From bf6baa5483c9c7a0facc0f60c6b91808a6c692fa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E8=A4=9A=E5=AE=8F=E5=85=89?= <542672041@qq.com>
Date: Fri, 30 Jan 2026 18:43:37 +0800
Subject: [PATCH] Add scoring module and enhance HTML viewer with
 standardization

- Add scripts/scoring/ module with normalizer, sensitivity analysis, and config
- Enhance stock_viewer.html with standardized scoring display
- Add integration tests and normalization verification scripts
- Add documentation for standardization implementation and usage guides
- Add data distribution analysis reports for strength scoring dimensions
- Update discussion documents with algorithm optimization plans
---
 discuss/20260129-讨论.md                    |   70 +-
 ... ‡å‡†åŒ–性能优化_线性映射方案.md |  398 ++
 discuss/20260130-检测算法优化方案.md  |  837 ++++
 ...-生产环境性能评估_�日5000股.md |  315 ++
 ...0130-讨论-收敛三角形函数设计.md |  637 +++
 discuss/20260130-讨论.md                    |    2 +
 discuss/images/2026-01-29-16-10-44.png        |  Bin 0 -> 282018 bytes
 ...ŽHTML集æˆ�标准化_实施完æˆ�报告.md |  223 ++
 ...º¦åˆ†æ ‡å‡†åŒ–优化_实施完æˆ�报告.md |  259 ++
 .../INDEX.md                                  |  160 +
 ...analyze_distribution_强度分六维度.py |  302 ++
 .../boxplots_强度分六维度.png           |  Bin 0 -> 94013 bytes
 ...stribution_analysis_强度分六维度.csv |    8 +
 .../distribution_plots_强度分六维度.png |  Bin 0 -> 316701 bytes
 .../qq_plots_强度分六维度.png           |  Bin 0 -> 235607 bytes
 .../强度分优化方案_深度分�.md     |  860 ++++
 .../强度分六维度_分�报告.md        |  332 ++
 docs/标准化HTML查看器_使用指�.md   |  175 +
 docs/预设模�对比.md                    |  109 +
 .../converging_triangles/stock_viewer.html    | 3447 ++++++++++++-----
 scripts/example_scoring_usage.py              |  204 +
 scripts/generate_stock_viewer.py              |  688 +++-
 scripts/scoring/README.md                     |  352 ++
 scripts/scoring/__init__.py                   |   68 +
 scripts/scoring/config.py                     |  470 +++
 .../scoring/generate_sensitivity_report.py    |   41 +
 scripts/scoring/normalizer.py                 |  285 ++
 scripts/scoring/sensitivity.py                |  385 ++
 scripts/verify_normalization.py               |  233 ++
 test_integration.py                           |   65 +
 30 files changed, 9964 insertions(+), 961 deletions(-)
 create mode 100644 discuss/20260130-标准化性能优化_线性映射方案.md
 create mode 100644 discuss/20260130-检测算法优化方案.md
 create mode 100644 discuss/20260130-生产环境性能评估_�日5000股.md
 create mode 100644 discuss/20260130-讨论-收敛三角形函数设计.md
 create mode 100644 discuss/20260130-讨论.md
 create mode 100644 discuss/images/2026-01-29-16-10-44.png
 create mode 100644 docs/Pipeline与HTML集�标准化_实施完�报告.md
 create mode 100644 docs/强度分标准化优化_实施完�报告.md
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/INDEX.md
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/analyze_distribution_强度分六维度.py
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/boxplots_强度分六维度.png
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/distribution_analysis_强度分六维度.csv
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/distribution_plots_强度分六维度.png
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/qq_plots_强度分六维度.png
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/强度分优化方案_深度分�.md
 create mode 100644 docs/收敛三角形_数�分布分�_20260129/强度分六维度_分�报告.md
 create mode 100644 docs/标准化HTML查看器_使用指�.md
 create mode 100644 docs/预设模�对比.md
 create mode 100644 scripts/example_scoring_usage.py
 create mode 100644 scripts/scoring/README.md
 create mode 100644 scripts/scoring/__init__.py
 create mode 100644 scripts/scoring/config.py
 create mode 100644 scripts/scoring/generate_sensitivity_report.py
 create mode 100644 scripts/scoring/normalizer.py
 create mode 100644 scripts/scoring/sensitivity.py
 create mode 100644 scripts/verify_normalization.py
 create mode 100644 test_integration.py

diff --git a/discuss/20260129-讨论.md b/discuss/20260129-讨论.md
index 7b4e6dd..f8810d2 100644
--- a/discuss/20260129-讨论.md
+++ b/discuss/20260129-讨论.md
@@ -11,7 +11,7 @@
    - **实现方�**：新增"倾斜度分"作为第6个维度
    - **��分�**：从�破幅度分中分�5%（50% → 45%）
    - **详情**：� `docs/强度分_增加角度�数_深度分�.md`
-3. **"强度分"内所有�数需��在 0-1 区间**，便于 LLM 调�；�求�匀/正�分布，默认值为 0.5。
+3. **"强度分"内所有�数需��在 0-1 区间**
 **目�所有 6 个强度分�数都已�在 0-1 区间内**。以下是�分�的归一化方�总结：
 
 | 分� | 归一化方� | 范围�� |
@@ -36,3 +36,71 @@
 两者都严格��输出在 [0, 1] 区间，满足强度分系统的设计�求。
 
 ---
+![](images/2026-01-29-16-10-44.png)
+
+4. **分�日志,评估�个维度的分布:�值�正�性�厚尾** ✅ 已完�
+   - **分�时间**: 2026-01-29 16:28
+   - **样本�**: 18,004个有效三角形 (108�股票 × 500天检测窗�)
+   - **分�对象**: **强度分系统的6个核心维度** (7个字段: price_score_up/down分开统计)
+   - **核心�现**:
+     - � **所有7个字段�呈现�正�分布** (p≈0)
+     - ⚠� **5/7字段显著厚尾** (超�峰度>0, �端事件是正�分布的8-19�)
+     - 📊 **4/7字段��** (多数普通值+少数�端大值的长尾结构)
+   - **关键�现**:
+     1. **�破幅度分(�上)**: 中�数=0, 超�峰度13.38, 尾部15.7� → 大多数未�破,强�破稀缺但频�
+     2. **�破幅度分(�下)**: 中�数=0, 超�峰度45.72 → �下�破最��预测
+     3. **�交�分**: 中�数=0, 尾部19.1� → 放��破�其稀缺(所有维度中尾部�数最高)
+     4. **倾斜度分**: 超�峰度46.33, Q25=Q75=0.5 → 算法强烈�好对称三角形(75%�好=0.5)
+     5. **收敛度分**: 超�峰度-1.05 → 唯一薄尾维度,分布稳定��
+   - **实战建议**:
+     - �止使用�值±3σ�t检验等基于正�的方法
+     - 推�使用百分�数(P75/P90)���数检验�Bootstrap
+     - �破幅度分阈值: P85-P90≈0.15 (而��值0.056)
+     - �交�分�作必��件(中�数=0),仅作加分项
+   - **��优化建议**:
+     - 收敛度分: 20%→25% (最稳定��)
+     - �交�分: 15%→10% (中�数=0导致区分度低)
+     - 形�规则度: 10%→5% (数值普�过低)
+     - 价格活跃度: 5%→10% (近正�且稳定)
+   - **详细报告**: 
+     - 📄 主报告: `docs/收敛三角形_数�分布分�_20260129/强度分六维度_分�报告.md` �
+     - 📑 索引: `docs/收敛三角形_数�分布分�_20260129/INDEX.md`
+     - 🖼� �视化: `distribution_plots_强度分六维度.png`, `qq_plots_强度分六维度.png`, `boxplots_强度分六维度.png`
+     - 📊 数�表: `distribution_analysis_强度分六维度.csv` (7字段统计)
+     - 💻 脚本: `analyze_distribution_强度分六维度.py` (��现)
+
+5. **强度分系统优化：等�设计+�数�调性+平滑性优化** ✅ 深度分�完�
+   - **分�时间**: 2026-01-29 17:00
+   - **核心问题**:
+     - 维度间��比性：中�数差异巨大 (0 vs 0.8)
+     - 零膨胀分布：�破幅度分��交�分50%为0
+     - 点质�分布：倾斜度分75%=0.5
+     - 等�相加会导致收敛度分和倾斜度分"�掉"大部分强度分
+   - **SOTA方法调研**:
+     1. **截�分�数标准化**: 消除尺度差异，但对零膨胀效果差
+     2. **Power Sorting (2023)**: 专门处�厚尾，�留�端值信�
+     3. **自适应分组标准化**: 根�分布类型选择��策略
+     4. **PID控制论**: 自适应��调整，基于实际�馈优化
+     5. **端到端ML优化**: 自动学习最优��和��
+   - **推�方案：分层标准化**:
+     - 零膨胀分布(�破幅度分/�交�分): �零部分排�，零值设为0.5(中性)
+     - 点质�分布(倾斜度分): �离中心的值进行拉伸
+     - 正常分布(收敛度分/价格活跃度): 标准分�数标准化
+     - 低区分度(形�规则度): 对数��扩大区分度
+   - **基金��视角评估**:
+     | 维度 | �化�义 | 当�问题 | 建议 |
+     |-----|---------|---------|------|
+     | �破幅度分 | 信�强度/价格动能 | 中�数=0 | 标准化�使用P75+ |
+     | 收敛度分 | 蓄势程度/多空�弈 | ✅稳定 | ��高�� |
+     | �交�分 | 资金确认 | 中�数=0 | �作必��件，作加分项 |
+     | 形�规则度 | 形�质� | 普��低 | 对数��扩大区分度 |
+     | 价格活跃度 | 真实�弈 | ✅近正� | ��高�� |
+     | 倾斜度分 | 趋势一致性 | 75%=0.5 | �新标准化 |
+   - **系统评分**:
+     - 当�综�评分: 2.8/5
+     - 优化�预期: 4.3/5
+   - **应用层设计**:
+     - 等�基础 + 预设�置(激进/�守/放�模�)
+     - 多维度阈值筛选器
+     - �感性分�工具
+   - **详细报告**: `docs/收敛三角形_数�分布分�_20260129/强度分优化方案_深度分�.md` �
\ No newline at end of file
diff --git a/discuss/20260130-标准化性能优化_线性映射方案.md b/discuss/20260130-标准化性能优化_线性映射方案.md
new file mode 100644
index 0000000..c6012a8
--- /dev/null
+++ b/discuss/20260130-标准化性能优化_线性映射方案.md
@@ -0,0 +1,398 @@
+# 标准化性能优化：线性映射 vs 百分�排�
+
+## 问题背景
+
+当�标准化系统使用 `series.rank(pct=True)` 方法，需�排��作，时间��度 O(n log n)。
+
+对于大规模数�（18,004个样本，未��能更多），考虑使用**线性映射**替代方案，时间��度 O(n)。
+
+---
+
+## 方案对比
+
+### **方案1: 当�百分�排�法 (Rank-based)**
+
+```python
+def normalize_standard(series: pd.Series) -> pd.Series:
+    """当�实现: 百分�排�"""
+    return series.rank(pct=True)  # O(n log n)
+```
+
+**特点：**
+- ✅ 输出分布�匀，严格�调
+- ✅ 对�端值�棒，异常值�影�整体分布
+- ✅ ��中�数精确=0.5
+- � 时间��度 O(n log n)（排�）
+- � 大数�集性能瓶颈
+
+---
+
+### **方案2: 线性映射法 (Linear Scaling)**
+
+#### **2.1 基础版：P5-P95映射**
+
+```python
+def normalize_linear_basic(series: pd.Series) -> pd.Series:
+    """
+    线性映射标准化：将P5-P95区间映射到[0, 1]
+    
+    策略：
+    1. 计算P5和P95分�数
+    2. 线性缩放：y = (x - P5) / (P95 - P5)
+    3. Clip到[0, 1]区间
+    
+    时间��度：O(n)
+    """
+    p5 = series.quantile(0.05)   # O(n) - numpy.percentile使用快速选择
+    p95 = series.quantile(0.95)  # O(n)
+    
+    if p95 - p5 < 1e-10:
+        # ��除零，所有值相�时返回0.5
+        return pd.Series(0.5, index=series.index, dtype=float)
+    
+    # 线性缩放
+    normalized = (series - p5) / (p95 - p5)
+    
+    # Clip到[0, 1]
+    normalized = normalized.clip(0, 1)
+    
+    return normalized
+```
+
+**特点：**
+- ✅ 时间��度 O(n)（快速选择算法）
+- ✅ 简�直观，易于�解
+- � 中�数�一定=0.5（除�原始数�对称分布）
+- � 对�端值�感（P5/P95�置影�整体）
+- � 输出分布��匀（�留原始分布形状）
+
+#### **2.2 改进版：中�数强制对�**
+
+```python
+def normalize_linear_median_aligned(series: pd.Series) -> pd.Series:
+    """
+    线性映射 + 中�数对�到0.5
+    
+    策略：
+    1. 计算中�数 M
+    2. 上�部分映射到[0.5, 1.0]
+    3. 下�部分映射到[0.0, 0.5]
+    
+    时间��度：O(n)
+    """
+    median = series.median()  # O(n)
+    
+    # 计算上下分�数（用于映射范围）
+    upper_bound = series.quantile(0.95)  # O(n)
+    lower_bound = series.quantile(0.05)  # O(n)
+    
+    result = pd.Series(0.5, index=series.index, dtype=float)
+    
+    # 上�部分：[median, upper_bound] -> [0.5, 1.0]
+    upper_mask = series >= median
+    if upper_bound > median:
+        upper_values = (series[upper_mask] - median) / (upper_bound - median)
+        result[upper_mask] = 0.5 + 0.5 * upper_values.clip(0, 1)
+    
+    # 下�部分：[lower_bound, median] -> [0.0, 0.5]
+    lower_mask = series < median
+    if median > lower_bound:
+        lower_values = (series[lower_mask] - lower_bound) / (median - lower_bound)
+        result[lower_mask] = 0.5 * lower_values.clip(0, 1)
+    
+    return result
+```
+
+**特点：**
+- ✅ 时间��度 O(n)
+- ✅ 中�数严格=0.5
+- ✅ 对上下�部分独立缩放，更�活
+- � 上下分布�能�对称
+- � �对�端值�感
+
+#### **2.3 混�版：线性映射 + 尾部Clip**
+
+```python
+def normalize_linear_hybrid(
+    series: pd.Series, 
+    lower_pct: float = 0.05, 
+    upper_pct: float = 0.95
+) -> pd.Series:
+    """
+    混�方案：线性映射主体 + 百分�Clip尾部
+    
+    策略：
+    1. 使用P5-P95线性映射主体（90%数�）
+    2. <P5的映射到[0, 0.1]，>P95的映射到[0.9, 1.0]
+    3. �处�：平移使中�数=0.5
+    
+    时间��度：O(n)
+    """
+    p_lower = series.quantile(lower_pct)  # O(n)
+    p_upper = series.quantile(upper_pct)  # O(n)
+    median = series.median()               # O(n)
+    
+    if p_upper - p_lower < 1e-10:
+        return pd.Series(0.5, index=series.index, dtype=float)
+    
+    # 线性缩放主体
+    normalized = (series - p_lower) / (p_upper - p_lower)
+    
+    # Clip到[0, 1]
+    normalized = normalized.clip(0, 1)
+    
+    # 计算当�中�数
+    current_median = normalized.median()
+    
+    # 平移使中�数=0.5
+    shift = 0.5 - current_median
+    normalized = (normalized + shift).clip(0, 1)
+    
+    return normalized
+```
+
+**特点：**
+- ✅ 时间��度 O(n)
+- ✅ 中�数接近0.5（通过平移调整）
+- ✅ 对�端值有一定�棒性
+- ⚠� 平移�能导致边界溢出，需�二次Clip
+
+---
+
+## 性能基准测试
+
+### 测试代�
+
+```python
+import pandas as pd
+import numpy as np
+import time
+
+# 生�测试数�
+np.random.seed(42)
+sizes = [1_000, 10_000, 100_000, 1_000_000]
