diff --git a/discuss/20260129-讨论.md b/discuss/20260129-讨论.md
index e69de29..7b4e6dd 100644
--- a/discuss/20260129-讨论.md
+++ b/discuss/20260129-讨论.md
@@ -0,0 +1,38 @@
+## 收敛三角形函数本身
+
+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。
+
+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
+ - **结论**：不建议合并，两者测量的是不同维度
+ - **优化**：重命名为"形态规则度"和"价格活跃度"，更直观
+ - **详情**：见 `docs/命名优化_拟合贴合度_边界利用率_重命名.md`
+
+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
+ - **实现方式**：新增"倾斜度分"作为第6个维度
+ - **权重分配**：从突破幅度分中分配5%（50% → 45%）
+ - **详情**：见 `docs/强度分_增加角度参数_深度分析.md`
+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。
+**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：
+
+| 分量 | 归一化方式 | 范围保证 |
+|------|-----------|---------|
+| **突破幅度分** (`price_score`) | `np.tanh(pct * 15.0)` | tanh 输出 [0, 1]（因为 pct ≥ 0） |
+| **收敛度分** (`convergence_score`) | `max(0, min(1, 1 - width_ratio))` | 显式 clamp 到 [0, 1] |
+| **成交量分** (`volume_score`) | `min(1, max(0, volume_ratio - 1))` | 显式 clamp 到 [0, 1] |
+| **形态规则度** (`geometry_score`) | `exp(-error * 20)` + clamp | 指数衰减 + 显式 clamp |
+| **价格活跃度** (`activity_score`) | 逐日计算 `1 - blank_ratio` 的平均 + clamp | 每日 clamp + 最终 clamp |
+| **倾斜度分** (`tilt_score`) | `(1 ± tilt) / 2` + clamp | 显式 clamp 到 [0, 1] |
+
+### 形态规则度和价格活跃度的归一化详解
+
+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：
+
+**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)
+
+**核心要点**：
+- **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
+- **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
+
+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。
+
+---
diff --git a/docs/命名优化_完成总结.md b/docs/命名优化_完成总结.md
new file mode 100644
index 0000000..b0d8d0b
--- /dev/null
+++ b/docs/命名优化_完成总结.md
@@ -0,0 +1,185 @@
+# 命名优化完成总结
+
+## ✅ 全部修改完成
+
+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"。
+
+---
+
+## 📊 修改统计
+
+### 文件总数：11 个
+
+#### 核心源文件（3个）
+1. ✅ `src/converging_triangle.py`
+2. ✅ `src/converging_triangle_optimized.py`
+3. ✅ `src/triangle_detector_api.py`
+
+#### 脚本文件（6个）
+4. ✅ `scripts/plot_converging_triangles.py`
+5. ✅ `scripts/test_full_pipeline.py`
+6. ✅ `scripts/test_optimization_comparison.py`
+7. ✅ `scripts/generate_stock_viewer.py`
+8. ✅ `scripts/README_performance_tests.md`
+9. ✅ `tests/test_renaming.py`
+
+#### 文档文件（3个）
+10. ✅ `docs/强度分组成梳理.md`
+11. ✅ `docs/命名优化_拟合贴合度_边界利用率_重命名.md`
+
+#### 讨论文件（1个）
+12. ✅ `discuss/20260129-讨论.md`
+
+---
+
+## 🔄 命名映射完整表
+
+| 类别 | 旧名称 | 新名称 | 位置 |
+|------|--------|--------|------|
+| **Python变量** | `fitting_score` | `geometry_score` | 所有Python文件 |
+| **Python变量** | `fitting_adherence` | `geometry_score` | 所有Python文件 |
+| **Python变量** | `boundary_utilization` | `activity_score` | 所有Python文件 |
+| **Python变量** | `utilization_score` | `activity_score` | 所有Python文件 |
+| **Python函数** | `calc_fitting_adherence()` | `calc_geometry_score()` | 所有Python文件 |
+| **Python函数** | `calc_boundary_utilization()` | `calc_activity_score()` | 所有Python文件 |
+| **Python常量** | `W_FITTING` | `W_GEOMETRY` | 权重配置 |
+| **Python常量** | `W_UTILIZATION` | `W_ACTIVITY` | 权重配置 |
+| **Python常量** | `UTILIZATION_FLOOR` | `ACTIVITY_FLOOR` | 惩罚阈值 |
+| **JavaScript字段** | `boundaryUtilization` | `activityScore` | HTML/JS文件 |
+| **中文术语** | "拟合贴合度" | "形态规则度" | 所有文档和UI |
+| **中文术语** | "边界利用率" | "价格活跃度" | 所有文档和UI |
+| **注释/文档** | "拟合贴合度分" | "形态规则度" | 注释和文档 |
+| **注释/文档** | "边界利用率分" | "价格活跃度" | 注释和文档 |
+| **图表标题** | "利用率惩罚" | "活跃度惩罚" | 图表生成代码 |
+
+---
+
+## 📝 强度分组成（最终版）
+
+```
+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 | 中文名称 | 权重 | 英文字段 | 说明 |
+|------|---------|------|----------|------|
+| 1 | 突破幅度分 | 50% | `price_score` | 价格突破边界的力度 |
+| 2 | 收敛度分 | 15% | `convergence_score` | 三角形收敛的紧密程度 |
+| 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`** | 形态的几何标准性 |
+| 5 | **价格活跃度** | 15% | **`activity_score`** | 价格振荡的充分性 |
+
+**空白惩罚**：当价格活跃度 < 20% 时，总强度 × 惩罚系数
+
+---
+
+## 🎯 重命名原因
+
+### 问题诊断
+1. **"拟合贴合度"** - 过于技术化，不够直观
+2. **"边界利用率"** - 容易让人误以为两者是同一维度
+
+### 解决方案
+- **"形态规则度"** (Geometry Score)
+ - 直观：一看就知道是衡量形态标准性
+ - 准确：测量枢轴点的几何规则性
+
+- **"价格活跃度"** (Activity Score)
+ - 直观：一看就知道是衡量价格活跃程度
+ - 准确：测量价格振荡的充分性
+
+### 核心差异
+
+| 维度 | 形态规则度 | 价格活跃度 |
+|------|----------|----------|
+| **测量对象** | 4-8个关键枢轴点 | 240天全部价格 |
+| **测量内容** | 几何规则性 | 振荡充分性 |
+| **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 |
+
+---
+
+## ✅ 验证清单
+
+### 代码验证
+- [x] 所有Python文件语法检查通过
+- [x] 函数导入测试成功
+- [x] 数据类字段验证通过
+- [x] Numba优化版本正常加载
+- [x] 所有脚本可以正常导入
+
+### UI验证
+- [x] 图表标题使用新名称
+- [x] HTML查看器使用新字段名
+- [x] JavaScript代码使用新字段名
+
+### 文档验证
+- [x] 所有Markdown文档已更新
+- [x] 代码注释已更新
+- [x] API文档已更新
+
+---
+
+## 📌 后续工作
+
+### 需要重新生成的内容
+1. **outputs/converging_triangles/stock_viewer.html**
+ - 需要运行 `generate_stock_viewer.py` 重新生成
+ - 会使用新的字段名 `activityScore`
+
+2. **所有PNG图表**
+ - 需要运行 `pipeline_converging_triangle.py` 重新生成
+ - 标题会显示"形态规则度"和"价格活跃度"
+
+### 运行命令
+```bash
+# 重新运行完整流程
+python scripts/pipeline_converging_triangle.py --clean --all-stocks
+
+# 或分步运行
+python scripts/detect_all_stocks.py
+python scripts/generate_stock_viewer.py
+```
+
+---
+
+## 💾 向后兼容性
+
+### ⚠️ 破坏性变更
+此次重命名是**破坏性变更**，旧代码需要更新：
+
+#### Python代码
+```python
+# ❌ 旧代码（不再工作）
+result.fitting_score
+result.boundary_utilization
+components.utilization_score
+
+# ✅ 新代码
+result.geometry_score
+result.activity_score
+components.activity_score
+```
+
+#### JavaScript代码
+```javascript
+// ❌ 旧代码（不再工作）
+stock.boundaryUtilization
+
+// ✅ 新代码
+stock.activityScore
+```
+
+---
+
+## 📚 相关文档
+
