测试分批次数据获取功能...\n")
|
||||
|
||||
# 测试A股数据
|
||||
test_a_stock_batch_data()
|
||||
|
||||
# 测试加密货币数据
|
||||
test_crypto_batch_data()
|
||||
|
||||
# 测试数据质量
|
||||
test_data_quality()
|
||||
|
||||
print("\n测试完成！")
|
||||
@@ -1,214 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
测试分型强度检测功能
|
||||
"""
|
||||
|
||||
from ChanKLC import ChanKLC
|
||||
import ChanKLU
|
||||
from ChanEnum import Chan_FX_TYPE, Chan_KLINE_DIR
|
||||
import requests
|
||||
import json
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from collections import Counter
|
||||
|
||||
def test_fx_strength():
|
||||
"""测试分型强度检测功能"""
|
||||
print('=== 分型强度检测功能测试 ===')
|
||||
|
||||
# 创建一个简单的测试KLU，使用正确的构造函数参数
|
||||
klu = ChanKLU.ChanKLU(
|
||||
time='2024-01-01 10:00:00',
|
||||
open=100.0,
|
||||
high=105.0,
|
||||
low=98.0,
|
||||
close=103.0,
|
||||
volume=1000
|
||||
)
|
||||
klu.rsi = 65.0
|
||||
klu.volume_ratio = 1.2
|
||||
klu.macdhist = 0.5
|
||||
|
||||
# 创建KLC对象
|
||||
klc = ChanKLC(klu, 1, Chan_KLINE_DIR.UP)
|
||||
klc.fx = Chan_FX_TYPE.TOP
|
||||
|
||||
# 测试强度计算
|
||||
strength = klc.calculate_fx_strength()
|
||||
level = klc.get_fx_strength_level()
|
||||
is_strong = klc.is_strong_fx()
|
||||
|
||||
print(f'分型强度分数: {strength}')
|
||||
print(f'分型强度等级: {level}')
|
||||
print(f'是否强分型: {is_strong}')
|
||||
|
||||
# 测试特征数据集成
|
||||
features = klc.get_feature_data()
|
||||
fx_features = {k: v for k, v in features.items() if 'fx_strength' in k}
|
||||
print('\n分型强度相关特征:')
|
||||
for key, value in fx_features.items():
|
||||
print(f' {key}: {value}')
|
||||
|
||||
print('\n✅ 分型强度检测功能正常工作!')
|
||||
return True
|
||||
|
||||
def test_fx_strength_distribution():
|
||||
"""测试分型强度分布情况"""
|
||||
|
||||
print("=== 分型强度分布分析 ===")
|
||||
|
||||
# 请求API数据
|
||||
url = "http://localhost:8123/api/analyze"
|
||||
params = {
|
||||
'symbol': 'SOL/USDT:USDT',
|
||||
'timeframe': '5m',
|
||||
'timezone': 'Asia/Shanghai'
|
||||
}
|
||||
|
||||
try:
|
||||
response = requests.get(url, params=params)
|
||||
response.raise_for_status()
|
||||
data = response.json()
|
||||
except Exception as e:
|
||||
print(f"❌ 请求API失败: {e}")
|
||||
return
|
||||
|
||||
# 提取分型强度数据
|
||||
fx_strengths = []
|
||||
fx_levels = []
|
||||
top_strengths = []
|
||||
bottom_strengths = []
|
||||
|
||||
for fx in data.get('klc_fx_info', []):
|
||||
strength = fx.get('fx_strength', 0)
|
||||
level = fx.get('fx_strength_level', 'Unknown')
|
||||
is_bottom = fx.get('is_bottom_fx', False)
|
||||
|
||||
fx_strengths.append(strength)
|
||||
fx_levels.append(level)
|
||||
|
||||
if is_bottom:
|
||||
bottom_strengths.append(strength)
|
||||
else:
|
||||
top_strengths.append(strength)
|
||||
|
||||
# 统计分析
|
||||
if fx_strengths:
|
||||
print(f"\n📊 基础统计:")
|
||||
print(f"总分型数量: {len(fx_strengths)}")
|
||||
print(f"平均强度: {np.mean(fx_strengths):.2f}")
|
||||
print(f"强度中位数: {np.median(fx_strengths):.2f}")
|
||||
print(f"强度标准差: {np.std(fx_strengths):.2f}")
|
||||
print(f"最高强度: {np.max(fx_strengths):.2f}")
|
||||
print(f"最低强度: {np.min(fx_strengths):.2f}")
|
||||
|
||||
print(f"\n🔝 顶分型统计:")
|
||||
if top_strengths:
|
||||
print(f"数量: {len(top_strengths)}")
|
||||
print(f"平均强度: {np.mean(top_strengths):.2f}")
|
||||
print(f"最高强度: {np.max(top_strengths):.2f}")
|
||||
|
||||
print(f"\n🔻 底分型统计:")
|
||||
if bottom_strengths:
|
||||
print(f"数量: {len(bottom_strengths)}")
|
||||
print(f"平均强度: {np.mean(bottom_strengths):.2f}")
|
||||
print(f"最高强度: {np.max(bottom_strengths):.2f}")
|
||||
|
||||
# 强度等级分布
|
||||
print(f"\n📈 强度等级分布:")
|
||||
level_counts = Counter(fx_levels)
|
||||
for level, count in level_counts.items():
|
||||
percentage = (count / len(fx_levels)) * 100
|
||||
print(f"{level}: {count} ({percentage:.1f}%)")
|
||||
|
||||
# 强度区间分布
|
||||
print(f"\n📊 强度区间分布:")
|
||||
ranges = [
|
||||
(0, 20, "极弱 (0-20)"),
|
||||
(20, 40, "弱 (20-40)"),
|
||||
(40, 60, "中等 (40-60)"),
|
||||
(60, 80, "强 (60-80)"),
|
||||
(80, 100, "极强 (80-100)")
|
||||
]
|
||||
|
||||
for min_val, max_val, label in ranges:
|
||||
count = sum(1 for s in fx_strengths if min_val <= s < max_val)
|
||||
percentage = (count / len(fx_strengths)) * 100
|
||||
print(f"{label}: {count} ({percentage:.1f}%)")
|
||||
|
||||
# 找出最强和最弱的分型
|
||||
print(f"\n⭐ 最强分型 (Top 5):")
|
||||
sorted_fx = sorted(data.get('klc_fx_info', []),
|
||||
key=lambda x: x.get('fx_strength', 0),
|
||||
reverse=True)[:5]
|
||||
|
||||
for i, fx in enumerate(sorted_fx, 1):
|
||||
fx_type = "底分型" if fx.get('is_bottom_fx', False) else "顶分型"
|
||||
print(f" {i}. {fx.get('time', 'N/A')} - {fx_type} - 强度: {fx.get('fx_strength', 0):.2f} - 等级: {fx.get('fx_strength_level', 'N/A')}")
|
||||
|
||||
print(f"\n💔 最弱分型 (Bottom 5):")
|
||||
weakest_fx = sorted(data.get('klc_fx_info', []),
|
||||
key=lambda x: x.get('fx_strength', 0))[:5]
|
||||
|
||||
for i, fx in enumerate(weakest_fx, 1):
|
||||
fx_type = "底分型" if fx.get('is_bottom_fx', False) else "顶分型"