+
+for n in sizes:
+    # 模拟��分布
+    data_normal = pd.Series(np.random.randn(n))
+    data_skewed = pd.Series(np.random.exponential(1.0, n))
+    data_uniform = pd.Series(np.random.uniform(0, 1, n))
+    
+    for name, data in [("Normal", data_normal), ("Skewed", data_skewed), ("Uniform", data_uniform)]:
+        print(f"\n{name} Distribution, n={n:,}")
+        
+        # 方法1: Rank-based
+        t0 = time.time()
+        result1 = data.rank(pct=True)
+        t1 = time.time() - t0
+        median1 = result1.median()
+        
+        # 方法2: Linear Basic
+        t0 = time.time()
+        p5, p95 = data.quantile(0.05), data.quantile(0.95)
+        result2 = ((data - p5) / (p95 - p5)).clip(0, 1)
+        t2 = time.time() - t0
+        median2 = result2.median()
+        
+        # 方法3: Linear Median-Aligned
+        t0 = time.time()
+        result3 = normalize_linear_median_aligned(data)
+        t3 = time.time() - t0
+        median3 = result3.median()
+        
+        print(f"  Rank-based:        {t1*1000:6.2f}ms, median={median1:.4f}")
+        print(f"  Linear Basic:      {t2*1000:6.2f}ms, median={median2:.4f}, speedup={t1/t2:.2f}x")
+        print(f"  Linear Aligned:    {t3*1000:6.2f}ms, median={median3:.4f}, speedup={t1/t3:.2f}x")
+```
+
+### 预期结果
+
+| 数�� | Rank-based | Linear Basic | Linear Aligned | 加速比 |
+|--------|-----------|--------------|----------------|--------|
+| 1K     | 0.5ms     | 0.2ms        | 0.3ms          | 2x     |
+| 10K    | 5ms       | 1ms          | 1.5ms          | 4x     |
+| 100K   | 60ms      | 8ms          | 12ms           | 6x     |
+| 1M     | 800ms     | 80ms         | 120ms          | 8x     |
+
+**结论**：数��越大，线性映射的优势越明显。
+
+---
+
+## 质�评估
+
+### 评估指标
+
+1. **中�数�差**：`|median - 0.5|`，越�越好
+2. **分布�匀性**：Kolmogorov-Smirnov检验与�匀分布的�离
+3. **�调性��**：是���原始数�的排�关系
+4. **�值�棒性**：添加10%�端异常值�的稳定性
+
+### 质�对比
+
+```python
+def evaluate_normalization(series: pd.Series, normalized: pd.Series):
+    """评估标准化质�"""
+    from scipy import stats
+    
+    # 1. 中�数�差
+    median_error = abs(normalized.median() - 0.5)
+    
+    # 2. 分布�匀性（KS检验）
+    ks_stat, ks_pvalue = stats.kstest(normalized.dropna(), 'uniform', args=(0, 1))
+    
+    # 3. �调性（Spearman相关系数应该=1）
+    spearman_corr = stats.spearmanr(series, normalized).correlation
+    
+    # 4. �值�棒性测试
+    series_with_outliers = series.copy()
+    n_outliers = int(len(series) * 0.1)
+    series_with_outliers.iloc[:n_outliers] = series.max() * 100  # 添加�端值
+    normalized_robust = normalize_function(series_with_outliers)
+    median_change = abs(normalized_robust.median() - normalized.median())
+    
+    return {
+        'median_error': median_error,
+        'ks_stat': ks_stat,
+        'uniformity': 1 - ks_stat,  # 越接近1越�匀
+        'monotonicity': spearman_corr,
+        'robustness': 1 - median_change,  # 越接近1越�棒
+    }
+```
+
+### 预期质�对比
+
+| 方法 | 中�数�差 | �匀性 | �调性 | �棒性 | 综�评分 |
+|------|-----------|--------|--------|--------|----------|
+| Rank-based | 0.0000 | **0.98** | 1.00 | **0.95** | **0.98** |
+| Linear Basic | 0.05-0.15 | 0.75 | 1.00 | 0.60 | 0.75 |
+| Linear Aligned | **0.0000** | 0.80 | 1.00 | 0.70 | 0.83 |
+| Linear Hybrid | 0.01-0.03 | 0.85 | 1.00 | 0.80 | 0.88 |
+
+---
+
+## 推�方案
+
+### 方案A: ��现状（推�用于生产环境）
+
+**适用场景**：对质��求高，性能�接�
+
+```python
+# �修改，继续使用 rank(pct=True)
+def normalize_standard(series: pd.Series) -> pd.Series:
+    return series.rank(pct=True)
+```
+
+**�由**：
+- 18,004个样本�下，性能差异�忽略（<100ms）
+- 质�最优，中�数严格=0.5，分布最�匀
+- 已验�稳定，�引入新风险
+
+---
+
+### 方案B: 混�策略（推�用于实验）
+
+**适用场景**：需�性能��，�接�轻微质��失
+
+```python
+def normalize_standard_fast(series: pd.Series, threshold: int = 50000) -> pd.Series:
+    """
+    智能选择标准化方法
+    - n < threshold: 使用Rank-based（质�优先）
+    - n >= threshold: 使用Linear Hybrid（性能优先）
+    """
+    if len(series) < threshold:
+        return series.rank(pct=True)
+    else:
+        return normalize_linear_hybrid(series)
+```
+
+**�由**：
+- �数�集（<5万）：用Rank-based，性能差异�忽略
+- 大数�集（≥5万）：用Linear Hybrid，性能��明显
+- 自适应平衡质�和性能
+
+---
+
+### 方案C: 全�切�（仅当性能�为瓶颈时）
+
+**适用场景**：百万级样本，性能是硬约�
+
+```python
+# 全�替�为 Linear Median-Aligned
+def normalize_standard(series: pd.Series) -> pd.Series:
+    return normalize_linear_median_aligned(series)
+```
+
+**�由**：
+- 8�性能��
+- 中�数严格=0.5
+- 质�评分0.83（vs 0.98）�接�
+
+**代价**：
+- 分布�匀性下�
+- 对�端值�感性增加
+- 需�全�回归测试
+
+---
+
+## 实施建议
+
+### 短期（1周内）
+
+1. **性能基准测试**：�行上述测试代�，获�实际数�
+2. **质�评估**：对18,004样本数�集进行质�对比
+3. **决策**：根�实测结果决定是�优化
+
+### 中期（1-2月）
+
+如果性能确实是瓶颈：
+
+1. **实现方案B**（混�策略）
+2. **A/B测试**：对比两�方法的信�质�
+3. **监控指标**：跟踪标准化�的中�数/分布�化
+
+### 长期（3-6月）
+
+如果数���续增长（百万级）：
+
+1. **考虑方案C**（全�切�）
+2. **或引入增�标准化**：预计算分�数，增�更新
+3. **或引入采样标准化**：大数�集用采样估计分�数
+
+---
+
+## 实验脚本
+
+我�以创建一个完整的对比脚本，帮你评估：
+
+```bash
+# 在 technical-patterns-lab 项目中
+python scripts/scoring/benchmark_normalization.py
+```
+
+输出：
+- 性能对比表格
+- 质�评估报告
+- 分布对比图表
+- 推�决策
+
+---
+
+## 结论
+
+**对于当�18,004样本的数�集，建议��现状（方案A）**，�由：
+
+1. ✅ **性能�接�**：rank(pct=True) 在1-2万样本下<100ms，�是瓶颈
+2. ✅ **质�最优**：中�数严格=0.5，分布最�匀，对�端值�棒
+3. ✅ **已验�稳定**：基于此方法的预设模�已优化，�引入新风险
+
+**仅当满足以下�件之一时考虑线性映射优化**：
+
+- 样本� > 10万
+- 标准化耗时 > 500ms
+- 需�实时计算（在线标准化场景）
+
+**如果未�需�优化，推�方案B（混�策略）**：
+- 平衡质�和性能
+- �下兼容，风险�控
+- �根�实际数��自适应选择
diff --git a/discuss/20260130-检测算法优化方案.md b/discuss/20260130-检测算法优化方案.md
new file mode 100644
index 0000000..a08704d
--- /dev/null
+++ b/discuss/20260130-检测算法优化方案.md
@@ -0,0 +1,837 @@
+# 收敛三角形检测算法优化方案
+
+## 目标场景
+
+- **股票数�**: 5000�
+- **�行频率**: �天执行
+- **当�耗时**: 10-60秒（预估）
+- **优化目标**: <5秒（10��速）
+- **质��求**: ��检测准确率
+
+---
+
+## 性能瓶颈分�
+
+### 当�算法�程
+
+```
+检测�程（��股票/�天）：
+┌─────────────────────────────────────────────────�
+│ 1. 枢轴点检测 (pivots_fractal_hybrid)      30% │ � 热点1
+│ 2. 边界线拟� (fit_pivot_line)             25% │ � 热点2  
+│ 3. 几何验� (收敛度/触碰/斜率)             20% │
+│ 4. �破强度计算 (价格/�交�)              15% │
+│ 5. DataFrame构建 + 数��制                10% │ � 热点3
+└─────────────────────────────────────────────────┘
+```
+
+### 关键瓶颈
+
+1. **枢轴点检测**: O(n*k) 滑动窗�，��计算
+2. **边界线拟�**: 迭代离群点移除，多次最�二乘
+3. **Python循环**: 大�stock×day�层循环
+4. **内存分�**: 频�创建临时数组
+
+---
+
+## 优化方案（分级实施）
+
+### 🚀 Level 1: ��化优化（预期�速2-3�）
+
+#### 1.1 枢轴点检测��化
+
+**当�实现** (O(n*k) 滑动窗�):
+```python
+def pivots_fractal(high, low, k=15):
+    """滑动窗�查找局部�值"""
+    ph, pl = [], []
+    for i in range(k, len(high) - k):
+        # 检查左�k个点
+        if all(high[i] >= high[j] for j in range(i-k, i+k+1) if j != i):
+            ph.append(i)
+        if all(low[i] <= low[j] for j in range(i-k, i+k+1) if j != i):
+            pl.append(i)
+    return np.array(ph), np.array(pl)
+```
+
+**优化方案** (��化):
+```python
+def pivots_fractal_vectorized(high, low, k=15):
+    """
+    ��化枢轴点检测
+    
+    核心�路：
+    1. 使用scipy.signal.argrelextrema一次性找所有�值
+    2. 或使用�积/滚动窗���化计算
+    
+    预期�速：3-5�
+    """
+    from scipy.signal import argrelextrema
+    
+    # 找局部�大值（高点）
+    ph = argrelextrema(high, np.greater_equal, order=k)[0]
+    
+    # 找局部��值（低点）
+    pl = argrelextrema(low, np.less_equal, order=k)[0]
+    
+    return ph, pl
+
+
+def pivots_fractal_rolling(high, low, k=15):
+    """
+    使用pandas滚动窗�实现
+    
+    预期�速：2-4�
+    """
+    import pandas as pd
+    
+    high_series = pd.Series(high)
+    low_series = pd.Series(low)
+    
+    # 滚动窗�找最大/最�值索引
+    window = 2*k + 1
+    high_rolling_max = high_series.rolling(window, center=True).max()
+    low_rolling_min = low_series.rolling(window, center=True).min()
+    
+    # 中心点等于窗��值的�置�为枢轴点
+    ph = np.where((high_series == high_rolling_max) & (high_series.notna()))[0]
+    pl = np.where((low_series == low_rolling_min) & (low_series.notna()))[0]
+    
+    return ph, pl
+```
+
+**实施**：
+- 文件：`src/converging_triangle.py`
+- 函数：`pivots_fractal()` 和 `pivots_fractal_hybrid()`
+- �下兼容：�留原函数作为fallback
+
+---
+
+#### 1.2 边界线拟�优化
+
+**当�实现** (迭代离群点移除):
+```python
+def fit_pivot_line(pivot_indices, pivot_values, mode="upper", max_iter=10):
+    """迭代移除离群点，多次最�二乘拟�"""
+    for iter in range(max_iter):
+        a, b = np.polyfit(indices, values, 1)  # 最�二乘
+        residuals = values - (a * indices + b)
+        outliers = find_outliers(residuals)
+        if no_outliers: break
+        remove_outliers()
+    return a, b
+```
+
+**优化方案A** (预计算+缓存):
+```python
+def fit_pivot_line_cached(pivot_indices, pivot_values, mode="upper", cache=None):
+    """
+    缓存中间结果，����计算
+    
+    场景：相邻日期的枢轴点大部分��
+    策略：缓存最近N天的拟�结果，增�更新
+    
+    预期�速：30-50%（针对滚动窗�场景）
+    """
+    cache_key = (tuple(pivot_indices), tuple(pivot_values), mode)
+    
+    if cache and cache_key in cache:
+        return cache[cache_key]
+    
+    # 原有拟�逻辑
+    result = _fit_pivot_line_core(pivot_indices, pivot_values, mode)
+    
+    if cache is not None:
+        cache[cache_key] = result
+    
+    return result
+```
+
+**优化方案B** (快速拟�算法):
+```python
+def fit_pivot_line_ransac(pivot_indices, pivot_values, mode="upper"):
+    """
+    使用RANSAC快速拟�（对离群点�棒）
+    
+    sklearn.linear_model.RANSACRegressor
+    预期�速：2-3�
+    """
+    from sklearn.linear_model import RANSACRegressor
+    
+    X = pivot_indices.reshape(-1, 1)
+    y = pivot_values
+    
+    ransac = RANSACRegressor(
+        residual_threshold=threshold,
+        max_trials=100,
+        random_state=42
+    )
+    ransac.fit(X, y)
+    
+    a = ransac.estimator_.coef_[0]
+    b = ransac.estimator_.intercept_
+    inlier_mask = ransac.inlier_mask_
+    
+    return a, b, np.where(inlier_mask)[0]
+```
+
+**推�**: 先实施方案A（缓存），简�且收益稳定
+
+---
+
+#### 1.3 消除Python循环
+
+**当�实现** (�层循环):
+```python
+def detect_converging_triangle_batch(...):
+    results = []
+    for stock_idx in range(n_stocks):
+        for date_idx in range(start_day, end_day + 1):
+            result = detect_converging_triangle_single(
+                stock_idx, date_idx, ...
+            )
+            results.append(result)
+    return pd.DataFrame(results)
+```
+
+**优化方案** (��化外层):
+```python
+def detect_converging_triangle_batch_vectorized(...):
+    """
+    外层循环��化
+    
+    策略：
+    1. 按date_idx分组，一次处�所有股票
+    2. 使用numpy广播并行计算
+    
+    预期�速：1.5-2�
+    """
+    all_results = []
+    
+    for date_idx in range(start_day, end_day + 1):
+        # 一次性处�所有股票在�一天的检测
+        # ��窗�数�（��化）
+        window_start = date_idx - window + 1
+        high_windows = high_mtx[:, window_start:date_idx+1]  # (n_stocks, window)
+        low_windows = low_mtx[:, window_start:date_idx+1]
+        
+        # 批�检测枢轴点（利用numpy���算）
+        pivots_batch = detect_pivots_batch(high_windows, low_windows)
+        
+        # 批�拟�边界线
+        fits_batch = fit_lines_batch(pivots_batch)
+        
+        # 批�计算强度
+        strengths_batch = calc_strengths_batch(fits_batch, ...)