+- **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`
+- **强度分说明**: `docs/强度分组成梳理.md`
+- **讨论记录**: `discuss/20260129-讨论.md`
+
+---
+
+**更新时间**: 2026-01-29
+**更新人员**: AI Assistant
+**变更类型**: 破坏性命名优化
+**影响范围**: 全项目
diff --git a/docs/命名优化_拟合贴合度_边界利用率_重命名.md b/docs/命名优化_拟合贴合度_边界利用率_重命名.md
new file mode 100644
index 0000000..563fa3c
--- /dev/null
+++ b/docs/命名优化_拟合贴合度_边界利用率_重命名.md
@@ -0,0 +1,204 @@
+# 命名优化：拟合贴合度 → 形态规则度，边界利用率 → 价格活跃度
+
+## 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） |
+| `boundary_utilization` | `activity_score` | 价格活跃度分数 |
+| `utilization_score` | `activity_score` | 价格活跃度分数（输出） |
+| `calc_fitting_adherence()` | `calc_geometry_score()` | 计算形态规则度 |
+| `calc_boundary_utilization()` | `calc_activity_score()` | 计算价格活跃度 |
+| `W_FITTING` | `W_GEOMETRY` | 形态规则度权重 |
+| `W_UTILIZATION` | `W_ACTIVITY` | 价格活跃度权重 |
+| `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 |
+|--------|--------|
+| `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
diff --git a/docs/强度分_增加角度参数_深度分析.md b/docs/强度分_增加角度参数_深度分析.md
new file mode 100644
index 0000000..934ef9a
--- /dev/null
+++ b/docs/强度分_增加角度参数_深度分析.md
@@ -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
diff --git a/docs/强度分_形态规则度和价格活跃度_归一化详解.md b/docs/强度分_形态规则度和价格活跃度_归一化详解.md
new file mode 100644
index 0000000..9fac7f7
--- /dev/null
+++ b/docs/强度分_形态规则度和价格活跃度_归一化详解.md
@@ -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
diff --git a/docs/强度分组成梳理.md b/docs/强度分组成梳理.md
new file mode 100644
index 0000000..3eabb00
--- /dev/null
+++ b/docs/强度分组成梳理.md
@@ -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
diff --git a/outputs/converging_triangles/stock_viewer.html b/outputs/converging_triangles/stock_viewer.html
index 0d1d316..bce79f2 100644
--- a/outputs/converging_triangles/stock_viewer.html
+++ b/outputs/converging_triangles/stock_viewer.html
@@ -1068,17 +1068,18 @@
"idx": 28,
"code": "SH603379",
"name": "三美股份",
- "strengthUp": 0.2718794483272821,
- "strengthDown": 0.09070852657198746,
+ "strengthUp": 0.26552211680191823,
+ "strengthDown": 0.10246828722215302,
"direction": "up",
"widthRatio": 0.08922158280365546,
"touchesUpper": 4,
"touchesLower": 4,
"volumeConfirmed": "False",
- "boundaryUtilization": 0.0918991906342708,
+ "activityScore": 0.0918991906342708,
+ "tiltScore": 0.5118548082524743,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.2718794483272821,
+ "strength": 0.26552211680191823,
"chartPath": "charts/20260120_SH603379_三美股份.png",
"chartPathDetail": "charts/20260120_SH603379_三美股份_detail.png"
},
@@ -1086,71 +1087,75 @@
"idx": 13,
"code": "SH600744",
"name": "华银电力",
- "strengthUp": 0.15706248130235445,
- "strengthDown": 0.05487856283191771,
+ "strengthUp": 0.1522090509643363,
+ "strengthDown": 0.06024352434094318,
"direction": "up",
"widthRatio": 0.2867596230842454,
"touchesUpper": 5,
"touchesLower": 2,
"volumeConfirmed": "True",
- "boundaryUtilization": 0.043431555801019366,
+ "activityScore": 0.043431555801019366,
+ "tiltScore": 0.4941072370149417,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.15706248130235445,
+ "strength": 0.1522090509643363,
"chartPath": "charts/20260120_SH600744_华银电力.png",
"chartPathDetail": "charts/20260120_SH600744_华银电力_detail.png"
},
- {
- "idx": 20,
- "code": "SH601609",
- "name": "金田股份",
- "strengthUp": 0.12491668196385437,
- "strengthDown": 0.08566223307080872,
- "direction": "up",
- "widthRatio": 0.03896315672868362,
- "touchesUpper": 4,
- "touchesLower": 6,
- "volumeConfirmed": "True",
- "boundaryUtilization": 0.06788056841717838,
- "date": 20260120,
- "hasTriangle": true,
- "strength": 0.12491668196385437,
- "chartPath": "charts/20260120_SH601609_金田股份.png",
- "chartPathDetail": "charts/20260120_SH601609_金田股份_detail.png"
- },
{
"idx": 95,
"code": "SZ300737",
"name": "科顺股份",
- "strengthUp": 0.12060013192131139,
- "strengthDown": 0.12060013192131139,
+ "strengthUp": 0.13400783708484898,
+ "strengthDown": 0.13400783708484898,
"direction": "none",
"widthRatio": 0.21325532904558048,
"touchesUpper": 6,
"touchesLower": 7,
"volumeConfirmed": "",
- "boundaryUtilization": 0.10726164130830072,
+ "activityScore": 0.10726164130830072,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.12060013192131139,
+ "strength": 0.13400783708484898,
"chartPath": "charts/20260120_SZ300737_科顺股份.png",
"chartPathDetail": "charts/20260120_SZ300737_科顺股份_detail.png"
},
+ {
+ "idx": 20,
+ "code": "SH601609",
+ "name": "金田股份",
+ "strengthUp": 0.12944397068032543,
+ "strengthDown": 0.09411496667658434,
+ "direction": "up",
+ "widthRatio": 0.03896315672868362,
+ "touchesUpper": 4,
+ "touchesLower": 6,
+ "volumeConfirmed": "True",
+ "activityScore": 0.06788056841717838,
+ "tiltScore": 0.4980944504664168,
+ "date": 20260120,
+ "hasTriangle": true,
+ "strength": 0.12944397068032543,
+ "chartPath": "charts/20260120_SH601609_金田股份.png",
+ "chartPathDetail": "charts/20260120_SH601609_金田股份_detail.png"
+ },
{
"idx": 91,
"code": "SZ300530",
"name": "领湃科技",
- "strengthUp": 0.11111761818295247,
- "strengthDown": 0.11111761818295247,
+ "strengthUp": 0.1208592686961565,
+ "strengthDown": 0.1208592686961565,
"direction": "none",
"widthRatio": 0.12780633375372022,
"touchesUpper": 3,
"touchesLower": 2,
"volumeConfirmed": "",
- "boundaryUtilization": 0.0779332041056322,
+ "activityScore": 0.0779332041056322,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.11111761818295247,
+ "strength": 0.1208592686961565,
"chartPath": "charts/20260120_SZ300530_领湃科技.png",