|
||||
print(f" {i}. {fx.get('time', 'N/A')} - {fx_type} - 强度: {fx.get('fx_strength', 0):.2f} - 等级: {fx.get('fx_strength_level', 'N/A')}")
|
||||
|
||||
# 生成直方图
|
||||
try:
|
||||
plt.figure(figsize=(12, 8))
|
||||
|
||||
# 主强度分布图
|
||||
plt.subplot(2, 2, 1)
|
||||
plt.hist(fx_strengths, bins=20, alpha=0.7, color='blue', edgecolor='black')
|
||||
plt.title('分型强度分布')
|
||||
plt.xlabel('强度分数')
|
||||
plt.ylabel('频次')
|
||||
plt.axvline(np.mean(fx_strengths), color='red', linestyle='--', label=f'平均值: {np.mean(fx_strengths):.2f}')
|
||||
plt.legend()
|
||||
|
||||
# 顶分型 vs 底分型对比
|
||||
plt.subplot(2, 2, 2)
|
||||
if top_strengths and bottom_strengths:
|
||||
plt.hist([top_strengths, bottom_strengths], bins=15, alpha=0.7,
|
||||
label=['顶分型', '底分型'], color=['red', 'green'])
|
||||
plt.title('顶分型 vs 底分型强度对比')
|
||||
plt.xlabel('强度分数')
|
||||
plt.ylabel('频次')
|
||||
plt.legend()
|
||||
|
||||
# 强度等级饼图
|
||||
plt.subplot(2, 2, 3)
|
||||
if level_counts:
|
||||
labels = list(level_counts.keys())
|
||||
sizes = list(level_counts.values())
|
||||
colors = ['red', 'orange', 'yellow', 'lightgreen', 'green'][:len(labels)]
|
||||
plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')
|
||||
plt.title('强度等级分布')
|
||||
|
||||
# 时间序列图
|
||||
plt.subplot(2, 2, 4)
|
||||
x_vals = range(len(fx_strengths))
|
||||
colors = ['red' if not fx.get('is_bottom_fx', False) else 'green'
|
||||
for fx in data.get('klc_fx_info', [])]
|
||||
plt.scatter(x_vals, fx_strengths, c=colors, alpha=0.6)
|
||||
plt.title('分型强度时间序列 (红=顶分型, 绿=底分型)')
|
||||
plt.xlabel('分型序号')
|
||||
plt.ylabel('强度分数')
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig('user_data/Chan/fx_strength_analysis.png', dpi=300, bbox_inches='tight')
|
||||
print(f"\n📈 图表已保存到: user_data/Chan/fx_strength_analysis.png")
|
||||
|
||||
except ImportError:
|
||||
print("\n📈 matplotlib 未安装，跳过图表生成")
|
||||
except Exception as e:
|
||||
print(f"\n❌ 生成图表失败: {e}")
|
||||
|
||||
else:
|
||||
print("❌ 未找到分型强度数据")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_fx_strength()
|
||||
test_fx_strength_distribution()
|
||||
@@ -1,107 +0,0 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
测试web接口返回的分型强度数据
|
||||
"""
|
||||
|
||||
import requests
|
||||
import json
|
||||
import sys
|
||||
import os
|
||||
|
||||
# 添加父目录到系统路径以便导入模块
|
||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
def test_web_api():
|
||||
"""测试web API接口返回的分型强度数据"""
|
||||
|
||||
print('=== 测试Web API分型强度数据 ===')
|
||||
|
||||
# 构建请求URL
|
||||
base_url = "http://localhost:8123"
|
||||
endpoint = "/api/analyze"
|
||||
|
||||
params = {
|
||||
'symbol': 'SOL/USDT:USDT',
|
||||
'timeframe': '5m',
|
||||
'timezone': 'Asia/Shanghai'
|
||||
}
|
||||
|
||||
try:
|
||||
print(f"发送请求到: {base_url}{endpoint}")
|
||||
print(f"参数: {params}")
|
||||
|
||||
# 发送请求
|
||||
response = requests.get(f"{base_url}{endpoint}", params=params, timeout=30)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
|
||||
# 检查是否有分型信息
|
||||
if 'klc_fx_info' in data:
|
||||
fx_info = data['klc_fx_info']
|
||||
print(f"\n找到 {len(fx_info)} 个分型")
|
||||
|
||||
# 显示前3个分型的详细信息
|
||||
for i, fx in enumerate(fx_info[:3]):
|
||||
print(f"\n分型 #{i+1}:")
|
||||
print(f" 时间: {fx.get('time', '无')}")
|
||||
print(f" 价格: {fx.get('price', '无')}")
|
||||
print(f" 分型类型: {fx.get('fx_type', '无')}")
|
||||
print(f" 是否底分型: {fx.get('is_bottom', '无')}")
|
||||
print(f" 强度分数: {fx.get('fx_strength', '缺失!')}")
|
||||
print(f" 强度等级: {fx.get('fx_strength_level', '缺失!')}")
|
||||
print(f" 是否强分型: {fx.get('is_strong_fx', '缺失!')}")
|
||||
|
||||
# 检查强度数据是否完整
|
||||
missing_strength_count = 0
|
||||
for fx in fx_info:
|
||||
if 'fx_strength' not in fx or 'fx_strength_level' not in fx or 'is_strong_fx' not in fx:
|
||||
missing_strength_count += 1
|
||||
|
||||
if missing_strength_count == 0:
|
||||
print(f"\n✅ 所有 {len(fx_info)} 个分型都包含完整的强度数据")
|
||||
else:
|
||||
print(f"\n❌ 有 {missing_strength_count} 个分型缺少强度数据")
|
||||
|
||||
else:
|
||||
print("\n❌ 响应中未找到分型信息 (klc_fx_info)")
|
||||
|
||||
# 检查小周期分型信息
|
||||
if 'element_klc_fx_info' in data:
|
||||
element_fx_info = data['element_klc_fx_info']
|
||||
print(f"\n找到 {len(element_fx_info)} 个小周期分型")
|
||||
|
||||
# 检查小周期强度数据
|
||||
missing_element_strength_count = 0
|
||||
for fx in element_fx_info:
|
||||
if 'fx_strength' not in fx or 'fx_strength_level' not in fx or 'is_strong_fx' not in fx:
|
||||
missing_element_strength_count += 1
|
||||
|
||||
if missing_element_strength_count == 0:
|
||||
print(f"✅ 所有 {len(element_fx_info)} 个小周期分型都包含完整的强度数据")
|
||||
else:
|
||||
print(f"❌ 有 {missing_element_strength_count} 个小周期分型缺少强度数据")
|
||||
|
||||
else:
|
||||
print(f"❌ 请求失败，状态码: {response.status_code}")
|
||||
print(f"响应内容: {response.text}")
|
||||
|
||||
except requests.exceptions.ConnectionError:
|
||||
print("❌ 无法连接到服务器，请确保web服务正在运行 (python web/app.py)")
|
||||