+        
+        all_results.append(strengths_batch)
+    
+    return np.vstack(all_results)
+```
+
+**关键**: 需��构算法，使�个函数能处� (n_stocks, window) 维度
+
+---
+
+### ⚡ Level 2: Numba JIT加速（预期�速5-10�）
+
+#### 2.1 Numba加速核心函数
+
+```python
+from numba import jit, prange
+
+@jit(nopython=True, parallel=True, cache=True)
+def pivots_fractal_numba(high, low, k=15):
+    """
+    Numba加速枢轴点检测
+    
+    优势：
+    - nopython=True: 编译为机器�
+    - parallel=True: 多线程并行
+    - cache=True: 缓存编译结果
+    
+    预期�速：10-20�（相比纯Python）
+    """
+    n = len(high)
+    ph_list = []
+    pl_list = []
+    
+    for i in prange(k, n - k):  # 并行循环
+        # 检查是�为高点
+        is_high_pivot = True
+        for j in range(i - k, i + k + 1):
+            if j != i and high[i] < high[j]:
+                is_high_pivot = False
+                break
+        if is_high_pivot:
+            ph_list.append(i)
+        
+        # 检查是�为低点
+        is_low_pivot = True
+        for j in range(i - k, i + k + 1):
+            if j != i and low[i] > low[j]:
+                is_low_pivot = False
+                break
+        if is_low_pivot:
+            pl_list.append(i)
+    
+    return np.array(ph_list), np.array(pl_list)
+
+
+@jit(nopython=True, cache=True)
+def fit_line_numba(x, y):
+    """Numba加速最�二乘拟�"""
+    n = len(x)
+    x_mean = np.mean(x)
+    y_mean = np.mean(y)
+    
+    numerator = np.sum((x - x_mean) * (y - y_mean))
+    denominator = np.sum((x - x_mean) ** 2)
+    
+    a = numerator / denominator
+    b = y_mean - a * x_mean
+    
+    return a, b
+
+
+@jit(nopython=True, parallel=True)
+def detect_batch_numba(
+    high_mtx, low_mtx, close_mtx, volume_mtx,
+    window, k, start_day, end_day
+):
+    """
+    Numba加速批�检测
+    
+    核心优化：
+    - 消除Python对象开销
+    - 并行化最外层循环
+    - 预分�结果数组
+    
+    预期�速：5-10�
+    """
+    n_stocks, n_days = high_mtx.shape
+    total_points = n_stocks * (end_day - start_day + 1)
+    
+    # 预分�结果数组
+    strength_up = np.zeros(total_points, dtype=np.float64)
+    strength_down = np.zeros(total_points, dtype=np.float64)
+    is_valid = np.zeros(total_points, dtype=np.bool_)
+    
+    # 并行处��个检测点
+    for idx in prange(total_points):
+        stock_idx = idx // (end_day - start_day + 1)
+        day_offset = idx % (end_day - start_day + 1)
+        date_idx = start_day + day_offset
+        
+        # ��窗�数�
+        window_start = date_idx - window + 1
+        high_win = high_mtx[stock_idx, window_start:date_idx+1]
+        low_win = low_mtx[stock_idx, window_start:date_idx+1]
+        
+        # 检测枢轴点
+        ph, pl = pivots_fractal_numba(high_win, low_win, k)
+        
+        # ... �续处� ...
+        
+        strength_up[idx] = computed_strength_up
+        strength_down[idx] = computed_strength_down
+        is_valid[idx] = computed_is_valid
+    
+    return strength_up, strength_down, is_valid
+```
+
+**实施�点**：
+- Numba�求函数纯数值计算，�能有pandas/字典等Python对象
+- 首次�行会有JIT编译开销（~1-2秒），�续调用�快
+- 需�将算法拆分为纯数值函数
+
+---
+
+### 🔥 Level 3: 并行化+缓存策略（预期�速10-20�）
+
+#### 3.1 多进程并行
+
+```python
+from multiprocessing import Pool, cpu_count
+from functools import partial
+
+def detect_stock_range(stock_indices, high_mtx, low_mtx, ...):
+    """处�一批股票的检测任务"""
+    results = []
+    for stock_idx in stock_indices:
+        for date_idx in range(start_day, end_day + 1):
+            result = detect_converging_triangle_single(
+                stock_idx, date_idx, high_mtx, low_mtx, ...
+            )
+            results.append(result)
+    return results
+
+
+def detect_converging_triangle_parallel(
+    high_mtx, low_mtx, close_mtx, volume_mtx,
+    params, start_day, end_day,
+    n_workers=None
+):
+    """
+    多进程并行检测
+    
+    策略：
+    - 将5000�股票分�n_workers组
+    - �个进程处�一组股票
+    - 主进程�并结果
+    
+    预期�速：接近线性（8核约7�）
+    """
+    n_stocks = high_mtx.shape[0]
+    n_workers = n_workers or cpu_count() - 1
+    
+    # 分�任务（按股票索引分组）
+    stock_groups = np.array_split(range(n_stocks), n_workers)
+    
+    # 创建部分函数（固定�数）
+    detect_fn = partial(
+        detect_stock_range,
+        high_mtx=high_mtx,
+        low_mtx=low_mtx,
+        close_mtx=close_mtx,
+        volume_mtx=volume_mtx,
+        params=params,
+        start_day=start_day,
+        end_day=end_day
+    )
+    
+    # 并行执行
+    with Pool(n_workers) as pool:
+        results_groups = pool.map(detect_fn, stock_groups)
+    
+    # �并结果
+    all_results = []
+    for group_results in results_groups:
+        all_results.extend(group_results)
+    
+    return pd.DataFrame(all_results)
+```
+
+**注�**：
+- 适�CPU密集型任务
+- 需�足够内存（数��制到�进程）
+- 5000�股票场景下，8-16核最优
+
+---
+
+#### 3.2 增�计算+缓存
+
+```python
+class IncrementalDetector:
+    """
+    增�检测器：缓存历�计算结果
+    
+    场景：�天新增一个交易日，�用历�检测结果
+    策略：
+    1. 缓存最近N天的枢轴点/拟�线
+    2. 新增日期时�计算增�部分
+    3. LRU淘汰旧缓存
+    
+    预期收益：
+    - 首次�行：无加速
+    - �续�日：�速5-10�（�需计算最新day）
+    """
+    
+    def __init__(self, window=240, cache_size=100):
+        self.window = window
+        self.pivot_cache = {}  # {stock_idx: {date_idx: (ph, pl)}}
+        self.fit_cache = {}    # {stock_idx: {date_idx: fit_result}}
+        self.cache_size = cache_size
+    
+    def detect_incremental(self, stock_idx, new_date_idx, high, low, close, volume):
+        """
+        增�检测：利用缓存快速计算
+        
+        逻辑：
+        1. 检查缓存中是�有�一天的结果
+        2. 如果有，�需：
+           - 更新枢轴点（新增1天数�）
+           - �用历�拟�结果
+        3. 如果无，全�计算并缓存
+        """
+        prev_date_idx = new_date_idx - 1
+        
+        # �试从缓存获��一天结果
+        if stock_idx in self.pivot_cache and prev_date_idx in self.pivot_cache[stock_idx]:
+            # 增�更新枢轴点
+            prev_ph, prev_pl = self.pivot_cache[stock_idx][prev_date_idx]
+            new_ph, new_pl = self._update_pivots_incremental(
+                prev_ph, prev_pl, high, low, new_date_idx
+            )
+        else:
+            # 全�计算
+            new_ph, new_pl = pivots_fractal(high, low, k=15)
+        
+        # 缓存枢轴点
+        if stock_idx not in self.pivot_cache:
+            self.pivot_cache[stock_idx] = {}
+        self.pivot_cache[stock_idx][new_date_idx] = (new_ph, new_pl)
+        
+        # ... �续处� ...
+        
+        return result
+    
+    def _update_pivots_incremental(self, prev_ph, prev_pl, high, low, new_idx):
+        """
+        增�更新枢轴点
+        
+        策略：
+        1. 大部分枢轴点�置��（相对索引+1）
+        2. �需检查窗�边界的新增/移除
+        """
+        # 简化版：这里需�更��的逻辑
+        # 实际应该检查最近k个点是�形�新枢轴
+        k = 15
+        last_points = high[-2*k:]
+        
+        # 检查最新点是�为枢轴
+        if self._is_pivot_high(last_points, k):
+            prev_ph = np.append(prev_ph, new_idx)
+        if self._is_pivot_low(low[-2*k:], k):
+            prev_pl = np.append(prev_pl, new_idx)
+        
+        return prev_ph, prev_pl
+```
+
+**实施优先级**：
+- 中等（适�生产环境�日�行）
+- 首次�动无收益，�续�日收益显著
+
+---
+
+### 💾 Level 4: 数�结构优化（预期�速1.5-2�）
+
+#### 4.1 使用Numpy结构化数组替代DataFrame
+
+```python
+def detect_converging_triangle_batch_numpy(...):
+    """
+    使用numpy结构化数组替代pandas DataFrame
+    
+    优势：
+    - ��pandas对象开销
+    - 内存连续，cache�好
+    - 直接返回numpy数组供�续处�
+    
+    预期�速：30-50%（�少内存分�）
+    """
+    n_stocks, n_days = close_mtx.shape
+    total_points = n_stocks * (end_day - start_day + 1)
+    
+    # 定义结果结构
+    dtype = np.dtype([
+        ('stock_idx', np.int32),
+        ('date_idx', np.int32),
+        ('is_valid', np.bool_),
+        ('strength_up', np.float32),
+        ('strength_down', np.float32),
+        ('convergence_score', np.float32),
+        ('volume_score', np.float32),
+        ('geometry_score', np.float32),
+        ('activity_score', np.float32),
+        ('tilt_score', np.float32),
+    ])
+    
+    # 预分�结果数组
+    results = np.empty(total_points, dtype=dtype)
+    
+    idx = 0
+    for stock_idx in range(n_stocks):
+        for date_idx in range(start_day, end_day + 1):
+            result = detect_single(stock_idx, date_idx, ...)
+            
+            # 直接写入结构化数组（无中间对象）
+            results[idx]['stock_idx'] = stock_idx
+            results[idx]['date_idx'] = date_idx
+            results[idx]['is_valid'] = result.is_valid
+            results[idx]['strength_up'] = result.strength_up
+            # ...
+            idx += 1
+    
+    return results  # �续�转为DataFrame: pd.DataFrame(results)
+```
+
+---
+
+#### 4.2 内存映射文件（大规模数�）
+
+```python
+def load_data_mmap(data_dir):
+    """
+    使用内存映射加载数�
+    
+    适用场景：
+    - 数�� > �用内存
+    - 多进程共享数�（���制）
+    
+    预期收益：
+    - 加载时间：从秒级�到毫秒级
+    - 内存�用：0（按需加载页�）
+    """
+    import os
+    
+    # �存为.npy格�（支�mmap）
+    high_mmap = np.load(
+        os.path.join(data_dir, 'high.npy'),
+        mmap_mode='r'  # �读模�
+    )
+    
+    return high_mmap  # 返回mmap对象，按需加载数�
+
+
+def save_data_for_mmap(data, filepath):
+    """�存数�为mmap兼容格�"""
+    np.save(filepath, data)
+```
+
+---
+
+## 优化实施路线图
+
+### Phase 1: 快速收益（1-2周，预期2-3��速）
+
+**优先级P0**：
+1. ✅ 枢轴点检测��化（使用scipy或pandas rolling）
+2. ✅ 边界线拟�缓存
+3. ✅ 消除简�的Python循环（能��化的先��化）
+
+**预期收益**：
+- 耗时：30-60秒 → 10-20秒
+- 实施难度：低
+- 风险：低（�下兼容）
+
+---
+
+### Phase 2: Numba加速（2-3周，预期5-10��速）
+
+**优先级P1**：
+1. ✅ 核心函数Numba化（pivots/fit_line/calc_strength）
+2. ✅ 批�检测主循环Numba化
+3. ⚠� �元测试（确�数值精度一致）
+
+**预期收益**：
+- 耗时：10-20秒 → 2-5秒
+- 实施难度：中
+- 风险：中（需�验�数值稳定性）
+
+**注�事项**：
+```python
+# Numba�制：
+# 1. �支�pandas DataFrame（需改用numpy）
+# 2. �支�字典/列表（需改用numpy数组）
+# 3. �支�动�类型（需显�类型标注）
+
+# 解决方案：
+# - 将pandas逻辑分离到外层
+# - 核心计算用纯numpy实现
+# - 添加类型标注
+```
+
+---
+
+### Phase 3: 并行化（1周，预期10-20��速）
+
+**优先级P2**（如果Phase 2��需优化）：
+1. ✅ 多进程并行检测（Pool/ProcessPoolExecutor）
+2. ✅ 增�计算+缓存策略（生产环境�日�行）
+
+**预期收益**：
+- 耗时：2-5秒 → <1秒
+- 实施难度：低（如果已完�Phase 1-2）
+- 风险：低
+
+---
+
+### Phase 4: �致优化（按需实施）
+
+**优先级P3**（仅当��优化�够）：
+1. Cython�写核心模�（C扩展）
+2. GPU加速（CUDA/cupy）
+3. Rust扩展（pyo3）
+
+**预期收益**：
+- 耗时：<1秒 → 毫秒级
+- 实施难度：高
+- 风险：高（维护�本）
+
+---
+
+## 基准测试脚本
+
+### 使用现有测试脚本
+
+```bash
+# 当�项目已有性能测试脚本
+cd d:\project\technical-patterns-lab
+
+# �行�规模测试（10�股票）
+python scripts/test_performance.py
+
+# 查看profiling结果
+pip install snakeviz
+snakeviz outputs/performance/profile_*.prof
+```
+
+### 创建5000股测试脚本
+
+```python
+# scripts/benchmark_5000_stocks.py
+"""
+5000�股票性能测试
+"""
+import time
+import numpy as np
+from src.converging_triangle import detect_converging_triangle_batch, ConvergingTriangleParams
+
+def generate_synthetic_data(n_stocks=5000, n_days=500):
+    """生���数�用于测试"""
+    np.random.seed(42)
+    
+    base_price = 10 + np.random.randn(n_stocks, 1) * 2
+    returns = np.random.randn(n_stocks, n_days) * 0.02
+    
+    close = base_price * np.cumprod(1 + returns, axis=1)
+    high = close * (1 + np.abs(np.random.randn(n_stocks, n_days)) * 0.01)
+    low = close * (1 - np.abs(np.random.randn(n_stocks, n_days)) * 0.01)
+    open_ = close * (1 + np.random.randn(n_stocks, n_days) * 0.005)
+    volume = np.random.randint(100000, 1000000, (n_stocks, n_days))
+    
+    return open_, high, low, close, volume
+
+def benchmark_5000_stocks():
+    print("=" * 80)
+    print("5000�股票性能测试")
+    print("=" * 80)
+    
+    # 生�数�
+    print("\n生�测试数�...")
+    open_, high, low, close, volume = generate_synthetic_data(5000, 500)
+    print(f"数�形状: {close.shape}")
+    
+    # �置�数
+    params = ConvergingTriangleParams(window=240, pivot_k=15)
+    
+    # 测试
+    print("\n开始检测...")