"chartPathDetail": "charts/20260120_SZ300530_领湃科技_detail.png"
},
@@ -1158,17 +1163,18 @@
"idx": 26,
"code": "SH603237",
"name": "五芳斋",
- "strengthUp": 0.1024610736254772,
- "strengthDown": 0.1024610736254772,
+ "strengthUp": 0.11693159036032842,
+ "strengthDown": 0.11693159036032842,
"direction": "none",
"widthRatio": 0.1737453100820701,
"touchesUpper": 5,
"touchesLower": 5,
"volumeConfirmed": "",
- "boundaryUtilization": 0.11576413387880975,
+ "activityScore": 0.11576413387880975,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.1024610736254772,
+ "strength": 0.11693159036032842,
"chartPath": "charts/20260120_SH603237_五芳斋.png",
"chartPathDetail": "charts/20260120_SH603237_五芳斋_detail.png"
},
@@ -1176,71 +1182,75 @@
"idx": 76,
"code": "SZ002802",
"name": "洪汇新材",
- "strengthUp": 0.10100693556969877,
- "strengthDown": 0.10100693556969877,
+ "strengthUp": 0.1157886059983914,
+ "strengthDown": 0.1157886059983914,
"direction": "none",
"widthRatio": 0.23884176697328596,
"touchesUpper": 5,
"touchesLower": 4,
"volumeConfirmed": "",
- "boundaryUtilization": 0.11825336342954114,
+ "activityScore": 0.11825336342954114,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.10100693556969877,
+ "strength": 0.1157886059983914,
"chartPath": "charts/20260120_SZ002802_洪汇新材.png",
"chartPathDetail": "charts/20260120_SZ002802_洪汇新材_detail.png"
},
- {
- "idx": 54,
- "code": "SZ000722",
- "name": "湖南发展",
- "strengthUp": 0.10089807923588778,
- "strengthDown": 0.10089807923588778,
- "direction": "none",
- "widthRatio": 0.16117444007433868,
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- "touchesLower": 2,
- "volumeConfirmed": "",
- "boundaryUtilization": 0.08798114509673645,
- "date": 20260120,
- "hasTriangle": true,
- "strength": 0.10089807923588778,
- "chartPath": "charts/20260120_SZ000722_湖南发展.png",
- "chartPathDetail": "charts/20260120_SZ000722_湖南发展_detail.png"
- },
{
"idx": 22,
"code": "SH603005",
"name": "晶方科技",
- "strengthUp": 0.1004173045532496,
- "strengthDown": 0.1004173045532496,
+ "strengthUp": 0.11219704543568702,
+ "strengthDown": 0.11219704543568702,
"direction": "none",
"widthRatio": 0.34612788685827206,
"touchesUpper": 4,
"touchesLower": 6,
"volumeConfirmed": "",
- "boundaryUtilization": 0.09423792705949945,
+ "activityScore": 0.09423792705949945,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.1004173045532496,
+ "strength": 0.11219704543568702,
"chartPath": "charts/20260120_SH603005_晶方科技.png",
"chartPathDetail": "charts/20260120_SH603005_晶方科技_detail.png"
},
+ {
+ "idx": 54,
+ "code": "SZ000722",
+ "name": "湖南发展",
+ "strengthUp": 0.11189572237297984,
+ "strengthDown": 0.11189572237297984,
+ "direction": "none",
+ "widthRatio": 0.16117444007433868,
+ "touchesUpper": 5,
+ "touchesLower": 2,
+ "volumeConfirmed": "",
+ "activityScore": 0.08798114509673645,
+ "tiltScore": 0.5,
+ "date": 20260120,
+ "hasTriangle": true,
+ "strength": 0.11189572237297984,
+ "chartPath": "charts/20260120_SZ000722_湖南发展.png",
+ "chartPathDetail": "charts/20260120_SZ000722_湖南发展_detail.png"
+ },
{
"idx": 79,
"code": "SZ002966",
"name": "苏州银行",
- "strengthUp": 0.09178123721898465,
- "strengthDown": 0.09178123721898465,
+ "strengthUp": 0.10487848984668484,
+ "strengthDown": 0.10487848984668484,
"direction": "none",
"widthRatio": 0.15024073729732276,
"touchesUpper": 3,
"touchesLower": 5,
"volumeConfirmed": "",
- "boundaryUtilization": 0.1047780210216016,
+ "activityScore": 0.1047780210216016,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.09178123721898465,
+ "strength": 0.10487848984668484,
"chartPath": "charts/20260120_SZ002966_苏州银行.png",
"chartPathDetail": "charts/20260120_SZ002966_苏州银行_detail.png"
},
@@ -1248,17 +1258,18 @@
"idx": 63,
"code": "SZ002142",
"name": "宁波银行",
- "strengthUp": 0.09042608326894579,
- "strengthDown": 0.09042608326894579,
+ "strengthUp": 0.10477713216177321,
+ "strengthDown": 0.10477713216177321,
"direction": "none",
"widthRatio": 0.13991351758720233,
"touchesUpper": 4,
"touchesLower": 6,
"volumeConfirmed": "",
- "boundaryUtilization": 0.11480839114261941,
+ "activityScore": 0.11480839114261941,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.09042608326894579,
+ "strength": 0.10477713216177321,
"chartPath": "charts/20260120_SZ002142_宁波银行.png",
"chartPathDetail": "charts/20260120_SZ002142_宁波银行_detail.png"
},
@@ -1266,17 +1277,18 @@
"idx": 98,
"code": "SZ300892",
"name": "品渥食品",
- "strengthUp": 0.08334022107272326,
- "strengthDown": 0.08334022107272326,
+ "strengthUp": 0.09790798372541941,
+ "strengthDown": 0.09790798372541941,
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"widthRatio": 0.2865112467693385,
"touchesUpper": 4,
"touchesLower": 3,
"volumeConfirmed": "",
- "boundaryUtilization": 0.11654210122156917,
+ "activityScore": 0.11654210122156917,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.08334022107272326,
+ "strength": 0.09790798372541941,
"chartPath": "charts/20260120_SZ300892_品渥食品.png",
"chartPathDetail": "charts/20260120_SZ300892_品渥食品_detail.png"
},
@@ -1284,17 +1296,18 @@
"idx": 72,
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+ "strengthUp": 0.03282153828304035,
+ "strengthDown": 0.03282153828304035,
"direction": "none",
"widthRatio": 0.14102846289352128,
"touchesUpper": 2,
"touchesLower": 4,
"volumeConfirmed": "",
- "boundaryUtilization": 0.040469628970140684,
+ "activityScore": 0.040469628970140684,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.027762834661772765,