except Exception as e:
|
||||
print(f"❌ 测试过程中出错: {e}")
|
||||
|
||||
def print_usage():
|
||||
"""打印使用说明"""
|
||||
print("\n=== 使用说明 ===")
|
||||
print("1. 确保web服务正在运行:")
|
||||
print(" cd user_data/Chan/web")
|
||||
print(" python app.py")
|
||||
print("\n2. 然后运行此测试脚本:")
|
||||
print(" python test_web_data.py")
|
||||
print("\n3. 检查控制台输出，确认分型强度数据是否正确返回")
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_web_api()
|
||||
print_usage()
|
||||
+236
-19
@@ -225,17 +225,35 @@ def get_a_stock_kl_data(symbol, timeframe, limit=1000, start_time=None, end_time
|
||||
|
||||
if start_time:
|
||||
try:
|
||||
# 尝试解析时间戳（毫秒）
|
||||
start_timestamp = int(start_time)
|
||||
start_date = datetime.fromtimestamp(start_timestamp / 1000).strftime('%Y-%m-%d')
|
||||
except:
|
||||
start_date = start_time
|
||||
except (ValueError, TypeError):
|
||||
# 如果不是时间戳，尝试解析datetime-local格式 (YYYY-MM-DDTHH:MM)
|
||||
try:
|
||||
if 'T' in str(start_time):
|
||||
# datetime-local格式：2025-05-19T06:07
|
||||
start_date = str(start_time).split('T')[0] # 只取日期部分
|
||||
else:
|
||||
start_date = str(start_time)
|
||||
except:
|
||||
start_date = start_time
|
||||
|
||||
if end_time:
|
||||
try:
|
||||
# 尝试解析时间戳（毫秒）
|
||||
end_timestamp = int(end_time)
|
||||
end_date = datetime.fromtimestamp(end_timestamp / 1000).strftime('%Y-%m-%d')
|
||||
except:
|
||||
end_date = end_time
|
||||
except (ValueError, TypeError):
|
||||
# 如果不是时间戳，尝试解析datetime-local格式
|
||||
try:
|
||||
if 'T' in str(end_time):
|
||||
# datetime-local格式：2025-05-26T06:07
|
||||
end_date = str(end_time).split('T')[0] # 只取日期部分
|
||||
else:
|
||||
end_date = str(end_time)
|
||||
except:
|
||||
end_date = end_time
|
||||
|
||||
# 如果用户指定了时间范围，优先获取该范围内的所有数据
|
||||
actual_limit = limit
|
||||
@@ -328,22 +346,51 @@ def analyze_chan(df):
|
||||
klc_fx_info = []
|
||||
for klc in klc_list:
|
||||
if hasattr(klc, 'klc_fx_type') and klc.klc_fx_type != Chan_KLC_FX.UNKNOWN:
|
||||
# 计算分型强度
|
||||
#fx_strength = klc.calculate_fx_strength()
|
||||
fx_strength = klc.cal_fx_strength()
|
||||
fx_strength_level = klc.get_fx_strength_level()
|
||||
is_strong_fx = klc.is_strong_fx()
|
||||
if fx_strength < 1:
|
||||
try:
|
||||
# 计算分型强度
|
||||
fx_strength = 0
|
||||
klc_fx_info.append({
|
||||
'time': klc.end_time,
|
||||
'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
|
||||
'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
|
||||
'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
|
||||
'fx_strength': fx_strength, # 分型强度分数 (0-100)
|
||||
'fx_strength_level': fx_strength_level, # 分型强度等级 (极强/强/中等/弱/极弱)
|
||||
'is_strong_fx': is_strong_fx # 是否为强分型
|
||||
})
|
||||
fx_strength_level = ""
|
||||
is_strong_fx = False
|
||||
|
||||
# 尝试调用分型强度计算方法
|
||||
if hasattr(klc, 'cal_fx_strength'):
|
||||
fx_strength = klc.cal_fx_strength()
|
||||
elif hasattr(klc, 'calculate_fx_strength'):
|
||||
fx_strength = klc.calculate_fx_strength()
|
||||
|
||||
# 尝试获取分型强度等级
|
||||
if hasattr(klc, 'get_fx_strength_level'):
|
||||
fx_strength_level = klc.get_fx_strength_level()
|
||||
|
||||
# 尝试判断是否为强分型
|
||||
if hasattr(klc, 'is_strong_fx'):
|
||||
is_strong_fx = klc.is_strong_fx()
|
||||
|
||||
# 如果分型强度小于1，设为0
|
||||
if fx_strength < 1:
|
||||
fx_strength = 0
|
||||
|
||||
klc_fx_info.append({
|
||||
'time': klc.end_time,
|
||||
'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
|
||||
'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
|
||||
'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
|
||||
'fx_strength': fx_strength, # 分型强度分数 (0-100)
|
||||
'fx_strength_level': fx_strength_level, # 分型强度等级 (极强/强/中等/弱/极弱)
|
||||
'is_strong_fx': is_strong_fx # 是否为强分型
|
||||
})
|
||||
except Exception as e:
|
||||
print(f"处理KLC分型信息时出错: {e}")
|
||||
# 如果出错，仍然添加基本信息，但分型强度为0
|
||||
klc_fx_info.append({
|
||||
'time': klc.end_time,
|
||||
'price': klc.low if klc.fx == Chan_FX_TYPE.BOTTOM else klc.high,
|
||||
'fx_type': str(klc.klc_fx_type).replace("Chan_KLC_FX.", ""),
|
||||
'is_bottom': klc.fx == Chan_FX_TYPE.BOTTOM,
|
||||
'fx_strength': 0,
|
||||
'fx_strength_level': "",
|
||||
'is_strong_fx': False
|
||||
})
|
||||
|
||||
return {
|
||||
'klc_list': klc_list,
|
||||
@@ -766,5 +813,175 @@ def search_stock():
|
||||
except Exception as e:
|
||||
return jsonify({'error': str(e)})
|
||||
|
||||
@app.route('/api/filter_stocks', methods=['POST'])
|
||||
def filter_stocks():
|
||||
"""筛选满足条件的A股股票"""
|
||||
try:
|
||||
data = request.get_json()
|
||||
start_time = data.get('start_time')
|
||||
end_time = data.get('end_time')
|
||||
timeframe = data.get('timeframe', '1d')
|
||||
fx_strength_threshold = data.get('fx_strength_threshold', 1.0)
|
||||
|
||||
if not start_time or not end_time:
|
||||
return jsonify({'error': '开始时间和结束时间不能为空'})
|
||||
|
||||
# 获取所有A股股票列表，如果失败则使用热门股票作为备用
|
||||
stock_list = []
|
||||
data_source = ""
|
||||
try:
|
||||
print("正在获取完整股票列表...")
