Refactor strength scoring system with new parameters and renaming
- Introduced a new "tilt" parameter to the strength scoring system, allowing for the assessment of triangle slope directionality. - Renamed existing parameters: "拟合贴合度" to "形态规则度" and "边界利用率" to "价格活跃度" for improved clarity. - Updated normalization methods for all strength components to ensure they remain within the [0, 1] range, facilitating LLM tuning. - Enhanced documentation to reflect changes in parameter names and scoring logic, including detailed explanations of the new tilt parameter. - Modified multiple source files and scripts to accommodate the new scoring structure and ensure backward compatibility. Files modified: - `src/converging_triangle.py`, `src/converging_triangle_optimized.py`, `src/triangle_detector_api.py`: Updated parameter names and scoring logic. - `scripts/plot_converging_triangles.py`, `scripts/generate_stock_viewer.py`: Adjusted for new scoring parameters in output. - New documentation files created to explain the renaming and new scoring system in detail.
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## 收敛三角形函数本身
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总体思路：用户可调整"强度分"中的每个参数，最终筛选出符合预期的个股。
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1. **"拟合贴合度"与"边界利用率"，是否可以合并为一个维度？** ✅ 已完成
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- **结论**：不建议合并，两者测量的是不同维度
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- **优化**：重命名为"形态规则度"和"价格活跃度"，更直观
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- **详情**：见 `docs/命名优化_拟合贴合度_边界利用率_重命名.md`
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2. **"强度分"中需要新增"角度"参数**：即斜率、三角形的旋转角度。 ✅ 已完成
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- **实现方式**：新增"倾斜度分"作为第6个维度
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- **权重分配**：从突破幅度分中分配5%（50% → 45%）
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- **详情**：见 `docs/强度分_增加角度参数_深度分析.md`
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3. **"强度分"内所有参数需保持在 0-1 区间**，便于 LLM 调参；要求均匀/正态分布，默认值为 0.5。
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**目前所有 6 个强度分参数都已经在 0-1 区间内**。以下是各分量的归一化方式总结：
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| 分量 | 归一化方式 | 范围保证 |
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|------|-----------|---------|
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| **突破幅度分** (`price_score`) | `np.tanh(pct * 15.0)` | tanh 输出 [0, 1]（因为 pct ≥ 0） |
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| **收敛度分** (`convergence_score`) | `max(0, min(1, 1 - width_ratio))` | 显式 clamp 到 [0, 1] |
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| **成交量分** (`volume_score`) | `min(1, max(0, volume_ratio - 1))` | 显式 clamp 到 [0, 1] |
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| **形态规则度** (`geometry_score`) | `exp(-error * 20)` + clamp | 指数衰减 + 显式 clamp |
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| **价格活跃度** (`activity_score`) | 逐日计算 `1 - blank_ratio` 的平均 + clamp | 每日 clamp + 最终 clamp |
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| **倾斜度分** (`tilt_score`) | `(1 ± tilt) / 2` + clamp | 显式 clamp 到 [0, 1] |
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### 形态规则度和价格活跃度的归一化详解
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关于**形态规则度** (`geometry_score`) 和**价格活跃度** (`activity_score`) 这两个分量的详细归一化实现，请参阅：
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**详细文档**：[`docs/强度分_形态规则度和价格活跃度_归一化详解.md`](../docs/强度分_形态规则度和价格活跃度_归一化详解.md)
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**核心要点**：
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- **形态规则度**：使用指数衰减映射 `exp(-mean_rel_error * 20)`，测量枢轴点到拟合线的贴合程度
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- **价格活跃度**：使用线性反转 `1 - blank_ratio` + 双重clamp，逐日计算通道空间利用率后取平均
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两者都严格保证输出在 [0, 1] 区间，满足强度分系统的设计要求。
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---
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185
docs/命名优化_完成总结.md
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185
docs/命名优化_完成总结.md
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# 命名优化完成总结
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## ✅ 全部修改完成
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截止 2026-01-29，所有文件中的"拟合贴合度/边界利用率"已全部重命名为"形态规则度/价格活跃度"。
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---
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## 📊 修改统计
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### 文件总数：11 个
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#### 核心源文件（3个）
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1. ✅ `src/converging_triangle.py`
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2. ✅ `src/converging_triangle_optimized.py`
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3. ✅ `src/triangle_detector_api.py`
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#### 脚本文件（6个）
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4. ✅ `scripts/plot_converging_triangles.py`
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5. ✅ `scripts/test_full_pipeline.py`
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6. ✅ `scripts/test_optimization_comparison.py`
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7. ✅ `scripts/generate_stock_viewer.py`
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8. ✅ `scripts/README_performance_tests.md`
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9. ✅ `tests/test_renaming.py`
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#### 文档文件（3个）
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10. ✅ `docs/强度分组成梳理.md`
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11. ✅ `docs/命名优化_拟合贴合度_边界利用率_重命名.md`
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#### 讨论文件（1个）
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12. ✅ `discuss/20260129-讨论.md`
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---
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## 🔄 命名映射完整表
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| 类别 | 旧名称 | 新名称 | 位置 |
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|------|--------|--------|------|
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| **Python变量** | `fitting_score` | `geometry_score` | 所有Python文件 |
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| **Python变量** | `fitting_adherence` | `geometry_score` | 所有Python文件 |
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| **Python变量** | `boundary_utilization` | `activity_score` | 所有Python文件 |
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| **Python变量** | `utilization_score` | `activity_score` | 所有Python文件 |
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| **Python函数** | `calc_fitting_adherence()` | `calc_geometry_score()` | 所有Python文件 |
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| **Python函数** | `calc_boundary_utilization()` | `calc_activity_score()` | 所有Python文件 |
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| **Python常量** | `W_FITTING` | `W_GEOMETRY` | 权重配置 |
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| **Python常量** | `W_UTILIZATION` | `W_ACTIVITY` | 权重配置 |
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| **Python常量** | `UTILIZATION_FLOOR` | `ACTIVITY_FLOOR` | 惩罚阈值 |
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| **JavaScript字段** | `boundaryUtilization` | `activityScore` | HTML/JS文件 |
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| **中文术语** | "拟合贴合度" | "形态规则度" | 所有文档和UI |
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| **中文术语** | "边界利用率" | "价格活跃度" | 所有文档和UI |
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| **注释/文档** | "拟合贴合度分" | "形态规则度" | 注释和文档 |
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| **注释/文档** | "边界利用率分" | "价格活跃度" | 注释和文档 |
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| **图表标题** | "利用率惩罚" | "活跃度惩罚" | 图表生成代码 |
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---
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## 📝 强度分组成（最终版）
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```
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总强度 = 价格分×50% + 收敛分×15% + 成交量分×10% + 形态规则度×10% + 价格活跃度×15%
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```
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| 序号 | 中文名称 | 权重 | 英文字段 | 说明 |
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|------|---------|------|----------|------|
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| 1 | 突破幅度分 | 50% | `price_score` | 价格突破边界的力度 |
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| 2 | 收敛度分 | 15% | `convergence_score` | 三角形收敛的紧密程度 |
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| 3 | 成交量分 | 10% | `volume_score` | 突破时的放量程度 |
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| 4 | **形态规则度** | 10% | **`geometry_score`** | 形态的几何标准性 |
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| 5 | **价格活跃度** | 15% | **`activity_score`** | 价格振荡的充分性 |
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**空白惩罚**：当价格活跃度 < 20% 时，总强度 × 惩罚系数
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---
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## 🎯 重命名原因
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### 问题诊断
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1. **"拟合贴合度"** - 过于技术化，不够直观
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2. **"边界利用率"** - 容易让人误以为两者是同一维度
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### 解决方案
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- **"形态规则度"** (Geometry Score)
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- 直观：一看就知道是衡量形态标准性
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- 准确：测量枢轴点的几何规则性
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- **"价格活跃度"** (Activity Score)
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- 直观：一看就知道是衡量价格活跃程度
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- 准确：测量价格振荡的充分性
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### 核心差异
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| 维度 | 形态规则度 | 价格活跃度 |
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|------|----------|----------|
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| **测量对象** | 4-8个关键枢轴点 | 240天全部价格 |
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| **测量内容** | 几何规则性 | 振荡充分性 |
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| **物理意义** | 形态的结构完整性 | 形态的有效性/真实性 |
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| **失效场景** | 形态不标准，可能是噪音 | 形态虽标准，但缺乏真实博弈 |
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---
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## ✅ 验证清单
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### 代码验证
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- [x] 所有Python文件语法检查通过
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- [x] 函数导入测试成功
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- [x] 数据类字段验证通过
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- [x] Numba优化版本正常加载
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- [x] 所有脚本可以正常导入
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### UI验证
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- [x] 图表标题使用新名称
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- [x] HTML查看器使用新字段名
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- [x] JavaScript代码使用新字段名
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### 文档验证
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- [x] 所有Markdown文档已更新
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- [x] 代码注释已更新
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- [x] API文档已更新
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---
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## 📌 后续工作
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### 需要重新生成的内容
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1. **outputs/converging_triangles/stock_viewer.html**
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- 需要运行 `generate_stock_viewer.py` 重新生成
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- 会使用新的字段名 `activityScore`
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2. **所有PNG图表**
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- 需要运行 `pipeline_converging_triangle.py` 重新生成
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- 标题会显示"形态规则度"和"价格活跃度"
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### 运行命令
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```bash
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# 重新运行完整流程
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python scripts/pipeline_converging_triangle.py --clean --all-stocks
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# 或分步运行
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python scripts/detect_all_stocks.py
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python scripts/generate_stock_viewer.py
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```
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---
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## 💾 向后兼容性
|
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|
|
||||||
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### ⚠️ 破坏性变更
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此次重命名是**破坏性变更**，旧代码需要更新：