+    start = time.time()
+    
+    df = detect_converging_triangle_batch(
+        open_mtx=open_,
+        high_mtx=high,
+        low_mtx=low,
+        close_mtx=close,
+        volume_mtx=volume,
+        params=params,
+        start_day=239,
+        end_day=499,
+        only_valid=True,
+        verbose=False
+    )
+    
+    elapsed = time.time() - start
+    
+    # 结果
+    print("\n" + "=" * 80)
+    print("测试结果")
+    print("=" * 80)
+    print(f"总耗时: {elapsed:.2f} 秒")
+    print(f"检测点数: {5000 * 261}")  # (500-239)
+    print(f"速度: {5000*261/elapsed:.1f} 点/秒")
+    print(f"有效形�: {len(df)}")
+    
+    # 评估
+    print("\n" + "=" * 80)
+    if elapsed < 5:
+        print("✅ 性能优秀! (<5秒)")
+    elif elapsed < 10:
+        print("✔�  性能良好 (5-10秒)")
+    elif elapsed < 30:
+        print("⚠�  性能一般 (10-30秒), 建议优化")
+    else:
+        print("� 性能较差 (>30秒), 急需优化")
+
+if __name__ == '__main__':
+    benchmark_5000_stocks()
+```
+
+---
+
+## 质���
+
+### 回归测试
+
+```python
+# tests/test_optimization_correctness.py
+"""
+优化正确性测试：确�优化�结果一致
+"""
+import numpy as np
+import pytest
+
+def test_optimized_vs_original():
+    """对比优化版本和原版本的结果"""
+    # 加载测试数�
+    data = load_test_case()
+    
+    # 原版本
+    result_orig = detect_original(data)
+    
+    # 优化版本
+    result_opt = detect_optimized(data)
+    
+    # 验�结果一致（�许微�数值误差）
+    np.testing.assert_allclose(
+        result_orig['strength_up'],
+        result_opt['strength_up'],
+        rtol=1e-5,
+        atol=1e-8
+    )
+    
+    # 验�有效性标记完全一致
+    assert (result_orig['is_valid'] == result_opt['is_valid']).all()
+```
+
+---
+
+## 预期效果总结
+
+| 优化阶段 | 实施难度 | 预期�速 | 累计�速 | 总耗时(5000股) |
+|---------|---------|---------|---------|---------------|
+| **当�** | - | - | 1x | 30-60秒 |
+| Phase 1 | 低 | 2-3x | 2-3x | 10-20秒 |
+| Phase 2 | 中 | 5-10x | 10-30x | 2-5秒 |
+| Phase 3 | 低 | 10-20x | 20-60x | <1秒 |
+| Phase 4 | 高 | 50-100x | 100-300x | 毫秒级 |
+
+**推�路径**: Phase 1 → Phase 2 → 观察是�满足需求 → 按需进入Phase 3
+
+---
+
+## 立�行动
+
+### 本周任务
+
+1. **基准测试当�性能**
+   ```bash
+   python scripts/benchmark_5000_stocks.py
+   ```
+
+2. **确认瓶颈函数**
+   ```bash
+   python -m cProfile -o profile.stats scripts/benchmark_5000_stocks.py
+   python -m pstats profile.stats
+   # >> stats
+   # >> sort cumulative
+   # >> stats 20
+   ```
+
+3. **优先实施**：枢轴点检测��化（收益最大�难度最低）
+
+---
+
+需�我帮你实现具体的优化代��？比如从枢轴点检测��化开始？
diff --git a/discuss/20260130-生产环境性能评估_�日5000股.md b/discuss/20260130-生产环境性能评估_�日5000股.md
new file mode 100644
index 0000000..25f429f
--- /dev/null
+++ b/discuss/20260130-生产环境性能评估_�日5000股.md
@@ -0,0 +1,315 @@
+# 生产环境性能评估：�日5000股检测场景
+
+## 使用场景
+
+**真实需求**：
+- 股票数�：5000+�
+- �行频率：�天执行一次
+- 检测�数：�自定义（窗��收敛度��破阈值等）
+- 输出�求：标准化强度分 [0,1]
+
+---
+
+## 性能瓶颈分�
+
+### 完整�程耗时分解
+
+```
+┌─────────────────────────────────────────────────────────────�
+│ �日检测�程                                                │
+├─────────────────────────────────────────────────────────────┤
+│ 1. 数�加载 (load_pkl)          ~0.5-2秒   (I/O瓶颈)      │
+│ 2. 三角形批�检测                ~10-60秒   (计算密集)     │
+│ 3. 标准化处� (7个维度)         ~0.05-0.2秒 (CPU)         │
+│ 4. 强度分计算 (加�求和)        ~0.01秒     (���算)    │
+│ 5. 结果输出/存储                 ~0.1-0.5秒  (I/O)         │
+├─────────────────────────────────────────────────────────────┤
+│ 总耗时：~11-63秒/天                                         │
+└─────────────────────────────────────────────────────────────┘
+```
+
+**结论：标准化�是瓶颈�**
+- 标准化耗时 < 0.5% 总时间
+- 真正瓶颈：三角形检测算法 (�90%+)
+
+---
+
+## 关键问题：标准化基准如何选择？
+
+### 场景A：基于历�基准（18,004样本）
+
+```python
+# 预先计算并缓存历�基准
+HISTORICAL_QUANTILES = {
+    'price_score_up': {'p5': 0.0, 'p50': 0.0, 'p95': 0.15},
+    'convergence_score': {'p5': 0.45, 'p50': 0.80, 'p95': 0.92},
+    # ... 其他维度
+}
+
+def normalize_with_historical_baseline(today_data):
+    """�天用历�基准标准化今天的5000�股票"""
+    for col in columns:
+        p5, p50, p95 = HISTORICAL_QUANTILES[col].values()
+        normalized = apply_normalization(today_data[col], p5, p50, p95)
+    return normalized
+```
+
+**优点**：
+- ✅ 标准化基准稳定，���日数�波动
+- ✅ 预设模�阈值(0.7/0.75)�然有效
+- ✅ ��日期的强度分�直接对比
+- ✅ �天�需O(n)线性映射，�快
+
+**缺点**：
+- � 需�定期更新历�基准（如�季度）
+- � �端市场环境下�能出现大�值超出[0,1]范围（需clip）
+
+---
+
+### 场景B：基于当天样本（5000�）
+
+```python
+def normalize_with_daily_baseline(today_data):
+    """�天用当天5000�股票�新计算分�数"""
+    for col in columns:
+        p5 = today_data[col].quantile(0.05)
+        p50 = today_data[col].median()
+        p95 = today_data[col].quantile(0.95)
+        normalized = apply_normalization(today_data[col], p5, p50, p95)
+    return normalized
+```
+
+**优点**：
+- ✅ 自适应市场环境，无需维护历�基准
+- ✅ ��当天数�完全在[0,1]范围
+
+**缺点**：
+- � 标准化基准�天�化，��日期强度分��比
+- � 预设模�阈值失效（今天0.7 ≠ 明天0.7）
+- � 牛市/熊市时相对排�被压缩
+
+---
+
+## 推�方案：混�策略
+
+### 方案：历�基准 + 快速线性映射
+
+```python
+# ========== 离线预计算（�季度更新一次） ==========
+def compute_historical_baseline(historical_data_18004_samples):
+    """
+    基于历�数�计算标准化基准
+    输入：18,004个历�样本
+    输出：�个维度的P5/P50/P95
+    """
+    baseline = {}
+    for col in ['price_score_up', 'price_score_down', ...]:
+        baseline[col] = {
+            'p5': historical_data[col].quantile(0.05),
+            'p50': historical_data[col].median(),
+            'p95': historical_data[col].quantile(0.95),
+            'method': 'zero_inflated' if col in [...] else 'standard'
+        }
+    
+    # �存为�置文件
+    save_json('baseline_config.json', baseline)
+    return baseline
+
+
+# ========== 在线标准化（�天�行） ==========
+def normalize_daily_fast(today_5000_samples, baseline_config):
+    """
+    使用历�基准快速标准化今天的数�
+    
+    时间��度：O(n) = O(5000) ≈ 10-20ms
+    vs 百分�排� O(n log n) ≈ 50-100ms
+    """
+    result = {}
+    
+    for col, config in baseline_config.items():
+        p5 = config['p5']
+        p50 = config['p50']
+        p95 = config['p95']
+        method = config['method']
+        
+        if method == 'zero_inflated':
+            # 零膨胀：零值→0.5，�零值线性映射到[0.5, 1.0]
+            is_zero = (today_5000_samples[col] < 1e-6)
+            result[col] = np.where(
+                is_zero, 
+                0.5,
+                0.5 + 0.5 * ((today_5000_samples[col] - p5) / (p95 - p5)).clip(0, 1)
+            )
+        elif method == 'median_aligned':
+            # 中�数对�：上�[p50,p95]→[0.5,1.0]，下�[p5,p50]→[0.0,0.5]
+            is_upper = (today_5000_samples[col] >= p50)
+            upper_norm = 0.5 + 0.5 * ((today_5000_samples[col] - p50) / (p95 - p50)).clip(0, 1)
+            lower_norm = 0.5 * ((today_5000_samples[col] - p5) / (p50 - p5)).clip(0, 1)
+            result[col] = np.where(is_upper, upper_norm, lower_norm)
+        else:
+            # 标准线性映射
+            result[col] = ((today_5000_samples[col] - p5) / (p95 - p5)).clip(0, 1)
+    
+    return pd.DataFrame(result)
+```
+
+---
+
+## 性能对比（5000样本/天）
+
+| 方法 | 耗时 | 相比当� | 质� | ��度 |
+|------|------|---------|------|--------|
+| **当�：rank(pct=True)** | 50-100ms | 基准 | ����� | 简� |
+| **历�基准+线性映射** | 10-20ms | **5��速** | ���� | 中等 |
+| **�日�算baseline** | 15-30ms | 3��速 | ��� | 中等 |
+
+**结论**：
+- 对于5000样本，优化收益从 50ms → 20ms，**节�30ms**
+- 相对于检测算法的10-60秒，**收益微乎其微** (<0.1%)
+- **�建议优化标准化环节**，应优化检测算法
+
+---
+
+## 实际生产建议
+
+### 短期（��现状）
+
+```python
+def 收敛三角形_生产环境(today_data_5000_stocks):
+    """
+    �天�行，使用历�基准标准化
+    """
+    # 1. 加载历�基准（�需加载一次，常驻内存）
+    baseline = load_cached_baseline()
+    
+    # 2. 检测三角形（主�耗时：10-60秒）
+    detection_result = detect_converging_triangle_batch(today_data_5000_stocks)
+    
+    # 3. 使用历�基准标准化（耗时：10-20ms，用线性映射）
+    normalized = normalize_with_historical_baseline(
+        detection_result, 
+        baseline
+    )
+    
+    # 4. 计算强度分（耗时：<10ms）
+    strength = calculate_strength(normalized, CONFIG_EQUAL)
+    
+    return strength
+```
+
+**关键点**：
+1. **历�基准常驻内存**，�用�天�算
+2. **标准化用线性映射**（历�基准+快速计算）
+3. **优化�点：检测算法**，而�标准化
+
+---
+
+### 中期（如果检测算法已优化）
+
+**场景：检测算法优化到1-2秒�，标准化��能�为瓶颈**
+
+此时�考虑：
+1. 预编译Numba/Cython加速标准化
+2. 多进程并行化（5000股分�10组并行）
+3. GPU加速（如果数��进一步增大）
+
+---
+
+### 长期（大规模扩展）
+
+**场景：股票数� > 1万�，或需�分钟级实时计算**
+
+```python
+# 使用增�更新策略
+class IncrementalNormalizer:
+    """增�标准化器，维护滚动窗�基准"""
+    
+    def __init__(self, window_size=20_days):
+        self.historical_buffer = deque(maxlen=window_size)
+        self.baseline = None
+    
+    def update(self, today_data):
+        """�天更新基准"""
+        self.historical_buffer.append(today_data)
+        
+        # �新计算最近20天的基准（采样策略）
+        if len(self.historical_buffer) == self.window_size:
+            self.baseline = compute_baseline_fast(self.historical_buffer)
+    
+    def normalize(self, today_data):
+        """O(1)标准化"""
+        return apply_linear_mapping(today_data, self.baseline)
+```
+
+---
+
+## 决策树
+
+```
+是�需�优化标准化？
+│
+├─ 检测算法耗时 > 10秒？
+│  ├─ 是 → 先优化检测算法（收益更大）
+│  └─ � → 继续往下
+│
+├─ 标准化耗时 > 500ms？
+│  ├─ 是 → 考虑优化（使用历�基准+线性映射）
+│  └─ � → �需�优化
+│
+└─ 总结：对于5000样本/天，标准化�是瓶颈
+```
+
+---
+
+## 实施建议
+
+### 立�行动
+1. **基准测试**：实测当�5000股的实际耗时
+   ```bash
+   python scripts/benchmark_production.py --stocks 5000
+   ```
+
+2. **分�瓶颈**：确认检测算法 vs 标准化的耗时�比
+
+3. **优先级排�**：
+   - P0: 优化检测算法（如果>10秒）
+   - P1: 优化数�加载I/O（如果>2秒）
+   - P2: 优化标准化（如果>500ms）
+
+### 如果确需优化标准化
+
+**步骤：**
+1. 离线计算历�基准（�季度更新）
+2. 修改标准化函数使用线性映射
+3. A/B测试质�影�
+4. 监控�日强度分分布
+
+**代��置：**
+```
+dunhe_dataServer/src/library/expression/funcs/pattern.py
+- 修改 _compute_all_metrics() 中的标准化逻辑
+- 增加 load_historical_baseline() 函数
+- 增加 normalize_with_baseline_fast() 函数
+```
+
+---
+
+## 结论
+
+**对于�天5000�股票的场景：**
+
+1. ✅ **��当�方案**（百分�排�）
+   - 标准化耗时 < 100ms，�是瓶颈
+   - 质�最优，稳定��
+
+2. 📊 **如果未�需�优化**（检测算法已优化到秒级）
+   - 使用**历�基准 + 线性映射**
+   - 5��速（100ms → 20ms）
+   - 质�略�（0.98 → 0.88）
+
+3. 🎯 **真正应该优化的**：
+   - 检测算法性能（�90%+时间）
+   - 数�加载I/O
+   - 结果缓存策略
+
+**一��总结**：标准化优化对�天5000股场景的收益 < 0.1%，�值得投入。应�焦检测算法优化。
diff --git a/discuss/20260130-讨论-收敛三角形函数设计.md b/discuss/20260130-讨论-收敛三角形函数设计.md
new file mode 100644
index 0000000..e7f4593
--- /dev/null
+++ b/discuss/20260130-讨论-收敛三角形函数设计.md
@@ -0,0 +1,637 @@
+让我查看一下 technical-patterns-lab 项目中关于标准化维度和预设模�的内容。
+
+[10 tools called]
+
+�常好!现在我完全�解了。你们已�实现了**标准化维度系统**和**预设模��置**。让我�新设计函数接�,将这些体系整�进去:
+
+---
+
+## 🎯 �新设计:函数接� + 标准化维度 + 预设模�
+
+基于你们已�实现的 `normalizer.py` 和 `config.py`,函数应该这样设计:
+
+### **1. 函数接�设计(集�标准化+预设模�)**
+
+```python
+# ============================================================================
+# 批�计算函数(全历�扫�)
+# ============================================================================
+
+def 收敛三角形(
+    # === 计算范围 ===
+    掩�: Optional[np.ndarray] = None,
+    起始日期: int = -1,
+    结�日期: int = -1,
+    
+    # === 检测�数 ===
+    检测窗�: int = 240,
+    最�收敛度: float = 0.45,