|
||||
stock_list = china_stock.get_stock_list()
|
||||
if stock_list and len(stock_list) > 0:
|
||||
print(f"成功获取完整股票列表: {len(stock_list)} 只股票")
|
||||
data_source = "完整股票列表"
|
||||
else:
|
||||
raise Exception("获取到的股票列表为空")
|
||||
except Exception as e:
|
||||
print(f"获取完整股票列表失败: {e}")
|
||||
print("使用热门股票列表作为备用...")
|
||||
try:
|
||||
popular_stocks = china_stock.get_popular_stocks()
|
||||
stock_list = [{'symbol': stock['symbol'], 'name': stock['name']} for stock in popular_stocks]
|
||||
print(f"使用热门股票列表: {len(stock_list)} 只股票")
|
||||
data_source = "热门股票列表"
|
||||
except Exception as e2:
|
||||
print(f"获取热门股票列表也失败: {e2}")
|
||||
# 检查是否是网络连接问题
|
||||
if "timeout" in str(e).lower() or "connection" in str(e).lower() or "network" in str(e).lower():
|
||||
return jsonify({
|
||||
'error': '网络连接超时，无法获取股票数据。请检查网络连接后重试。',
|
||||
'error_type': 'network_error',
|
||||
'suggestion': '请确保网络连接正常，或稍后重试。'
|
||||
})
|
||||
else:
|
||||
return jsonify({'error': f'无法获取股票列表: {str(e)}'})
|
||||
|
||||
if not stock_list:
|
||||
return jsonify({
|
||||
'error': '无法获取股票列表，请检查网络连接后重试',
|
||||
'error_type': 'network_error',
|
||||
'suggestion': '请确保网络连接正常，或稍后重试。'
|
||||
})
|
||||
|
||||
results = []
|
||||
processed_count = 0
|
||||
total_count = len(stock_list)
|
||||
failed_count = 0
|
||||
|
||||
print(f"开始筛选股票，总数: {total_count}, 时间范围: {start_time} 到 {end_time}, 周期: {timeframe}")
|
||||
|
||||
for stock in stock_list:
|
||||
try:
|
||||
symbol = stock['symbol']
|
||||
name = stock['name']
|
||||
processed_count += 1
|
||||
|
||||
# 每处理20只股票打印一次进度
|
||||
if processed_count % 20 == 0:
|
||||
print(f"已处理 {processed_count}/{total_count} 只股票，成功: {len(results)}, 失败: {failed_count}")
|
||||
|
||||
# 获取股票K线数据
|
||||
df = get_a_stock_kl_data(symbol, timeframe, start_time=start_time, end_time=end_time)
|
||||
|
||||
if df is None or len(df) < 3:
|
||||
failed_count += 1
|
||||
# 如果连续失败太多，可能是网络问题
|
||||
if failed_count > 10 and len(results) == 0:
|
||||
print(f"连续失败 {failed_count} 次，可能是网络问题")
|
||||
return jsonify({
|
||||
'error': '网络连接不稳定，无法获取股票数据。请检查网络连接后重试。',
|
||||
'error_type': 'network_error',
|
||||
'processed_count': processed_count,
|
||||
'failed_count': failed_count
|
||||
})
|
||||
continue
|
||||
|
||||
# 进行缠论分析
|
||||
analysis_result = analyze_chan(df)
|
||||
|
||||
if not analysis_result or 'klc_fx_info' not in analysis_result:
|
||||
continue
|
||||
|
||||
klc_fx_info = analysis_result['klc_fx_info']
|
||||
|
||||
# 检查最近2个KLC是否有满足条件的分型
|
||||
recent_klcs = klc_fx_info[-2:] if len(klc_fx_info) >= 2 else klc_fx_info
|
||||
|
||||
for klc_info in recent_klcs:
|
||||
fx_strength = klc_info.get('fx_strength', 0)
|
||||
fx_type = klc_info.get('fx_type', 'UNKNOWN')
|
||||
|
||||
# 检查是否满足条件：分型强度>=阈值 且 分型类型不为UNKNOWN
|
||||
if fx_strength >= fx_strength_threshold and fx_type != 'UNKNOWN':
|
||||
# 获取当前价格（最新收盘价）
|
||||
current_price = df['close'].iloc[-1] if len(df) > 0 else None
|
||||
fx_price = klc_info.get('price', 0)
|
||||
|
||||
# 计算涨跌幅
|
||||
change_percent = 0
|
||||
if current_price and fx_price and fx_price > 0:
|
||||
change_percent = ((current_price - fx_price) / fx_price) * 100
|
||||
|
||||
# 格式化分型类型显示
|
||||
fx_type_display = format_fx_type(fx_type)
|
||||
|
||||
results.append({
|
||||
'symbol': symbol,
|
||||
'name': name,
|
||||
'fx_time': klc_info.get('time', ''),
|
||||
'fx_type': fx_type_display,
|
||||
'fx_strength': fx_strength,
|
||||
'fx_price': fx_price,
|
||||
'current_price': current_price,
|
||||
'change_percent': change_percent
|
||||
})
|
||||
break # 找到一个满足条件的就跳出循环
|
||||
|
||||
except Exception as e:
|
||||
print(f"处理股票 {symbol} 时出错: {str(e)}")
|
||||
failed_count += 1
|
||||
continue
|
||||
|
||||
print(f"筛选完成，共找到 {len(results)} 只满足条件的股票")
|
||||
|
||||
# 按分型强度降序排列
|
||||
results.sort(key=lambda x: x['fx_strength'], reverse=True)
|
||||
|
||||
return jsonify({
|
||||
'results': results,
|
||||
'total_processed': processed_count,
|
||||
'total_found': len(results),
|
||||
'failed_count': failed_count,
|
||||
'data_source': data_source,
|
||||
'message': f'使用{data_source}进行筛选，共处理{processed_count}只股票，找到{len(results)}只满足条件的股票'
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
print(f"筛选股票时发生错误: {str(e)}")
|
||||
# 检查是否是网络连接问题
|
||||
if "timeout" in str(e).lower() or "connection" in str(e).lower() or "network" in str(e).lower():
|
||||
return jsonify({
|
||||
'error': '网络连接超时，请检查网络连接后重试。',
|
||||
'error_type': 'network_error',
|
||||
'suggestion': '请确保网络连接正常，或稍后重试。'
|
||||
})
|
||||
else:
|
||||
return jsonify({'error': str(e)})
|
||||
|
||||
def format_fx_type(fx_type):
|
||||
"""格式化分型类型显示"""
|
||||
fx_type_map = {
|
||||