+ "strength": 0.03282153828304035,
"chartPath": "charts/20260120_SZ000039_中集集团.png",
"chartPathDetail": "charts/20260120_SZ000039_中集集团_detail.png"
},
@@ -2058,17 +2113,18 @@
"idx": 78,
"code": "SZ002910",
"name": "庄园牧场",
- "strengthUp": 0.02595109833198887,
- "strengthDown": 0.02595109833198887,
+ "strengthUp": 0.029462837189994914,
+ "strengthDown": 0.029462837189994914,
"direction": "none",
"widthRatio": 0.057656261422220724,
"touchesUpper": 2,
"touchesLower": 3,
"volumeConfirmed": "",
- "boundaryUtilization": 0.028093910864048357,
+ "activityScore": 0.028093910864048357,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.02595109833198887,
+ "strength": 0.029462837189994914,
"chartPath": "charts/20260120_SZ002910_庄园牧场.png",
"chartPathDetail": "charts/20260120_SZ002910_庄园牧场_detail.png"
},
@@ -2076,17 +2132,18 @@
"idx": 64,
"code": "SZ002192",
"name": "融捷股份",
- "strengthUp": 0.02338013665457102,
- "strengthDown": 0.02338013665457102,
+ "strengthUp": 0.028213852163709897,
+ "strengthDown": 0.028213852163709897,
"direction": "none",
"widthRatio": 0.23256546452034127,
"touchesUpper": 2,
"touchesLower": 4,
"volumeConfirmed": "",
- "boundaryUtilization": 0.03866972407311103,
+ "activityScore": 0.03866972407311103,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.02338013665457102,
+ "strength": 0.028213852163709897,
"chartPath": "charts/20260120_SZ002192_融捷股份.png",
"chartPathDetail": "charts/20260120_SZ002192_融捷股份_detail.png"
},
@@ -2094,17 +2151,18 @@
"idx": 44,
"code": "SH688472",
"name": "阿特斯",
- "strengthUp": 0.022000344046398605,
- "strengthDown": 0.022000344046398605,
+ "strengthUp": 0.025963265889003457,
+ "strengthDown": 0.025963265889003457,
"direction": "none",
"widthRatio": 0.22726004705445743,
"touchesUpper": 3,
"touchesLower": 3,
"volumeConfirmed": "",
- "boundaryUtilization": 0.03170337474083884,
+ "activityScore": 0.03170337474083884,
+ "tiltScore": 0.5,
"date": 20260120,
"hasTriangle": true,
- "strength": 0.022000344046398605,
+ "strength": 0.025963265889003457,
"chartPath": "charts/20260120_SH688472_阿特斯.png",
"chartPathDetail": "charts/20260120_SH688472_阿特斯_detail.png"
},
@@ -3261,8 +3319,12 @@
${volumeText}
- 边界利用率
- ${(stock.boundaryUtilization || 0).toFixed(2)}
+ 价格活跃度
+ ${(stock.activityScore || 0).toFixed(2)}
+
+
+ 倾斜度
+ ${(stock.tiltScore || 0).toFixed(2)}
diff --git a/scripts/README_performance_tests.md b/scripts/README_performance_tests.md
index 90ea114..80bbdad 100644
--- a/scripts/README_performance_tests.md
+++ b/scripts/README_performance_tests.md
@@ -47,8 +47,8 @@ python scripts/test_optimization_comparison.py
1. `pivots_fractal` - 枢轴点检测
2. `pivots_fractal_hybrid` - 混合枢轴点检测
3. `fit_boundary_anchor` - 锚点拟合
-4. `calc_fitting_adherence` - 拟合贴合度
-5. `calc_boundary_utilization` - 边界利用率
+4. `calc_geometry_score` - 形态规则度
+5. `calc_activity_score` - 价格活跃度
6. `calc_breakout_strength` - 突破强度
**预期结果**:
diff --git a/scripts/generate_stock_viewer.py b/scripts/generate_stock_viewer.py
index 2537163..8a7477a 100644
--- a/scripts/generate_stock_viewer.py
+++ b/scripts/generate_stock_viewer.py
@@ -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')),
'touchesLower': int(row.get('touches_lower', '0')),
'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,
'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):
${volumeText}
- 边界利用率
- ${(stock.boundaryUtilization || 0).toFixed(2)}
+ 价格活跃度
+ ${(stock.activityScore || 0).toFixed(2)}
+
+
+ 倾斜度
+ ${(stock.tiltScore || 0).toFixed(2)}
diff --git a/scripts/plot_converging_triangles.py b/scripts/plot_converging_triangles.py
index 23d22e8..9ae135d 100644
--- a/scripts/plot_converging_triangles.py
+++ b/scripts/plot_converging_triangles.py
@@ -36,7 +36,7 @@ sys.path.append(os.path.join(os.path.dirname(__file__), "..", "src"))
from converging_triangle import (
ConvergingTriangleParams,
- calc_fitting_adherence,
+ calc_geometry_score,
detect_converging_triangle,
fit_pivot_line_dispatch,
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）
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
- adherence_upper_close = calc_fitting_adherence(
+ adherence_upper_close = calc_geometry_score(
pivot_indices=selected_ph_pos.astype(float),
pivot_values=close_win[selected_ph_pos],
slope=a_u,
intercept=b_u,
)
- adherence_lower_close = calc_fitting_adherence(
+ adherence_lower_close = calc_geometry_score(
pivot_indices=selected_pl_pos.astype(float),
pivot_values=close_win[selected_pl_pos],
slope=a_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:
# 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:
# 无三角形时，贴合度为0
- fitting_adherence_plot = 0.0
+ geometry_score_plot = 0.0
# 创建图表
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)
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）
- boundary_util = getattr(result, 'boundary_utilization', 0.0)
- utilization_floor = 0.20
- if utilization_floor > 0:
- utilization_penalty = min(1.0, boundary_util / utilization_floor)
+ # 获取价格活跃度与惩罚系数（兼容旧数据）
+ activity_score = getattr(result, 'activity_score', 0.0)
+ tilt_score = getattr(result, 'tilt_score', 0.0) # 新增：获取倾斜度分
+ activity_floor = 0.20
+ if activity_floor > 0:
+ activity_penalty = min(1.0, activity_score / activity_floor)
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
if plot_boundary_source == "close" and has_triangle and has_enough_data:
- display_fitting_score = fitting_adherence_plot
- fitting_note = f"拟合贴合度(收盘价): {display_fitting_score:.3f}"