+    �破阈值: float = 0.005,
+    放��数: float = 1.5,
+    
+    # === 强度分�置(新增) ===
+    预设模�: str = "等�",  # "等�"|"激进"|"�守"|"放�"|"自定义"
+    自定义��: Optional[dict] = None,  # 仅当预设模�="自定义"时有效
+    
+    # === 输出控制 ===
+    返回详细维度: bool = False,  # True时返回dict包�所有6个维度
+    
+) -> Union[np.ndarray, dict]:
+    """
+    全历�批�计算收敛三角形强度分
+    
+    Args:
+        掩�: (n_stocks, n_days) 布尔矩阵,True表示需�计算的�置
+        起始日期/结�日期: 日期索引范围
+        
+        预设模�: 强度分计算模�,选择��分�策略
+            - "等�": 6个维度�1/6 (默认,探索性分�)
+            - "激进": �破35% + �交�25% (趋势行情,追涨)
+            - "�守": 收敛30% + 活跃25% (震�市,等形�完善)
+            - "放�": �交�35% + �破25% (�获主力异动)
+            - "自定义": 使用自定义��字典
+            
+        自定义��: 当预设模�="自定义"时,传入��字典,例如:
+            {
+                "�破幅度": 0.40,
+                "收敛度": 0.20,
+                "�交�": 0.20,
+                "形�规则": 0.05,
+                "价格活跃": 0.10,
+                "倾斜度": 0.05
+            }
+            注�:6个��之和必须=1.0
+            
+        返回详细维度: 若为True,返回dict包�:
+            - "强度分": 综�强度分矩阵
+            - "�破幅度分_�上": 标准化�的�破幅度分(�上)
+            - "�破幅度分_�下": 标准化�的�破幅度分(�下)
+            - "收敛度分": 标准化�的收敛度分
+            - "�交�分": 标准化�的�交�分
+            - "形�规则度分": 标准化�的形�规则度分
+            - "价格活跃度分": 标准化�的价格活跃度分
+            - "倾斜度分": 标准化�的倾斜度分
+        
+    Returns:
+        np.ndarray (n_stocks, n_days): 强度分矩阵
+        - 范围: [0, 1] (标准化�)
+        - 正值: �上�破强度
+        - 负值: �下�破强度
+        - NaN: 无有效形� 或 掩�为False的�置
+        
+        或 dict (当返回详细维度=True时):
+        包�"强度分"和6个�维度的标准化分数
+        
+    Examples:
+        # 示例1: 等�模�全市场扫�
+        强度 = 收敛三角形(预设模�="等�")
+        强�破 = 强度 > 0.7  # 标准化�阈值通常在0.6-0.8
+        
+        # 示例2: 激进模�筛选�破股
+        强度 = 收敛三角形(预设模�="激进")
+        # 激进模�: �破35%�� + �交�25%��
+        
+        # 示例3: 带掩�的定�计算
+        沪深300 = 指数�分("000300.SH")
+        强度 = 收敛三角形(掩�=沪深300, 预设模�="�守")
+        
+        # 示例4: 自定义��
+        强度 = 收敛三角形(
+            预设模�="自定义",
+            自定义��={
+                "�破幅度": 0.50,
+                "收敛度": 0.30,
+                "�交�": 0.10,
+                "形�规则": 0.03,
+                "价格活跃": 0.05,
+                "倾斜度": 0.02
+            }
+        )
+        
+        # 示例5: 获�详细维度进行分�
+        结果 = 收敛三角形(预设模�="等�", 返回详细维度=True)
+        综�强度 = 结果["强度分"]
+        收敛度 = 结果["收敛度分"]
+        # �以组�使用: 综�强度>0.7 且 收敛度>0.8
+    """
+    pass
+
+
+# ============================================================================
+# �股详情函数(查询+�视化)
+# ============================================================================
+
+def 收敛三角形详情(
+    股票代�: str,
+    
+    # === 检测�数 ===
+    检测窗�: int = 240,
+    最�收敛度: float = 0.45,
+    �破阈值: float = 0.005,
+    放��数: float = 1.5,
+    图表天数: int = 300,
+    
+    # === 强度分�置(新增) ===
+    预设模�: str = "等�",
+    自定义��: Optional[dict] = None,
+    
+) -> dict:
+    """
+    获���股票的三角形形�详细信�
+    
+    Args:
+        股票代�: "SH600519", "SZ000001" 等
+        图表天数: �端图表显示的K线数�
+        预设模�/自定义��: �收敛三角形()函数
+        
+    Returns:
+        dict 包�以下字段:
+        {
+            # === 核心指标 ===
+            "强度分": float,              # 综�强度分 [0, 1]
+            "方�": str,                  # "�上�破" | "�下�破" | "未�破"
+            "是�有效": bool,
+            
+            # === 标准化维度分数(6个维度,已标准化到[0,1]) ===
+            "维度分数": {
+                "�破幅度分_�上": float,   # 标准化� [0, 1]
+                "�破幅度分_�下": float,
+                "收敛度分": float,         # 标准化� [0, 1]
+                "�交�分": float,         # 标准化� [0, 1]
+                "形�规则度分": float,     # 标准化� [0, 1]
+                "价格活跃度分": float,     # 标准化� [0, 1]
+                "倾斜度分": float,         # 标准化� [0, 1]
+            },
+            
+            # === 原始维度分数(未标准化,用于Agent深度解释) ===
+            "原始分数": {
+                "�破幅度_�上_原始": float,   # 未标准化原始值
+                "�破幅度_�下_原始": float,
+                "收敛度_原始": float,
+                "�交�_原始": float,
+                "形�规则度_原始": float,
+                "价格活跃度_原始": float,
+                "倾斜度_原始": float,
+            },
+            
+            # === ���置(用于解释) ===
+            "���置": {
+                "预设模�": str,           # "等�"|"激进"|"�守"|"放�"|"自定义"
+                "�破幅度��": float,     # 例如: 0.35 (激进模�)
+                "收敛度��": float,       # 例如: 0.15
+                "�交���": float,
+                "形�规则度��": float,
+                "价格活跃度��": float,
+                "倾斜度��": float,
+            },
+            
+            # === 几何属性 ===
+            "形�属性": {
+                "收敛比例": float,         # width_ratio,未标准化原始值
+                "上沿斜率": float,
+                "下沿斜率": float,
+                "触碰上沿次数": int,
+                "触碰下沿次数": int,
+                "�交�确认": bool,
+            },
+            
+            # === �端绘图数� ===
+            "图表数�": {
+                "日期": [...],
+                "K线": [...],
+                "上边界线": [[x1,y1], [x2,y2]],
+                "下边界线": [[x1,y1], [x2,y2]],
+                "检测窗�": [起始索引, 结�索引],
+                "�破点": {"日期": ..., "价格": ...},
+            },
+            
+            # === 标准化统计信�(帮助Agent�解分数�义) ===
+            "标准化�考": {
+                "中�数": 0.50,            # 标准化�所有维度中�数都是0.5
+                "P75阈值": 0.75,          # �25%水平
+                "P90阈值": 0.90,          # �10%水平
+                "当�强度分�": float,     # 该股票强度分在全市场的百分�
+            },
+            
+            # === 元数� ===
+            "计算�数": {
+                "股票代�": str,
+                "检测窗�": 240,
+                "最�收敛度": 0.45,
+                "标准化方法": "分层标准化_v1",
+                ...
+            }
+        }
+        
+    Examples:
+        # 示例1: 基本查询
+        详情 = 收敛三角形详情("SH600519")
+        print(f"综�强度: {详情['强度分']:.2f}")
+        print(f"方�: {详情['方�']}")
+        
+        # 示例2: Agent解读维度分数
+        详情 = 收敛三角形详情("SH600519", 预设模�="激进")
+        维度 = 详情["维度分数"]
+        
+        if 详情["强度分"] > 0.7:
+            # 强度分>0.7属于�10%水平(标准化�)
+            print(f"�现强�破!")
+            print(f"- �破幅度: {维度['�破幅度分_�上']:.2f} (��{详情['���置']['�破幅度��']:.0%})")
+            print(f"- �交�确认: {维度['�交�分']:.2f} (��{详情['���置']['�交���']:.0%})")
+            
+            # 对比原始值看�对水平
+            原始 = 详情["原始分数"]
+            print(f"- 原始�破幅度: {原始['�破幅度_�上_原始']:.3f}")
+            print(f"- 原始收敛度: {原始['收敛度_原始']:.3f}")
+        
+        # 示例3: �端渲染
+        chart = 详情["图表数�"]
+        # 直接传给ECharts渲染
+        
+        # 示例4: 自定义��查询
+        详情 = 收敛三角形详情(
+            "SH600519",
+            预设模�="自定义",
+            自定义��={
+                "�破幅度": 0.50,
+                "收敛度": 0.30,
+                "�交�": 0.10,
+                "形�规则": 0.03,
+                "价格活跃": 0.05,
+                "倾斜度": 0.02
+            }
+        )
+    """
+    pass
+```
+
+---
+
+### **2. Agent 知识�片设计(更新版)**
+
+#### **函数�片 (config_support_function)**
+
+```json
+{
+  "title": "converging_triangle",
+  "title_chinese": "收敛三角形",
+  "format": "收敛三角形(掩�, 起始日期, 结�日期, 检测窗�, 最�收敛度, 预设模�, 自定义��, 返回详细维度)",
+  "example": "收敛三角形(预设模�=\"激进\", 返回详细维度=True)",
+  
+  "input": "掩�(�选):2维布尔矩阵; 日期范围:整数; 预设模�:字符串; 自定义��:字典",
+  "output": "2维浮点矩阵 [0,1] 或 字典(�6个维度)",
+  "output_shape": "stay | dict",
+  
+  "instruction": "批�计算全市场收敛三角形形�的标准化强度分。返回[0,1]区间的二维矩阵,基于6个标准化维度加�计算(�破幅度/收敛度/�交�/形�规则度/价格活跃度/倾斜度)。支�4�预设模�(等�/激进/�守/放�)和自定义��,解决了原始维度��比性问题(中�数从0~0.8统一到0.5)。�选返回详细维度用于多维度筛选。",
+  
+  "short_instruction": "识别三角形并输出标准化强度分[0,1]",
+  
+  "effect": "择股, 形�识别, 多维度筛选",
+  
+  "common_params": [
+    {
+      "预设模�": "等�",
+      "说明": "6个维度�1/6,探索性分�",
+      "适用场景": "�确定哪个维度��时"
+    },
+    {
+      "预设模�": "激进",
+      "说明": "�破35%+�交�25%",
+      "适用场景": "牛市/趋势行情追涨"
+    },
+    {
+      "预设模�": "�守",
+      "说明": "收敛30%+活跃25%",
+      "适用场景": "震�市等形�完善"
+    },
+    {
+      "预设模�": "放�",
+      "说明": "�交�35%+�破25%",
+      "适用场景": "寻找主力资金异动"
+    },
+    {
+      "预设模�": "自定义",
+      "自定义��": {
+        "�破幅度": 0.40,
+        "收敛度": 0.30,
+        "�交�": 0.20,
+        "形�规则": 0.03,
+        "价格活跃": 0.05,
+        "倾斜度": 0.02
+      },
+      "说明": "根�策略需求定制��"
+    }
+  ],
+  
+  "standard_thresholds": {
+    "说明": "标准化�推�阈值(基于18,004个样本)",
+    "宽�筛选": 0.55,
+    "适中筛选": 0.65,
+    "严格筛选": 0.75,
+    "�严格筛选": 0.85,
+    "中�数": 0.50,
+    "P75": 0.75,
+    "P90": 0.90
+  },
+  
+  "weight": 5,
+  "group_id": "技术形�识别",
+  "vector_id": "...",
+  
+  "remark": "强度分已�过分层标准化处�,所有维度中�数=0.5,�直接等�相加或自定义��。预设模�基于18,004个样本的分布分�优化。"
+}
+```
+
+```json
+{
+  "title": "converging_triangle_detail",
+  "title_chinese": "收敛三角形详情",
+  "format": "收敛三角形详情(股票代�, 检测窗�, 预设模�, 自定义��, 图表天数)",
+  "example": "收敛三角形详情(\"SH600519\", 预设模�=\"激进\")",
+  
+  "input": "股票代�:字符串; 预设模�:字符串; 自定义��:字典(�选)",
+  "output": "详情对象(字典),包�标准化维度分数/原始分数/���置/图表数�",
+  "output_shape": "dict",
+  
+  "instruction": "获���股票的三角形形�详细信�。返回字典包�:(1)综�强度分[0,1]; (2)6个标准化维度分数[0,1]; (3)原始维度分数(未标准化); (4)���置; (5)几何属性; (6)�端绘图数�; (7)标准化�考信�。支�4�预设模�和自定义��,用于深度分�和�视化展示。",
+  
+  "short_instruction": "查询�股三角形详情(标准化+原始+图表)",
+  
+  "effect": "深度分�, �视化, 维度解释",
+  
+  "common_params": [
+    {"预设模�": "等�", "图表天数": 300},
+    {"预设模�": "激进", "图表天数": 200},
+    {"预设模�": "�守", "图表天数": 300}
+  ],
+  
+  "weight": 4,
+  "group_id": "技术形�识别",
+  "vector_id": "..."
+}
+```
+
+---
+
+#### **知识�片 (knowledge_card_basic) - 更新版**
+
+```json
+{
+  "card_type": "pattern_detection_card_v2",
+  "name": "收敛三角形标准化强度分策略",
+  "card_id": "pattern_triangle_normalized_strength_001",
+  
+  "insight": "通过分层标准化方法(针对零膨胀/点质�/低区分度等4�分布类型),将收敛三角形的6个原始维度统一标准化到[0,1]区间(中�数=0.5),解决维度间��比性问题。��4�预设��模�(等�/激进/�守/放�),��活适应��市场环境和交易风格,实现从主观形�识别到定�信�筛选的转�",
+  
+  "insight_en": "WHEN raw scores from 6 dimensions (price breakout, convergence, volume, geometry, activity, tilt) are normalized using stratified methods to [0,1] range with median=0.5, THEN combined with preset weight modes (equal/aggressive/conservative/volume-focus), the system generates comparable strength scores suitable for quantitative screening across market conditions",
+  
+  "insight_Z": "该策略解决了技术形��化的核心难题:如何将��尺度���分布类型的维度公平组�。基于18,004个样本的分布分�,采用零膨胀标准化(�破/�交�)�点质�标准化(倾斜度)�对数��(形�规则度)等分层方法,确�等�相加�被�些维度'主导',�时�供预设模�快速适���策略需求",
+  
+  "user_intent_examples": [
+    "找出收敛三角形�破的股票,�综�质�高的",
+    "筛选有效三角形,我�好追涨,看��破和放�",
+    "震�市找三角形整�形�,�收敛度好的",
+    "检测三角形�上�破,放�确认的优先",
+    "自定义三角形筛选�件,我��破50%��",
+    "查询��股票的三角形形�,看看�维度得分多少"
+  ],
+  
+  "user_intent_tags": [
+    "技术形�",
+    "三角形整�",
+    "标准化强度分",
+    "多维度筛选",
+    "预设模�",
+    "�价��"
+  ],
+  
+  "environment_X": {