'TOP1': '顶分型1',
|
||||
'TOP2': '顶分型2',
|
||||
'TOP3': '顶分型3',
|
||||
'BOTTOM1': '底分型1',
|
||||
'BOTTOM2': '底分型2',
|
||||
'BOTTOM3': '底分型3',
|
||||
'TOP': '顶分型',
|
||||
'BOTTOM': '底分型'
|
||||
}
|
||||
return fx_type_map.get(fx_type, fx_type)
|
||||
|
||||
if __name__ == '__main__':
|
||||
app.run(debug=True, host='0.0.0.0', port=8123)
|
||||
+46
-13
@@ -21,26 +21,59 @@ class ChinaStockData:
|
||||
def get_stock_list(self):
|
||||
"""获取A股股票列表"""
|
||||
try:
|
||||
# 获取沪深A股实时行情
|
||||
stock_info = ak.stock_zh_a_spot_em()
|
||||
import requests
|
||||
# 设置较短的超时时间，避免长时间等待
|
||||
import akshare as ak
|
||||
|
||||
print("正在获取A股股票列表...")
|
||||
|
||||
# 尝试获取沪深A股实时行情，设置超时时间
|
||||
try:
|
||||
# 临时设置requests的默认超时
|
||||
original_timeout = getattr(requests, 'timeout', None)
|
||||
requests.timeout = 10 # 10秒超时
|
||||
|
||||
stock_info = ak.stock_zh_a_spot_em()
|
||||
|
||||
# 恢复原始超时设置
|
||||
if original_timeout:
|
||||
requests.timeout = original_timeout
|
||||
else:
|
||||
delattr(requests, 'timeout')
|
||||
|
||||
except Exception as network_error:
|
||||
print(f"网络请求失败: {network_error}")
|
||||
# 网络失败时返回空列表，让调用方使用备用方案
|
||||
return []
|
||||
|
||||
if stock_info is None or len(stock_info) == 0:
|
||||
print("获取到的股票数据为空")
|
||||
return []
|
||||
|
||||
# 增加到前2000只股票，提供更多选择
|
||||
stock_list = []
|
||||
for index, row in stock_info.head(2000).iterrows():
|
||||
# 过滤掉ST股票和停牌股票
|
||||
stock_name = str(row['名称'])
|
||||
if 'ST' not in stock_name and '*' not in stock_name:
|
||||
stock_list.append({
|
||||
'symbol': row['代码'],
|
||||
'name': row['名称'],
|
||||
'price': float(row['最新价']) if pd.notna(row['最新价']) else 0.0,
|
||||
'change_pct': float(row['涨跌幅']) if pd.notna(row['涨跌幅']) else 0.0,
|
||||
'volume': float(row['成交量']) if pd.notna(row['成交量']) else 0.0,
|
||||
'amount': float(row['成交额']) if pd.notna(row['成交额']) else 0.0
|
||||
})
|
||||
try:
|
||||
# 过滤掉ST股票和停牌股票
|
||||
stock_name = str(row['名称'])
|
||||
if 'ST' not in stock_name and '*' not in stock_name:
|
||||
stock_list.append({
|
||||
'symbol': row['代码'],
|
||||
'name': row['名称'],
|
||||
'price': float(row['最新价']) if pd.notna(row['最新价']) else 0.0,
|
||||
'change_pct': float(row['涨跌幅']) if pd.notna(row['涨跌幅']) else 0.0,
|
||||
'volume': float(row['成交量']) if pd.notna(row['成交量']) else 0.0,
|
||||
'amount': float(row['成交额']) if pd.notna(row['成交额']) else 0.0
|
||||
})
|
||||
except Exception as row_error:
|
||||
print(f"处理股票数据行时出错: {row_error}")
|
||||
continue
|
||||
|
||||
# 按成交金额排序，优先显示活跃股票
|
||||
stock_list.sort(key=lambda x: x['amount'], reverse=True)
|
||||
print(f"成功获取 {len(stock_list)} 只股票")
|
||||
return stock_list
|
||||
|
||||
except Exception as e:
|
||||
print(f"获取股票列表失败: {e}")
|
||||
return []
|
||||
|
||||
+448
-1
@@ -74,9 +74,51 @@
|
||||
}
|
||||
.data-container {
|
||||
margin-top: 10px;
|
||||
position: relative;
|
||||
z-index: 10;
|
||||
background-color: white;
|
||||
border-radius: 8px;
|
||||
padding: 15px;
|
||||
box-shadow: 0 2px 8px rgba(0,0,0,0.1);
|
||||
}
|
||||
.nav-tabs {
|
||||
margin-bottom: 10px;
|
||||
position: relative;
|
||||
z-index: 20;
|
||||
background-color: white;
|
||||
border-radius: 8px 8px 0 0;
|
||||
padding: 10px 10px 0 10px;
|
||||
}
|
||||
.nav-tabs .nav-link {
|
||||
border-radius: 6px 6px 0 0;
|
||||
margin-right: 5px;
|
||||
font-weight: 500;
|
||||
transition: all 0.2s ease;
|
||||
}
|
||||
.nav-tabs .nav-link:hover {
|
||||
background-color: #f8f9fa;
|
||||
border-color: #dee2e6;
|
||||
}
|
||||
.nav-tabs .nav-link.active {
|
||||
background-color: #0d6efd;
|
||||
color: white;
|
||||
border-color: #0d6efd;
|
||||
}
|
||||
/* 特别突出显示股票筛选tab */
|
||||
#stock-filter-tab {
|
||||
background-color: #28a745 !important;
|
||||
color: white !important;
|
||||
border-color: #28a745 !important;
|
||||
font-weight: bold !important;
|
||||
box-shadow: 0 2px 4px rgba(40, 167, 69, 0.3) !important;
|
||||
}
|
||||
#stock-filter-tab:hover {
|
||||
background-color: #218838 !important;
|
||||
border-color: #1e7e34 !important;
|
||||
}
|
||||
#stock-filter-tab.active {
|
||||
background-color: #155724 !important;
|
||||
border-color: #155724 !important;
|
||||
}
|
||||
.table-container {
|
||||
overflow-x: auto;
|
||||
@@ -478,6 +520,9 @@
|
||||
<li class="nav-item" role="presentation">
|
||||
<button class="nav-link" id="trade-points-tab" data-bs-toggle="tab" data-bs-target="#trade-points" type="button" role="tab">买卖点</button>
|
||||
</li>
|
||||
<li class="nav-item" role="presentation">
|
||||
<button class="nav-link" id="stock-filter-tab" data-bs-toggle="tab" data-bs-target="#stock-filter" type="button" role="tab">股票筛选</button>
|
||||
</li>
|
||||
</ul>
|
||||