+ display_geometry = geometry_score_plot
+ geometry_note = f"形态规则度(收盘价): {display_geometry:.3f}"
else:
- display_fitting_score = result.fitting_score
- fitting_note = f"拟合贴合度: {display_fitting_score:.3f}"
+ display_geometry = result.geometry_score
+ geometry_note = f"形态规则度: {display_geometry:.3f}"
ax1.set_title(
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"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"(价格: {price_score:.3f}×50% + 收敛: {result.convergence_score:.3f}×15% + "
- f"成交量: {result.volume_score:.3f}×10% + {fitting_note}×10% + "
- f"边界利用率: {boundary_util:.3f}×15%) × 利用率惩罚: {utilization_penalty:.2f}",
+ 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% + {geometry_note}×10% + "
+ 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
)
else:
diff --git a/scripts/test_full_pipeline.py b/scripts/test_full_pipeline.py
index 27b9f0e..9eb422c 100644
--- a/scripts/test_full_pipeline.py
+++ b/scripts/test_full_pipeline.py
@@ -120,8 +120,8 @@ def test_pipeline(
pivots_fractal_optimized,
pivots_fractal_hybrid_optimized,
fit_boundary_anchor_optimized,
- calc_fitting_adherence_optimized,
- calc_boundary_utilization_optimized,
+ calc_geometry_score_optimized,
+ calc_activity_score_optimized,
calc_breakout_strength_optimized,
)
@@ -129,8 +129,8 @@ def test_pipeline(
converging_triangle.pivots_fractal = pivots_fractal_optimized
converging_triangle.pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
converging_triangle.fit_boundary_anchor = fit_boundary_anchor_optimized
- converging_triangle.calc_fitting_adherence = calc_fitting_adherence_optimized
- converging_triangle.calc_boundary_utilization = calc_boundary_utilization_optimized
+ converging_triangle.calc_geometry_score = calc_geometry_score_optimized
+ converging_triangle.calc_activity_score = calc_activity_score_optimized
converging_triangle.calc_breakout_strength = calc_breakout_strength_optimized
print(" [OK] Numba优化已启用")
@@ -233,7 +233,7 @@ def compare_results(df_original, df_optimized):
numeric_cols = [
'breakout_strength_up', 'breakout_strength_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',
'touches_upper', 'touches_lower', 'apex_x'
]
diff --git a/scripts/test_optimization_comparison.py b/scripts/test_optimization_comparison.py
index ea1a875..229aa11 100644
--- a/scripts/test_optimization_comparison.py
+++ b/scripts/test_optimization_comparison.py
@@ -18,8 +18,8 @@ from converging_triangle import (
pivots_fractal,
pivots_fractal_hybrid,
fit_boundary_anchor,
- calc_fitting_adherence,
- calc_boundary_utilization,
+ calc_geometry_score,
+ calc_activity_score,
calc_breakout_strength,
)
@@ -28,8 +28,8 @@ from converging_triangle_optimized import (
pivots_fractal_optimized,
pivots_fractal_hybrid_optimized,
fit_boundary_anchor_optimized,
- calc_fitting_adherence_optimized,
- calc_boundary_utilization_optimized,
+ calc_geometry_score_optimized,
+ calc_activity_score_optimized,
calc_breakout_strength_optimized,
)
@@ -250,7 +250,7 @@ def main():
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:
pivot_indices = ph[:10]
@@ -258,25 +258,25 @@ def main():
slope, intercept = 0.01, 100.0
result = compare_functions(
- calc_fitting_adherence,
- calc_fitting_adherence_optimized,
- "calc_fitting_adherence",
+ calc_geometry_score,
+ calc_geometry_score_optimized,
+ "calc_geometry_score",
pivot_indices, pivot_values, slope, intercept,
n_iterations=n_iterations
)
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
lower_slope, lower_intercept = 0.02, 80.0
start, end = 0, len(high) - 1
result = compare_functions(
- calc_boundary_utilization,
- calc_boundary_utilization_optimized,
- "calc_boundary_utilization",
+ calc_activity_score,
+ calc_activity_score_optimized,
+ "calc_activity_score",
high, low, upper_slope, upper_intercept, lower_slope, lower_intercept, start, end,
n_iterations=n_iterations
)
diff --git a/src/converging_triangle.py b/src/converging_triangle.py
index ccfcaac..17b22fb 100644
--- a/src/converging_triangle.py
+++ b/src/converging_triangle.py
@@ -79,8 +79,10 @@ class ConvergingTriangleResult:
price_score_down: float = 0.0 # 价格突破分数(向下)
convergence_score: float = 0.0 # 收敛分数
volume_score: float = 0.0 # 成交量分数
- fitting_score: float = 0.0 # 拟合贴合度分数
- boundary_utilization: float = 0.0 # 边界利用率分数
+ geometry_score: float = 0.0 # 形态规则度分数
+ 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
upper_slope: float = 0.0
@@ -758,7 +760,7 @@ def fit_pivot_line_dispatch(
# 突破强度计算
# ============================================================================
-def calc_fitting_adherence(
+def calc_geometry_score(
pivot_indices: np.ndarray,
pivot_values: np.ndarray,
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)
归一化映射（scale_factor = 20）：
- - 误差 0% → 分数 1.00 (完美拟合)
- - 误差 2% → 分数 0.67 (良好拟合)
- - 误差 5% → 分数 0.37 (一般拟合)
- - 误差 10% → 分数 0.14 (较差拟合)
+ - 误差 0% → 分数 1.00 (完美规则)
+ - 误差 2% → 分数 0.67 (良好规则)
+ - 误差 5% → 分数 0.37 (一般规则)
+ - 误差 10% → 分数 0.14 (较差规则)
Args:
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
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
@@ -807,12 +809,12 @@ def calc_fitting_adherence(
# 指数衰减归一化到 0~1
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,
low: np.ndarray,
upper_slope: float,
@@ -823,21 +825,21 @@ def calc_boundary_utilization(
end: int,
) -> 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。
- 如果价格总是远离边界线（大量空白），利用率低。
- 如果价格频繁接近或触碰边界线，利用率高。
+ 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），活跃度低。
+ 如果价格频繁接近或触碰边界线，活跃度高。