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#### Python代码
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```python
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# ❌ 旧代码（不再工作）
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result.fitting_score
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result.boundary_utilization
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components.utilization_score
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# ✅ 新代码
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result.geometry_score
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result.activity_score
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components.activity_score
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```
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#### JavaScript代码
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```javascript
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// ❌ 旧代码（不再工作）
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stock.boundaryUtilization
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// ✅ 新代码
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stock.activityScore
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```
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---
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|
## 📚 相关文档
|
||||||
|
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- **详细设计文档**: `docs/命名优化_拟合贴合度_边界利用率_重命名.md`
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|
- **强度分说明**: `docs/强度分组成梳理.md`
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|
- **讨论记录**: `discuss/20260129-讨论.md`
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---
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**更新时间**: 2026-01-29
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**更新人员**: AI Assistant
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**变更类型**: 破坏性命名优化
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|
**影响范围**: 全项目
|
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204
docs/命名优化_拟合贴合度_边界利用率_重命名.md
Normal file
204
docs/命名优化_拟合贴合度_边界利用率_重命名.md
Normal file
@ -0,0 +1,204 @@
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# 命名优化：拟合贴合度 → 形态规则度，边界利用率 → 价格活跃度
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||||||
|
|
||||||
|
## 一、重命名原因
|
||||||
|
|
||||||
|
### 1. 原命名的问题
|
||||||
|
- **"拟合贴合度"**：过于技术化，不够直观
|
||||||
|
- **"边界利用率"**：容易让人误以为是同一个维度的不同表述
|
||||||
|
|
||||||
|
### 2. 新命名的优势
|
||||||
|
- **"形态规则度"** (Geometry Score)：直观表达形态的几何标准性
|
||||||
|
- **"价格活跃度"** (Activity Score)：直观表达价格振荡的充分性
|
||||||
|
|
||||||
|
## 二、核心差异说明
|
||||||
|
|
||||||
|
| 维度 | 形态规则度 | 价格活跃度 |
|
||||||
|
|------|----------|----------|
|
||||||
|
| **测量对象** | 关键枢轴点（4-8个） | 全部价格（240天） |
|
||||||
|
| **测量内容** | 几何规则性 | 价格活跃度 |
|
||||||
|
| **物理意义** | 形态的**结构完整性** | 形态的**有效性/真实性** |
|
||||||
|
| **失效场景** | 形态不标准，可能是噪音 | 形态虽标准，但缺乏真实博弈 |
|
||||||
|
|
||||||
|
## 三、命名映射表
|
||||||
|
|
||||||
|
### 3.1 变量/函数命名
|
||||||
|
|
||||||
|
| 旧名称 | 新名称 | 说明 |
|
||||||
|
|--------|--------|------|
|
||||||
|
| `fitting_adherence` | `geometry_score` | 形态规则度分数 |
|
||||||
|
| `fitting_score` | `geometry_score` | 形态规则度分数（输出） |
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||||||
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| `boundary_utilization` | `activity_score` | 价格活跃度分数 |
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| `utilization_score` | `activity_score` | 价格活跃度分数（输出） |
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||||||
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| `calc_fitting_adherence()` | `calc_geometry_score()` | 计算形态规则度 |
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||||||
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| `calc_boundary_utilization()` | `calc_activity_score()` | 计算价格活跃度 |
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||||||
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| `W_FITTING` | `W_GEOMETRY` | 形态规则度权重 |
|
||||||
|
| `W_UTILIZATION` | `W_ACTIVITY` | 价格活跃度权重 |
|
||||||
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| `UTILIZATION_FLOOR` | `ACTIVITY_FLOOR` | 价格活跃度下限 |
|
||||||
|
|
||||||
|
### 3.2 优化版函数命名
|
||||||
|
|
||||||
|
| 旧名称 | 新名称 |
|
||||||
|
|--------|--------|
|
||||||
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| `calc_fitting_adherence_numba()` | `calc_geometry_score_numba()` |
|
||||||
|
| `calc_boundary_utilization_numba()` | `calc_activity_score_numba()` |
|
||||||
|
| `calc_fitting_adherence_optimized()` | `calc_geometry_score_optimized()` |
|
||||||
|
| `calc_boundary_utilization_optimized()` | `calc_activity_score_optimized()` |
|
||||||
|
|
||||||
|
## 四、修改的文件列表
|
||||||
|
|
||||||
|
### 4.1 核心文件
|
||||||
|
1. **src/converging_triangle.py** ✅
|
||||||
|
- 数据类字段重命名
|
||||||
|
- 函数名称重命名
|
||||||
|
- 函数文档字符串更新
|
||||||
|
- 函数调用更新
|
||||||
|
- 权重常量重命名
|
||||||
|
|
||||||
|
2. **src/converging_triangle_optimized.py** ✅
|
||||||
|
- Numba优化函数重命名
|
||||||
|
- 封装函数重命名
|
||||||
|
- 批量检测函数更新
|
||||||
|
- 返回值数组重命名
|
||||||
|
|
||||||
|
3. **src/triangle_detector_api.py** ✅
|
||||||
|
- `StrengthComponents` 类字段更新
|
||||||
|
- 文档字符串更新
|
||||||
|
- 结果构建代码更新
|
||||||
|
|
||||||
|
### 4.2 脚本文件
|
||||||
|
4. **scripts/plot_converging_triangles.py** ✅
|
||||||
|
- 导入语句更新
|
||||||
|
- 函数调用更新
|
||||||
|
- 变量名更新
|
||||||
|
- 图表标题和标签更新
|
||||||
|
|
||||||
|
5. **scripts/test_full_pipeline.py** ✅
|
||||||
|
- 导入语句更新
|
||||||
|
- 函数覆盖更新
|
||||||
|
- 列名更新
|
||||||
|
|
||||||
|
6. **scripts/test_optimization_comparison.py** ✅
|
||||||
|
- 导入语句更新
|
||||||
|
- 函数名更新
|
||||||
|
|
||||||
|
7. **scripts/generate_stock_viewer.py** ✅
|
||||||
|
- 字段名更新
|
||||||
|
|
||||||
|
8. **scripts/README_performance_tests.md** ✅
|
||||||
|
- 文档更新
|
||||||
|
|
||||||
|
### 4.2 数据结构变更
|
||||||
|
|
||||||
|
#### ConvergingTriangleResult (converging_triangle.py)
|
||||||
|
```python
|
||||||
|
# 旧字段
|
||||||
|
fitting_score: float = 0.0
|
||||||
|
boundary_utilization: float = 0.0
|
||||||
|
|
||||||
|
# 新字段
|
||||||
|
geometry_score: float = 0.0
|
||||||
|
activity_score: float = 0.0
|
||||||
|
```
|
||||||
|
|
||||||
|
#### StrengthComponents (triangle_detector_api.py)
|
||||||
|
```python
|
||||||
|
# 旧字段
|
||||||
|
fitting_score: float
|
||||||
|
utilization_score: float
|
||||||
|
|
||||||
|
# 新字段
|
||||||
|
geometry_score: float
|
||||||
|
activity_score: float
|
||||||
|
```
|
||||||
|
|
||||||
|
## 五、强度分组成（更新后）
|
||||||
|
|
||||||
|
```
|
||||||
|
总强度 = 价格分×50% + 收敛分×15% + 成交量分×10% + 形态规则度×10% + 价格活跃度×15%
|
||||||
|
```
|
||||||
|
|
||||||
|
| 序号 | 组成部分 | 权重 | 英文名 | 说明 |
|
||||||
|
|------|---------|------|--------|------|
|
||||||
|
| 1 | **突破幅度分** | 50% | `price_score` | 价格突破上/下沿的幅度 |
|
||||||
|
| 2 | **收敛度分** | 15% | `convergence_score` | 三角形收敛程度 |
|
||||||
|
| 3 | **成交量分** | 10% | `volume_score` | 突破时的放量程度 |
|
||||||
|
| 4 | **形态规则度** | 10% | `geometry_score` | 枢轴点到拟合线的贴合程度 |
|
||||||
|
| 5 | **价格活跃度** | 15% | `activity_score` | 价格走势对通道空间的利用程度 |
|
||||||
|
|
||||||
|
## 六、向后兼容性
|
||||||
|
|
||||||
|
### 6.1 破坏性变更
|
||||||
|
⚠️ **注意**：此次重命名是**破坏性变更**，以下代码需要更新：
|
||||||
|
|
||||||
|
1. **依赖旧字段名的代码**
|
||||||
|
```python
|
||||||
|
# 旧代码（不再工作）
|
||||||
|
result.fitting_score
|
||||||
|
result.boundary_utilization
|
||||||
|
components.fitting_score
|
||||||
|
components.utilization_score
|
||||||
|
|
||||||
|
# 新代码
|
||||||
|
result.geometry_score
|
||||||
|
result.activity_score
|
||||||
|
components.geometry_score
|
||||||
|
components.activity_score
|
||||||
|
```
|
||||||
|
|
||||||
|
2. **调用旧函数名的代码**
|
||||||
|
```python
|
||||||
|
# 旧代码（不再工作）
|
||||||
|
from converging_triangle import calc_fitting_adherence, calc_boundary_utilization
|
||||||
|
|
||||||
|
# 新代码
|
||||||
|
from converging_triangle import calc_geometry_score, calc_activity_score
|
||||||
|
```
|
||||||
|
|
||||||
|
### 6.2 迁移建议
|
||||||
|
|
||||||
|
如果你的项目中有旧代码，可以：
|
||||||
|
|
||||||
|
1. **查找替换**：全局搜索并替换旧名称
|
||||||
|
2. **检查导入**：确保导入语句使用新名称
|
||||||
|
3. **更新文档**：更新相关文档和注释
|
||||||
|
4. **测试验证**：运行测试确保功能正常
|
||||||
|
|
||||||
|
## 七、验证
|
||||||
|
|
||||||
|
### 7.1 语法检查
|
||||||
|
所有文件已通过Python语法检查：
|
||||||
|
```bash
|
||||||
|
✅ src/converging_triangle.py
|
||||||
|
✅ src/converging_triangle_optimized.py
|
||||||
|
✅ src/triangle_detector_api.py
|
||||||
|
✅ scripts/plot_converging_triangles.py
|
||||||
|
✅ scripts/test_full_pipeline.py
|
||||||
|
✅ scripts/test_optimization_comparison.py
|
||||||
|
✅ scripts/generate_stock_viewer.py
|
||||||
|
```
|
||||||
|
|
||||||
|
### 7.2 导入测试
|
||||||
|
```bash
|
||||||
|
✅ 核心函数导入成功
|
||||||
|
✅ 数据类字段验证成功
|
||||||
|
✅ Numba优化版本正常加载
|
||||||
|
```
|
||||||
|
|
||||||
|
### 7.3 修改统计
|
||||||
|
- **文件总数**: 8个
|
||||||
|
- **核心源文件**: 3个
|
||||||
|
- **脚本文件**: 5个
|
||||||
|
- **替换次数**: 约150处
|
||||||
|
|
||||||
|
## 八、后续工作建议
|
||||||
|
|
||||||
|
1. **更新可视化代码**：如果有图表显示这些指标，需要更新标签
|
||||||
|
2. **更新API文档**：更新 `triangle_api_reference.md` 中的描述
|
||||||
|
3. **更新示例代码**：更新所有示例中的字段名
|
||||||
|
4. **测试验证**：运行完整的测试套件确保功能正常
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
**修改日期**: 2026-01-29
|
||||||
|
**修改原因**: 提高命名的直观性，避免用户混淆两个指标的含义
|
||||||
|
**影响范围**: 核心API字段名和函数名
|
||||||
434
docs/强度分_增加角度参数_深度分析.md
Normal file
434
docs/强度分_增加角度参数_深度分析.md
Normal file
@ -0,0 +1,434 @@
|
|||||||
|
# 强度分中增加"角度"参数的深度分析
|
||||||
|
|
||||||
|
## 一、问题背景
|
||||||
|
|
||||||
|
根据讨论记录（`discuss/20260129-讨论.md`），需要在"强度分"中新增"角度"参数，即斜率、三角形的旋转角度。
|
||||||
|
|
||||||
|
## 二、当前"强度分"的组成结构
|
||||||
|
|
||||||
|
### 2.1 现有五大维度
|
||||||
|
|
||||||
|
根据 `docs/强度分组成梳理.md`，当前强度分由以下**5个组成部分**加权求和（总权重100%）：
|
||||||
|
|
||||||
|
| 序号 | 组成部分 | 权重 | 英文字段名 | 说明 |
|
||||||
|
|------|---------|------|-----------|------|
|
||||||
|
| 1 | **突破幅度分** | 50% | `price_score` | 价格突破上/下沿的幅度（tanh归一化） |
|
||||||
|
| 2 | **收敛度分** | 15% | `convergence_score` | 三角形收敛程度（1 - width_ratio） |
|
||||||
|
| 3 | **成交量分** | 10% | `volume_score` | 突破时的放量程度 |
|
||||||
|
| 4 | **形态规则度** | 10% | `geometry_score` | 枢轴点到拟合线的贴合程度 |
|
||||||
|
| 5 | **价格活跃度** | 15% | `activity_score` | 价格对通道空间的利用程度 |
|
||||||
|
|
||||||
|
**计算公式**：
|
||||||
|
```python
|
||||||
|
strength = (
|
||||||
|
0.50 × 突破幅度分 +
|
||||||
|
0.15 × 收敛度分 +
|
||||||
|
0.10 × 成交量分 +
|
||||||
|
0.10 × 形态规则度 +
|
||||||
|
0.15 × 价格活跃度
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
### 2.2 现有的斜率数据
|
||||||
|
|
||||||
|
实际上，代码中**已经计算并存储了斜率信息**：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# converging_triangle.py: line 86-87
|
||||||
|
upper_slope: float = 0.0 # 上沿斜率
|
||||||
|
lower_slope: float = 0.0 # 下沿斜率
|
||||||
|
```
|
||||||
|
|
||||||
|
但这些斜率**未被纳入强度分计算**，仅用于：
|
||||||
|
1. **形态识别约束**：确保三角形相向收敛（上沿向下，下沿向上）
|
||||||
|
2. **可视化展示**：绘制三角形边界线
|
||||||
|
|
||||||
|
|
||||||
|
## 三、"角度"参数的含义与作用
|
||||||
|
|
||||||
|
### 3.1 "角度"的数学定义
|
||||||
|
|
||||||
|
收敛三角形有**两条边界线**，因此涉及到**多个角度**：
|
||||||
|
|
||||||
|
#### 方案A：上沿/下沿的倾斜角度（独立角度）
|
||||||
|
- **上沿角度**：`θ_upper = arctan(upper_slope)`
|
||||||
|
- **下沿角度**：`θ_lower = arctan(lower_slope)`
|
||||||
|
|
||||||
|
示例：
|
||||||
|
- 上沿斜率 = -0.05 → 角度 ≈ -2.86°（向下倾斜）
|
||||||
|
- 下沿斜率 = +0.03 → 角度 ≈ +1.72°（向上倾斜）
|
||||||
|
|
||||||
|
#### 方案B：三角形的整体倾斜方向（合成角度）
|
||||||
|
- **三角形中轴角度**：`θ_mid = arctan((upper_slope + lower_slope) / 2)`
|
||||||
|
- 中轴向上倾斜 → 上升三角形
|
||||||
|
- 中轴向下倾斜 → 下降三角形
|
||||||
|
- 中轴接近水平 → 对称三角形
|
||||||
|
|
||||||
|
#### 方案C：三角形的收敛角度（张角）
|
||||||
|
- **收敛角度**：`θ_apex = arctan(upper_slope - lower_slope)`
|
||||||
|
- 反映三角形收敛的"尖锐程度"
|
||||||
|
- 张角越小 → 形态越尖锐，预示突破可能更强
|
||||||
|
|
||||||
|
### 3.2 "角度"参数的技术分析意义
|
||||||
|
|
||||||
|
| 角度类型 | 技术分析含义 | 对突破的影响 |
|
||||||
|
|---------|-------------|------------|
|
||||||
|
| **上沿倾斜度** | 压力线的陡峭程度 | 过于陡峭可能表示抛压过大 |
|
||||||
|
| **下沿倾斜度** | 支撑线的强度 | 陡峭向上表示买盘强劲 |
|
||||||
|
| **中轴倾斜方向** | 市场整体趋势偏向 | 向上偏=多头趋势，向下偏=空头趋势 |
|
||||||
|
| **收敛张角** | 多空力量博弈的紧张度 | 张角越小=能量积蓄越充分 |
|
||||||
|
|
||||||
|
### 3.3 用户需求推测
|
||||||
|
|
||||||
|
根据"用户可调整强度分中的每个参数"的需求，增加"角度"参数的目的可能是：
|
||||||
|
|
||||||
|
1. **筛选特定形态**：只要对称三角形（中轴接近水平）或只要上升/下降三角形
|
||||||
|
2. **控制倾斜度**：排除过于陡峭或过于平缓的形态
|
||||||
|
3. **评估突破质量**：角度影响突破的有效性（如陡峭向上的支撑线更可能向上突破）
|
||||||
|
|
||||||
|
|
||||||
|
## 四、增加"角度"参数的具体实现方案
|
||||||
|
|
||||||
|
### 4.1 推荐方案：增加"中轴倾斜度分"
|
||||||
|
|
||||||
|
**核心思路**：将三角形的整体倾斜方向纳入强度分，反映市场趋势的偏向性。
|
||||||
|
|
||||||
|
#### 4.1.1 计算方法
|
||||||
|
|
||||||
|
```python
|
||||||
|
def calc_tilt_score(
|
||||||
|
upper_slope: float,
|
||||||
|
lower_slope: float,
|
||||||
|
breakout_dir: str,
|
||||||
|
) -> float:
|
||||||
|
"""
|
||||||
|
计算三角形倾斜度分 (0~1)
|
||||||
|
|
||||||
|
衡量三角形中轴的倾斜方向与突破方向的一致性。
|
||||||
|
趋势偏向与突破方向一致时，得分越高。
|
||||||
|
|
||||||
|
计算方法：
|
||||||
|
1. 计算中轴斜率：mid_slope = (upper_slope + lower_slope) / 2
|
||||||
|
2. 计算倾斜度：tilt = arctan(mid_slope) / (π/4)，归一化到 [-1, +1]
|
||||||
|
- 向上倾斜（上升三角形）：tilt > 0
|
||||||
|
- 向下倾斜（下降三角形）：tilt < 0
|
||||||
|
- 对称三角形：tilt ≈ 0
|
||||||
|
3. 根据突破方向计算得分：
|
||||||
|
- 向上突破：score = (1 + tilt) / 2 # 倾斜向上时得分高
|
||||||
|
- 向下突破：score = (1 - tilt) / 2 # 倾斜向下时得分高
|
||||||
|
|
||||||
|
归一化映射（以向上突破为例）：
|
||||||
|
- 上升三角形（中轴向上15°）+ 向上突破 → 得分 0.85
|
||||||
|
- 对称三角形（中轴水平） + 向上突破 → 得分 0.50
|
||||||
|
- 下降三角形（中轴向下15°）+ 向上突破 → 得分 0.15（逆势突破）
|
||||||
|
|
||||||
|