<div class="data-source-info alert alert-info py-2 mt-1 mb-2" style="display:none;">
|
||||
<small id="dataSourceText"></small>
|
||||
@@ -592,6 +637,81 @@
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
<div class="tab-pane fade" id="stock-filter" role="tabpanel">
|
||||
<div class="container-fluid">
|
||||
<div class="row mb-3">
|
||||
<div class="col-md-12">
|
||||
<h5>A股强分型筛选</h5>
|
||||
<p class="text-muted">筛选最近2个K线合并(KLC)中有一个满足分型强度≥1.0且分型类型不为UNKNOWN的A股股票</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row mb-3">
|
||||
<div class="col-md-3">
|
||||
<label for="filterStartTime" class="form-label">开始时间:</label>
|
||||
<input type="datetime-local" id="filterStartTime" class="form-control">
|
||||
</div>
|
||||
<div class="col-md-3">
|
||||
<label for="filterEndTime" class="form-label">结束时间:</label>
|
||||
<input type="datetime-local" id="filterEndTime" class="form-control">
|
||||
</div>
|
||||
<div class="col-md-3">
|
||||
<label for="filterTimeframe" class="form-label">时间周期:</label>
|
||||
<select id="filterTimeframe" class="form-select">
|
||||
<option value="5m">5分钟</option>
|
||||
<option value="15m">15分钟</option>
|
||||
<option value="30m">30分钟</option>
|
||||
<option value="1h">1小时</option>
|
||||
<option value="4h">4小时</option>
|
||||
<option value="1d" selected>1日</option>
|
||||
<option value="1w">1周</option>
|
||||
<option value="1M">1月</option>
|
||||
</select>
|
||||
</div>
|
||||
<div class="col-md-3">
|
||||
<label for="fxStrengthThreshold" class="form-label">分型强度阈值:</label>
|
||||
<input type="number" id="fxStrengthThreshold" class="form-control" value="1.0" min="0" max="100" step="0.1">
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row mb-3">
|
||||
<div class="col-md-12">
|
||||
<button class="btn btn-primary" onclick="filterStocks()">
|
||||
<i class="bi bi-search"></i> 开始筛选
|
||||
</button>
|
||||
<button class="btn btn-secondary ms-2" onclick="exportFilterResults()">
|
||||
<i class="bi bi-download"></i> 导出结果
|
||||
</button>
|
||||
<span id="filterProgress" class="ms-3" style="display:none;">
|
||||
<i class="bi bi-hourglass-split"></i> 正在筛选中...
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="row">
|
||||
<div class="col-md-12">
|
||||
<div class="table-container">
|
||||
<table id="stockFilterTable" class="display compact" style="width:100%">
|
||||
<thead>
|
||||
<tr>
|
||||
<th>股票代码</th>
|
||||
<th>股票名称</th>
|
||||
<th>分型时间</th>
|
||||
<th>分型类型</th>
|
||||
<th>分型强度</th>
|
||||
<th>分型价格</th>
|
||||
<th>当前价格</th>
|
||||
<th>涨跌幅(%)</th>
|
||||
<th>操作</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody></tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
@@ -841,6 +961,12 @@
|
||||
updateChartDisplay();
|
||||
});
|
||||
|
||||
// 添加K线周期切换事件监听器
|
||||
$('input[name="klinePeriod"]').change(function() {
|
||||
console.log('K线周期切换:', $(this).attr('id'), $(this).is(':checked'));
|
||||
updateChartDisplay();
|
||||
});
|
||||
|
||||
// 当选择不同的元素时间周期时
|
||||
$('#elementTimeframe').change(function() {
|
||||
const elementTimeframe = $(this).val();
|
||||
@@ -1198,7 +1324,7 @@
|
||||
}
|
||||
|
||||
// 检查是否使用小周期K线数据
|
||||
const useElementPeriod = $('#useElementPeriod').is(':checked') &&
|
||||
const useElementPeriod = $('#elementPeriodKline').is(':checked') &&
|
||||
currentData.element_kline_data &&
|
||||
Array.isArray(currentData.element_kline_data);
|
||||
|
||||
@@ -4004,6 +4130,52 @@
|
||||
updateTables(currentData);
|
||||
}
|
||||
});
|
||||
|
||||
// 页面加载完成后初始化
|
||||
$(document).ready(function() {
|
||||
// 设置默认的筛选时间（最近7天）
|
||||
const now = new Date();
|
||||
const weekAgo = new Date(now.getTime() - 7 * 24 * 60 * 60 * 1000);
|
||||
|
||||
$('#filterEndTime').val(now.toISOString().slice(0, 16));
|
||||
$('#filterStartTime').val(weekAgo.toISOString().slice(0, 16));
|
||||
|
||||
// 初始化股票筛选表格
|
||||
initStockFilterTable();
|
||||
|
||||
// 突出显示股票筛选tab
|
||||
setTimeout(function() {
|
||||
const stockFilterTab = $('#stock-filter-tab');
|
||||
if (stockFilterTab.length > 0) {
|
||||
console.log('股票筛选tab已找到，开始突出显示');
|
||||
|
||||
// 添加闪烁效果来吸引注意
|
||||
stockFilterTab.addClass('animate__animated animate__pulse');
|
||||
|
||||
// 滚动到tab区域
|
||||
$('html, body').animate({
|
||||
scrollTop: $('.data-container').offset().top - 100
|
||||
}, 1000);
|
||||
|
||||
// 添加提示信息
|
||||
const alertDiv = $(`
|
||||
<div class="alert alert-info alert-dismissible fade show" role="alert" style="position: fixed; top: 20px; right: 20px; z-index: 9999; max-width: 400px;">
|
||||
<strong>新功能！</strong> 股票筛选功能已添加，请查看绿色的"股票筛选"标签页。
|
||||
<button type="button" class="btn-close" data-bs-dismiss="alert"></button>