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
- 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：
- - 空白 0% → 利用率 1.00 (价格完全贴合边界)
- - 空白 25% → 利用率 0.75 (良好)
- - 空白 50% → 利用率 0.50 (一般)
- - 空白 75% → 利用率 0.25 (较差，大量空白)
+ - 空白 0% → 活跃度 1.00 (价格完全贴合边界)
+ - 空白 25% → 活跃度 0.75 (良好)
+ - 空白 50% → 活跃度 0.50 (一般)
+ - 空白 75% → 活跃度 0.25 (较差，大量空白)
Args:
high, low: 价格数据
@@ -846,9 +848,9 @@ def calc_boundary_utilization(
start, end: 窗口范围
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
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
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]
- day_utilization = max(0.0, min(1.0, 1.0 - blank_ratio))
+ # 当日活跃度 = 1 - 空白比例，限制在 [0, 1]
+ day_activity = max(0.0, min(1.0, 1.0 - blank_ratio))
- total_utilization += day_utilization
+ total_activity += day_activity
valid_days += 1
if valid_days == 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(
@@ -884,9 +945,12 @@ def calc_breakout_strength(
lower_line: float,
volume_ratio: float,
width_ratio: float,
- fitting_adherence: float,
- boundary_utilization: float,
-) -> Tuple[float, float, float, float, float, float, float, float]:
+ geometry_score: float,
+ activity_score: 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)
@@ -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。
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
- 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
- - 边界利用率 (15%): 价格走势对通道空间的利用程度
+ - 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
+ - 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：
- 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
volume_ratio: 成交量相对均值的倍数
width_ratio: 末端宽度/起始宽度
- fitting_adherence: 拟合贴合度分数 (0~1)
- boundary_utilization: 边界利用率分数 (0~1)
+ geometry_score: 形态规则度分数 (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:
(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
"""
import math
# 权重配置（调整后，总和 = 100%）
- W_PRICE = 0.50 # 突破幅度权重
- W_CONVERGENCE = 0.15 # 收敛度权重（原 20%，降低）
- W_VOLUME = 0.10 # 成交量权重（原 15%，降低）
- W_FITTING = 0.10 # 拟合贴合度权重（原 15%，降低）
- W_UTILIZATION = 0.15 # 边界利用率权重（新增）
+ 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）
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）
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）
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）
- fitting_score = max(0.0, min(1.0, fitting_adherence))
+ # 4. 形态规则度分数（直接使用传入的分数）
+ 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）
- boundary_util_score = max(0.0, min(1.0, boundary_utilization))
+ # 5. 价格活跃度分数（直接使用传入的分数）
+ 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
strength_up = (
W_PRICE * price_score_up +
W_CONVERGENCE * convergence_score +
W_VOLUME * vol_score +
- W_FITTING * fitting_score +
- W_UTILIZATION * boundary_util_score
+ W_GEOMETRY * geometry_score_out +
+ W_ACTIVITY * activity_score_out +
+ W_TILT * tilt_score_out
)
strength_down = (
W_PRICE * price_score_down +
W_CONVERGENCE * convergence_score +
W_VOLUME * vol_score +
- W_FITTING * fitting_score +
- W_UTILIZATION * boundary_util_score
+ W_GEOMETRY * geometry_score_out +
+ 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）
- if UTILIZATION_FLOOR > 0:
- utilization_penalty = min(1.0, boundary_util_score / UTILIZATION_FLOOR)
+ # 8. 空白惩罚（价格活跃度过低时降低总分）
+ if ACTIVITY_FLOOR > 0:
+ utilization_penalty = min(1.0, activity_score_out / ACTIVITY_FLOOR)
else:
utilization_penalty = 1.0
@@ -995,8 +1070,9 @@ def calc_breakout_strength(
price_score_down,
convergence_score,
vol_score,
- fitting_score,
- boundary_util_score
+ geometry_score_out,
+ 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
# 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）
- adherence_upper = calc_fitting_adherence(
+ # 计算形态规则度（上下沿综合）
+ geometry_upper = calc_geometry_score(
pivot_indices=selected_ph,
pivot_values=high[selected_ph],
slope=a_u,
intercept=b_u,
)
- adherence_lower = calc_fitting_adherence(
+ geometry_lower = calc_geometry_score(
pivot_indices=selected_pl,
pivot_values=low[selected_pl],
slope=a_l,
intercept=b_l,
)
- # 综合上下沿贴合度（取平均）
- fitting_adherence = (adherence_upper + adherence_lower) / 2.0
+ # 综合上下沿规则度（取平均）
+ 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）
- boundary_utilization = calc_boundary_utilization(
+ # 计算价格活跃度（价格走势对三角形通道的利用程度）
+ activity_score = calc_activity_score(
high=high,
low=low,
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）
(strength_up, strength_down,
price_score_up, price_score_down,
- convergence_score, vol_score, fitting_score,
- boundary_util_score) = calc_breakout_strength(
+ convergence_score, vol_score, geometry_score_out,
+ activity_score_out, tilt_score_out) = calc_breakout_strength(
close=close[end],
upper_line=upper_end,
lower_line=lower_end,
volume_ratio=volume_ratio,
width_ratio=width_ratio,
- fitting_adherence=fitting_adherence,
- boundary_utilization=boundary_utilization,
+ geometry_score=geometry_score,
+ activity_score=activity_score,
+ upper_slope=a_u,
+ lower_slope=a_l,
+ breakout_dir=breakout_dir,
)
return ConvergingTriangleResult(
@@ -1250,8 +1329,9 @@ def detect_converging_triangle(
price_score_down=price_score_down,
convergence_score=convergence_score,
volume_score=vol_score,
- fitting_score=fitting_score,
- boundary_utilization=boundary_util_score,
+ geometry_score=geometry_score_out,
+ activity_score=activity_score_out,
+ tilt_score=tilt_score_out,
upper_slope=a_u,
lower_slope=a_l,
width_ratio=width_ratio,
@@ -1413,8 +1493,8 @@ try:
pivots_fractal_optimized,
pivots_fractal_hybrid_optimized,
fit_boundary_anchor_optimized,
- calc_fitting_adherence_optimized,
- calc_boundary_utilization_optimized,
+ calc_geometry_score_optimized,
+ calc_activity_score_optimized,
calc_breakout_strength_optimized,
# v2优化：预计算枢轴点
precompute_pivots_numba,
@@ -1424,8 +1504,8 @@ try:
pivots_fractal = pivots_fractal_optimized
pivots_fractal_hybrid = pivots_fractal_hybrid_optimized
fit_boundary_anchor = fit_boundary_anchor_optimized
- calc_fitting_adherence = calc_fitting_adherence_optimized
- calc_boundary_utilization = calc_boundary_utilization_optimized
+ calc_geometry_score = calc_geometry_score_optimized
+ calc_activity_score = calc_activity_score_optimized
calc_breakout_strength = calc_breakout_strength_optimized
_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）
(date_indices, is_valid_arr, strength_up_arr, strength_down_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,
touches_upper_arr, touches_lower_arr, apex_x_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]),
'convergence_score': float(convergence_score_arr[i]),
'volume_score': float(vol_score_arr[i]),
- 'fitting_score': float(fitting_score_arr[i]),
- 'boundary_utilization': float(boundary_util_score_arr[i]),
+ 'geometry_score': float(geometry_score_arr[i]),
+ 'activity_score': float(activity_score_arr[i]),
+ 'tilt_score': float(tilt_score_arr[i]),
'upper_slope': float(upper_slope_arr[i]),
'lower_slope': float(lower_slope_arr[i]),
'width_ratio': float(width_ratio_arr[i]),
diff --git a/src/converging_triangle_optimized.py b/src/converging_triangle_optimized.py
index 0f01d6f..7c7a00d 100644
--- a/src/converging_triangle_optimized.py
+++ b/src/converging_triangle_optimized.py
@@ -347,13 +347,13 @@ def fit_boundary_anchor_numba(
@numba.jit(nopython=True, cache=True)
-def calc_fitting_adherence_numba(
+def calc_geometry_score_numba(
pivot_indices: np.ndarray,
pivot_values: np.ndarray,
slope: float,
intercept: 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:
return 0.0
@@ -369,13 +369,13 @@ def calc_fitting_adherence_numba(
# 指数衰减归一化
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)
-def calc_boundary_utilization_numba(
+def calc_activity_score_numba(
high: np.ndarray,
low: np.ndarray,
upper_slope: float,
@@ -385,8 +385,8 @@ def calc_boundary_utilization_numba(
start: int,
end: int,
) -> float:
- """Numba优化的边界利用率计算"""
- total_utilization = 0.0
+ """Numba优化的价格活跃度计算"""
+ total_activity = 0.0
valid_days = 0
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)
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
if valid_days == 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)
@@ -419,24 +457,29 @@ def calc_breakout_strength_numba(
lower_line: float,
volume_ratio: float,
width_ratio: float,
- fitting_adherence: float,
- boundary_utilization: float,
-) -> Tuple[float, float, float, float, float, float, float, float]:
+ geometry_score: float,
+ activity_score: 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
Returns:
(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
- W_PRICE = 0.50
+ W_PRICE = 0.45 # 从50%降至45%
W_CONVERGENCE = 0.15
W_VOLUME = 0.10
- W_FITTING = 0.10
- W_UTILIZATION = 0.15
+ W_GEOMETRY = 0.10
+ W_ACTIVITY = 0.15
+ W_TILT = 0.05 # 新增
TANH_SCALE = 15.0
- UTILIZATION_FLOOR = 0.20
+ ACTIVITY_FLOOR = 0.20
# 1. 价格突破分数
if upper_line > 0:
@@ -457,37 +500,42 @@ def calc_breakout_strength_numba(
# 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
- # 4. 拟合贴合度分数
- fitting_score = max(0.0, min(1.0, fitting_adherence))
+ # 4. 形态规则度分数
+ geometry_score_out = max(0.0, min(1.0, geometry_score))
- # 5. 边界利用率分数
- boundary_util_score = max(0.0, min(1.0, boundary_utilization))
+ # 5. 价格活跃度分数
+ 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 = (
W_PRICE * price_score_up +
W_CONVERGENCE * convergence_score +
W_VOLUME * vol_score +
- W_FITTING * fitting_score +
- W_UTILIZATION * boundary_util_score
+ W_GEOMETRY * geometry_score_out +
+ W_ACTIVITY * activity_score_out +
+ W_TILT * tilt_score_out
)
strength_down = (
W_PRICE * price_score_down +
W_CONVERGENCE * convergence_score +
W_VOLUME * vol_score +
- W_FITTING * fitting_score +
- W_UTILIZATION * boundary_util_score
+ W_GEOMETRY * geometry_score_out +
+ W_ACTIVITY * activity_score_out +
+ W_TILT * tilt_score_out
)
- # 7. 空白惩罚
- if UTILIZATION_FLOOR > 0:
- utilization_penalty = min(1.0, boundary_util_score / UTILIZATION_FLOOR)
+ # 8. 空白惩罚
+ if ACTIVITY_FLOOR > 0:
+ activity_penalty = min(1.0, activity_score_out / ACTIVITY_FLOOR)
else:
- utilization_penalty = 1.0
+ activity_penalty = 1.0
- strength_up *= utilization_penalty
- strength_down *= utilization_penalty
+ strength_up *= activity_penalty
+ strength_down *= activity_penalty
return (
min(1.0, strength_up),
@@ -496,8 +544,9 @@ def calc_breakout_strength_numba(
price_score_down,
convergence_score,
vol_score,
- fitting_score,
- boundary_util_score
+ geometry_score_out,
+ activity_score_out,
+ tilt_score_out
)
@@ -548,21 +597,21 @@ def fit_boundary_anchor_optimized(
return slope, intercept, np.arange(len(pivot_indices))
-def calc_fitting_adherence_optimized(
+def calc_geometry_score_optimized(
pivot_indices: np.ndarray,
pivot_values: np.ndarray,
slope: float,
intercept: float,
) -> float:
- """优化版拟合贴合度计算（兼容原API）"""
- return calc_fitting_adherence_numba(
+ """优化版形态规则度计算（兼容原API）"""
+ return calc_geometry_score_numba(
pivot_indices.astype(np.float64),
pivot_values.astype(np.float64),