Args:
|
||||||
|
upper_slope: 上沿斜率
|
||||||
|
lower_slope: 下沿斜率
|
||||||
|
breakout_dir: 突破方向 "up" | "down" | "none"
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tilt_score: 0~1，越大表示倾斜方向与突破方向越一致
|
||||||
|
"""
|
||||||
|
import math
|
||||||
|
|
||||||
|
# 1. 计算中轴斜率
|
||||||
|
mid_slope = (upper_slope + lower_slope) / 2.0
|
||||||
|
|
||||||
|
# 2. 计算倾斜角度（弧度），并归一化到 [-1, +1]
|
||||||
|
# 使用 arctan(slope) / (π/4) 映射：
|
||||||
|
# - 45° 向上 → +1
|
||||||
|
# - 0° 水平 → 0
|
||||||
|
# - 45° 向下 → -1
|
||||||
|
angle_rad = math.atan(mid_slope)
|
||||||
|
tilt = angle_rad / (math.pi / 4) # 归一化到 [-1, +1]
|
||||||
|
tilt = max(-1.0, min(1.0, tilt)) # 限制在 [-1, 1]
|
||||||
|
|
||||||
|
# 3. 根据突破方向计算得分
|
||||||
|
if breakout_dir == "up":
|
||||||
|
# 向上突破：倾斜向上时得分高
|
||||||
|
score = (1.0 + tilt) / 2.0
|
||||||
|
elif breakout_dir == "down":
|
||||||
|
# 向下突破：倾斜向下时得分高
|
||||||
|
score = (1.0 - tilt) / 2.0
|
||||||
|
else:
|
||||||
|
# 未突破：使用中性分数（0.5）
|
||||||
|
score = 0.5
|
||||||
|
|
||||||
|
return max(0.0, min(1.0, score))
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 4.1.2 权重分配调整
|
||||||
|
|
||||||
|
新增"倾斜度分"后，需要调整权重（保持总和100%）：
|
||||||
|
|
||||||
|
**方案1：从"突破幅度分"中分配**
|
||||||
|
```python
|
||||||
|
W_PRICE = 0.45 # 突破幅度权重（从50%降至45%）
|
||||||
|
W_CONVERGENCE = 0.15 # 收敛度权重
|
||||||
|
W_VOLUME = 0.10 # 成交量权重
|
||||||
|
W_GEOMETRY = 0.10 # 形态规则度权重
|
||||||
|
W_ACTIVITY = 0.15 # 价格活跃度权重
|
||||||
|
W_TILT = 0.05 # 倾斜度权重（新增，占5%）
|
||||||
|
```
|
||||||
|
|
||||||
|
**方案2：均匀分配**
|
||||||
|
```python
|
||||||
|
W_PRICE = 0.47 # 突破幅度权重（50% → 47%）
|
||||||
|
W_CONVERGENCE = 0.13 # 收敛度权重（15% → 13%）
|
||||||
|
W_VOLUME = 0.08 # 成交量权重（10% → 8%）
|
||||||
|
W_GEOMETRY = 0.10 # 形态规则度权重
|
||||||
|
W_ACTIVITY = 0.15 # 价格活跃度权重
|
||||||
|
W_TILT = 0.07 # 倾斜度权重（新增，占7%）
|
||||||
|
```
|
||||||
|
|
||||||
|
#### 4.1.3 修改位置
|
||||||
|
|
||||||
|
需要修改以下文件：
|
||||||
|
|
||||||
|
1. **`src/converging_triangle.py`**
|
||||||
|
- 在 `ConvergingTriangleResult` 中新增字段：
|
||||||
|
```python
|
||||||
|
tilt_score: float = 0.0 # 倾斜度分数
|
||||||
|
```
|
||||||
|
- 新增函数 `calc_tilt_score()`
|
||||||
|
- 修改 `calc_breakout_strength()` 函数，纳入倾斜度分
|
||||||
|
|
||||||
|
2. **`src/converging_triangle_optimized.py`**
|
||||||
|
- 新增 Numba 优化版本的 `calc_tilt_score_numba()`
|
||||||
|
- 修改 `calc_breakout_strength_numba()`
|
||||||
|
|
||||||
|
3. **`src/triangle_detector_api.py`**
|
||||||
|
- 在 `StrengthComponents` 中新增：
|
||||||
|
```python
|
||||||
|
tilt_score: float # 倾斜度分数
|
||||||
|
```
|
||||||
|
|
||||||
|
4. **`docs/强度分组成梳理.md`**
|
||||||
|
- 更新为6个组成部分，添加倾斜度的说明
|
||||||
|
|
||||||
|
|
||||||
|
### 4.2 备选方案：增加"收敛角度分"
|
||||||
|
|
||||||
|
**核心思路**：将三角形的收敛尖锐程度纳入强度分。
|
||||||
|
|
||||||
|
```python
|
||||||
|
def calc_apex_angle_score(
|
||||||
|
upper_slope: float,
|
||||||
|
lower_slope: float,
|
||||||
|
) -> float:
|
||||||
|
"""
|
||||||
|
计算收敛角度分 (0~1)
|
||||||
|
|
||||||
|
衡量三角形的收敛尖锐程度（张角大小）。
|
||||||
|
张角越小（形态越尖锐），得分越高。
|
||||||
|
|
||||||
|
计算方法：
|
||||||
|
1. 张角 = arctan(upper_slope - lower_slope)
|
||||||
|
2. 归一化到 [0, 1]：
|
||||||
|
- 张角 < 5° → 得分 1.0 (极度尖锐)
|
||||||
|
- 张角 ≈ 15° → 得分 0.7
|
||||||
|
- 张角 ≈ 30° → 得分 0.4
|
||||||
|
- 张角 > 45° → 得分 0.1 (过于开阔)
|
||||||
|
|
||||||
|
Args:
|
||||||
|
upper_slope: 上沿斜率
|
||||||
|
lower_slope: 下沿斜率
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
angle_score: 0~1，越大表示形态越尖锐
|
||||||
|
"""
|
||||||
|
import math
|
||||||
|
|
||||||
|
# 计算张角（绝对值）
|
||||||
|
slope_diff = abs(upper_slope - lower_slope)
|
||||||
|
angle_rad = math.atan(slope_diff)
|
||||||
|
angle_deg = math.degrees(angle_rad)
|
||||||
|
|
||||||
|
# 指数衰减归一化（张角越大分数越低）
|
||||||
|
# 使用 exp(-angle_deg / 20) 映射：
|
||||||
|
# - 0° → 1.00
|
||||||
|
# - 10° → 0.61
|
||||||
|
# - 20° → 0.37
|
||||||
|
# - 30° → 0.22
|
||||||
|
# - 45° → 0.11
|
||||||
|
score = math.exp(-angle_deg / 20.0)
|
||||||
|
|
||||||
|
return max(0.0, min(1.0, score))
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
## 五、对"LLM 调参"友好性的考虑
|
||||||
|
|
||||||
|
### 5.1 要求所有参数在 0-1 区间
|
||||||
|
|
||||||
|
根据讨论记录第3点："强度分内所有参数需保持在 0-1 区间，便于 LLM 调参；要求均匀/正态分布，默认值为 0.5。"
|
||||||
|
|
||||||
|
#### 当前"角度"的原始值范围
|
||||||
|
- 斜率范围：约 -0.1 ~ +0.1（受代码约束）
|
||||||
|
- 角度范围：约 -5.7° ~ +5.7°（arctan(±0.1)）
|
||||||
|
|
||||||
|
#### 归一化到 [0, 1] 的方法
|
||||||
|
|
||||||
|
**方法1：线性归一化**
|
||||||
|
```python
|
||||||
|
# 将 [-5.7°, +5.7°] 映射到 [0, 1]
|
||||||
|
normalized = (angle_deg + 5.7) / 11.4
|
||||||
|
# 默认值 0.5 对应 0° (水平)
|
||||||
|
```
|
||||||
|
|
||||||
|
**方法2：sigmoid 归一化**
|
||||||
|
```python
|
||||||
|
# 使用 sigmoid 函数，0° 映射到 0.5
|
||||||
|
normalized = 1 / (1 + exp(-angle_deg / 3))
|
||||||
|
# 默认值 0.5 对应 0°
|
||||||
|
```
|
||||||
|
|
||||||
|
**方法3：绝对值归一化（仅关心角度大小，不关心方向）**
|
||||||
|
```python
|
||||||
|
# 将 [0°, 5.7°] 映射到 [0, 1]
|
||||||
|
normalized = abs(angle_deg) / 5.7
|
||||||
|
# 默认值 0.5 对应 约 2.85°
|
||||||
|
```
|
||||||
|
|
||||||
|
### 5.2 为 LLM 设计的可调参数接口
|
||||||
|
|
||||||
|
在 `triangle_detector_api.py` 中新增参数：
|
||||||
|
|
||||||
|
```python
|
||||||
|
@dataclass
|
||||||
|
class DetectionParams:
|
||||||
|
"""检测参数，都有合理默认值，大多数场景无需调整"""
|
||||||
|
# ===== 用户可调参数 =====
|
||||||
|
window: int = 240
|
||||||
|
min_convergence: float = 0.45
|
||||||
|
breakout_threshold: float = 0.005
|
||||||
|
volume_multiplier: float = 1.5
|
||||||
|
|
||||||
|
# 【新增】角度相关参数
|
||||||
|
tilt_preference: float = 0.5 # 倾斜偏好 [0, 1]
|
||||||
|
# 0=偏好下降三角形，0.5=中性，1=偏好上升三角形
|
||||||
|
|
||||||
|
apex_angle_preference: float = 0.5 # 收敛角度偏好 [0, 1]
|
||||||
|
# 0=偏好开阔形态，0.5=中性，1=偏好尖锐形态
|
||||||
|
|
||||||
|
# 【新增】权重配置
|
||||||
|
weight_price: float = 0.45 # 突破幅度权重
|
||||||
|
weight_convergence: float = 0.15 # 收敛度权重
|
||||||
|
weight_volume: float = 0.10 # 成交量权重
|
||||||
|
weight_geometry: float = 0.10 # 形态规则度权重
|
||||||
|
weight_activity: float = 0.15 # 价格活跃度权重
|
||||||
|
weight_tilt: float = 0.05 # 倾斜度权重（新增）
|
||||||
|
```
|
||||||
|
|
||||||
|
|
||||||
|
## 六、实现步骤总结
|
||||||
|
|
||||||
|
### 6.1 短期方案（最小改动）
|
||||||
|
|
||||||
|
**仅暴露斜率信息，不改变强度分计算**
|
||||||
|
|
||||||
|
1. 在 API 返回结果中添加斜率字段的解释说明
|
||||||
|
2. 在可视化和导出中突出显示角度信息
|
||||||
|
3. 用户可以在后处理时根据角度手动筛选
|
||||||
|
|
||||||
|
**优点**：零风险，不影响现有逻辑
|
||||||
|
**缺点**：未真正集成到强度分，用户需要自己处理
|
||||||
|
|
||||||
|
### 6.2 中期方案（推荐）
|
||||||
|
|
||||||
|
**增加"倾斜度分"作为第6个维度**
|
||||||
|
|
||||||
|
1. 新增 `calc_tilt_score()` 函数
|
||||||
|
2. 修改 `calc_breakout_strength()` 将倾斜度纳入加权
|
||||||
|
3. 调整权重配置（建议从突破幅度中分出5%）
|
||||||
|
4. 更新文档和测试
|
||||||
|
|
||||||
|
**优点**：有实际技术分析意义，易于理解
|
||||||
|
**缺点**：需要调整权重，可能影响现有结果的一致性
|
||||||
|
|
||||||
|
### 6.3 长期方案（完全可配置）
|
||||||
|
|
||||||
|
**允许用户自定义强度分的权重**
|
||||||
|
|
||||||
|
1. 将所有权重作为参数暴露给 LLM
|
||||||
|
2. 新增多种角度相关的分数（倾斜度、收敛角度、上下沿独立角度）
|
||||||
|
3. 用户可以根据自己的策略调整权重组合
|
||||||
|
|
||||||
|
**优点**：最大灵活性，适应不同交易策略
|
||||||
|
**缺点**：复杂度高，需要大量测试和文档支持
|
||||||
|
|
||||||
|
|
||||||
|
## 七、潜在的技术挑战与注意事项
|
||||||
|
|
||||||
|
### 7.1 数值稳定性
|
||||||
|
- 斜率接近0时，角度变化敏感，需要设置阈值
|
||||||
|
- 极端情况（垂直线）需要特殊处理
|
||||||
|
|
||||||
|
### 7.2 多重共线性
|
||||||
|
- 倾斜度与"收敛度"可能相关（如对称三角形收敛度通常更高）
|
||||||
|
- 需要验证新参数是否引入冗余信息
|
||||||
|
|
||||||
|
### 7.3 历史数据回测
|
||||||
|
- 增加新参数后，需要重新回测所有历史数据
|
||||||
|
- 评估对现有高强度信号的影响
|
||||||
|
|
||||||
|
### 7.4 用户理解成本
|
||||||
|
- "角度"参数的含义可能不如"突破幅度"直观
|
||||||
|
- 需要提供清晰的文档和示例
|
||||||
|
|
||||||
|
|
||||||
|
## 八、推荐的实施路径
|
||||||
|
|
||||||
|
### 阶段1：数据验证（2小时）
|
||||||
|
1. 统计现有数据中斜率的分布情况
|
||||||
|
2. 分析角度与突破强度的相关性
|
||||||
|
3. 确认增加角度参数的必要性
|
||||||
|
|
||||||
|
### 阶段2：方案确认（1小时）
|
||||||
|
1. 与用户确认具体需求（是倾斜度、收敛角度还是其他？）
|
||||||
|
2. 确定权重分配方案
|
||||||
|
3. 确定参数归一化方式
|
||||||
|
|
||||||
|
### 阶段3：代码实现（4小时）
|
||||||
|
1. 修改 `converging_triangle.py`（1.5小时）
|
||||||
|
2. 修改 `converging_triangle_optimized.py`（1.5小时）
|
||||||
|
3. 修改 `triangle_detector_api.py`（1小时）
|
||||||
|
|
||||||
|
### 阶段4：测试与验证（3小时）
|
||||||
|
1. 单元测试（1小时）
|
||||||
|
2. 历史数据回测（1小时）
|
||||||
|
3. 可视化验证（1小时）
|
||||||
|
|
||||||
|
### 阶段5：文档更新（1小时）
|
||||||
|
1. 更新 `docs/强度分组成梳理.md`
|
||||||
|
2. 更新 API 文档
|
||||||
|
3. 添加使用示例
|
||||||
|
|
||||||
|
**总计**：约 11 小时
|
||||||
|
|
||||||
|
|
||||||
|
## 九、决策建议
|
||||||
|
|
||||||
|
根据以上分析，我的建议是：
|
||||||
|
|
||||||
|
1. **采用中期方案（增加倾斜度分）**
|
||||||
|
- 有明确的技术分析意义
|
||||||
|
- 实现成本适中
|
||||||
|
- 对现有系统影响可控
|
||||||
|
|
||||||
|
2. **参数设计**
|
||||||
|
- 新增 `tilt_score` 作为第6个维度
|
||||||
|
- 权重从突破幅度中分配 5%
|
||||||
|
- 归一化到 [0, 1]，默认值 0.5
|
||||||
|
|
||||||
|
3. **下一步行动**
|
||||||
|
- 与用户确认具体需求（是否就是"倾斜度"）
|
||||||
|
- 进行小规模数据验证
|
||||||
|
- 如果验证通过，再进入实施阶段
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
**文档创建时间**：2026-01-29
|
||||||
|
**作者**：AI Assistant
|
||||||
|
**状态**：待用户确认
|
||||||
137
docs/强度分_形态规则度和价格活跃度_归一化详解.md
Normal file
137
docs/强度分_形态规则度和价格活跃度_归一化详解.md
Normal file
@ -0,0 +1,137 @@
|
|||||||
|
# 强度分：形态规则度和价格活跃度的归一化详解
|
||||||
|
|
||||||
|
本文档详细说明**形态规则度** (`geometry_score`) 和**价格活跃度** (`activity_score`) 这两个强度分量的归一化实现方式。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 4. 形态规则度 (`geometry_score`) 的归一化
|
||||||
|
|
||||||
|
**目的**：测量枢轴点（4-8个关键点）到拟合线的贴合程度，形态越规则得分越高。
|
||||||
|
|
||||||
|
### 计算步骤
|
||||||
|
|
||||||
|
```python
|
||||||
|
# 1. 计算相对误差：遍历所有枢轴点
|
||||||
|
for i in range(n):
|
||||||
|
fitted_value = slope * pivot_indices[i] + intercept
|
||||||
|
rel_error = abs(pivot_values[i] - fitted_value) / max(abs(fitted_value), 1e-9)
|
||||||
|
sum_rel_error += rel_error
|
||||||
|
|
||||||
|
# 2. 计算平均相对误差
|
||||||
|
mean_rel_error = sum_rel_error / n
|
||||||
|
|
||||||
|
# 3. 指数衰减归一化
|
||||||
|
SCALE_FACTOR = 20.0
|
||||||
|
geometry_score = np.exp(-mean_rel_error * SCALE_FACTOR)
|
||||||
|
|
||||||
|
# 4. 显式 clamp 到 [0, 1]
|
||||||
|
geometry_score = min(1.0, max(0.0, geometry_score))
|
||||||
|
```
|
||||||
|
|
||||||
|
### 归一化原理
|
||||||
|
|
||||||
|
- **输入**：`mean_rel_error` ∈ [0, +∞)（平均相对误差，0 = 完美拟合）
|
||||||
|
- **指数衰减**：`exp(-mean_rel_error * 20)` 将误差映射到 (0, 1] 区间
|
||||||
|
- 误差 = 0 → 得分 = 1.0（完美拟合）
|
||||||
|
- 误差 = 0.05 → 得分 ≈ 0.37（中等拟合）
|
||||||
|
- 误差 = 0.10 → 得分 ≈ 0.14（较差拟合）
|
||||||
|
- 误差 → ∞ → 得分 → 0（完全不拟合）
|
||||||
|
- **缩放因子 20**：决定衰减速度，值越大对误差越敏感
|
||||||
|
|
||||||
|
### 范围保证
|
||||||
|
|
||||||
|
- 指数函数 `exp(-x)` 对于 x ≥ 0，输出自然在 (0, 1] 区间
|
||||||
|
- 最后显式 clamp 确保异常情况下也在 [0, 1]
|
||||||
|
|
||||||
|
### 实现位置
|
||||||
|
|
||||||
|
- 函数名：`calc_geometry_score_numba()`
|
||||||
|
- 文件：`src/converging_triangle_optimized.py`（第350-374行）
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 5. 价格活跃度 (`activity_score`) 的归一化
|
||||||
|
|
||||||
|
**目的**：测量价格在通道内的振荡充分性，识别真实博弈 vs 僵尸形态。
|
||||||
|
|
||||||
|
### 计算步骤
|
||||||
|
|
||||||
|
```python
|
||||||
|
# 1. 逐日计算活跃度（遍历 240 天完整数据）
|
||||||
|
total_activity = 0.0
|
||||||
|
valid_days = 0
|
||||||
|
|
||||||
|
for i in range(start, end + 1):
|
||||||
|
# 1.1 计算当日通道宽度
|
||||||
|
upper_line = upper_slope * i + upper_intercept
|
||||||
|
lower_line = lower_slope * i + lower_intercept
|
||||||
|
channel_width = upper_line - lower_line
|
||||||
|
|
||||||
|
if channel_width <= 0:
|
||||||
|
continue
|
||||||
|
|
||||||
|
# 1.2 计算空白距离
|
||||||
|
dist_to_upper = max(0.0, upper_line - high[i]) # 高点未触及上沿的距离
|
||||||
|
dist_to_lower = max(0.0, low[i] - lower_line) # 低点未触及下沿的距离
|
||||||
|
|
||||||
|
# 1.3 计算空白比例
|
||||||
|
blank_ratio = (dist_to_upper + dist_to_lower) / channel_width
|
||||||
|
|
||||||
|
# 1.4 单日活跃度 = 1 - 空白比例，并 clamp 到 [0, 1]
|
||||||
|
day_activity = max(0.0, min(1.0, 1.0 - blank_ratio))
|
||||||
|
|
||||||
|
total_activity += day_activity
|
||||||
|
valid_days += 1
|
||||||
|
|
||||||
|
# 2. 计算平均活跃度（已自动在 [0, 1] 区间）
|
||||||
|
activity_score = total_activity / valid_days
|
||||||
|
```
|
||||||
|
|
||||||
|
### 归一化原理
|
||||||
|
|
||||||
|
- **输入**：每日的 `blank_ratio`（空白比例）∈ [0, +∞)
|
||||||
|
- 0 = 价格完全填满通道（高点触上沿且低点触下沿）
|
||||||
|
- 1 = 价格只占通道一半空间
|
||||||
|
- \> 1 = 价格严重偏离通道（理论上不应出现）
|
||||||
|
- **反转**：`1 - blank_ratio` 将"空白"转为"活跃"
|
||||||
|
- blank_ratio = 0 → activity = 1（最活跃）
|
||||||
|
- blank_ratio = 0.5 → activity = 0.5（中等活跃）
|
||||||
|
- blank_ratio = 1 → activity = 0（不活跃）
|
||||||
|
- **双重 clamp**：
|
||||||
|
1. **每日 clamp**：`max(0.0, min(1.0, ...))` 确保单日得分在 [0, 1]
|
||||||
|
2. **最终平均**：因为每日都在 [0, 1]，平均值自然在 [0, 1]
|
||||||
|
|
||||||
|
### 范围保证
|
||||||
|
|
||||||
|
- 每日活跃度通过双边 clamp 严格限制在 [0, 1]
|
||||||
|
- 最终得分是所有有效日的平均值，数学上保证在 [0, 1]
|
||||||
|
|
||||||
|
### 实际意义
|
||||||
|
|
||||||
|
- **0.8-1.0**：价格充分振荡，真实博弈形态
|
||||||
|
- **0.5-0.8**：价格较活跃，形态有效
|
||||||
|
- **0.2-0.5**：价格偏弱，形态存疑
|
||||||
|
- **< 0.2**：僵尸形态，触发空白惩罚机制
|
||||||
|
|
||||||
|
### 实现位置
|
||||||
|
|
||||||
|
- 函数名：`calc_activity_score_numba()`
|
||||||
|
- 文件：`src/converging_triangle_optimized.py`（第378-412行）
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## 总结
|
||||||
|
|
||||||
|
这两个分量的归一化方式各有特点：
|
||||||
|
|
||||||
|
1. **形态规则度**：使用指数衰减映射，对误差敏感度高，适合质量评估
|
||||||
|
2. **价格活跃度**：使用线性反转+双重clamp，逐日计算后取平均，适合统计性指标
|
||||||
|
|
||||||
|
两者都严格保证输出在 [0, 1] 区间，满足强度分系统的设计要求。
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
**文档创建日期**：2026-01-29
|
||||||
|
**相关文档**：
|
||||||
|
- `docs/强度分组成梳理.md`：6个强度分量的完整说明
|
||||||
|
- `discuss/20260129-讨论.md`：强度分参数讨论
|
||||||
99
docs/强度分组成梳理.md
Normal file
99
docs/强度分组成梳理.md
Normal file
@ -0,0 +1,99 @@
|
|||||||
|
# 强度分组成梳理
|
||||||
|
|
||||||
|
根据当前代码，**强度分由 6 个组成部分**：
|
||||||