|
||||
</div>
|
||||
`);
|
||||
$('body').append(alertDiv);
|
||||
|
||||
// 10秒后自动隐藏提示
|
||||
setTimeout(() => {
|
||||
alertDiv.alert('close');
|
||||
}, 10000);
|
||||
|
||||
} else {
|
||||
console.error('未找到股票筛选tab');
|
||||
}
|
||||
}, 2000);
|
||||
});
|
||||
});
|
||||
|
||||
// 自动刷新相关变量
|
||||
@@ -5103,6 +5275,281 @@
|
||||
window.astockStatusInterval = setInterval(updateAStockTradingStatus, 30000);
|
||||
console.log('A股交易时间状态更新器已启动');
|
||||
}
|
||||
|
||||
// 股票筛选相关函数
|
||||
let stockFilterTable = null;
|
||||
|
||||
// 初始化股票筛选表格
|
||||
function initStockFilterTable() {
|
||||
// 简单的表格初始化，不使用DataTable
|
||||
console.log('初始化股票筛选表格');
|
||||
}
|
||||
|
||||
// 筛选股票
|
||||
function filterStocks() {
|
||||
const startTime = $('#filterStartTime').val();
|
||||
const endTime = $('#filterEndTime').val();
|
||||
const timeframe = $('#filterTimeframe').val();
|
||||
const threshold = parseFloat($('#fxStrengthThreshold').val());
|
||||
|
||||
if (!startTime || !endTime) {
|
||||
alert('请选择开始时间和结束时间');
|
||||
return;
|
||||
}
|
||||
|
||||
if (new Date(startTime) >= new Date(endTime)) {
|
||||
alert('开始时间必须早于结束时间');
|
||||
return;
|
||||
}
|
||||
|
||||
// 显示进度指示器
|
||||
$('#filterProgress').show();
|
||||
$('#stockFilterTable tbody').empty();
|
||||
|
||||
// 发送筛选请求
|
||||
$.ajax({
|
||||
url: '/api/filter_stocks',
|
||||
method: 'POST',
|
||||
contentType: 'application/json',
|
||||
data: JSON.stringify({
|
||||
start_time: startTime,
|
||||
end_time: endTime,
|
||||
timeframe: timeframe,
|
||||
fx_strength_threshold: threshold
|
||||
}),
|
||||
timeout: 300000, // 5分钟超时
|
||||
success: function(response) {
|
||||
$('#filterProgress').hide();
|
||||
|
||||
if (response.error) {
|
||||
// 根据错误类型显示不同的错误信息
|
||||
if (response.error_type === 'network_error') {
|
||||
showNetworkErrorAlert(response.error, response.suggestion);
|
||||
} else {
|
||||
alert('筛选失败: ' + response.error);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
// 更新表格数据
|
||||
updateStockFilterTable(response.results);
|
||||
|
||||
// 显示统计信息
|
||||
const totalCount = response.results.length;
|
||||
const processedCount = response.total_processed || 0;
|
||||
const failedCount = response.failed_count || 0;
|
||||
const dataSource = response.data_source || '未知';
|
||||
|
||||
let message = `筛选完成！使用${dataSource}，共处理 ${processedCount} 只股票，找到 ${totalCount} 只满足条件的股票`;
|
||||
if (failedCount > 0) {
|
||||
message += `，${failedCount} 只股票数据获取失败`;
|
||||
}
|
||||
|
||||
// 显示成功提示
|
||||
showSuccessAlert(message);
|
||||
},
|
||||
error: function(xhr, status, error) {
|
||||
$('#filterProgress').hide();
|
||||
console.error('筛选请求失败:', error, status, xhr);
|
||||
|
||||
// 根据错误类型显示不同的错误信息
|
||||
if (status === 'timeout') {
|
||||
showNetworkErrorAlert(
|
||||
'请求超时，可能是网络连接不稳定或数据量较大',
|
||||
'请检查网络连接，或尝试缩小时间范围后重试'
|
||||
);
|
||||
} else if (xhr.responseJSON && xhr.responseJSON.error_type === 'network_error') {
|
||||
showNetworkErrorAlert(xhr.responseJSON.error, xhr.responseJSON.suggestion);
|
||||
} else {
|
||||
alert('筛选请求失败: ' + (xhr.responseJSON?.error || error || '未知错误'));
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
// 显示网络错误提示
|
||||
function showNetworkErrorAlert(errorMessage, suggestion) {
|
||||
const alertDiv = $(`
|
||||
<div class="alert alert-warning alert-dismissible fade show" role="alert">
|
||||
<h6><i class="fas fa-exclamation-triangle"></i> 网络连接问题</h6>
|
||||
<p><strong>错误信息：</strong>${errorMessage}</p>
|
||||
<p><strong>建议：</strong>${suggestion}</p>
|
||||
<hr>
|
||||
<p class="mb-0">
|
||||
<small>
|
||||
<i class="fas fa-info-circle"></i>
|
||||
如果问题持续存在，请检查网络连接或联系管理员
|
||||
</small>
|
||||
</p>
|
||||
<button type="button" class="btn-close" data-bs-dismiss="alert"></button>
|
||||
</div>
|
||||
`);
|
||||
$('#stock-filter .container-fluid').prepend(alertDiv);
|
||||
|
||||
// 10秒后自动隐藏提示
|
||||
setTimeout(() => {
|
||||
alertDiv.alert('close');
|
||||
}, 10000);
|
||||
}
|
||||
|
||||
// 显示成功提示
|
||||
function showSuccessAlert(message) {
|
||||
const alertDiv = $(`
|
||||
<div class="alert alert-success alert-dismissible fade show" role="alert">
|
||||
<i class="fas fa-check-circle"></i> ${message}
|
||||
<button type="button" class="btn-close" data-bs-dismiss="alert"></button>
|
||||
</div>
|
||||
`);
|
||||
$('#stock-filter .container-fluid').prepend(alertDiv);
|
||||
|
||||
// 5秒后自动隐藏提示
|
||||
setTimeout(() => {
|
||||
alertDiv.alert('close');
|
||||
}, 5000);
|
||||
}
|
||||
|
||||
// 更新股票筛选表格
|
||||
function updateStockFilterTable(results) {
|
||||
const tbody = $('#stockFilterTable tbody');
|
||||
tbody.empty();
|
||||
|
||||