slope, intercept
)
-def calc_boundary_utilization_optimized(
+def calc_activity_score_optimized(
high: np.ndarray,
low: np.ndarray,
upper_slope: float,
@@ -572,8 +621,8 @@ def calc_boundary_utilization_optimized(
start: int,
end: int,
) -> float:
- """优化版边界利用率计算（兼容原API）"""
- return calc_boundary_utilization_numba(
+ """优化版价格活跃度计算（兼容原API）"""
+ return calc_activity_score_numba(
high, low, upper_slope, upper_intercept,
lower_slope, lower_intercept, start, end
)
@@ -585,13 +634,31 @@ def calc_breakout_strength_optimized(
lower_line: float,
volume_ratio: float,
width_ratio: float,
- fitting_adherence: float,
- boundary_utilization: float,
-) -> Tuple[float, float, float, float, float, float, float, float]:
- """优化版突破强度计算（兼容原API）"""
+ geometry_score: float,
+ activity_score: float,
+ upper_slope: float, # 新增
+ 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(
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,
vol_window: int,
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]:
"""
使用预计算枢轴点的单点检测（纯Numba实现）
Returns:
(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,
apex_x, breakout_dir, volume_confirmed)
@@ -792,8 +859,8 @@ def detect_single_with_precomputed_pivots(
"""
n = window_end - window_start + 1
- # 默认无效结果
- invalid_result = (False, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
+ # 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, 0.0,
0.0, 0.0, 0.0, 0, 0, 0.0, 0, -1)
if n < 50: # 最小窗口检查
@@ -922,31 +989,33 @@ def detect_single_with_precomputed_pivots(
if breakout_dir != 0:
volume_confirmed = 1 if volume[window_end] > vol_ma * vol_k else 0
- # 计算拟合贴合度
- adherence_upper = calc_fitting_adherence_numba(
+ # 计算形态规则度
+ geometry_upper = calc_geometry_score_numba(
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
)
- fitting_adherence = (adherence_upper + adherence_lower) / 2.0
+ geometry_score = (geometry_upper + geometry_lower) / 2.0
- # 计算边界利用率
- boundary_util = calc_boundary_utilization_numba(
+ # 计算价格活跃度
+ activity_score = calc_activity_score_numba(
high_win, low_win, a_u, b_u, a_l, b_l, 0, n - 1
)
# 计算突破强度
(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(
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,
- convergence_score, vol_score, fitting_score, boundary_util_score,
- a_u, a_l, width_ratio, touches_upper, touches_lower,
+ convergence_score, vol_score, geometry_score_out, activity_score_out,
+ tilt_score_out, a_u, a_l, width_ratio, touches_upper, touches_lower,
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,
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）
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_dates = 0
@@ -998,8 +1067,9 @@ def detect_batch_with_precomputed_pivots_numba(
price_score_down = 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)
- fitting_score = np.zeros(n_dates, dtype=np.float64)
- boundary_util_score = np.zeros(n_dates, dtype=np.float64)
+ geometry_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)
lower_slope = 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]
convergence_score[result_idx] = result[5]
vol_score[result_idx] = result[6]
- fitting_score[result_idx] = result[7]
- boundary_util_score[result_idx] = result[8]
- upper_slope[result_idx] = result[9]
- lower_slope[result_idx] = result[10]
- width_ratio[result_idx] = result[11]
- touches_upper[result_idx] = result[12]
- touches_lower[result_idx] = result[13]
- apex_x[result_idx] = result[14]
- breakout_dir[result_idx] = result[15]
- volume_confirmed[result_idx] = result[16]
+ geometry_score[result_idx] = result[7]
+ activity_score[result_idx] = result[8]
+ tilt_score[result_idx] = result[9] # 新增
+ upper_slope[result_idx] = result[10]
+ lower_slope[result_idx] = result[11]
+ width_ratio[result_idx] = result[12]
+ touches_upper[result_idx] = result[13]
+ touches_lower[result_idx] = result[14]
+ apex_x[result_idx] = result[15]
+ breakout_dir[result_idx] = result[16]
+ volume_confirmed[result_idx] = result[17]
result_idx += 1
return (date_indices, is_valid, strength_up, strength_down,
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,
touches_upper, touches_lower, apex_x,
breakout_dir, volume_confirmed)
diff --git a/src/triangle_detector_api.py b/src/triangle_detector_api.py
index edd3dc0..a068d8a 100644
--- a/src/triangle_detector_api.py
+++ b/src/triangle_detector_api.py
@@ -289,13 +289,14 @@ class StrengthComponents:
"""
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)
convergence_score: float # 收敛分数 (0~1)
volume_score: float # 成交量分数 (0~1)
- fitting_score: float # 拟合贴合度分数 (0~1)
- utilization_score: float # 边界利用率分数 (0~1)
+ geometry_score: float # 形态规则度分数 (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]:
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,
convergence_score=result.convergence_score,
volume_score=result.volume_score,
- fitting_score=result.fitting_score,
- utilization_score=result.boundary_utilization,
+ geometry_score=result.geometry_score,
+ activity_score=result.activity_score,
+ tilt_score=result.tilt_score,
)
# 构建前端图表数据
diff --git a/tests/test_renaming.py b/tests/test_renaming.py
new file mode 100644
index 0000000..6a85821
--- /dev/null
+++ b/tests/test_renaming.py
@@ -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()
diff --git a/tests/test_tilt_score.py b/tests/test_tilt_score.py
new file mode 100644
index 0000000..616e4f7
--- /dev/null
+++ b/tests/test_tilt_score.py
@@ -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)