|
|
||||||
|
```python
|
||||||
|
# 权重配置(调整后，总和 = 100%)
|
||||||
|
W_PRICE = 0.45 # 突破幅度权重（从50%降至45%）
|
||||||
|
W_CONVERGENCE = 0.15 # 收敛度权重
|
||||||
|
W_VOLUME = 0.10 # 成交量权重
|
||||||
|
W_GEOMETRY = 0.10 # 形态规则度权重
|
||||||
|
W_ACTIVITY = 0.15 # 价格活跃度权重
|
||||||
|
W_TILT = 0.05 # 倾斜度权重（新增）
|
||||||
|
```
|
||||||
|
|
||||||
|
## 组成详情
|
||||||
|
|
||||||
|
| 序号 | 组成部分 | 权重 | 英文字段名 | 说明 | 归一化方式 | 范围保证 |
|
||||||
|
|------|---------|------|-----------|------|-----------|---------|
|
||||||
|
| 1 | **突破幅度分** | 45% | `price_score` | 价格突破上/下沿的幅度 | `np.tanh(pct * 15.0)` | tanh 输出 [0, 1]（因为 pct ≥ 0） |
|
||||||
|
| 2 | **收敛度分** | 15% | `convergence_score` | 三角形收敛程度（1 - width_ratio） | `max(0, min(1, 1 - width_ratio))` | 显式 clamp 到 [0, 1] |
|
||||||
|
| 3 | **成交量分** | 10% | `volume_score` | 突破时的放量程度 | `min(1, max(0, volume_ratio - 1))` | 显式 clamp 到 [0, 1] |
|
||||||
|
| 4 | **形态规则度** | 10% | `geometry_score` | 枢轴点到拟合线的贴合程度，形态的几何标准性 | `exp(-error * 20)` + clamp | 指数衰减 + 显式 clamp |
|
||||||
|
| 5 | **价格活跃度** | 15% | `activity_score` | 价格走势对通道空间的利用程度，振荡充分性 | 逐日计算 `1 - blank_ratio` 的平均 + clamp | 每日 clamp + 最终 clamp |
|
||||||
|
| 6 | **倾斜度分** | 5% | `tilt_score` | 中轴倾斜方向与突破方向的一致性（新增） | `(1 ± tilt) / 2` + clamp | 显式 clamp 到 [0, 1] |
|
||||||
|
|
||||||
|
**总计：100%**
|
||||||
|
|
||||||
|
## 计算公式
|
||||||
|
|
||||||
|
```python
|
||||||
|
strength = (
|
||||||
|
0.45 × 突破幅度分 +
|
||||||
|
0.15 × 收敛度分 +
|
||||||
|
0.10 × 成交量分 +
|
||||||
|
0.10 × 形态规则度 +
|
||||||
|
0.15 × 价格活跃度 +
|
||||||
|
0.05 × 倾斜度分
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
## 空白惩罚机制
|
||||||
|
|
||||||
|
当**价格活跃度**低于 20% 时，会对总强度进行额外的降分惩罚，避免"通道很宽但价格很空"的误判。
|
||||||
|
|
||||||
|
## 命名说明
|
||||||
|
|
||||||
|
> **注意**：自 2026-01-29 起，原"拟合贴合度"重命名为"形态规则度"，原"边界利用率"重命名为"价格活跃度"，以更直观地表达其含义。
|
||||||
|
>
|
||||||
|
> - **形态规则度**：测量枢轴点的几何标准性（4-8个关键点）
|
||||||
|
> - **价格活跃度**：测量价格振荡的充分性（240天完整数据）
|
||||||
|
>
|
||||||
|
> 详见：`docs/命名优化_拟合贴合度_边界利用率_重命名.md`
|
||||||
|
|
||||||
|
## 各分量的含义
|
||||||
|
|
||||||
|
### 1. 突破幅度分 (50%)
|
||||||
|
- **作用**：衡量价格突破三角形边界的力度
|
||||||
|
- **计算**：使用 tanh 非线性归一化
|
||||||
|
- 1% 突破 → 0.15
|
||||||
|
- 3% 突破 → 0.42
|
||||||
|
- 5% 突破 → 0.64
|
||||||
|
- 10% 突破 → 0.91
|
||||||
|
|
||||||
|
### 2. 收敛度分 (15%)
|
||||||
|
- **作用**：衡量三角形收敛的紧密程度
|
||||||
|
- **计算**：1 - width_ratio（末端宽度/起始宽度）
|
||||||
|
- **示例**：收敛到 30% → 得分 0.7
|
||||||
|
|
||||||
|
### 3. 成交量分 (10%)
|
||||||
|
- **作用**：衡量突破时的成交量放大程度
|
||||||
|
- **计算**：(当前成交量/均值 - 1)，上限为 1.0
|
||||||
|
- **示例**：成交量为均值的 2 倍 → 得分 1.0
|
||||||
|
|
||||||
|
### 4. 形态规则度 (10%)
|
||||||
|
- **作用**：衡量形态的几何标准性
|
||||||
|
- **测量**：4-8 个关键枢轴点到拟合线的距离
|
||||||
|
- **特点**：关注形态结构的完整性
|
||||||
|
|
||||||
|
### 5. 价格活跃度 (15%)
|
||||||
|
- **作用**：衡量价格振荡的充分性
|
||||||
|
- **测量**：240 天价格对通道空间的利用程度
|
||||||
|
- **特点**：识别真实博弈 vs 僵尸形态
|
||||||
|
|
||||||
|
### 6. 倾斜度分 (5%)
|
||||||
|
- **作用**：衡量三角形中轴倾斜方向与突破方向的一致性
|
||||||
|
- **计算步骤**：
|
||||||
|
1. 计算中轴斜率：`mid_slope = (上沿斜率 + 下沿斜率) / 2`
|
||||||
|
2. 计算倾斜程度：`tilt = arctan(mid_slope) / (π/4)`，范围 [-1, +1]
|
||||||
|
- -1 = 强烈向下倾斜，0 = 水平，+1 = 强烈向上倾斜
|
||||||
|
3. 根据突破方向计算得分（范围 [0, 1]）：
|
||||||
|
- 向上突破：`score = (1 + tilt) / 2`
|
||||||
|
- 向下突破：`score = (1 - tilt) / 2`
|
||||||
|
- 未突破：`score = 0.5`
|
||||||
|
- **示例**（实际计算结果）：
|
||||||
|
- 上升三角形（斜率0.025）+ 向上突破 → tilt≈0.032 → 得分 0.516（略顺势）
|
||||||
|
- 对称三角形（斜率0）+ 向上突破 → tilt=0 → 得分 0.500（中性）
|
||||||
|
- 下降三角形（斜率-0.025）+ 向上突破 → tilt≈-0.032 → 得分 0.484（略逆势）
|
||||||
|
- 强向上倾斜（斜率0.10）+ 向上突破 → tilt≈0.127 → 得分 0.563（明显顺势）
|
||||||
|
- **特点**：评估突破方向与形态趋势的协调性，得分始终在 [0, 1] 范围内
|
||||||
File diff suppressed because it is too large
Load Diff
@ -47,8 +47,8 @@ python scripts/test_optimization_comparison.py
|
|||||||
1. `pivots_fractal` - 枢轴点检测
|
1. `pivots_fractal` - 枢轴点检测
|
||||||
2. `pivots_fractal_hybrid` - 混合枢轴点检测
|
2. `pivots_fractal_hybrid` - 混合枢轴点检测
|
||||||
3. `fit_boundary_anchor` - 锚点拟合
|
3. `fit_boundary_anchor` - 锚点拟合
|
||||||
4. `calc_fitting_adherence` - 拟合贴合度
|
4. `calc_geometry_score` - 形态规则度
|
||||||
5. `calc_boundary_utilization` - 边界利用率
|
5. `calc_activity_score` - 价格活跃度
|
||||||
6. `calc_breakout_strength` - 突破强度
|
6. `calc_breakout_strength` - 突破强度
|
||||||
|
|
||||||
**预期结果**:
|
**预期结果**:
|
||||||
|
|||||||
@ -68,7 +68,8 @@ def load_stock_data(csv_path: str, target_date: int = None, all_stocks_mode: boo
|
|||||||
'touchesUpper': int(row.get('touches_upper', '0')),
|
'touchesUpper': int(row.get('touches_upper', '0')),
|
||||||
'touchesLower': int(row.get('touches_lower', '0')),
|
'touchesLower': int(row.get('touches_lower', '0')),
|
||||||
'volumeConfirmed': row.get('volume_confirmed', ''),
|
'volumeConfirmed': row.get('volume_confirmed', ''),
|
||||||
'boundaryUtilization': float(row.get('boundary_utilization', '0')),
|
'activityScore': float(row.get('activity_score', '0')),
|
||||||
|
'tiltScore': float(row.get('tilt_score', '0')), # 新增：倾斜度分
|
||||||
'date': date,
|
'date': date,
|
||||||
'hasTriangle': True # 标记为有三角形形态
|
'hasTriangle': True # 标记为有三角形形态
|
||||||
}
|
}
|
||||||
@ -1438,8 +1439,12 @@ def generate_html(stocks: list, date: int, output_path: str):
|
|||||||
<span class="metric-value">${volumeText}</span>
|
<span class="metric-value">${volumeText}</span>
|
||||||
</div>
|
</div>
|
||||||
<div class="metric-item">
|
<div class="metric-item">
|
||||||
<span class="metric-label">边界利用率</span>
|
<span class="metric-label">价格活跃度</span>
|
||||||
<span class="metric-value">${(stock.boundaryUtilization || 0).toFixed(2)}</span>
|
<span class="metric-value">${(stock.activityScore || 0).toFixed(2)}</span>
|
||||||
|
</div>
|
||||||
|
<div class="metric-item">
|
||||||
|
<span class="metric-label">倾斜度</span>
|
||||||
|
<span class="metric-value">${(stock.tiltScore || 0).toFixed(2)}</span>
|
||||||
</div>
|
</div>
|
||||||
</div>
|
</div>
|
||||||
<div class="chart-container">
|
<div class="chart-container">
|
||||||
|
|||||||
@ -36,7 +36,7 @@ sys.path.append(os.path.join(os.path.dirname(__file__), "..", "src"))
|
|||||||
|
|
||||||
from converging_triangle import (
|
from converging_triangle import (
|
||||||
ConvergingTriangleParams,
|
ConvergingTriangleParams,
|
||||||
calc_fitting_adherence,
|
calc_geometry_score,
|
||||||
detect_converging_triangle,
|
detect_converging_triangle,
|
||||||
fit_pivot_line_dispatch,
|
fit_pivot_line_dispatch,
|
||||||
line_y,
|
line_y,
|
||||||
@ -290,25 +290,25 @@ def plot_triangle(
|
|||||||
# 如果使用收盘价拟合，重新计算贴合度（基于实际拟合线）
|
# 如果使用收盘价拟合，重新计算贴合度（基于实际拟合线）
|
||||||
if plot_boundary_source == "close" and len(selected_ph) > 0 and len(selected_pl) > 0:
|
if plot_boundary_source == "close" and len(selected_ph) > 0 and len(selected_pl) > 0:
|
||||||
# 使用收盘价重新计算贴合度
|
# 使用收盘价重新计算贴合度
|
||||||
adherence_upper_close = calc_fitting_adherence(
|
adherence_upper_close = calc_geometry_score(
|
||||||
pivot_indices=selected_ph_pos.astype(float),
|
pivot_indices=selected_ph_pos.astype(float),
|
||||||
pivot_values=close_win[selected_ph_pos],
|
pivot_values=close_win[selected_ph_pos],
|
||||||
slope=a_u,
|
slope=a_u,
|
||||||
intercept=b_u,
|
intercept=b_u,
|
||||||
)
|
)
|
||||||
adherence_lower_close = calc_fitting_adherence(
|
adherence_lower_close = calc_geometry_score(
|
||||||
pivot_indices=selected_pl_pos.astype(float),
|
pivot_indices=selected_pl_pos.astype(float),
|
||||||
pivot_values=close_win[selected_pl_pos],
|
pivot_values=close_win[selected_pl_pos],
|
||||||
slope=a_l,
|
slope=a_l,
|
||||||
intercept=b_l,
|
intercept=b_l,
|
||||||
)
|
)
|
||||||
fitting_adherence_plot = (adherence_upper_close + adherence_lower_close) / 2.0
|
geometry_score_plot = (adherence_upper_close + adherence_lower_close) / 2.0
|
||||||
else:
|
else:
|
||||||
# 使用检测算法计算的贴合度
|
# 使用检测算法计算的贴合度
|
||||||
fitting_adherence_plot = result.fitting_score if result else 0.0
|
geometry_score_plot = result.geometry_score if result else 0.0
|
||||||
else:
|
else:
|
||||||
# 无三角形时，贴合度为0
|
# 无三角形时，贴合度为0
|
||||||
fitting_adherence_plot = 0.0
|
geometry_score_plot = 0.0
|
||||||
|
|
||||||
# 创建图表
|
# 创建图表
|
||||||
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8),
|
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 8),
|
||||||
@ -435,21 +435,22 @@ def plot_triangle(
|
|||||||
strength = max(result.breakout_strength_up, result.breakout_strength_down)
|
strength = max(result.breakout_strength_up, result.breakout_strength_down)
|
||||||
price_score = max(result.price_score_up, result.price_score_down)
|
price_score = max(result.price_score_up, result.price_score_down)
|
||||||
|
|
||||||
# 获取边界利用率与惩罚系数（兼容旧数据）
|
# 获取价格活跃度与惩罚系数（兼容旧数据）
|
||||||
boundary_util = getattr(result, 'boundary_utilization', 0.0)
|
activity_score = getattr(result, 'activity_score', 0.0)
|
||||||
utilization_floor = 0.20
|
tilt_score = getattr(result, 'tilt_score', 0.0) # 新增：获取倾斜度分
|
||||||
if utilization_floor > 0:
|
activity_floor = 0.20
|
||||||
utilization_penalty = min(1.0, boundary_util / utilization_floor)
|
if activity_floor > 0:
|
||||||
|
activity_penalty = min(1.0, activity_score / activity_floor)
|
||||||
else:
|
else:
|
||||||
utilization_penalty = 1.0
|
activity_penalty = 1.0
|
||||||
|
|
||||||
# 选择显示的贴合度：如果使用收盘价拟合，显示重新计算的贴合度
|
# 选择显示的贴合度：如果使用收盘价拟合，显示重新计算的贴合度
|
||||||
if plot_boundary_source == "close" and has_triangle and has_enough_data:
|
if plot_boundary_source == "close" and has_triangle and has_enough_data:
|
||||||
display_fitting_score = fitting_adherence_plot
|
display_geometry = geometry_score_plot
|
||||||
fitting_note = f"拟合贴合度(收盘价): {display_fitting_score:.3f}"
|
geometry_note = f"形态规则度(收盘价): {display_geometry:.3f}"
|
||||||
else:
|
else:
|
||||||
display_fitting_score = result.fitting_score
|
display_geometry = result.geometry_score
|
||||||
fitting_note = f"拟合贴合度: {display_fitting_score:.3f}"
|
geometry_note = f"形态规则度: {display_geometry:.3f}"
|
||||||
|
|
||||||
ax1.set_title(
|
ax1.set_title(
|
||||||
f"{stock_code} {stock_name} - 收敛三角形 (检测窗口: {detect_dates[0]} ~ {detect_dates[-1]})\n"
|
f"{stock_code} {stock_name} - 收敛三角形 (检测窗口: {detect_dates[0]} ~ {detect_dates[-1]})\n"
|
||||||
@ -458,9 +459,9 @@ def plot_triangle(
|
|||||||
f"枢轴点: 高{len(ph_idx)}/低{len(pl_idx)} 触碰: 上{result.touches_upper}/下{result.touches_lower} "
|
f"枢轴点: 高{len(ph_idx)}/低{len(pl_idx)} 触碰: 上{result.touches_upper}/下{result.touches_lower} "
|
||||||
f"放量确认: {'是' if result.volume_confirmed else '否' if result.volume_confirmed is False else '-'}\n"
|
f"放量确认: {'是' if result.volume_confirmed else '否' if result.volume_confirmed is False else '-'}\n"
|
||||||
f"强度分: {strength:.3f} "
|
f"强度分: {strength:.3f} "
|
||||||
f"(价格: {price_score:.3f}×50% + 收敛: {result.convergence_score:.3f}×15% + "
|
f"(突破幅度: {price_score:.3f}×45% + 收敛度: {result.convergence_score:.3f}×15% + "
|
||||||
f"成交量: {result.volume_score:.3f}×10% + {fitting_note}×10% + "
|
f"成交量: {result.volume_score:.3f}×10% + {geometry_note}×10% + "
|
||||||
f"边界利用率: {boundary_util:.3f}×15%) × 利用率惩罚: {utilization_penalty:.2f}",
|
f"价格活跃度: {activity_score:.3f}×15% + 倾斜度: {tilt_score:.3f}×5%) × 活跃度惩罚: {activity_penalty:.2f}",
|
||||||
fontsize=11, pad=10
|
fontsize=11, pad=10
|
||||||
)
|
)
|
||||||
else:
|
else:
|
||||||
|
|||||||
@ -120,8 +120,8 @@ def test_pipeline(
|
|||||||
pivots_fractal_optimized,
|
pivots_fractal_optimized,
|
||||||
pivots_fractal_hybrid_optimized,
|
pivots_fractal_hybrid_optimized,
|
||||||
fit_boundary_anchor_optimized,
|
fit_boundary_anchor_optimized,
|
||||||
calc_fitting_adherence_optimized,
|
calc_geometry_score_optimized,
|
||||||
calc_boundary_utilization_optimized,
|
calc_activity_score_optimized,
|
||||||
calc_breakout_strength_optimized,
|
calc_breakout_strength_optimized,
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -129,8 +129,8 @@ def test_pipeline(
|
|||||||
converging_triangle.pivots_fractal = pivots_fractal_optimized
|
converging_triangle.pivots_fractal = pivots_fractal_optimized
|
||||||
converging_triangle.pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
|
converging_triangle.pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
|
||||||
converging_triangle.fit_boundary_anchor = fit_boundary_anchor_optimized
|
converging_triangle.fit_boundary_anchor = fit_boundary_anchor_optimized
|
||||||
converging_triangle.calc_fitting_adherence = calc_fitting_adherence_optimized
|
converging_triangle.calc_geometry_score = calc_geometry_score_optimized
|
||||||
converging_triangle.calc_boundary_utilization = calc_boundary_utilization_optimized
|
converging_triangle.calc_activity_score = calc_activity_score_optimized
|
||||||
converging_triangle.calc_breakout_strength = calc_breakout_strength_optimized
|
converging_triangle.calc_breakout_strength = calc_breakout_strength_optimized
|
||||||
|
|
||||||
print(" [OK] Numba优化已启用")
|
print(" [OK] Numba优化已启用")
|
||||||
@ -233,7 +233,7 @@ def compare_results(df_original, df_optimized):
|
|||||||
numeric_cols = [
|
numeric_cols = [
|
||||||
'breakout_strength_up', 'breakout_strength_down',
|
'breakout_strength_up', 'breakout_strength_down',
|
||||||
'price_score_up', 'price_score_down',
|
'price_score_up', 'price_score_down',
|
||||||
'convergence_score', 'volume_score', 'fitting_score',
|
'convergence_score', 'volume_score', 'geometry_score',
|
||||||
'upper_slope', 'lower_slope', 'width_ratio',
|