if (!results || results.length === 0) {
|
||||
tbody.append('<tr><td colspan="9" class="text-center">没有找到满足条件的股票</td></tr>');
|
||||
return;
|
||||
}
|
||||
|
||||
// 按分型强度降序排列
|
||||
results.sort((a, b) => b.fx_strength - a.fx_strength);
|
||||
|
||||
// 添加新数据
|
||||
results.forEach(function(stock) {
|
||||
const strengthClass = getStrengthClass(stock.fx_strength);
|
||||
const changeClass = stock.change_percent >= 0 ? 'text-danger' : 'text-success';
|
||||
const changeSign = stock.change_percent >= 0 ? '+' : '';
|
||||
|
||||
const row = `
|
||||
<tr>
|
||||
<td>${stock.symbol}</td>
|
||||
<td>${stock.name}</td>
|
||||
<td>${stock.fx_time}</td>
|
||||
<td>${stock.fx_type}</td>
|
||||
<td><span class="${strengthClass}">${stock.fx_strength.toFixed(2)}</span></td>
|
||||
<td>${stock.fx_price.toFixed(2)}</td>
|
||||
<td>${stock.current_price ? stock.current_price.toFixed(2) : '-'}</td>
|
||||
<td><span class="${changeClass}">${changeSign}${(stock.change_percent || 0).toFixed(2)}%</span></td>
|
||||
<td><button class="btn btn-sm btn-outline-primary" onclick="analyzeStock('${stock.symbol}')">分析</button></td>
|
||||
</tr>
|
||||
`;
|
||||
tbody.append(row);
|
||||
});
|
||||
}
|
||||
|
||||
// 获取分型强度对应的CSS类
|
||||
function getStrengthClass(strength) {
|
||||
if (strength >= 2.0) return 'text-danger fw-bold';
|
||||
else if (strength >= 1.5) return 'text-warning fw-bold';
|
||||
else if (strength >= 1.0) return 'text-info';
|
||||
else return '';
|
||||
}
|
||||
|
||||
// 分析特定股票
|
||||
function analyzeStock(symbol) {
|
||||
// 切换到主分析页面
|
||||
$('#stock-filter-tab').removeClass('active');
|
||||
$('#kline-tab').addClass('active');
|
||||
$('#stock-filter').removeClass('show active');
|
||||
$('#kline').addClass('show active');
|
||||
|
||||
// 切换数据源为A股
|
||||
$('#dataSource').val('a_stock');
|
||||
$('#dataSource').trigger('change');
|
||||
|
||||
// 等待数据源切换完成后设置股票代码
|
||||
setTimeout(() => {
|
||||
$('#astockSymbol').val(symbol);
|
||||
// 触发分析
|
||||
updateChart();
|
||||
}, 500);
|
||||
}
|
||||
|
||||
// 导出筛选结果
|
||||
function exportFilterResults() {
|
||||
const tbody = $('#stockFilterTable tbody tr');
|
||||
|
||||
if (tbody.length === 0 || (tbody.length === 1 && tbody.find('td').length === 1)) {
|
||||
alert('没有可导出的数据');
|
||||
return;
|
||||
}
|
||||
|
||||
// 创建CSV内容
|
||||
const headers = ['股票代码', '股票名称', '分型时间', '分型类型', '分型强度', '分型价格', '当前价格', '涨跌幅(%)'];
|
||||
let csvContent = headers.join(',') + '\n';
|
||||
|
||||
tbody.each(function() {
|
||||
const cells = $(this).find('td');
|
||||
if (cells.length > 1) { // 排除"没有数据"的行
|
||||
const row = [];
|
||||
cells.slice(0, 8).each(function() { // 只取前8列，排除操作列
|
||||
row.push($(this).text().trim());
|
||||
});
|
||||
csvContent += row.join(',') + '\n';
|
||||
}
|
||||
});
|
||||
|
||||
// 创建下载链接
|
||||
const blob = new Blob(['\ufeff' + csvContent], { type: 'text/csv;charset=utf-8;' });
|
||||
const link = document.createElement('a');
|
||||
const url = URL.createObjectURL(blob);
|
||||
link.setAttribute('href', url);
|
||||
link.setAttribute('download', `股票筛选结果_${new Date().toISOString().slice(0, 10)}.csv`);
|
||||
link.style.visibility = 'hidden';
|
||||
document.body.appendChild(link);
|
||||
link.click();
|
||||
document.body.removeChild(link);
|
||||
}
|
||||
|
||||
// 页面加载完成后初始化
|
||||
$(document).ready(function() {
|
||||
// 设置默认的筛选时间（最近7天）
|
||||
const now = new Date();
|
||||
const weekAgo = new Date(now.getTime() - 7 * 24 * 60 * 60 * 1000);
|
||||
|
||||
$('#filterEndTime').val(now.toISOString().slice(0, 16));
|
||||
$('#filterStartTime').val(weekAgo.toISOString().slice(0, 16));
|
||||
|
||||
// 初始化股票筛选表格
|
||||
initStockFilterTable();
|
||||
|
||||
// 突出显示股票筛选tab
|
||||
setTimeout(function() {
|
||||
const stockFilterTab = $('#stock-filter-tab');
|
||||
if (stockFilterTab.length > 0) {
|
||||
console.log('股票筛选tab已找到，开始突出显示');
|
||||
|
||||
// 滚动到tab区域
|
||||
$('html, body').animate({
|
||||
scrollTop: $('.data-container').offset().top - 100
|
||||
}, 1000);
|
||||
|
||||
// 添加提示信息
|
||||
const alertDiv = $(`
|
||||
<div class="alert alert-info alert-dismissible fade show" role="alert" style="position: fixed; top: 20px; right: 20px; z-index: 9999; max-width: 400px;">
|
||||
<strong>新功能！</strong> 股票筛选功能已添加，请查看绿色的"股票筛选"标签页。
|
||||
<button type="button" class="btn-close" data-bs-dismiss="alert"></button>
|
||||
</div>
|
||||
`);
|
||||
$('body').append(alertDiv);
|
||||
|
||||
// 10秒后自动隐藏提示
|
||||
setTimeout(() => {
|
||||
alertDiv.alert('close');
|
||||
}, 10000);
|
||||
|
||||
} else {
|
||||
console.error('未找到股票筛选tab');
|
||||
}
|
||||
}, 2000);
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user