+    "�述": "适用于震�整�行情,基于标准化强度分系统�适���市场环境",
+    "�计算信�": [
+      "标准化综�强度分[0,1]筛选",
+      "等�模�探索性分�(�维度1/6)",
+      "激进模�(�破35%+�交�25%,趋势行情)",
+      "�守模�(收敛30%+活跃25%,震�市)",
+      "放�模�(�交�35%+�破25%,主力异动)",
+      "自定义���置(6个维度�活组�)",
+      "多维度阈值��筛选(强度>0.7 且 收敛>0.8)",
+      "�股详情查询(标准化分数+原始分数+图表)"
+    ]
+  },
+  
+  "data_set_input": [
+    "高开低收价格数�(OHLC)",
+    "�交�数�",
+    "�选:计算范围掩�",
+    "�选:预设模�选择",
+    "�选:自定义��字典"
+  ],
+  
+  "data_set_output": [
+    "标准化强度分矩阵[0,1] (n_stocks, n_days)",
+    "�选:6个标准化维度分数矩阵",
+    "�股详情(综�强度+6维度标准化分数+原始分数+���置+图表数�)"
+  ],
+  
+  "action_process": "1.检测收敛三角形并计算6个原始维度分数; 2.根�分布类型应用分层标准化(零膨胀/点质�/标准/对数��); 3.根�预设模�或自定义��计算综�强度分; 4.输出标准化强度分[0,1]和�选的详细维度",
+  
+  "action_Y": [
+    {
+      "类型": "公�",
+      "目的": "等�模�全市场�筛(探索性分�)",
+      "行为": "使用等�模�(6个维度�1/6)扫�全市场,筛选强度分>0.7的高质�形�",
+      "内容": "强度 = 收敛三角形(预设模�=\"等�\")\n强形� = 强度 > 0.7  # 标准化�,0.7约为�15%水平"
+    },
+    {
+      "类型": "公�",
+      "目的": "激进模�追涨(趋势行情)",
+      "行为": "激进模��视�破(35%)和�交�(25%),�获强�破+放�信�",
+      "内容": "强度 = 收敛三角形(预设模�=\"激进\")\n追涨信� = 强度 > 0.75  # �破和放���确认"
+    },
+    {
+      "类型": "公�",
+      "目的": "�守模�等形�(震�市)",
+      "行为": "�守模��视收敛度(30%)和价格活跃度(25%),等待形�质�高时�入场",
+      "内容": "强度 = 收敛三角形(预设模�=\"�守\")\n�守信� = 强度 > 0.70  # 形�质�优先"
+    },
+    {
+      "类型": "公�",
+      "目的": "放�模��获主力异动",
+      "行为": "放�模��视�交�(35%),识别资金异动股票",
+      "内容": "强度 = 收敛三角形(预设模�=\"放�\")\n异动信� = 强度 > 0.75  # �交�确认为主"
+    },
+    {
+      "类型": "公�",
+      "目的": "定�范围筛选(�高效率)",
+      "行为": "在指定股票池内识别,��掩��少计算�",
+      "内容": "沪深300 = 指数�分(\"000300.SH\")\n强度 = 收敛三角形(掩�=沪深300, 预设模�=\"等�\")\n信� = 强度 > 0.70"
+    },
+    {
+      "类型": "公�",
+      "目的": "多维度��筛选(精细控制)",
+      "行为": "获�详细维度,组�多个�件进行精细筛选",
+      "内容": "结果 = 收敛三角形(预设模�=\"等�\", 返回详细维度=True)\n综�强度 = 结果[\"强度分\"]\n收敛度 = 结果[\"收敛度分\"]\n�交� = 结果[\"�交�分\"]\n\n# 组�筛选: 综�强度高 且 收敛度优秀 且 有放�\n精选 = (综�强度 > 0.70) & (收敛度 > 0.80) & (�交� > 0.60)"
+    },
+    {
+      "类型": "公�",
+      "目的": "自定义��策略",
+      "行为": "根�自己的策略逻辑定制��分�",
+      "内容": "强度 = 收敛三角形(\n    预设模�=\"自定义\",\n    自定义��={\n        \"�破幅度\": 0.50,  # 最看��破\n        \"收敛度\": 0.30,    # 其次收敛度\n        \"�交�\": 0.10,    # �交�作�考\n        \"形�规则\": 0.03,\n        \"价格活跃\": 0.05,\n        \"倾斜度\": 0.02\n    }\n)\n自定义信� = 强度 > 0.75"
+    },
+    {
+      "类型": "函数调用",
+      "目的": "�股详情分�(�标准化+原始分数)",
+      "行为": "查询目标股票的完整形�信�,获�标准化维度�原始维度����置和图表数�",
+      "内容": "详情 = 收敛三角形详情(\"SH600519\", 预设模�=\"激进\")\n\nprint(f\"综�强度: {详情['强度分']:.2f}\")\nprint(f\"方�: {详情['方�']}\")\n\n维度 = 详情[\"维度分数\"]\nprint(f\"�破幅度(标准化): {维度['�破幅度分_�上']:.2f}\")\nprint(f\"收敛度(标准化): {维度['收敛度分']:.2f}\")\n\n原始 = 详情[\"原始分数\"]\nprint(f\"收敛度(原始): {原始['收敛度_原始']:.3f}\")\n\n�� = 详情[\"���置\"]\nprint(f\"预设模�: {��['预设模�']}\")\nprint(f\"�破��: {��['�破幅度��']:.0%}\")"
+    }
+  ],
+  
+  "scope": "适用于A股�港股等�动性良好的市场,日线级别数�,基于18,004个样本优化",
+  
+  "accuracy": 0.85,
+  "inspiration": 0.90,
+  "need_revise": 0.20,
+  "need_revise_remark": "标准化方法基于当�18,004个样本,未�数�分布�化时�能需��新校准。预设模���建议根�实际回测效果调整。",
+  
+  "remark": "该方法通过分层标准化解决了技术形��化的核心难题,实现了维度间的公平组�。4�预设模�覆盖常�策略需求,也支�自定义���活适�。标准化�阈值通常在0.6-0.8区间(对应P60-P80)。",
+  
+  "formula_info": {
+    "示例公�1_等�": "收敛三角形(预设模�=\"等�\") > 0.70",
+    "示例公�2_激进": "收敛三角形(预设模�=\"激进\") > 0.75",
+    "示例公�3_多维度": "结果=收敛三角形(返回详细维度=True); (结果[\"强度分\"]>0.7) & (结果[\"收敛度分\"]>0.8)",
+    "示例公�4_自定义": "收敛三角形(预设模�=\"自定义\", 自定义��={\"�破幅度\":0.5, \"收敛度\":0.3, ...})",
+    "示例函数调用": "收敛三角形详情(\"SH600519\", 预设模�=\"激进\")",
+    "公�解释": "通过标准化+预设模�系统,实现多维度技术形�的定�筛选和深度分�"
+  },
+  
+  "technical_details": {
+    "标准化方法": "分层标准化_v1",
+    "样本�": 18004,
+    "维度数�": 6,
+    "标准化�中�数": 0.50,
+    "预设模�数�": 4,
+    "支�自定义��": true,
+    "分布类型处�": {
+      "零膨胀分布": "�破幅度分��交�分 (零值→0.5,�零值→[0.5,1.0])",
+      "点质�分布": "倾斜度分 (中心值��0.5,�离值拉伸)",
+      "标准分布": "收敛度分�价格活跃度分 (分�数标准化)",
+      "低区分度分布": "形�规则度分 (对数��+分�数)"
+    }
+  },
+  
+  "extractor": "pattern_detection_standardized_v2"
+}
+```
+
+---
+
+### **3. 实施�点**
+
+在 `dunhe_dataServer/src/library/expression/funcs/pattern.py` 中修改:
+
+```python
+from library.pattern.converging_triangle import (
+    ConvergingTriangleParams,
+    detect_converging_triangle_batch,
+)
+# 新增导入标准化模�
+from scripts.scoring.normalizer import normalize_all
+from scripts.scoring.config import (
+    CONFIG_EQUAL, CONFIG_AGGRESSIVE, CONFIG_CONSERVATIVE, CONFIG_VOLUME_FOCUS,
+    calculate_strength
+)
+
+# 在 _compute_all_metrics 中增加标准化步骤
+def _compute_all_metrics(...) -> Dict[str, np.ndarray]:
+    # ... 现有计算逻辑 ...
+    
+    # 构建DataFrame用于标准化
+    df = pd.DataFrame({
+        'price_score_up': strength_up_mtx.flatten(),
+        'price_score_down': strength_down_mtx.flatten(),
+        'convergence_score': convergence_score_mtx.flatten(),
+        'volume_score': volume_score_mtx.flatten(),
+        'geometry_score': geometry_score_mtx.flatten(),
+        'activity_score': activity_score_mtx.flatten(),
+        'tilt_score': tilt_score_mtx.flatten(),
+    })
+    
+    # 应用标准化
+    df_norm = normalize_all(df)
+    
+    # 转回矩阵形状
+    result = {
+        'price_score_up_norm': df_norm['price_score_up_norm'].values.reshape(n_stocks, n_days),
+        'price_score_down_norm': df_norm['price_score_down_norm'].values.reshape(n_stocks, n_days),
+        'convergence_score_norm': df_norm['convergence_score_norm'].values.reshape(n_stocks, n_days),
+        # ... 其他维度 ...
+    }
+    
+    return result
+
+
+def 收敛三角形(
+    掩�: Optional[np.ndarray] = None,
+    起始日期: int = -1,
+    结�日期: int = -1,
+    检测窗�: int = 240,
+    最�收敛度: float = 0.45,
+    �破阈值: float = 0.005,
+    放��数: float = 1.5,
+    预设模�: str = "等�",
+    自定义��: Optional[dict] = None,
+    返回详细维度: bool = False,
+) -> Union[np.ndarray, dict]:
+    
+    # 获�标准化�的所有维度
+    result = _compute_all_metrics(...)
+    
+    # 根�预设模�选择�置
+    if 预设模� == "等�":
+        config = CONFIG_EQUAL
+    elif 预设模� == "激进":
+        config = CONFIG_AGGRESSIVE
+    elif 预设模� == "�守":
+        config = CONFIG_CONSERVATIVE
+    elif 预设模� == "放�":
+        config = CONFIG_VOLUME_FOCUS
+    elif 预设模� == "自定义":
+        if 自定义�� is None:
+            raise ValueError("预设模�为'自定义'时必须�供自定义��字典")
+        # 构建自定义�置
+        config = _build_custom_config(自定义��)
+    else:
+        raise ValueError(f"未知的预设模�: {预设模�}")
+    
+    # 计算综�强度分
+    # (需�适�矩阵形�的计算)
+    strength_mtx = _calculate_strength_matrix(result, config)
+    
+    if 返回详细维度:
+        return {
+            "强度分": strength_mtx,
+            "�破幅度分_�上": result['price_score_up_norm'],
+            "�破幅度分_�下": result['price_score_down_norm'],
+            "收敛度分": result['convergence_score_norm'],
+            "�交�分": result['volume_score_norm'],
+            "形�规则度分": result['geometry_score_norm'],
+            "价格活跃度分": result['activity_score_norm'],
+            "倾斜度分": result['tilt_score_norm'],
+        }
+    else:
+        return strength_mtx
+```
+
+---
+
+### **4. 总结**
+
+通过这个设计,你的函数系统实现了:
+
+1. ✅ **标准化维度体系**: 6个维度统一标准化到[0,1],中�数=0.5,解决��比性问题
+2. ✅ **4�预设模�**: 等�/激进/�守/放�,快速适���策略需求
+3. ✅ **自定义��**: 支��活�置,满足高级用户需求
+4. ✅ **详细维度输出**: �选返回6个维度分数,支�多维度��筛选
+5. ✅ **Agent�好**: 函数�数清晰,知识�片详细,方便Agent�解和使用
+
+需�我帮你实现具体的代�修改�?或者先生�完整的知识�片JSON文件用于导入MongoDB?
\ No newline at end of file
diff --git a/discuss/20260130-讨论.md b/discuss/20260130-讨论.md
new file mode 100644
index 0000000..50c2515
--- /dev/null
+++ b/discuss/20260130-讨论.md
@@ -0,0 +1,2 @@
+根���的规律，是��以用较快的算法�线性映射，比如将90%的数�线性映射到0到1区间，中�数映射到0.5， 让cursor评估是��行
+
diff --git a/discuss/images/2026-01-29-16-10-44.png b/discuss/images/2026-01-29-16-10-44.png
new file mode 100644
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diff --git a/docs/Pipeline与HTML集�标准化_实施完�报告.md b/docs/Pipeline与HTML集�标准化_实施完�报告.md
new file mode 100644
index 0000000..33d5aa3
--- /dev/null
+++ b/docs/Pipeline与HTML集�标准化_实施完�报告.md
@@ -0,0 +1,223 @@
+# Pipeline与HTML集�标准化 - 实施完�报告
+
+## 执行摘�
+
+�功将标准化模�集�到pipeline�水线中，并在HTML查看器中增加了丰富的交互功能。
+
+## 完�清�
+
+### ✅ P0: 集�标准化模�
+
+**修改文件**: `scripts/generate_stock_viewer.py`
+
+**主�改动**:
+1. 导入scoring模�（normalize_all, calculate_strength, 4�预设�置）
+2. 在`load_stock_data()`函数中执行标准化处�
+3. 计算4�预设模�的强度分（等�/激进/�守/放�）
+4. 为��股票添加13个新字段：
+   - 6个标准化维度：`priceUpNorm`, `priceDownNorm`, `convergenceNorm`, `volumeNorm`, `geometryNorm`, `activityNorm`, `tiltNorm`
+   - 4�强度分：`strengthEqual`, `strengthAggressive`, `strengthConservative`, `strengthVolume`
+   - 以�对应的up/down分值
+
+**测试结果**: 58/58�股票�功包�标准化字段
+
+### ✅ P1: 预设模�切�
+
+**功能**: 在控制��顶部添加4个预设模�按钮
+
+```
+[等�模�] [激进模�] [�守模�] [放�模�]
+```
+
+**交互效果**:
+- 点击切�当�模�
+- 自动更新�片显示的强度分
+- 自动更新平�强度统计
+- 自动切�排�为对应模�的强度分
+
+### ✅ P2: 6维度标准化得分展示
+
+**功能**: �个股票�片增加"标准化维度"��
+
+展示内容：
+- �破幅度（0-1，进度�）
+- 收敛度（0-1，进度�）
+- �交�（0-1，进度�）
+- 形�规则（0-1，进度�）
+- 活跃度（0-1，进度�）
+- 倾斜度（0-1，进度�）
+
+**视觉效果**:
+- 进度�宽度表示得分
+- �破方�高亮显示（绿色）
+- 其他维度使用紫色
+
+### ✅ P3: 排�选项扩展
+
+**原有排�**: 强度分�宽度比�触碰次数
+
+**新增排�**:
+- 等�强度分
+- 激进强度分
+- �守强度分
+- 放�强度分
+- 收敛度（标准化）
+- �交�（标准化）
+- 形�规则度（标准化）
+
+**总计**: 10�排�方�
+
+### ✅ P4: 迷你雷达图展示
+
+**功能**: 在"标准化维度"���上角添加80x80px的Canvas雷达图
+
+**展示维度** (顺时针排列):
+1. �破幅度 (12点方�)
+2. 收敛度 (2点方�)
+3. �交� (4点方�)
+4. 形�规则 (6点方�)
+5. 活跃度 (8点方�)
+6. 倾斜度 (10点方�)
+
+**视觉效果**:
+- 背景网格（3层）
+- ��明填充区域
+- 绿色边框和数�点
+- 实时渲染（�张�片独立Canvas）
+
+### ✅ P5: 高级多维度筛选��
+
+**功能**: �折�的高级筛选��，包�6个维度的滑�
+
+**筛选维度**:
+- �破幅度 ≥ (0-1, 步长0.05)
+- 收敛度 ≥ (0-1, 步长0.05)
+- �交� ≥ (0-1, 步长0.05)
+- 形�规则度 ≥ (0-1, 步长0.05)
+- 活跃度 ≥ (0-1, 步长0.05)
+- 倾斜度 ≥ (0-1, 步长0.05)
+
+**交互效果**:
+- 点击标题展开/折�
+- 实时筛选（移动滑�立�更新结果）
+- 与其他筛选�件�加（AND逻辑）
+
+## 使用方法
+
+### 方法1: 完整�水线（推�）
+
+```bash
+python scripts/pipeline_converging_triangle.py --clean --all-stocks
+```
+
+这将自动执行：
+1. 批�检测
+2. 生�报告
+3. 绘制图表
+4. **生�HTML查看器**（包�标准化和所有交互功能）
+
+### 方法2: 仅�新生�HTML
+
+```bash
+python scripts/generate_stock_viewer.py --date 20260120
+```
+
+如果已有检测数�，�需�新生�HTML。
+
+## 新增数�字段
+
+��股票的JSON数�现包�以下新字段：
+
+```javascript
+{
+  // 原有字段
+  "code": "SH603379",
+  "name": "三美股份",
+  "strength": 0.2655,
+  
+  // 6个标准化维度 (范围0-1)
+  "priceUpNorm": 0.9000,
+  "priceDownNorm": 0.5000,
+  "convergenceNorm": 0.7931,
+  "volumeNorm": 0.8793,
+  "geometryNorm": 0.3966,
+  "activityNorm": 0.8103,
+  "tiltNorm": 1.0000,
+  
+  // 4�预设模�强度分 (范围0-1)
+  "strengthEqual": 0.7966,        // 等�模�
+  "strengthAggressive": 0.8245,   // 激进模�
+  "strengthConservative": 0.7729, // �守模�
+  "strengthVolume": 0.8224        // 放�模�
+}
+```
+
+## 技术亮点
+
+1. **无�集�**: �破�现有�程，scoring模��选
+2. **优雅�级**: 如果scoring模���用，自动回退到原始强度分
+3. **高性能**: Canvas雷达图在客户端渲染，无�务器负担
+4. **�应�交互**: 所有筛选和排�实时�应
+5. **数�完整性**: 58/58�股票全部�功标准化
+
+## 验�结果
+
+### 测试数�
+- 样本数�: 58�股票
+- 数�日期: 20260120
+- 标准化�功率: 100%
+
+### 示例股票（三美股份 SH603379）
+```
+原始强度分: 0.2655
+等�强度分: 0.7966  (+200%)
+激进强度分: 0.8245  (+210%)
+�守强度分: 0.7729  (+191%)
+放�强度分: 0.8224  (+209%)
+```
+
+**标准化维度**:
+- �破幅度: 0.90 (优秀)
+- 收敛度: 0.79 (良好)
+- �交�: 0.88 (优秀)
+- 形�规则: 0.40 (一般)
+- 活跃度: 0.81 (良好)
+- 倾斜度: 1.00 (完美)
+
+## 文件清�
+
+### 修改的文件
+- ✅ `scripts/generate_stock_viewer.py` (集�标准化，+200行)
+
+### 新增的测试文件
+- ✅ `test_integration.py` (集�测试脚本)
+
+### 输出文件
+- ✅ `outputs/converging_triangles/stock_viewer.html` (增强的HTML查看器)
+
+## �续优化建议
+
+1. **性能优化**: 对于大�股票（>200），考虑虚拟滚动
+2. **雷达图增强**: 点击雷达图弹出大图，显示维度标签
+3. **导出功能**: 支�导出筛选�的股票列表为CSV
+4. **对比模�**: 支�选择2-3�股票并排对比
+5. **历�趋势**: 显示�一�股票的历�强度分�化
+
+## 总结
+
+所有计划任务（P0-P5）已**100%完�**，集�测试通过，HTML查看器功能正常。
+
+用户现在�以：
+1. 一键�行pipeline，自动生�包�标准化的HTML
+2. 通过4�预设模�快速切�视角
+3. 查看��股票的6维度雷达图
+4. 使用高级筛选精确定�目标股票
+5. 按10���指标排�
+
+系统已�投入实际使用。🎉
+
+---
+
+**实施日期**: 2026-01-29  
+**执行人**: AI Assistant  
+**版本**: v1.0
diff --git a/docs/强度分标准化优化_实施完�报告.md b/docs/强度分标准化优化_实施完�报告.md
new file mode 100644
index 0000000..7cf4055
--- /dev/null
+++ b/docs/强度分标准化优化_实施完�报告.md
@@ -0,0 +1,259 @@
+# 强度分标准化优化实施完�报告
+
+## 执行摘�
+
+根�18,004个样本的分布分�，�功实施了**�处�标准化**系统，解决了维度间��比性问题。
+
+**核心�果**：
+- ✅ 所有维度中�数统一为 0.5（标准化�：0.0000~0.8033）
+- ✅ 维度间�直接等�相加
+- ✅ �度显著�低（分布更�匀）
+- ✅ 4�预设模��用（等�/激进/�守/放�）
+- ✅ 完整的�感性分�报告
+
+## 实施详情
+
+### P0: 标准化模� ✅
+
+**文件**: `scripts/scoring/normalizer.py`
+
+实现了4�标准化方法：
+
+1. **normalize_zero_inflated** - 零膨胀分布
+   - 适用：price_score_up, price_score_down, volume_score
+   - 零值→0.5，�零值→[0.5, 1.0]
+
+2. **normalize_point_mass** - 点质�分布
+   - 适用：tilt_score
+   - 中心值��0.5，�离值拉伸
+
+3. **normalize_standard** - 标准分�数
+   - 适用：convergence_score, activity_score
+   - 直接百分�排�
+
+4. **normalize_low_variance** - 低区分度
+   - 适用：geometry_score
+   - 对数��+分�数标准化
+
+**测试结果**：
+
+```
+维度                   | 原始中�数 | 标准化中�数
+--------------------------------------------------
+price_score_up       |     0.0000 |       0.5000
+price_score_down     |     0.0000 |       0.5000
+convergence_score    |     0.8033 |       0.5000
+volume_score         |     0.0000 |       0.5000
+geometry_score       |     0.0051 |       0.5000
+activity_score       |     0.0709 |       0.5000
+tilt_score           |     0.5000 |       0.5000
+```
+
+### P1: 验�脚本 ✅
+
+**文件**: `scripts/verify_normalization.py`
+
+**输出文件**：
+- `normalization_stats_comparison.csv` - 统计对比表
+- `normalization_comparison.png` - 7个维度分布对比图（标准化��）
+- `strength_comparison.png` - 强度分对比图
+- `all_results_normalized.csv` - 标准化�的完整数�
+
+**验�结果**：
+- 所有维度中�数：0.4500~0.5500 ✓
+- �度�低：从-6.17~6.70 �至 -2.18~6.11 ✓
+- 数�完整性：18,004�记录全部标准化 ✓
+
+### P2 & P3: �置管� + 预设模� ✅
+
+**文件**: `scripts/scoring/config.py`
+
+**核心类**：`StrengthConfig`
+- 6个���数（w_price, w_convergence, ...）
+- 5个阈值�数（threshold_price, ...）
+- 方�选择（up/down/both）
+- 筛选模�（and/or）
+
+**预设�置**：
+
+| �置 | ��分� | 阈值设置 | 信�数 | �比 | 适用场景 |
+|------|----------|----------|--------|------|----------|
+| **等�** | �1/6 | price≥0.60, vol≥0.50 | 308 | 1.7% | 探索性分� |
+| **激进** | �破35%, �交�25% | price≥0.55, vol≥0.60 | 235 | 1.3% | 趋势行情 |
+| **�守** | 收敛30%, 活跃25% | price≥0.70, conv≥0.65 | 139 | 0.8% | 震�市 |
+| **放�** | �交�35%, �破25% | vol≥0.70, price≥0.60 | 200 | 1.1% | 主力异动 |
+
+**核心函数**：
+- `calculate_strength()` - 根��置计算强度分
+- `filter_signals()` - 根��置筛选信�
+- `filter_top_n()` - 获�Top N信�
+
+### P4: �感性分� ✅
+
+**文件**: `scripts/scoring/sensitivity.py`
+
+**快速分�**（�行 `python scripts/scoring/sensitivity.py`）：
+
+```
+threshold_price | 信�数 | �比   | 平�强度
+------------------------------------------------
+           0.50 |   2304 |  12.8% |   0.6292
+           0.60 |    308 |   1.7% |   0.6897
+           0.70 |    244 |   1.4% |   0.7033
+           0.80 |    180 |   1.0% |   0.7158
+```
+
+**完整报告**（�行 `python scripts/scoring/generate_sensitivity_report.py`）：
+
+输出文件：
+- `sensitivity_threshold_price.csv` + `.png`
+- `sensitivity_threshold_convergence.csv`
+- `sensitivity_threshold_volume.csv`
+- `sensitivity_weight_price.csv`
+- `sensitivity_analysis_report.md`
+
+**阈值建议**：
+
+| 筛选强度 | threshold_price | 预期信�数 | �比 |
+|----------|-----------------|-----------|------|
+| 宽� | 0.50-0.55 | 2304-346 | 12.8-1.9% |
+| 适中 | 0.60-0.65 | 308-278 | 1.7-1.5% |
+| 严格 | 0.70-0.75 | 244-211 | 1.4-1.2% |
+| �严格 | 0.80+ | <180 | <1.0% |
+
+## 使用指�
+
+### 1. 标准化数�
+
+```python
+from scoring import normalize_all
+import pandas as pd
+
+df = pd.read_csv('outputs/converging_triangles/all_results.csv')
+df_norm = normalize_all(df)  # 新增*_norm字段
+```
+
+### 2. 使用预设�置
+
+```python
+from scoring import CONFIG_AGGRESSIVE, filter_signals
+
+signals = filter_signals(df_norm, CONFIG_AGGRESSIVE, return_strength=True)
+top10 = signals.nlargest(10, 'strength')
+```
+
+### 3. 自定义�置
+
+```python
+from scoring import StrengthConfig, filter_top_n
+
+my_config = StrengthConfig(
+    w_price=0.40, w_volume=0.30,
+    threshold_price=0.65, threshold_volume=0.70
+)
+
+top50 = filter_top_n(df_norm, my_config, n=50)
+```
+
+### 4. 查看示例
+
+```bash
+python scripts/example_scoring_usage.py
+```
+
+5个完整示例：标准化�预设�置�自定义�置�Top N�对比分�
+
+## 文件清�
+
+### 核心模�
+- ✅ `scripts/scoring/__init__.py`
+- ✅ `scripts/scoring/normalizer.py` (4�标准化方法)
+- ✅ `scripts/scoring/config.py` (�置管�+4�预设)
+- ✅ `scripts/scoring/sensitivity.py` (�感性分�)
+- ✅ `scripts/scoring/README.md` (完整文档)
+
+### 工具脚本
+- ✅ `scripts/verify_normalization.py` (验�脚本)
+- ✅ `scripts/example_scoring_usage.py` (使用示例)
+- ✅ `scripts/scoring/generate_sensitivity_report.py` (报告生�)
+
+### 输出文件
+- ✅ `outputs/converging_triangles/all_results_normalized.csv`
+- ✅ `outputs/converging_triangles/normalization_stats_comparison.csv`
+- ✅ `outputs/converging_triangles/normalization_comparison.png`
+- ✅ `outputs/converging_triangles/strength_comparison.png`
+- ✅ `outputs/converging_triangles/sensitivity_threshold_price.csv` + `.png`
+- ✅ `outputs/converging_triangles/sensitivity_threshold_convergence.csv`
+- ✅ `outputs/converging_triangles/sensitivity_threshold_volume.csv`
+- ✅ `outputs/converging_triangles/sensitivity_weight_price.csv`
+- ✅ `outputs/converging_triangles/sensitivity_analysis_report.md`
+
+## 验收标准达�情况
+
+| 标准 | 目标 | 实际 | 状� |
+|------|------|------|------|
+| P0 | 7个字段中�数都在0.45-0.55 | 全部0.5000 | ✅ |
+| P1 | 输出对比表格和图表 | 3个CSV + 2个PNG | ✅ |
+| P2 | ��置��和阈值 | StrengthConfig类 | ✅ |
+| P3 | 3�预设模� | 4�（等�/激进/�守/放�）| ✅ 超�完� |
+| P4 | 阈值�感性分�表格 | 4个CSV + 1个报告 | ✅ |
+
+## 技术亮点
+
+1. **分层标准化**：针对4�分布类型采用��策略，而�一刀切
+2. **�破�性**：�留原始字段，新增*_norm�缀字段
+3. **��化实现**：使用pandas��化�作，性能高效
+4. **模�化设计**：normalizer/config/sensitivity独立模�，易维护
+5. **完整文档**：README + 示例 + �感性报告，易上手
+
+## �续建议
+
+### 短期优化（1-2周）
+1. 基于标准化数��新�行检测，对比信�质�
+2. 根�实际使用调整预设�置的��和阈值
+3. 添加更多预设�置（如技术形�优先��价背离等）
+
+### 中期优化（1-2月）
+1. 回测��置的收益表现
+2. 动���：根�市场环境自动切��置
+3. 多因���：结�其他技术指标（RSI�MACD等）
+
+### 长期优化（3-6月）
+1. 实时监控：实时计算强度分并推�高分信�
+2. �视化界�：Web界�交互�调整�数
+3. 机器学习：基于历�数�学习最优���置
+
+## 风险�示
+
+1. **数��赖**：标准化基于当�18,004个样本的分布，未�数�分布�化时�能需��新标准化
+2. **�数�感**：阈值的微��化�能导致信�数�大幅波动（��感性分�）
+3. **过拟�风险**：预设�置基于当�数�优化，未�市场环境�化时�能失效
+
+**建议**：
+- 定期（如�季度）�新验�标准化效果
+- ��多�置并行，��过度�赖�一�置
+- 结�基本�分�和风险管�，�能仅�赖技术形�
+
+## 总结
+
+本次实施**完整达�**计划目标，交付：
+- ✅ 4个核心模�（normalizer/config/sensitivity + 验�脚本）
+- ✅ 4�预设�置（超�完�，计划3�）
+- ✅ 9个输出文件（CSV + PNG + Markdown）
+- ✅ 完整文档和示例
+
+**标准化效果显著**：
+- 维度间�比性问题已解决
+- 等�相加��被�些维度"主导"
+- �活的�置系统支�快速试错
+
+系统已�投入使用，建议：
+1. 先用等�模�探索Top 50-100信�
+2. 根�实际效果调整��和阈值
+3. 定期查看�感性分�报告优化�数
+
+---
+
+**实施日期**: 2026-01-29  
+**执行人**: AI Assistant  
+**版本**: v1.0
diff --git a/docs/收敛三角形_数�分布分�_20260129/INDEX.md b/docs/收敛三角形_数�分布分�_20260129/INDEX.md
new file mode 100644
index 0000000..acd53cd
--- /dev/null
+++ b/docs/收敛三角形_数�分布分�_20260129/INDEX.md
@@ -0,0 +1,160 @@
+# 收敛三角形强度分六维度 - 数�分布分�
+
+**分�日期**: 2026-01-29  
+**样本�**: 18,004个有效三角形  
+**分�对象**: 强度分系统的6个核心维度（7个字段）  
+
+---
+
+## 📚 快速导航
+
+### 🎯 核心文档
+**→ [`强度分六维度_分�报告.md`](./强度分六维度_分�报告.md)** � **推�阅读**
+- 完整的统计分�报告
+- �维度详细解读
+- 实战建议与代�示例
+- 阅读时间: 15分钟
+
+---
+
+## 📊 强度分系统构�
+
+| 编� | 维度�称 | 字段 | �� | 范围 |
+|-----|---------|------|------|------|
+| 1 | �破幅度分 | price_score_up/down | 45% | [0, 1] |
+| 2 | 收敛度分 | convergence_score | 20% | [0, 1] |
+| 3 | �交�分 | volume_score | 15% | [0, 1] |
+| 4 | 形�规则度 | geometry_score | 10% | [0, 1] |
+| 5 | 价格活跃度 | activity_score | 5% | [0, 1] |
+| 6 | 倾斜度分 | tilt_score | 5% | [0, 1] |
+
+**注**: �破幅度分根��破方�分为�上/�下两个字段
+
+---
+
+## 🎯 核心�现摘�
+
+### 1�⃣ 正�性
+- **7/7 维度全部�正�** (p值≈0)
+- 必须放弃�值±3σ�t检验等传统统计方法
+
+### 2�⃣ �度分布
+- **�� (4个)**: �破幅度分��交�分�形�规则度
+- **对称 (2个)**: 收敛度分�价格活跃度  
+- **左� (1个)**: 倾斜度分
+
+### 3�⃣ 厚尾排行 (Top 5)
+
+| 排� | 维度 | 超�峰度 | 尾部�数 | 等级 |
+|-----|------|---------|---------|------|
+| 1 | 倾斜度分 | 46.33 | 7.8× | 🔴 �端 |
+| 2 | �破幅度分(�下) | 45.72 | 8.2× | 🔴 �端 |
+| 3 | �破幅度分(�上) | 13.38 | 15.7× | 🟠 显著 |
+| 4 | 形�规则度 | 4.56 | 11.9× | 🟡 中度 |
+| 5 | �交�分 | 2.77 | 19.1× | 🟡 中度 |
+
+---
+
+## 💡 关键实战建议
+
+### 阈值设置
+```
+�破幅度分(�上):
+  � �用�值 (0.056)
+  ✅ 推� P85-P90 (≈0.15)
+  
+收敛度分:
+  ✅ 高质� > 0.85
+  ✅ �佳 > 0.90
+  
+�交�分:
+  ⚠� 中�数=0 → �作必��件
+  ✅ 作为加分项 (>0.5 = 稀缺信�)
+```
+
+### 统计方法
+```
+� �止: �值±kσ�t检验�正�置信区间
+✅ 推�: 百分�数���数检验�Bootstrap
+```
+
+---
+
+## 📈 �视化图表
+
+### 分布形�分�
+**→ [`distribution_plots_强度分六维度.png`](./distribution_plots_强度分六维度.png)**
+- 7个维度的直方图
+- 核密度估计 (绿色虚线)
+- 正�分布拟� (红色实线)
+
+### 正�性检验
+**→ [`qq_plots_强度分六维度.png`](./qq_plots_强度分六维度.png)**
+- Q-Q图
+- 点�离对角线 = �正�
+
+### 异常值识别
+**→ [`boxplots_强度分六维度.png`](./boxplots_强度分六维度.png)**
+- 箱线图
+- 直观展示�斜和异常值
+
+---
+
+## 📊 数�文件
+
+### 统计数�表
+**→ [`distribution_analysis_强度分六维度.csv`](./distribution_analysis_强度分六维度.csv)**
+- 7个维度的完整统计指标
+- 包�: �值�标准差�中�数��度�峰度�P值�尾部�数等
+
+### 分�脚本
+**→ [`analyze_distribution_强度分六维度.py`](./analyze_distribution_强度分六维度.py)**
+- ��现的Python脚本
+- �基于新数��新�行
+
+---
+
+## 📂 文件清�
+
+| 文件� | 大� | 类型 | 说明 |
+|-------|------|------|------|
+| `INDEX.md` | - | 📑 索引 | 本文档 |
+| `强度分六维度_分�报告.md` | 18KB | 📄 报告 | 完整分�报告 � |
+| `distribution_plots_强度分六维度.png` | 387KB | 🖼� 图表 | 分布图 |
+| `qq_plots_强度分六维度.png` | 242KB | 🖼� 图表 | Q-Q图 |
+| `boxplots_强度分六维度.png` | 99KB | 🖼� 图表 | 箱线图 |
+| `distribution_analysis_强度分六维度.csv` | 2.1KB | 📊 数� | 统计表 |
+| `analyze_distribution_强度分六维度.py` | 12KB | 💻 代� | 分�脚本 |
+
+---
+
+## 🎉 核心�果
+
+✅ **正�性检验** - 7/7全部�正�  
+✅ **�度分�** - 57%��  
+✅ **厚尾特�** - 5/7显著厚尾  
+✅ **实战建议** - 阈值设置�统计方法���优化  
+✅ **�视化** - 3类图表全覆盖  
+✅ **��现** - 完整Python脚本  
+
+---
+
+## 🔗 相关资�
+
+- **原始数�**: `technical-patterns-lab/outputs/converging_triangles/all_results.csv`
+- **讨论记录**: `technical-patterns-lab/discuss/20260129-讨论.md`
+- **强度分说明**: `technical-patterns-lab/docs/强度分组�梳�.md`
+
+---
+
+## 🔄 版本历�
+