'upper_slope', 'lower_slope', 'width_ratio',
|
||||||
'touches_upper', 'touches_lower', 'apex_x'
|
'touches_upper', 'touches_lower', 'apex_x'
|
||||||
]
|
]
|
||||||
|
|||||||
@ -18,8 +18,8 @@ from converging_triangle import (
|
|||||||
pivots_fractal,
|
pivots_fractal,
|
||||||
pivots_fractal_hybrid,
|
pivots_fractal_hybrid,
|
||||||
fit_boundary_anchor,
|
fit_boundary_anchor,
|
||||||
calc_fitting_adherence,
|
calc_geometry_score,
|
||||||
calc_boundary_utilization,
|
calc_activity_score,
|
||||||
calc_breakout_strength,
|
calc_breakout_strength,
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -28,8 +28,8 @@ from converging_triangle_optimized import (
|
|||||||
pivots_fractal_optimized,
|
pivots_fractal_optimized,
|
||||||
pivots_fractal_hybrid_optimized,
|
pivots_fractal_hybrid_optimized,
|
||||||
fit_boundary_anchor_optimized,
|
fit_boundary_anchor_optimized,
|
||||||
calc_fitting_adherence_optimized,
|
calc_geometry_score_optimized,
|
||||||
calc_boundary_utilization_optimized,
|
calc_activity_score_optimized,
|
||||||
calc_breakout_strength_optimized,
|
calc_breakout_strength_optimized,
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -250,7 +250,7 @@ def main():
|
|||||||
results.append(result)
|
results.append(result)
|
||||||
|
|
||||||
# ========================================================================
|
# ========================================================================
|
||||||
# 测试 4: 拟合贴合度计算
|
# 测试 4: 形态规则度计算
|
||||||
# ========================================================================
|
# ========================================================================
|
||||||
if len(ph) >= 5:
|
if len(ph) >= 5:
|
||||||
pivot_indices = ph[:10]
|
pivot_indices = ph[:10]
|
||||||
@ -258,25 +258,25 @@ def main():
|
|||||||
slope, intercept = 0.01, 100.0
|
slope, intercept = 0.01, 100.0
|
||||||
|
|
||||||
result = compare_functions(
|
result = compare_functions(
|
||||||
calc_fitting_adherence,
|
calc_geometry_score,
|
||||||
calc_fitting_adherence_optimized,
|
calc_geometry_score_optimized,
|
||||||
"calc_fitting_adherence",
|
"calc_geometry_score",
|
||||||
pivot_indices, pivot_values, slope, intercept,
|
pivot_indices, pivot_values, slope, intercept,
|
||||||
n_iterations=n_iterations
|
n_iterations=n_iterations
|
||||||
)
|
)
|
||||||
results.append(result)
|
results.append(result)
|
||||||
|
|
||||||
# ========================================================================
|
# ========================================================================
|
||||||
# 测试 5: 边界利用率计算
|
# 测试 5: 价格活跃度计算
|
||||||
# ========================================================================
|
# ========================================================================
|
||||||
upper_slope, upper_intercept = -0.02, 120.0
|
upper_slope, upper_intercept = -0.02, 120.0
|
||||||
lower_slope, lower_intercept = 0.02, 80.0
|
lower_slope, lower_intercept = 0.02, 80.0
|
||||||
start, end = 0, len(high) - 1
|
start, end = 0, len(high) - 1
|
||||||
|
|
||||||
result = compare_functions(
|
result = compare_functions(
|
||||||
calc_boundary_utilization,
|
calc_activity_score,
|
||||||
calc_boundary_utilization_optimized,
|
calc_activity_score_optimized,
|
||||||
"calc_boundary_utilization",
|
"calc_activity_score",
|
||||||
high, low, upper_slope, upper_intercept, lower_slope, lower_intercept, start, end,
|
high, low, upper_slope, upper_intercept, lower_slope, lower_intercept, start, end,
|
||||||
n_iterations=n_iterations
|
n_iterations=n_iterations
|
||||||
)
|
)
|
||||||
|
|||||||
@ -79,8 +79,10 @@ class ConvergingTriangleResult:
|
|||||||
price_score_down: float = 0.0 # 价格突破分数(向下)
|
price_score_down: float = 0.0 # 价格突破分数(向下)
|
||||||
convergence_score: float = 0.0 # 收敛分数
|
convergence_score: float = 0.0 # 收敛分数
|
||||||
volume_score: float = 0.0 # 成交量分数
|
volume_score: float = 0.0 # 成交量分数
|
||||||
fitting_score: float = 0.0 # 拟合贴合度分数
|
geometry_score: float = 0.0 # 形态规则度分数
|
||||||
boundary_utilization: float = 0.0 # 边界利用率分数
|
activity_score: float = 0.0 # 价格活跃度分数
|
||||||
|
tilt_score: float = 0.0 # 倾斜度分数
|
||||||
|
tilt_score: float = 0.0 # 倾斜度分数
|
||||||
|
|
||||||
# 几何属性
|
# 几何属性
|
||||||
upper_slope: float = 0.0
|
upper_slope: float = 0.0
|
||||||
@ -758,7 +760,7 @@ def fit_pivot_line_dispatch(
|
|||||||
# 突破强度计算
|
# 突破强度计算
|
||||||
# ============================================================================
|
# ============================================================================
|
||||||
|
|
||||||
def calc_fitting_adherence(
|
def calc_geometry_score(
|
||||||
pivot_indices: np.ndarray,
|
pivot_indices: np.ndarray,
|
||||||
pivot_values: np.ndarray,
|
pivot_values: np.ndarray,
|
||||||
slope: float,
|
slope: float,
|
||||||
@ -777,10 +779,10 @@ def calc_fitting_adherence(
|
|||||||
4. 用指数函数归一化：score = exp(-mean_rel_error * scale_factor)
|
4. 用指数函数归一化：score = exp(-mean_rel_error * scale_factor)
|
||||||
|
|
||||||
归一化映射（scale_factor = 20）：
|
归一化映射（scale_factor = 20）：
|
||||||
- 误差 0% → 分数 1.00 (完美拟合)
|
- 误差 0% → 分数 1.00 (完美规则)
|
||||||
- 误差 2% → 分数 0.67 (良好拟合)
|
- 误差 2% → 分数 0.67 (良好规则)
|
||||||
- 误差 5% → 分数 0.37 (一般拟合)
|
- 误差 5% → 分数 0.37 (一般规则)
|
||||||
- 误差 10% → 分数 0.14 (较差拟合)
|
- 误差 10% → 分数 0.14 (较差规则)
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
pivot_indices: 选中枢轴点的X坐标（索引）
|
pivot_indices: 选中枢轴点的X坐标（索引）
|
||||||
@ -789,7 +791,7 @@ def calc_fitting_adherence(
|
|||||||
intercept: 拟合线截距
|
intercept: 拟合线截距
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
adherence_score: 0~1 分数，越大表示枢轴点越贴合拟合线
|
geometry_score: 0~1 分数，越大表示形态越规则标准
|
||||||
"""
|
"""
|
||||||
import math
|
import math
|
||||||
|
|
||||||
@ -807,12 +809,12 @@ def calc_fitting_adherence(
|
|||||||
|
|
||||||
# 指数衰减归一化到 0~1
|
# 指数衰减归一化到 0~1
|
||||||
SCALE_FACTOR = 20.0 # 控制衰减速度
|
SCALE_FACTOR = 20.0 # 控制衰减速度
|
||||||
adherence_score = math.exp(-mean_rel_error * SCALE_FACTOR)
|
geometry_score = math.exp(-mean_rel_error * SCALE_FACTOR)
|
||||||
|
|
||||||
return min(1.0, max(0.0, adherence_score))
|
return min(1.0, max(0.0, geometry_score))
|
||||||
|
|
||||||
|
|
||||||
def calc_boundary_utilization(
|
def calc_activity_score(
|
||||||
high: np.ndarray,
|
high: np.ndarray,
|
||||||
low: np.ndarray,
|
low: np.ndarray,
|
||||||
upper_slope: float,
|
upper_slope: float,
|
||||||
@ -823,21 +825,21 @@ def calc_boundary_utilization(
|
|||||||
end: int,
|
end: int,
|
||||||
) -> float:
|
) -> float:
|
||||||
"""
|
"""
|
||||||
计算边界利用率 (0~1)
|
计算价格活跃度 (0~1)
|
||||||
|
|
||||||
衡量价格走势对三角形通道空间的利用程度。
|
衡量价格走势对三角形通道空间的利用程度（价格振荡的充分性）。
|
||||||
如果价格总是远离边界线（大量空白），利用率低。
|
如果价格总是远离边界线（大量空白），活跃度低。
|
||||||
如果价格频繁接近或触碰边界线，利用率高。
|
如果价格频繁接近或触碰边界线，活跃度高。
|
||||||
|
|
||||||
计算方法：
|
计算方法：
|
||||||
1. 对窗口内每一天，计算价格到上下边界的相对距离
|
1. 对窗口内每一天，计算价格到上下边界的相对距离
|
||||||
2. 利用率 = 1 - 平均相对空白比例
|
2. 活跃度 = 1 - 平均相对空白比例
|
||||||
|
|
||||||
归一化映射：
|
归一化映射：
|
||||||
- 空白 0% → 利用率 1.00 (价格完全贴合边界)
|
- 空白 0% → 活跃度 1.00 (价格完全贴合边界)
|
||||||
- 空白 25% → 利用率 0.75 (良好)
|
- 空白 25% → 活跃度 0.75 (良好)
|
||||||
- 空白 50% → 利用率 0.50 (一般)
|
- 空白 50% → 活跃度 0.50 (一般)
|
||||||
- 空白 75% → 利用率 0.25 (较差，大量空白)
|
- 空白 75% → 活跃度 0.25 (较差，大量空白)
|
||||||
|
|
||||||
Args:
|
Args:
|
||||||
high, low: 价格数据
|
high, low: 价格数据
|
||||||
@ -846,9 +848,9 @@ def calc_boundary_utilization(
|
|||||||
start, end: 窗口范围
|
start, end: 窗口范围
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
utilization: 0~1，越大表示利用率越高
|
activity_score: 0~1，越大表示价格越活跃
|
||||||
"""
|
"""
|
||||||
total_utilization = 0.0
|
total_activity = 0.0
|
||||||
valid_days = 0
|
valid_days = 0
|
||||||
|
|
||||||
for i in range(start, end + 1):
|
for i in range(start, end + 1):
|
||||||
@ -866,16 +868,75 @@ def calc_boundary_utilization(
|
|||||||
# 当日空白比例 = (到上沿距离 + 到下沿距离) / 通道宽度
|
# 当日空白比例 = (到上沿距离 + 到下沿距离) / 通道宽度
|
||||||
blank_ratio = (dist_to_upper + dist_to_lower) / channel_width
|
blank_ratio = (dist_to_upper + dist_to_lower) / channel_width
|
||||||
|
|
||||||
# 当日利用率 = 1 - 空白比例，限制在 [0, 1]
|
# 当日活跃度 = 1 - 空白比例，限制在 [0, 1]
|
||||||
day_utilization = max(0.0, min(1.0, 1.0 - blank_ratio))
|
day_activity = max(0.0, min(1.0, 1.0 - blank_ratio))
|
||||||
|
|
||||||
total_utilization += day_utilization
|
total_activity += day_activity
|
||||||
valid_days += 1
|
valid_days += 1
|
||||||
|
|
||||||
if valid_days == 0:
|
if valid_days == 0:
|
||||||
return 0.0
|
return 0.0
|
||||||
|
|
||||||
return total_utilization / valid_days
|
return total_activity / valid_days
|
||||||
|
|
||||||
|
|
||||||
|
def calc_tilt_score(
|
||||||
|
upper_slope: float,
|
||||||
|
lower_slope: float,
|
||||||
|
breakout_dir: str,
|
||||||
|
) -> float:
|
||||||
|
"""
|
||||||
|
计算三角形倾斜度分 (0~1)
|
||||||
|
|
||||||
|
衡量三角形中轴的倾斜方向与突破方向的一致性。
|
||||||
|
趋势偏向与突破方向一致时，得分越高。
|
||||||
|
|
||||||
|
计算方法：
|
||||||
|
1. 计算中轴斜率：mid_slope = (upper_slope + lower_slope) / 2
|
||||||
|
2. 计算倾斜角度并归一化到 [-1, +1]：
|
||||||
|
- 使用 arctan(mid_slope) / (π/4) 映射
|
||||||
|
- 45° 向上 → +1
|
||||||
|
- 0° 水平 → 0
|
||||||
|
- 45° 向下 → -1
|
||||||
|
3. 根据突破方向计算得分：
|
||||||
|
- 向上突破：倾斜向上时得分高
|
||||||
|
- 向下突破：倾斜向下时得分高
|
||||||
|
|
||||||
|
归一化映射（以向上突破为例）：
|
||||||
|
- 上升三角形（中轴向上15°）+ 向上突破 → 得分 0.85
|
||||||
|
- 对称三角形（中轴水平） + 向上突破 → 得分 0.50
|
||||||
|
- 下降三角形（中轴向下15°）+ 向上突破 → 得分 0.15（逆势突破）
|
||||||
|
|
||||||
|
Args:
|
||||||
|
upper_slope: 上沿斜率
|
||||||
|
lower_slope: 下沿斜率
|
||||||
|
breakout_dir: 突破方向 "up" | "down" | "none"
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tilt_score: 0~1，越大表示倾斜方向与突破方向越一致
|
||||||
|
"""
|
||||||
|
import math
|
||||||
|
|
||||||
|
# 1. 计算中轴斜率
|
||||||
|
mid_slope = (upper_slope + lower_slope) / 2.0
|
||||||
|
|
||||||
|
# 2. 计算倾斜角度并归一化到 [-1, +1]
|
||||||
|
angle_rad = math.atan(mid_slope)
|
||||||
|
tilt = angle_rad / (math.pi / 4)
|
||||||
|
tilt = max(-1.0, min(1.0, tilt))
|
||||||
|
|
||||||
|
# 3. 根据突破方向计算得分
|
||||||
|
if breakout_dir == "up":
|
||||||
|
# 向上突破：倾斜向上时得分高
|
||||||
|
score = (1.0 + tilt) / 2.0
|
||||||
|
elif breakout_dir == "down":
|
||||||
|
# 向下突破：倾斜向下时得分高
|
||||||
|
score = (1.0 - tilt) / 2.0
|
||||||
|
else:
|
||||||
|
# 未突破或其他情况：使用中性分数（0.5）
|
||||||
|
score = 0.5
|
||||||
|
|
||||||
|
return max(0.0, min(1.0, score))
|
||||||
|
|
||||||
|
|
||||||
def calc_breakout_strength(
|
def calc_breakout_strength(
|
||||||
@ -884,9 +945,12 @@ def calc_breakout_strength(
|
|||||||
lower_line: float,
|
lower_line: float,
|
||||||
volume_ratio: float,
|
volume_ratio: float,
|
||||||
width_ratio: float,
|
width_ratio: float,
|
||||||
fitting_adherence: float,
|
geometry_score: float,
|
||||||
boundary_utilization: float,
|
activity_score: float,
|
||||||
) -> Tuple[float, float, float, float, float, float, float, float]:
|
upper_slope: float, # 新增
|
||||||
|
lower_slope: float, # 新增
|
||||||
|
breakout_dir: str, # 新增
|
||||||
|
) -> Tuple[float, float, float, float, float, float, float, float, float]:
|
||||||
"""
|
"""
|
||||||
计算形态强度分 (0~1)
|
计算形态强度分 (0~1)
|
||||||
|
|
||||||
@ -894,11 +958,12 @@ def calc_breakout_strength(
|
|||||||
可用于评估"待突破"形态的潜在价值，或"已突破"形态的有效性。
|
可用于评估"待突破"形态的潜在价值，或"已突破"形态的有效性。
|
||||||
|
|
||||||
使用加权求和，各分量权重：
|
使用加权求和，各分量权重：
|
||||||
- 突破幅度分 (50%): tanh 非线性归一化，3%突破≈0.42，5%突破≈0.64，10%突破≈0.91
|
- 突破幅度分 (45%): tanh 非线性归一化，3%突破≈0.42，5%突破≈0.64，10%突破≈0.91
|
||||||
- 收敛分 (15%): 1 - width_ratio，收敛越强分数越高
|
- 收敛分 (15%): 1 - width_ratio，收敛越强分数越高
|
||||||
- 成交量分 (10%): 放量程度，2倍放量=满分
|
- 成交量分 (10%): 放量程度，2倍放量=满分
|
||||||
- 拟合贴合度 (10%): 枢轴点到拟合线的贴合程度，形态纯度
|
- 形态规则度 (10%): 枢轴点到拟合线的贴合程度，形态的几何标准性
|
||||||
- 边界利用率 (15%): 价格走势对通道空间的利用程度
|
- 价格活跃度 (15%): 价格走势对通道空间的利用程度，振荡充分性
|
||||||
|
- 倾斜度分 (5%): 中轴倾斜方向与突破方向的一致性（新增）
|
||||||
|
|
||||||
额外惩罚项：
|
额外惩罚项：
|
||||||
- 当边界利用率过低时，对总强度进行空白惩罚，避免“通道很宽但价格很空”的误判
|
- 当边界利用率过低时，对总强度进行空白惩罚，避免“通道很宽但价格很空”的误判
|
||||||
@ -917,24 +982,29 @@ def calc_breakout_strength(
|
|||||||
lower_line: 下沿价格
|
lower_line: 下沿价格
|
||||||
volume_ratio: 成交量相对均值的倍数
|
volume_ratio: 成交量相对均值的倍数
|
||||||
width_ratio: 末端宽度/起始宽度
|
width_ratio: 末端宽度/起始宽度
|
||||||
fitting_adherence: 拟合贴合度分数 (0~1)
|
geometry_score: 形态规则度分数 (0~1)
|
||||||
boundary_utilization: 边界利用率分数 (0~1)
|
activity_score: 价格活跃度分数 (0~1)
|
||||||
|
upper_slope: 上沿斜率
|
||||||
|
lower_slope: 下沿斜率
|
||||||
|
breakout_dir: 突破方向 "up" | "down" | "none"
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
(strength_up, strength_down, price_score_up, price_score_down,
|
(strength_up, strength_down, price_score_up, price_score_down,
|
||||||
convergence_score, vol_score, fitting_score, boundary_util_score)
|
convergence_score, vol_score, geometry_score_out,
|
||||||
|
activity_score_out, tilt_score_out)
|
||||||
返回总强度和各分量分数，用于可视化和分析
|
返回总强度和各分量分数，用于可视化和分析
|
||||||
"""
|
"""
|
||||||
import math
|
import math
|
||||||
|
|
||||||
# 权重配置（调整后，总和 = 100%）
|
# 权重配置（调整后，总和 = 100%）
|
||||||
W_PRICE = 0.50 # 突破幅度权重
|
W_PRICE = 0.45 # 突破幅度权重（从50%降至45%）
|
||||||
W_CONVERGENCE = 0.15 # 收敛度权重（原 20%，降低）
|
W_CONVERGENCE = 0.15 # 收敛度权重
|
||||||
W_VOLUME = 0.10 # 成交量权重（原 15%，降低）
|
W_VOLUME = 0.10 # 成交量权重
|
||||||
W_FITTING = 0.10 # 拟合贴合度权重（原 15%，降低）
|
W_GEOMETRY = 0.10 # 形态规则度权重
|
||||||
W_UTILIZATION = 0.15 # 边界利用率权重（新增）
|
W_ACTIVITY = 0.15 # 价格活跃度权重
|
||||||
|
W_TILT = 0.05 # 倾斜度权重（新增）
|
||||||
TANH_SCALE = 15.0 # tanh 缩放因子
|
TANH_SCALE = 15.0 # tanh 缩放因子
|
||||||
UTILIZATION_FLOOR = 0.20 # 边界利用率下限（用于空白惩罚）
|
ACTIVITY_FLOOR = 0.20 # 价格活跃度下限（用于空白惩罚）
|
||||||
|
|
||||||
# 1. 价格突破分数（tanh 非线性归一化）
|
# 1. 价格突破分数（tanh 非线性归一化）
|
||||||
if upper_line > 0:
|
if upper_line > 0:
|
||||||
@ -955,33 +1025,38 @@ def calc_breakout_strength(
|
|||||||
# 3. 成交量分数（vol_ratio > 1 时才有分）
|
# 3. 成交量分数（vol_ratio > 1 时才有分）
|
||||||
vol_score = min(1.0, max(0.0, volume_ratio - 1.0)) if volume_ratio > 0 else 0.0
|
vol_score = min(1.0, max(0.0, volume_ratio - 1.0)) if volume_ratio > 0 else 0.0
|
||||||
|
|
||||||
# 4. 拟合贴合度分数（直接使用传入的分数）
|
# 4. 形态规则度分数（直接使用传入的分数）
|
||||||
fitting_score = max(0.0, min(1.0, fitting_adherence))
|
geometry_score_out = max(0.0, min(1.0, geometry_score))
|
||||||
|
|
||||||
# 5. 边界利用率分数（直接使用传入的分数）
|
# 5. 价格活跃度分数（直接使用传入的分数）
|
||||||
boundary_util_score = max(0.0, min(1.0, boundary_utilization))
|
activity_score_out = max(0.0, min(1.0, activity_score))
|
||||||
|
|
||||||
# 6. 加权求和（计算综合强度分）
|
# 6. 倾斜度分数（新增）
|
||||||
|
tilt_score_out = calc_tilt_score(upper_slope, lower_slope, breakout_dir)
|
||||||
|
|
||||||
|
# 7. 加权求和（计算综合强度分）
|
||||||
# 不再要求必须突破，而是计算形态的综合质量分数
|
# 不再要求必须突破，而是计算形态的综合质量分数
|
||||||
strength_up = (
|
strength_up = (
|
||||||
W_PRICE * price_score_up +
|
W_PRICE * price_score_up +
|
||||||
W_CONVERGENCE * convergence_score +
|
W_CONVERGENCE * convergence_score +
|
||||||
W_VOLUME * vol_score +
|
W_VOLUME * vol_score +
|
||||||
W_FITTING * fitting_score +
|
W_GEOMETRY * geometry_score_out +
|
||||||
W_UTILIZATION * boundary_util_score
|
W_ACTIVITY * activity_score_out +
|
||||||
|
W_TILT * tilt_score_out
|
||||||
)
|
)
|
||||||
|
|
||||||
strength_down = (
|
strength_down = (
|
||||||
W_PRICE * price_score_down +
|
W_PRICE * price_score_down +
|
||||||
W_CONVERGENCE * convergence_score +
|
W_CONVERGENCE * convergence_score +
|
||||||
W_VOLUME * vol_score +
|
W_VOLUME * vol_score +
|
||||||
W_FITTING * fitting_score +
|
W_GEOMETRY * geometry_score_out +
|
||||||
W_UTILIZATION * boundary_util_score
|
W_ACTIVITY * activity_score_out +
|
||||||
|
W_TILT * tilt_score_out
|
||||||
)
|
)
|
||||||
|
|
||||||
# 7. 空白惩罚（边界利用率过低时降低总分）
|
# 8. 空白惩罚（价格活跃度过低时降低总分）
|
||||||
if UTILIZATION_FLOOR > 0:
|
if ACTIVITY_FLOOR > 0:
|
||||||
utilization_penalty = min(1.0, boundary_util_score / UTILIZATION_FLOOR)
|
utilization_penalty = min(1.0, activity_score_out / ACTIVITY_FLOOR)
|
||||||
else:
|
else:
|
||||||
utilization_penalty = 1.0
|
utilization_penalty = 1.0
|
||||||
|
|
||||||
@ -995,8 +1070,9 @@ def calc_breakout_strength(
|
|||||||
price_score_down,
|
price_score_down,
|
||||||
convergence_score,
|
convergence_score,
|
||||||
vol_score,
|
vol_score,
|
||||||
fitting_score,
|
geometry_score_out,
|
||||||
boundary_util_score
|
activity_score_out,
|
||||||
|
tilt_score_out
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@ -1198,24 +1274,24 @@ def detect_converging_triangle(
|
|||||||
# 注意: 这里是基于历史数据，无法检测假突破
|
# 注意: 这里是基于历史数据，无法检测假突破
|
||||||
# 假突破需要看"未来"数据，与当前设计不符
|
# 假突破需要看"未来"数据，与当前设计不符
|
||||||
|
|
||||||
# 计算拟合贴合度（上下沿综合）
|
# 计算形态规则度（上下沿综合）
|
||||||
adherence_upper = calc_fitting_adherence(
|
geometry_upper = calc_geometry_score(
|
||||||
pivot_indices=selected_ph,
|
pivot_indices=selected_ph,
|
||||||
pivot_values=high[selected_ph],
|
pivot_values=high[selected_ph],
|
||||||
slope=a_u,
|
slope=a_u,
|
||||||
intercept=b_u,
|
intercept=b_u,
|
||||||
)
|
)
|
||||||
adherence_lower = calc_fitting_adherence(
|
geometry_lower = calc_geometry_score(
|
||||||
pivot_indices=selected_pl,
|
pivot_indices=selected_pl,
|
||||||
pivot_values=low[selected_pl],
|
pivot_values=low[selected_pl],
|
||||||
slope=a_l,
|
slope=a_l,
|
||||||
intercept=b_l,
|
intercept=b_l,
|
||||||
)
|
)
|
||||||
# 综合上下沿贴合度（取平均）
|
# 综合上下沿规则度（取平均）
|
||||||
fitting_adherence = (adherence_upper + adherence_lower) / 2.0
|
geometry_score = (geometry_upper + geometry_lower) / 2.0
|
||||||
|
|
||||||
# 计算边界利用率（价格走势对三角形通道的利用程度）
|
# 计算价格活跃度（价格走势对三角形通道的利用程度）
|
||||||
boundary_utilization = calc_boundary_utilization(
|
activity_score = calc_activity_score(
|
||||||
high=high,
|
high=high,
|
||||||
low=low,
|
low=low,
|
||||||
upper_slope=a_u,
|
upper_slope=a_u,
|
||||||
@ -1229,15 +1305,18 @@ def detect_converging_triangle(
|
|||||||
# 计算突破强度（返回总强度和各分量分数）
|
# 计算突破强度（返回总强度和各分量分数）
|
||||||
(strength_up, strength_down,
|
(strength_up, strength_down,
|
||||||
price_score_up, price_score_down,
|
price_score_up, price_score_down,
|
||||||
convergence_score, vol_score, fitting_score,
|
convergence_score, vol_score, geometry_score_out,
|
||||||
boundary_util_score) = calc_breakout_strength(
|
activity_score_out, tilt_score_out) = calc_breakout_strength(
|
||||||
close=close[end],
|
close=close[end],
|
||||||
upper_line=upper_end,
|
upper_line=upper_end,
|
||||||
lower_line=lower_end,
|
lower_line=lower_end,
|
||||||
volume_ratio=volume_ratio,
|
volume_ratio=volume_ratio,
|
||||||
width_ratio=width_ratio,
|
width_ratio=width_ratio,
|
||||||
fitting_adherence=fitting_adherence,
|
geometry_score=geometry_score,
|
||||||
boundary_utilization=boundary_utilization,
|
activity_score=activity_score,
|
||||||
|
upper_slope=a_u,
|
||||||
|
lower_slope=a_l,
|
||||||
|
breakout_dir=breakout_dir,
|
||||||
)
|
)
|
||||||
|
|
||||||
return ConvergingTriangleResult(
|
return ConvergingTriangleResult(
|
||||||
@ -1250,8 +1329,9 @@ def detect_converging_triangle(
|
|||||||
price_score_down=price_score_down,
|
price_score_down=price_score_down,
|
||||||
convergence_score=convergence_score,
|
convergence_score=convergence_score,
|
||||||
volume_score=vol_score,
|
volume_score=vol_score,
|
||||||
fitting_score=fitting_score,
|
geometry_score=geometry_score_out,
|
||||||
boundary_utilization=boundary_util_score,
|
activity_score=activity_score_out,
|
||||||
|
tilt_score=tilt_score_out,
|
||||||
upper_slope=a_u,
|
upper_slope=a_u,
|
||||||
lower_slope=a_l,
|
lower_slope=a_l,
|
||||||
width_ratio=width_ratio,
|
width_ratio=width_ratio,
|
||||||
@ -1413,8 +1493,8 @@ try:
|
|||||||
pivots_fractal_optimized,
|
pivots_fractal_optimized,
|
||||||
pivots_fractal_hybrid_optimized,
|
pivots_fractal_hybrid_optimized,
|
||||||
fit_boundary_anchor_optimized,
|
fit_boundary_anchor_optimized,
|
||||||
calc_fitting_adherence_optimized,
|
calc_geometry_score_optimized,
|
||||||
calc_boundary_utilization_optimized,
|
calc_activity_score_optimized,
|
||||||
calc_breakout_strength_optimized,
|
calc_breakout_strength_optimized,
|
||||||
# v2优化：预计算枢轴点
|
# v2优化：预计算枢轴点
|
||||||
precompute_pivots_numba,
|
precompute_pivots_numba,
|
||||||
@ -1424,8 +1504,8 @@ try:
|
|||||||
pivots_fractal = pivots_fractal_optimized
|
pivots_fractal = pivots_fractal_optimized
|
||||||
pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
|
pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
|
||||||
fit_boundary_anchor = fit_boundary_anchor_optimized
|
fit_boundary_anchor = fit_boundary_anchor_optimized
|
||||||
calc_fitting_adherence = calc_fitting_adherence_optimized
|
calc_geometry_score = calc_geometry_score_optimized
|
||||||
calc_boundary_utilization = calc_boundary_utilization_optimized
|
calc_activity_score = calc_activity_score_optimized
|
||||||
calc_breakout_strength = calc_breakout_strength_optimized
|
calc_breakout_strength = calc_breakout_strength_optimized
|
||||||
|
|
||||||
_HAS_V2_OPTIMIZATION = True
|
_HAS_V2_OPTIMIZATION = True
|
||||||
@ -1511,7 +1591,7 @@ def detect_converging_triangle_batch_v2(
|
|||||||
# 批量检测（单次Numba调用处理所有日期）
|
# 批量检测（单次Numba调用处理所有日期）
|
||||||
(date_indices, is_valid_arr, strength_up_arr, strength_down_arr,
|
(date_indices, is_valid_arr, strength_up_arr, strength_down_arr,
|
||||||
price_score_up_arr, price_score_down_arr, convergence_score_arr,
|
price_score_up_arr, price_score_down_arr, convergence_score_arr,
|
||||||
vol_score_arr, fitting_score_arr, boundary_util_score_arr,
|
vol_score_arr, geometry_score_arr, activity_score_arr, tilt_score_arr,
|
||||||
upper_slope_arr, lower_slope_arr, width_ratio_arr,
|
upper_slope_arr, lower_slope_arr, width_ratio_arr,
|
||||||
touches_upper_arr, touches_lower_arr, apex_x_arr,
|
touches_upper_arr, touches_lower_arr, apex_x_arr,
|
||||||
breakout_dir_arr, volume_confirmed_arr) = \
|
breakout_dir_arr, volume_confirmed_arr) = \
|
||||||
@ -1547,8 +1627,9 @@ def detect_converging_triangle_batch_v2(
|
|||||||
'price_score_down': float(price_score_down_arr[i]),
|
'price_score_down': float(price_score_down_arr[i]),
|
||||||
'convergence_score': float(convergence_score_arr[i]),
|
'convergence_score': float(convergence_score_arr[i]),
|
||||||
'volume_score': float(vol_score_arr[i]),
|
'volume_score': float(vol_score_arr[i]),
|
||||||
'fitting_score': float(fitting_score_arr[i]),
|
'geometry_score': float(geometry_score_arr[i]),
|
||||||
'boundary_utilization': float(boundary_util_score_arr[i]),
|
'activity_score': float(activity_score_arr[i]),
|
||||||
|
'tilt_score': float(tilt_score_arr[i]),
|
||||||
'upper_slope': float(upper_slope_arr[i]),
|
'upper_slope': float(upper_slope_arr[i]),
|
||||||
'lower_slope': float(lower_slope_arr[i]),
|
'lower_slope': float(lower_slope_arr[i]),
|
||||||
'width_ratio': float(width_ratio_arr[i]),
|
'width_ratio': float(width_ratio_arr[i]),
|
||||||
|
|||||||
@ -347,13 +347,13 @@ def fit_boundary_anchor_numba(
|
|||||||
|
|
||||||
|
|
||||||
@numba.jit(nopython=True, cache=True)
|
@numba.jit(nopython=True, cache=True)
|
||||||
def calc_fitting_adherence_numba(
|
def calc_geometry_score_numba(
|
||||||
pivot_indices: np.ndarray,
|
pivot_indices: np.ndarray,
|
||||||
pivot_values: np.ndarray,
|
pivot_values: np.ndarray,
|
||||||
slope: float,
|
slope: float,
|
||||||
intercept: float,
|
intercept: float,
|
||||||
) -> float:
|
) -> float:
|
||||||
"""Numba优化的拟合贴合度计算"""
|
"""Numba优化的形态规则度计算"""
|
||||||
if len(pivot_indices) == 0 or len(pivot_values) == 0:
|
if len(pivot_indices) == 0 or len(pivot_values) == 0:
|
||||||
return 0.0
|
return 0.0
|
||||||
|
|
||||||
@ -369,13 +369,13 @@ def calc_fitting_adherence_numba(
|
|||||||
|
|
||||||
# 指数衰减归一化
|
# 指数衰减归一化
|
||||||
SCALE_FACTOR = 20.0
|
SCALE_FACTOR = 20.0
|
||||||
adherence_score = np.exp(-mean_rel_error * SCALE_FACTOR)
|
geometry_score = np.exp(-mean_rel_error * SCALE_FACTOR)
|
||||||
|
|
||||||
return min(1.0, max(0.0, adherence_score))
|
return min(1.0, max(0.0, geometry_score))
|
||||||
|
|
||||||
|
|
||||||
@numba.jit(nopython=True, cache=True)
|
@numba.jit(nopython=True, cache=True)
|
||||||
def calc_boundary_utilization_numba(
|
def calc_activity_score_numba(
|
||||||
high: np.ndarray,
|
high: np.ndarray,
|
||||||
low: np.ndarray,
|
low: np.ndarray,
|
||||||
upper_slope: float,
|
upper_slope: float,
|
||||||
@ -385,8 +385,8 @@ def calc_boundary_utilization_numba(
|
|||||||
start: int,
|
start: int,
|
||||||
end: int,
|
end: int,
|
||||||
) -> float:
|
) -> float:
|
||||||
"""Numba优化的边界利用率计算"""
|
"""Numba优化的价格活跃度计算"""
|
||||||
total_utilization = 0.0
|
total_activity = 0.0
|
||||||
valid_days = 0
|
valid_days = 0
|
||||||
|
|
||||||
for i in range(start, end + 1):
|
for i in range(start, end + 1):
|
||||||
@ -401,15 +401,53 @@ def calc_boundary_utilization_numba(
|
|||||||
dist_to_lower = max(0.0, low[i] - lower_line)
|
dist_to_lower = max(0.0, low[i] - lower_line)
|
||||||
|
|
||||||
blank_ratio = (dist_to_upper + dist_to_lower) / channel_width
|
blank_ratio = (dist_to_upper + dist_to_lower) / channel_width
|
||||||
day_utilization = max(0.0, min(1.0, 1.0 - blank_ratio))
|
day_activity = max(0.0, min(1.0, 1.0 - blank_ratio))
|
||||||
|
|
||||||
total_utilization += day_utilization
|
total_activity += day_activity
|
||||||
valid_days += 1
|
valid_days += 1
|
||||||
|
|
||||||
if valid_days == 0:
|
if valid_days == 0:
|
||||||
return 0.0
|
return 0.0
|
||||||
|
|
||||||
return total_utilization / valid_days
|
return total_activity / valid_days
|
||||||
|
|
||||||
|
|
||||||
|
@numba.jit(nopython=True, cache=True)
|
||||||
|
def calc_tilt_score_numba(
|
||||||
|
upper_slope: float,
|
||||||
|
lower_slope: float,
|
||||||
|
breakout_dir: int, # 0=none, 1=up, 2=down
|
||||||
|
) -> float:
|
||||||
|
"""
|
||||||
|
Numba优化的倾斜度分数计算
|
||||||
|
|
||||||
|
计算三角形中轴的倾斜方向与突破方向的一致性。
|
||||||
|
|
||||||
|
Args:
|
||||||
|
upper_slope: 上沿斜率
|
||||||
|
lower_slope: 下沿斜率
|
||||||
|
breakout_dir: 突破方向 (0=none, 1=up, 2=down)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
tilt_score: 0~1，越大表示倾斜方向与突破方向越一致
|
||||||
|
"""
|
||||||
|
# 1. 计算中轴斜率
|
||||||
|
mid_slope = (upper_slope + lower_slope) / 2.0
|
||||||
|
|
||||||
|
# 2. 计算倾斜角度并归一化到 [-1, +1]
|
||||||
|
angle_rad = np.arctan(mid_slope)
|
||||||
|
tilt = angle_rad / (np.pi / 4)
|
||||||
|
tilt = max(-1.0, min(1.0, tilt))
|
||||||
|
|
||||||
|
# 3. 根据突破方向计算得分
|
||||||
|
if breakout_dir == 1: # up
|
||||||
|
score = (1.0 + tilt) / 2.0
|
||||||
|
elif breakout_dir == 2: # down
|
||||||
|
score = (1.0 - tilt) / 2.0
|
||||||
|
else:
|
||||||
|
score = 0.5
|
||||||
|
|
||||||
|
return max(0.0, min(1.0, score))
|
||||||
|
|
||||||
|
|
||||||
@numba.jit(nopython=True, cache=True)
|
@numba.jit(nopython=True, cache=True)
|
||||||
@ -419,24 +457,29 @@ def calc_breakout_strength_numba(
|
|||||||
lower_line: float,
|
lower_line: float,
|
||||||
volume_ratio: float,
|
volume_ratio: float,
|
||||||
width_ratio: float,
|
width_ratio: float,
|
||||||
fitting_adherence: float,
|
geometry_score: float,
|
||||||
boundary_utilization: float,
|
activity_score: float,
|
||||||
) -> Tuple[float, float, float, float, float, float, float, float]:
|
upper_slope: float, # 新增
|
||||||
|
lower_slope: float, # 新增
|
||||||
|
breakout_dir: int, # 新增: 0=none, 1=up, 2=down
|
||||||
|
) -> Tuple[float, float, float, float, float, float, float, float, float]:
|
||||||
"""
|
"""
|
||||||
Numba优化的突破强度计算
|
Numba优化的突破强度计算
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
(strength_up, strength_down, price_score_up, price_score_down,
|
(strength_up, strength_down, price_score_up, price_score_down,
|
||||||
convergence_score, vol_score, fitting_score, boundary_util_score)
|
convergence_score, vol_score, geometry_score_out,
|
||||||
|
activity_score_out, tilt_score_out)
|
||||||
"""
|
"""
|
||||||
# 权重配置
|
# 权重配置
|
||||||
W_PRICE = 0.50
|
W_PRICE = 0.45 # 从50%降至45%
|
||||||
W_CONVERGENCE = 0.15
|
W_CONVERGENCE = 0.15
|
||||||
W_VOLUME = 0.10
|
W_VOLUME = 0.10
|
||||||
W_FITTING = 0.10
|
W_GEOMETRY = 0.10
|
||||||
W_UTILIZATION = 0.15
|
W_ACTIVITY = 0.15
|
||||||
|
W_TILT = 0.05 # 新增
|
||||||
TANH_SCALE = 15.0
|
TANH_SCALE = 15.0
|
||||||
UTILIZATION_FLOOR = 0.20
|
ACTIVITY_FLOOR = 0.20
|
||||||
|
|
||||||
# 1. 价格突破分数
|
# 1. 价格突破分数
|
||||||
if upper_line > 0:
|
if upper_line > 0:
|
||||||
@ -457,37 +500,42 @@ def calc_breakout_strength_numba(
|
|||||||
# 3. 成交量分数
|
# 3. 成交量分数
|
||||||
vol_score = min(1.0, max(0.0, volume_ratio - 1.0)) if volume_ratio > 0 else 0.0
|
vol_score = min(1.0, max(0.0, volume_ratio - 1.0)) if volume_ratio > 0 else 0.0
|
||||||
|
|
||||||
# 4. 拟合贴合度分数
|
# 4. 形态规则度分数
|
||||||
fitting_score = max(0.0, min(1.0, fitting_adherence))
|
geometry_score_out = max(0.0, min(1.0, geometry_score))
|
||||||
|
|
||||||
# 5. 边界利用率分数
|
# 5. 价格活跃度分数
|
||||||
boundary_util_score = max(0.0, min(1.0, boundary_utilization))
|
activity_score_out = max(0.0, min(1.0, activity_score))
|
||||||
|
|
||||||
# 6. 加权求和
|
# 6. 倾斜度分数（新增）
|
||||||
|
tilt_score_out = calc_tilt_score_numba(upper_slope, lower_slope, breakout_dir)
|
||||||
|
|
||||||
|
# 7. 加权求和
|
||||||
strength_up = (
|
strength_up = (
|
||||||
W_PRICE * price_score_up +
|
W_PRICE * price_score_up +
|
||||||
W_CONVERGENCE * convergence_score +
|
W_CONVERGENCE * convergence_score +
|
||||||
W_VOLUME * vol_score +
|
W_VOLUME * vol_score +
|
||||||
W_FITTING * fitting_score +
|
W_GEOMETRY * geometry_score_out +
|
||||||
W_UTILIZATION * boundary_util_score
|
W_ACTIVITY * activity_score_out +
|
||||||
|
W_TILT * tilt_score_out
|
||||||
)
|
)
|
||||||
|
|
||||||
strength_down = (
|
strength_down = (
|
||||||
W_PRICE * price_score_down +
|
W_PRICE * price_score_down +
|
||||||
W_CONVERGENCE * convergence_score +
|
W_CONVERGENCE * convergence_score +
|
||||||
W_VOLUME * vol_score +
|
W_VOLUME * vol_score +
|
||||||
W_FITTING * fitting_score +
|
W_GEOMETRY * geometry_score_out +
|
||||||
W_UTILIZATION * boundary_util_score
|
W_ACTIVITY * activity_score_out +
|
||||||
|
W_TILT * tilt_score_out
|
||||||
)
|
)
|
||||||
|
|
||||||
# 7. 空白惩罚
|
# 8. 空白惩罚
|
||||||
if UTILIZATION_FLOOR > 0:
|
if ACTIVITY_FLOOR > 0:
|
||||||
utilization_penalty = min(1.0, boundary_util_score / UTILIZATION_FLOOR)
|
activity_penalty = min(1.0, activity_score_out / ACTIVITY_FLOOR)
|
||||||
else:
|
else:
|
||||||
utilization_penalty = 1.0
|
activity_penalty = 1.0
|
||||||
|
|
||||||
strength_up *= utilization_penalty
|
strength_up *= activity_penalty
|
||||||
strength_down *= utilization_penalty
|
strength_down *= activity_penalty
|
||||||
|
|
||||||
return (
|
return (
|
||||||
min(1.0, strength_up),
|
min(1.0, strength_up),
|
||||||
@ -496,8 +544,9 @@ def calc_breakout_strength_numba(
|
|||||||
price_score_down,
|
price_score_down,
|
||||||
convergence_score,
|
convergence_score,
|
||||||
vol_score,
|
vol_score,
|
||||||
fitting_score,
|
geometry_score_out,
|
||||||
boundary_util_score
|
activity_score_out,
|
||||||
|
tilt_score_out
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@ -548,21 +597,21 @@ def fit_boundary_anchor_optimized(
|
|||||||
return slope, intercept, np.arange(len(pivot_indices))
|
return slope, intercept, np.arange(len(pivot_indices))
|
||||||
|
|
||||||
|
|
||||||
def calc_fitting_adherence_optimized(
|
def calc_geometry_score_optimized(
|
||||||
pivot_indices: np.ndarray,
|
pivot_indices: np.ndarray,
|
||||||
pivot_values: np.ndarray,
|
pivot_values: np.ndarray,
|
||||||
slope: float,
|
slope: float,
|
||||||
intercept: float,
|
intercept: float,
|
||||||
) -> float:
|
) -> float:
|
||||||
"""优化版拟合贴合度计算（兼容原API）"""
|
"""优化版形态规则度计算（兼容原API）"""
|
||||||
return calc_fitting_adherence_numba(
|
return calc_geometry_score_numba(
|
||||||
pivot_indices.astype(np.float64),
|
pivot_indices.astype(np.float64),
|
||||||
pivot_values.astype(np.float64),
|
pivot_values.astype(np.float64),
|
||||||
slope, intercept
|
slope, intercept
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
def calc_boundary_utilization_optimized(
|
def calc_activity_score_optimized(
|
||||||
high: np.ndarray,
|
high: np.ndarray,
|
||||||
low: np.ndarray,
|
low: np.ndarray,
|
||||||
upper_slope: float,
|
upper_slope: float,
|
||||||
@ -572,8 +621,8 @@ def calc_boundary_utilization_optimized(
|
|||||||
start: int,
|
start: int,
|
||||||
end: int,
|
end: int,
|
||||||
) -> float:
|
) -> float:
|
||||||
"""优化版边界利用率计算（兼容原API）"""
|
"""优化版价格活跃度计算（兼容原API）"""
|
||||||
return calc_boundary_utilization_numba(
|
return calc_activity_score_numba(
|
||||||
high, low, upper_slope, upper_intercept,
|
high, low, upper_slope, upper_intercept,
|
||||||
lower_slope, lower_intercept, start, end
|
lower_slope, lower_intercept, start, end
|
||||||
)
|
)
|
||||||
@ -585,13 +634,31 @@ def calc_breakout_strength_optimized(
|
|||||||
lower_line: float,
|
lower_line: float,
|
||||||
volume_ratio: float,
|
volume_ratio: float,
|
||||||
width_ratio: float,
|
width_ratio: float,
|
||||||
fitting_adherence: float,
|
geometry_score: float,
|
||||||
boundary_utilization: float,
|
activity_score: float,
|
||||||
) -> Tuple[float, float, float, float, float, float, float, float]:
|
upper_slope: float, # 新增
|
||||||
"""优化版突破强度计算（兼容原API）"""
|
lower_slope: float, # 新增
|
||||||
|
breakout_dir, # 新增，可以是 str 或 int
|
||||||
|
) -> Tuple[float, float, float, float, float, float, float, float, float]:
|
||||||
|
"""优化版突破强度计算（兼容原API）
|
||||||
|
|
||||||
|
支持 breakout_dir 为 str ("up"/"down"/"none") 或 int (1/2/0)
|
||||||
|
"""
|
||||||
|
# 转换breakout_dir为int（如果输入是str）
|
||||||
|
if isinstance(breakout_dir, str):
|
||||||
|
if breakout_dir == "up":
|
||||||
|
breakout_dir_int = 1
|
||||||
|
elif breakout_dir == "down":
|
||||||
|
breakout_dir_int = 2
|
||||||
|
else: # "none"
|
||||||
|
breakout_dir_int = 0
|
||||||
|
else:
|
||||||
|
breakout_dir_int = breakout_dir
|
||||||
|
|
||||||
return calc_breakout_strength_numba(
|
return calc_breakout_strength_numba(
|
||||||
close, upper_line, lower_line, volume_ratio,
|
close, upper_line, lower_line, volume_ratio,
|
||||||
width_ratio, fitting_adherence, boundary_utilization
|
width_ratio, geometry_score, activity_score,
|
||||||
|
upper_slope, lower_slope, breakout_dir_int
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
@ -776,14 +843,14 @@ def detect_single_with_precomputed_pivots(
|
|||||||
break_tol: float,
|
break_tol: float,
|
||||||
vol_window: int,
|
vol_window: int,
|
||||||
vol_k: float,
|
vol_k: float,
|
||||||
) -> Tuple[bool, float, float, float, float, float, float, float, float,
|
) -> Tuple[bool, float, float, float, float, float, float, float, float, float,
|
||||||
float, float, float, int, int, float, int, int]:
|
float, float, float, int, int, float, int, int]:
|
||||||
"""
|
"""
|
||||||
使用预计算枢轴点的单点检测（纯Numba实现）
|
使用预计算枢轴点的单点检测（纯Numba实现）
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
(is_valid, strength_up, strength_down, price_score_up, price_score_down,
|
(is_valid, strength_up, strength_down, price_score_up, price_score_down,
|
||||||
convergence_score, vol_score, fitting_score, boundary_util_score,
|
convergence_score, vol_score, geometry_score, activity_score, tilt_score,
|
||||||
upper_slope, lower_slope, width_ratio, touches_upper, touches_lower,
|
upper_slope, lower_slope, width_ratio, touches_upper, touches_lower,
|
||||||
apex_x, breakout_dir, volume_confirmed)
|
apex_x, breakout_dir, volume_confirmed)
|
||||||
|
|
||||||
@ -792,8 +859,8 @@ def detect_single_with_precomputed_pivots(
|
|||||||
"""
|
"""
|
||||||
n = window_end - window_start + 1
|
n = window_end - window_start + 1
|
||||||
|
|
||||||
# 默认无效结果
|
# 默认无效结果（18个元素，包含tilt_score）
|
||||||
invalid_result = (False, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
invalid_result = (False, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
|
||||||
0.0, 0.0, 0.0, 0, 0, 0.0, 0, -1)
|
0.0, 0.0, 0.0, 0, 0, 0.0, 0, -1)
|
||||||
|
|
||||||
if n < 50: # 最小窗口检查
|
if n < 50: # 最小窗口检查
|
||||||
@ -922,31 +989,33 @@ def detect_single_with_precomputed_pivots(
|
|||||||
if breakout_dir != 0:
|
if breakout_dir != 0:
|
||||||
volume_confirmed = 1 if volume[window_end] > vol_ma * vol_k else 0
|
volume_confirmed = 1 if volume[window_end] > vol_ma * vol_k else 0
|
||||||
|
|
||||||
# 计算拟合贴合度
|
# 计算形态规则度
|
||||||
adherence_upper = calc_fitting_adherence_numba(
|
geometry_upper = calc_geometry_score_numba(
|
||||||
ph_indices.astype(np.float64), ph_values, a_u, b_u
|
ph_indices.astype(np.float64), ph_values, a_u, b_u
|
||||||
)
|
)
|
||||||
adherence_lower = calc_fitting_adherence_numba(
|
geometry_lower = calc_geometry_score_numba(
|
||||||
pl_indices.astype(np.float64), pl_values, a_l, b_l
|
pl_indices.astype(np.float64), pl_values, a_l, b_l
|
||||||
)
|
)
|
||||||
fitting_adherence = (adherence_upper + adherence_lower) / 2.0
|
geometry_score = (geometry_upper + geometry_lower) / 2.0
|
||||||
|
|
||||||
# 计算边界利用率
|
# 计算价格活跃度
|
||||||
boundary_util = calc_boundary_utilization_numba(
|
activity_score = calc_activity_score_numba(
|
||||||
high_win, low_win, a_u, b_u, a_l, b_l, 0, n - 1
|
high_win, low_win, a_u, b_u, a_l, b_l, 0, n - 1
|
||||||
)
|
)
|
||||||
|
|
||||||
# 计算突破强度
|
# 计算突破强度
|
||||||
(strength_up, strength_down, price_score_up, price_score_down,
|
(strength_up, strength_down, price_score_up, price_score_down,
|
||||||
convergence_score, vol_score, fitting_score, boundary_util_score) = \
|
convergence_score, vol_score, geometry_score_out, activity_score_out,
|
||||||
|
tilt_score_out) = \
|
||||||
calc_breakout_strength_numba(
|
calc_breakout_strength_numba(
|
||||||
close_val, upper_end, lower_end, volume_ratio,
|
close_val, upper_end, lower_end, volume_ratio,
|
||||||
width_ratio, fitting_adherence, boundary_util
|
width_ratio, geometry_score, activity_score,
|
||||||
|
a_u, a_l, breakout_dir
|
||||||
)
|
)
|
||||||
|
|
||||||
return (True, strength_up, strength_down, price_score_up, price_score_down,
|
return (True, strength_up, strength_down, price_score_up, price_score_down,
|
||||||
convergence_score, vol_score, fitting_score, boundary_util_score,
|
convergence_score, vol_score, geometry_score_out, activity_score_out,
|
||||||
a_u, a_l, width_ratio, touches_upper, touches_lower,
|
tilt_score_out, a_u, a_l, width_ratio, touches_upper, touches_lower,
|
||||||
apex_x, breakout_dir, volume_confirmed)
|
apex_x, breakout_dir, volume_confirmed)
|
||||||
|
|
||||||
|
|
||||||
@ -973,12 +1042,12 @@ def detect_batch_with_precomputed_pivots_numba(
|
|||||||
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray,
|
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray,
|
||||||
np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray,
|
np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray,
|
||||||
np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray,
|
np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray,
|
||||||
np.ndarray, np.ndarray, np.ndarray]:
|
np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
|
||||||
"""
|
"""
|
||||||
使用预计算枢轴点的批量检测（单只股票，多日期）
|
使用预计算枢轴点的批量检测（单只股票，多日期）
|
||||||
|
|
||||||
Returns:
|
Returns:
|
||||||
多个数组，每个元素对应一个检测点
|
多个数组，每个元素对应一个检测点（新增 tilt_score）
|
||||||
"""
|
"""
|
||||||
n_valid = len(valid_indices)
|
n_valid = len(valid_indices)
|
||||||
n_dates = 0
|
n_dates = 0
|
||||||
@ -998,8 +1067,9 @@ def detect_batch_with_precomputed_pivots_numba(
|
|||||||
price_score_down = np.zeros(n_dates, dtype=np.float64)
|
price_score_down = np.zeros(n_dates, dtype=np.float64)
|
||||||
convergence_score = np.zeros(n_dates, dtype=np.float64)
|
convergence_score = np.zeros(n_dates, dtype=np.float64)
|
||||||
vol_score = np.zeros(n_dates, dtype=np.float64)
|
vol_score = np.zeros(n_dates, dtype=np.float64)
|
||||||
fitting_score = np.zeros(n_dates, dtype=np.float64)
|
geometry_score = np.zeros(n_dates, dtype=np.float64)
|
||||||
boundary_util_score = np.zeros(n_dates, dtype=np.float64)
|
activity_score = np.zeros(n_dates, dtype=np.float64)
|
||||||
|
tilt_score = np.zeros(n_dates, dtype=np.float64) # 新增
|
||||||
upper_slope = np.zeros(n_dates, dtype=np.float64)
|
upper_slope = np.zeros(n_dates, dtype=np.float64)
|
||||||
lower_slope = np.zeros(n_dates, dtype=np.float64)
|
lower_slope = np.zeros(n_dates, dtype=np.float64)
|
||||||
width_ratio = np.zeros(n_dates, dtype=np.float64)
|
width_ratio = np.zeros(n_dates, dtype=np.float64)
|
||||||
@ -1039,22 +1109,23 @@ def detect_batch_with_precomputed_pivots_numba(
|
|||||||
price_score_down[result_idx] = result[4]
|
price_score_down[result_idx] = result[4]
|
||||||
convergence_score[result_idx] = result[5]
|
convergence_score[result_idx] = result[5]
|
||||||
vol_score[result_idx] = result[6]
|
vol_score[result_idx] = result[6]
|
||||||
fitting_score[result_idx] = result[7]
|
geometry_score[result_idx] = result[7]
|
||||||
boundary_util_score[result_idx] = result[8]
|
activity_score[result_idx] = result[8]
|
||||||
upper_slope[result_idx] = result[9]
|
tilt_score[result_idx] = result[9] # 新增
|
||||||
lower_slope[result_idx] = result[10]
|
upper_slope[result_idx] = result[10]
|
||||||
width_ratio[result_idx] = result[11]
|
lower_slope[result_idx] = result[11]
|
||||||
touches_upper[result_idx] = result[12]
|
width_ratio[result_idx] = result[12]
|
||||||
touches_lower[result_idx] = result[13]
|
touches_upper[result_idx] = result[13]
|
||||||
apex_x[result_idx] = result[14]
|
touches_lower[result_idx] = result[14]
|
||||||
breakout_dir[result_idx] = result[15]
|
apex_x[result_idx] = result[15]
|
||||||
volume_confirmed[result_idx] = result[16]
|
breakout_dir[result_idx] = result[16]
|
||||||
|
volume_confirmed[result_idx] = result[17]
|
||||||
|
|
||||||
result_idx += 1
|
result_idx += 1
|
||||||
|
|
||||||
return (date_indices, is_valid, strength_up, strength_down,
|
return (date_indices, is_valid, strength_up, strength_down,
|
||||||
price_score_up, price_score_down, convergence_score,
|
price_score_up, price_score_down, convergence_score,
|
||||||
vol_score, fitting_score, boundary_util_score,
|
vol_score, geometry_score, activity_score, tilt_score,
|
||||||
upper_slope, lower_slope, width_ratio,
|
upper_slope, lower_slope, width_ratio,
|
||||||
touches_upper, touches_lower, apex_x,
|
touches_upper, touches_lower, apex_x,
|
||||||
breakout_dir, volume_confirmed)
|
breakout_dir, volume_confirmed)
|
||||||
|
|||||||
@ -289,13 +289,14 @@ class StrengthComponents:
|
|||||||
"""
|
"""
|
||||||
强度分分量（用于分析和可视化）
|
强度分分量（用于分析和可视化）
|
||||||
|
|
||||||
总强度 = 价格分×50% + 收敛分×15% + 成交量分×10% + 拟合分×10% + 边界利用率×15%
|
总强度 = 价格分×45% + 收敛分×15% + 成交量分×10% + 形态规则度×10% + 价格活跃度×15% + 倾斜度×5%
|
||||||
"""
|
"""
|
||||||
price_score: float # 价格突破分数 (0~1)
|
price_score: float # 价格突破分数 (0~1)
|
||||||
convergence_score: float # 收敛分数 (0~1)
|
convergence_score: float # 收敛分数 (0~1)
|
||||||
volume_score: float # 成交量分数 (0~1)
|
volume_score: float # 成交量分数 (0~1)
|
||||||
fitting_score: float # 拟合贴合度分数 (0~1)
|
geometry_score: float # 形态规则度分数 (0~1)
|
||||||
utilization_score: float # 边界利用率分数 (0~1)
|
activity_score: float # 价格活跃度分数 (0~1)
|
||||||
|
tilt_score: float # 倾斜度分数 (0~1，新增)
|
||||||
|
|
||||||
def to_dict(self) -> Dict[str, float]:
|
def to_dict(self) -> Dict[str, float]:
|
||||||
return asdict(self)
|
return asdict(self)
|
||||||
@ -475,8 +476,9 @@ def detect_triangle(
|
|||||||
price_score=result.price_score_up if result.breakout_dir != "down" else result.price_score_down,
|
price_score=result.price_score_up if result.breakout_dir != "down" else result.price_score_down,
|
||||||
convergence_score=result.convergence_score,
|
convergence_score=result.convergence_score,
|
||||||
volume_score=result.volume_score,
|
volume_score=result.volume_score,
|
||||||
fitting_score=result.fitting_score,
|
geometry_score=result.geometry_score,
|
||||||
utilization_score=result.boundary_utilization,
|
activity_score=result.activity_score,
|
||||||
|
tilt_score=result.tilt_score,
|
||||||
)
|
)
|
||||||
|
|
||||||
# 构建前端图表数据
|
# 构建前端图表数据
|
||||||
|
|||||||
142
tests/test_renaming.py
Normal file
142
tests/test_renaming.py
Normal file
@ -0,0 +1,142 @@
|
|||||||
|
"""
|
||||||
|
测试重命名后的API是否正常工作
|
||||||
|
"""
|
||||||
|
import numpy as np
|
||||||
|
from converging_triangle import (
|
||||||
|
calc_geometry_score,
|
||||||
|
calc_activity_score,
|
||||||
|
calc_breakout_strength,
|
||||||
|
)
|
||||||
|
|
||||||
|
def test_geometry_score():
|
||||||
|
"""测试形态规则度计算"""
|
||||||
|
pivot_indices = np.array([0, 10, 20, 30])
|
||||||
|
pivot_values = np.array([100.0, 95.0, 90.0, 85.0])
|
||||||
|
slope = -0.5
|
||||||
|
intercept = 100.0
|
||||||
|
|
||||||
|
score = calc_geometry_score(pivot_indices, pivot_values, slope, intercept)
|
||||||
|
print(f"✅ 形态规则度计算成功: {score:.4f}")
|
||||||
|
assert 0 <= score <= 1, "分数应在 0~1 之间"
|
||||||
|
return score
|
||||||
|
|
||||||
|
def test_activity_score():
|
||||||
|
"""测试价格活跃度计算"""
|
||||||
|
high = np.array([105, 102, 100, 98, 96, 94, 92, 90, 88, 86])
|
||||||
|
low = np.array([95, 92, 90, 88, 86, 84, 82, 80, 78, 76])
|
||||||
|
|
||||||
|
upper_slope = -1.0
|
||||||
|
upper_intercept = 105.0
|
||||||
|
lower_slope = -1.0
|
||||||
|
lower_intercept = 95.0
|
||||||
|
|
||||||
|
score = calc_activity_score(high, low, upper_slope, upper_intercept,
|
||||||
|
lower_slope, lower_intercept, 0, 9)
|
||||||
|
print(f"✅ 价格活跃度计算成功: {score:.4f}")
|
||||||
|
assert 0 <= score <= 1, "分数应在 0~1 之间"
|
||||||
|
return score
|
||||||
|
|
||||||
|
def test_breakout_strength():
|
||||||
|
"""测试突破强度计算（使用新的参数名）"""
|
||||||
|
geometry_score = 0.8
|
||||||
|
activity_score = 0.7
|
||||||
|
|
||||||
|
strength_up, strength_down, price_up, price_down, conv, vol, geom, act = \
|
||||||
|
calc_breakout_strength(
|
||||||
|
close=105.0,
|
||||||
|
upper_line=100.0,
|
||||||
|
lower_line=90.0,
|
||||||
|
volume_ratio=1.5,
|
||||||
|
width_ratio=0.3,
|
||||||
|
geometry_score=geometry_score,
|
||||||
|
activity_score=activity_score,
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"✅ 突破强度计算成功:")
|
||||||
|
print(f" - 向上强度: {strength_up:.4f}")
|
||||||
|
print(f" - 形态规则度: {geom:.4f}")
|
||||||
|
print(f" - 价格活跃度: {act:.4f}")
|
||||||
|
|
||||||
|
assert 0 <= strength_up <= 1, "强度应在 0~1 之间"
|
||||||
|
assert abs(geom - geometry_score) < 0.01, "形态规则度应正确传递"
|
||||||
|
assert abs(act - activity_score) < 0.01, "价格活跃度应正确传递"
|
||||||
|
|
||||||
|
return strength_up, strength_down
|
||||||
|
|
||||||
|
def test_imports():
|
||||||
|
"""测试新命名的导入"""
|
||||||
|
try:
|
||||||
|
from converging_triangle import calc_geometry_score, calc_activity_score
|
||||||
|
from converging_triangle_optimized import (
|
||||||
|
calc_geometry_score_optimized,
|
||||||
|
calc_activity_score_optimized,
|
||||||
|
)
|
||||||
|
print("✅ 所有函数导入成功")
|
||||||
|
return True
|
||||||
|
except ImportError as e:
|
||||||
|
print(f"❌ 导入失败: {e}")
|
||||||
|
return False
|
||||||
|
|
||||||
|
def test_dataclass_fields():
|
||||||
|
"""测试数据类字段重命名"""
|
||||||
|
from converging_triangle import ConvergingTriangleResult
|
||||||
|
from triangle_detector_api import StrengthComponents
|
||||||
|
|
||||||
|
# 检查 ConvergingTriangleResult 字段
|
||||||
|
result = ConvergingTriangleResult()
|
||||||
|
assert hasattr(result, 'geometry_score'), "应有 geometry_score 字段"
|
||||||
|
assert hasattr(result, 'activity_score'), "应有 activity_score 字段"
|
||||||
|
assert not hasattr(result, 'fitting_score'), "不应有旧的 fitting_score 字段"
|
||||||
|
assert not hasattr(result, 'boundary_utilization'), "不应有旧的 boundary_utilization 字段"
|
||||||
|
print("✅ ConvergingTriangleResult 字段正确")
|
||||||
|
|
||||||
|
# 检查 StrengthComponents 字段
|
||||||
|
comp = StrengthComponents(
|
||||||
|
price_score=0.5,
|
||||||
|
convergence_score=0.6,
|
||||||
|
volume_score=0.4,
|
||||||
|
geometry_score=0.7,
|
||||||
|
activity_score=0.8,
|
||||||
|
)
|
||||||
|
assert comp.geometry_score == 0.7, "geometry_score 应正确设置"
|
||||||
|
assert comp.activity_score == 0.8, "activity_score 应正确设置"
|
||||||
|
print("✅ StrengthComponents 字段正确")
|
||||||
|
|
||||||
|
return True
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
print("=" * 60)
|
||||||
|
print("测试重命名后的API")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
try:
|
||||||
|
# 1. 测试导入
|
||||||
|
print("\n[1/5] 测试函数导入...")
|
||||||
|
test_imports()
|
||||||
|
|
||||||
|
# 2. 测试数据类字段
|
||||||
|
print("\n[2/5] 测试数据类字段...")
|
||||||
|
test_dataclass_fields()
|
||||||
|
|
||||||
|
# 3. 测试形态规则度
|
||||||
|
print("\n[3/5] 测试形态规则度计算...")
|
||||||
|
test_geometry_score()
|
||||||
|
|
||||||
|
# 4. 测试价格活跃度
|
||||||
|
print("\n[4/5] 测试价格活跃度计算...")
|
||||||
|
test_activity_score()
|
||||||
|
|
||||||
|
# 5. 测试突破强度
|
||||||
|
print("\n[5/5] 测试突破强度计算...")
|
||||||
|
test_breakout_strength()
|
||||||
|
|
||||||
|
print("\n" + "=" * 60)
|
||||||
|
print("✅ 所有测试通过！重命名成功！")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
except Exception as e:
|
||||||
|
print("\n" + "=" * 60)
|
||||||
|
print(f"❌ 测试失败: {e}")
|
||||||
|
print("=" * 60)
|
||||||
|
import traceback
|
||||||
|
traceback.print_exc()
|
||||||
125
tests/test_tilt_score.py
Normal file
125
tests/test_tilt_score.py
Normal file
@ -0,0 +1,125 @@
|
|||||||
|
"""
|
||||||
|
测试倾斜度分数计算功能
|
||||||
|
|
||||||
|
测试 calc_tilt_score() 函数的正确性
|
||||||
|
"""
|
||||||
|
import sys
|
||||||
|
import os
|
||||||
|
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
|
||||||
|
|
||||||
|
from converging_triangle import calc_tilt_score
|
||||||
|
|
||||||
|
|
||||||
|
def test_basic_cases():
|
||||||
|
"""测试基本情况"""
|
||||||
|
print("\n=== 测试基本情况 ===")
|
||||||
|
|
||||||
|
# 1. 对称三角形 + 向上突破（中轴水平）
|
||||||
|
score = calc_tilt_score(-0.03, 0.03, "up")
|
||||||
|
print(f"对称三角形 + 向上突破: {score:.3f} (期望 = 0.5)")
|
||||||
|
assert abs(score - 0.5) < 0.01, f"对称三角形应该得0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
# 2. 对称三角形 + 向下突破
|
||||||
|
score = calc_tilt_score(-0.03, 0.03, "down")
|
||||||
|
print(f"对称三角形 + 向下突破: {score:.3f} (期望 = 0.5)")
|
||||||
|
assert abs(score - 0.5) < 0.01, f"对称三角形应该得0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
# 3. 任意形态 + 未突破
|
||||||
|
score = calc_tilt_score(0.01, -0.01, "none")
|
||||||
|
print(f"任意形态 + 未突破: {score:.3f} (期望 = 0.5)")
|
||||||
|
assert score == 0.5, f"未突破应该得0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
|
||||||
|
def test_upward_tilt():
|
||||||
|
"""测试向上倾斜的情况"""
|
||||||
|
print("\n=== 测试向上倾斜 ===")
|
||||||
|
|
||||||
|
# 轻微向上倾斜 + 向上突破（顺势）
|
||||||
|
score = calc_tilt_score(0.0, 0.05, "up")
|
||||||
|
print(f"向上倾斜 + 向上突破: {score:.3f} (期望 > 0.5)")
|
||||||
|
assert score > 0.5, f"向上倾斜向上突破应该 > 0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
# 轻微向上倾斜 + 向下突破（逆势）
|
||||||
|
score = calc_tilt_score(0.0, 0.05, "down")
|
||||||
|
print(f"向上倾斜 + 向下突破: {score:.3f} (期望 < 0.5)")
|
||||||
|
assert score < 0.5, f"向上倾斜向下突破应该 < 0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
# 强烈向上倾斜 + 向上突破
|
||||||
|
score = calc_tilt_score(0.0, 0.20, "up")
|
||||||
|
print(f"强向上倾斜 + 向上突破: {score:.3f} (期望明显 > 0.5)")
|
||||||
|
assert score > 0.55, f"强向上倾斜向上突破应该 > 0.55，实际: {score:.3f}"
|
||||||
|
|
||||||
|
|
||||||
|
def test_downward_tilt():
|
||||||
|
"""测试向下倾斜的情况"""
|
||||||
|
print("\n=== 测试向下倾斜 ===")
|
||||||
|
|
||||||
|
# 轻微向下倾斜 + 向下突破（顺势）
|
||||||
|
score = calc_tilt_score(-0.05, 0.0, "down")
|
||||||
|
print(f"向下倾斜 + 向下突破: {score:.3f} (期望 > 0.5)")
|
||||||
|
assert score > 0.5, f"向下倾斜向下突破应该 > 0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
# 轻微向下倾斜 + 向上突破（逆势）
|
||||||
|
score = calc_tilt_score(-0.05, 0.0, "up")
|
||||||
|
print(f"向下倾斜 + 向上突破: {score:.3f} (期望 < 0.5)")
|
||||||
|
assert score < 0.5, f"向下倾斜向上突破应该 < 0.5，实际: {score:.3f}"
|
||||||
|
|
||||||
|
# 强烈向下倾斜 + 向下突破
|
||||||
|
score = calc_tilt_score(-0.20, 0.0, "down")
|
||||||
|
print(f"强向下倾斜 + 向下突破: {score:.3f} (期望明显 > 0.5)")
|
||||||
|
assert score > 0.55, f"强向下倾斜向下突破应该 > 0.55，实际: {score:.3f}"
|
||||||
|
|
||||||
|
|
||||||
|
def test_bounds():
|
||||||
|
"""测试边界"""
|
||||||
|
print("\n=== 测试边界 ===")
|
||||||
|
|
||||||
|
test_cases = [
|
||||||
|
(0.1, 0.1, "up"),
|
||||||
|
(-0.1, -0.1, "down"),
|
||||||
|
(0.0, 0.0, "none"),
|
||||||
|
(0.05, -0.05, "up"),
|
||||||
|
(-0.05, 0.05, "down"),
|
||||||
|
(0.5, 0.5, "up"), # 极端情况
|
||||||
|
(-0.5, -0.5, "down"), # 极端情况
|
||||||
|
]
|
||||||
|
|
||||||
|
for upper, lower, direction in test_cases:
|
||||||
|
score = calc_tilt_score(upper, lower, direction)
|
||||||
|
print(f" 斜率({upper:+.2f}, {lower:+.2f}) + {direction:5s}: {score:.3f}")
|
||||||
|
assert 0.0 <= score <= 1.0, f"分数超出 [0, 1] 范围: {score}"
|
||||||
|
|
||||||
|
print("所有分数都在 [0, 1] 范围内")
|
||||||
|
|
||||||
|
|
||||||
|
def run_all_tests():
|
||||||
|
"""运行所有测试"""
|
||||||
|
print("=" * 60)
|
||||||
|
print("倾斜度分数计算 - 单元测试")
|
||||||
|
print("=" * 60)
|
||||||
|
|
||||||
|
try:
|
||||||
|
test_basic_cases()
|
||||||
|
test_upward_tilt()
|
||||||
|
test_downward_tilt()
|
||||||
|
test_bounds()
|
||||||
|
|
||||||
|
print("\n" + "=" * 60)
|
||||||
|
print("[PASS] 所有测试通过！")
|
||||||
|
print("=" * 60)
|
||||||
|
return True
|
||||||
|
except AssertionError as e:
|
||||||
|
print("\n" + "=" * 60)
|
||||||
|
print(f"[FAIL] 测试失败: {e}")
|
||||||
|
print("=" * 60)
|
||||||
|
return False
|
||||||
|
except Exception as e:
|
||||||
|
print("\n" + "=" * 60)
|
||||||
|
print(f"[ERROR] 运行错误: {e}")
|
||||||
|
print("=" * 60)
|
||||||
|
return False
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
success = run_all_tests()
|
||||||
|
sys.exit(0 if success else 1)
|
||||||
Loading…
x
Reference in New Issue
Block a user