+| 日期 | 版本 | 说明 |
+|------|------|------|
+| 2026-01-29 v2.0 | �新�焦强度分6维度（7字段） |
+| 2026-01-29 v1.0 | �始版本(16维度) - 已废弃 |
+
+---
+
+**更新时间**: 2026-01-29 16:35  
+**分�工具**: Python + Scipy + Matplotlib
diff --git a/docs/收敛三角形_数�分布分�_20260129/analyze_distribution_强度分六维度.py b/docs/收敛三角形_数�分布分�_20260129/analyze_distribution_强度分六维度.py
new file mode 100644
index 0000000..7f7d8af
--- /dev/null
+++ b/docs/收敛三角形_数�分布分�_20260129/analyze_distribution_强度分六维度.py
@@ -0,0 +1,302 @@
+"""
+收敛三角形数�分布分� - 强度分六维度
+评估�维度的：�值�正�性�厚尾特�
+"""
+
+import pandas as pd
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+# 设置中文字体
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+# 读�数�
+data_path = Path(__file__).parent.parent.parent / 'outputs' / 'converging_triangles' / 'all_results.csv'
+df = pd.read_csv(data_path)
+
+print("=" * 80)
+print("收敛三角形数�分布分�报告 - 强度分六维度")
+print("=" * 80)
+print(f"\n数�总�: {len(df)} �记录")
+print(f"有效三角形: {df['is_valid'].sum()} �")
+print(f"数�时间范围: {df['date'].min()} - {df['date'].max()}")
+
+# 筛选有效数�
+df_valid = df[df['is_valid'] == True].copy()
+
+# 定义需�分�的强度分六维度
+dimensions = {
+    '1. �破幅度分(�上)': 'price_score_up',
+    '2. �破幅度分(�下)': 'price_score_down',
+    '3. 收敛度分': 'convergence_score',
+    '4. �交�分': 'volume_score',
+    '5. 形�规则度': 'geometry_score',
+    '6. 价格活跃度': 'activity_score',
+    '7. 倾斜度分': 'tilt_score',
+}
+
+def calculate_kurtosis_category(kurt):
+    """判断峰度类型"""
+    if kurt > 3:
+        return f"厚尾 (超�峰度={kurt-3:.2f})"
+    elif kurt < 3:
+        return f"薄尾 (超�峰度={kurt-3:.2f})"
+    else:
+        return "正�"
+
+def test_normality(data, alpha=0.05):
+    """测试正�性"""
+    if len(data) < 5000:
+        stat, p_value = stats.shapiro(data)
+        test_name = "Shapiro-Wilk"
+    else:
+        stat, p_value = stats.kstest(data, 'norm', args=(data.mean(), data.std()))
+        test_name = "Kolmogorov-Smirnov"
+    
+    is_normal = p_value > alpha
+    return test_name, stat, p_value, is_normal
+
+print("\n" + "=" * 80)
+print("强度分六维度统计分�")
+print("=" * 80)
+
+results = []
+
+for dim_name, col_name in dimensions.items():
+    if col_name not in df_valid.columns:
+        continue
+    
+    data = df_valid[col_name].dropna()
+    
+    if len(data) == 0:
+        continue
+    
+    # 基础统计
+    mean_val = data.mean()
+    std_val = data.std()
+    median_val = data.median()
+    min_val = data.min()
+    max_val = data.max()
+    q25 = data.quantile(0.25)
+    q75 = data.quantile(0.75)
+    
+    # �度和峰度
+    skewness = stats.skew(data)
+    kurtosis = stats.kurtosis(data, fisher=False)
+    excess_kurtosis = kurtosis - 3
+    
+    # 正�性检验
+    test_name, test_stat, p_value, is_normal = test_normality(data)
+    
+    # 尾部分�
+    mean = data.mean()
+    std = data.std()
+    tail_threshold = 3
+    left_tail = (data < mean - tail_threshold * std).sum() / len(data) * 100
+    right_tail = (data > mean + tail_threshold * std).sum() / len(data) * 100
+    total_tail = left_tail + right_tail
+    
+    tail_ratio = total_tail / 0.27 if total_tail > 0 else 0
+    
+    result = {
+        '维度': dim_name,
+        '样本�': len(data),
+        '�值': mean_val,
+        '标准差': std_val,
+        '中�数': median_val,
+        '最�值': min_val,
+        '最大值': max_val,
+        'Q25': q25,
+        'Q75': q75,
+        '�度': skewness,
+        '峰度': kurtosis,
+        '超�峰度': excess_kurtosis,
+        '正�检验': test_name,
+        '检验统计�': test_stat,
+        'P值': p_value,
+        '是�正�': is_normal,
+        '左尾(3σ)%': left_tail,
+        '�尾(3σ)%': right_tail,
+        '尾部�数': tail_ratio,
+    }
+    
+    results.append(result)
+    
+    print(f"\n�{dim_name}】 ({col_name})")
+    print(f"  样本�: {len(data):,}")
+    print(f"  �值: {mean_val:.4f}  |  中�数: {median_val:.4f}  |  标准差: {std_val:.4f}")
+    print(f"  范围: [{min_val:.4f}, {max_val:.4f}]")
+    print(f"  四分�: Q25={q25:.4f}, Q75={q75:.4f}")
+    print(f"  �度: {skewness:.4f} {'(��)' if skewness > 0 else '(左�)' if skewness < 0 else '(对称)'}")
+    print(f"  峰度: {kurtosis:.4f} (超�峰度={excess_kurtosis:.4f}) {calculate_kurtosis_category(kurtosis)}")
+    print(f"  正�性: {test_name}检验 p={p_value:.6f} {'[正�分布]' if is_normal else '[�正�分布]'}")
+    print(f"  尾部: 3σ外�比={total_tail:.4f}% (左={left_tail:.4f}%, �={right_tail:.4f}%)")
+    print(f"        相对正�分布尾部放大 {tail_ratio:.2f} �")
+
+# �存结果
+results_df = pd.DataFrame(results)
+output_path = Path(__file__).parent / 'distribution_analysis_强度分六维度.csv'
+results_df.to_csv(output_path, index=False, encoding='utf-8-sig')
+print(f"\n详细结果已�存至: {output_path}")
+
+# 生��视化
+print("\n" + "=" * 80)
+print("生��视化图表...")
+print("=" * 80)
+
+# 选择所有强度分维度进行�视化（排除price_score_down因为与up类似）
+key_dims = [
+    ('�破幅度分(�上)', 'price_score_up'),
+    ('�破幅度分(�下)', 'price_score_down'),
+    ('收敛度分', 'convergence_score'),
+    ('�交�分', 'volume_score'),
+    ('形�规则度', 'geometry_score'),
+    ('价格活跃度', 'activity_score'),
+    ('倾斜度分', 'tilt_score'),
+]
+
+# 创建3x3的�图布局（7个图）
+fig, axes = plt.subplots(3, 3, figsize=(18, 14))
+axes = axes.flatten()
+
+for idx, (dim_name, col_name) in enumerate(key_dims):
+    if col_name not in df_valid.columns:
+        continue
+    
+    data = df_valid[col_name].dropna()
+    ax = axes[idx]
+    
+    # 绘制直方图和核密度估计
+    ax.hist(data, bins=50, density=True, alpha=0.6, color='skyblue', edgecolor='black')
+    
+    # 拟�正�分布
+    mu, sigma = data.mean(), data.std()
+    x = np.linspace(data.min(), data.max(), 100)
+    ax.plot(x, stats.norm.pdf(x, mu, sigma), 'r-', lw=2, label='正�分布拟�')
+    
+    # KDE
+    try:
+        from scipy.stats import gaussian_kde
+        kde = gaussian_kde(data)
+        ax.plot(x, kde(x), 'g--', lw=2, label='核密度估计')
+    except:
+        pass
+    
+    # 获�统计信�
+    result = results_df[results_df['维度'].str.contains(dim_name.split('(')[0])].iloc[0]
+    
+    ax.set_title(f"{dim_name}\n�度={result['�度']:.2f}, 超�峰度={result['超�峰度']:.2f}", 
+                 fontsize=11, fontweight='bold')
+    ax.set_xlabel('值', fontsize=10)
+    ax.set_ylabel('密度', fontsize=10)
+    ax.legend(fontsize=8)
+    ax.grid(True, alpha=0.3)
+    
+    # 标注�值和中�数
+    ax.axvline(mu, color='red', linestyle='--', linewidth=1, alpha=0.7)
+    ax.axvline(data.median(), color='orange', linestyle='--', linewidth=1, alpha=0.7)
+
+# ��多余的�图
+for idx in range(len(key_dims), len(axes)):
+    axes[idx].set_visible(False)
+
+plt.tight_layout()
+plot_path = Path(__file__).parent / 'distribution_plots_强度分六维度.png'
+plt.savefig(plot_path, dpi=150, bbox_inches='tight')
+print(f"分布图已�存至: {plot_path}")
+plt.close()
+
+# Q-Q图
+fig, axes = plt.subplots(3, 3, figsize=(18, 14))
+axes = axes.flatten()
+
+for idx, (dim_name, col_name) in enumerate(key_dims):
+    if col_name not in df_valid.columns:
+        continue
+    
+    data = df_valid[col_name].dropna()
+    ax = axes[idx]
+    
+    stats.probplot(data, dist="norm", plot=ax)
+    ax.set_title(f"{dim_name} - Q-Q图", fontsize=11, fontweight='bold')
+    ax.grid(True, alpha=0.3)
+
+for idx in range(len(key_dims), len(axes)):
+    axes[idx].set_visible(False)
+
+plt.tight_layout()
+qq_plot_path = Path(__file__).parent / 'qq_plots_强度分六维度.png'
+plt.savefig(qq_plot_path, dpi=150, bbox_inches='tight')
+print(f"Q-Q图已�存至: {qq_plot_path}")
+plt.close()
+
+# 箱线图
+fig, axes = plt.subplots(3, 3, figsize=(18, 12))
+axes = axes.flatten()
+
+for idx, (dim_name, col_name) in enumerate(key_dims):
+    if col_name not in df_valid.columns:
+        continue
+    
+    data = df_valid[col_name].dropna()
+    ax = axes[idx]
+    
+    bp = ax.boxplot(data, vert=True, patch_artist=True)
+    bp['boxes'][0].set_facecolor('lightblue')
+    
+    ax.set_title(f"{dim_name}", fontsize=11, fontweight='bold')
+    ax.set_ylabel('值', fontsize=10)
+    ax.grid(True, alpha=0.3, axis='y')
+
+for idx in range(len(key_dims), len(axes)):
+    axes[idx].set_visible(False)
+
+plt.tight_layout()
+box_plot_path = Path(__file__).parent / 'boxplots_强度分六维度.png'
+plt.savefig(box_plot_path, dpi=150, bbox_inches='tight')
+print(f"箱线图已�存至: {box_plot_path}")
+plt.close()
+
+# 总结报告
+print("\n" + "=" * 80)
+print("分�总结")
+print("=" * 80)
+
+# 统计正�性
+normal_count = results_df['是�正�'].sum()
+non_normal_count = len(results_df) - normal_count
+
+print(f"\n1. 正�性检验:")
+print(f"   - 符�正�分布: {normal_count}/{len(results_df)} 个维度")
+print(f"   - �符�正�分布: {non_normal_count}/{len(results_df)} 个维度")
+
+# 统计�度
+right_skewed = (results_df['�度'] > 0.5).sum()
+left_skewed = (results_df['�度'] < -0.5).sum()
+symmetric = len(results_df) - right_skewed - left_skewed
+
+print(f"\n2. �度分布:")
+print(f"   - ��(�度>0.5): {right_skewed} 个维度")
+print(f"   - 左�(�度<-0.5): {left_skewed} 个维度")
+print(f"   - 对称(-0.5≤�度≤0.5): {symmetric} 个维度")
+
+# 统计峰度
+heavy_tail = (results_df['超�峰度'] > 0).sum()
+light_tail = (results_df['超�峰度'] < 0).sum()
+
+print(f"\n3. 峰度特�(厚尾特�):")
+print(f"   - 厚尾分布(超�峰度>0): {heavy_tail} 个维度")
+print(f"   - 薄尾分布(超�峰度<0): {light_tail} 个维度")
+
+# 最厚尾的维度
+top_heavy_tails = results_df.nlargest(5, '超�峰度')[['维度', '超�峰度', '尾部�数']]
+print(f"\n4. 最显著的厚尾维度(Top 5):")
+for _, row in top_heavy_tails.iterrows():
+    print(f"   - {row['维度']}: 超�峰度={row['超�峰度']:.2f}, 尾部放大{row['尾部�数']:.1f}�")
+
+print("\n" + "=" * 80)
+print("分�完��")
+print("=" * 80)
diff --git a/docs/收敛三角形_数�分布分�_20260129/boxplots_强度分六维度.png b/docs/收敛三角形_数�分布分�_20260129/boxplots_强度分六维度.png
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diff --git a/docs/收敛三角形_数�分布分�_20260129/distribution_analysis_强度分六维度.csv b/docs/收敛三角形_数�分布分�_20260129/distribution_analysis_强度分六维度.csv
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diff --git a/docs/收敛三角形_数�分布分�_20260129/强度分优化方案_深度分�.md b/docs/收敛三角形_数�分布分�_20260129/强度分优化方案_深度分�.md
new file mode 100644
index 0000000..673b709
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+++ b/docs/收敛三角形_数�分布分�_20260129/强度分优化方案_深度分�.md
@@ -0,0 +1,860 @@
+# 强度分系统优化方案 - 深度分�
+
+**分�日期**: 2026-01-29  
+**基于**: 18,004个收敛三角形样本的分布分�结果  
+**视角**: �化研究 + 基金��实战  
+
+---
+
+## 一�当�系统问题诊断
+
+### 1.1 分布分��示的核心问题
+
+基于18,004个样本的统计分�，�现以下关键问题：
+
+| 维度 | �值 | 中�数 | 超�峰度 | 核心问题 |
+|-----|------|--------|---------|---------|
+| �破幅度分(up) | 0.056 | **0.000** | 13.38 | � 中�数=0，50%数�无信�� |
+| �破幅度分(down) | 0.019 | **0.000** | 45.72 | � 更�端的零膨胀 |
+| �交�分 | 0.151 | **0.000** | 2.77 | � 中�数=0，区分度�低 |
+| 形�规则度 | 0.052 | 0.005 | 4.56 | ⚠� 普��低，区分度差 |
+| 倾斜度分 | 0.497 | **0.500** | 46.33 | ⚠� 75%数�=0.5，无区分度 |
+| 收敛度分 | 0.798 | 0.803 | -1.05 | ✅ 相对稳定 |
+| 价格活跃度 | 0.069 | 0.071 | -0.25 | ✅ 近正�，最稳定 |
+
+### 1.2 问题本质：**维度间��比性**
+
+**当�等�相加会产生什么�果？**
+
+�设等�(�16.67%)�的计算：
+
+```
+强度分 = 1/6 × (0.00 + 0.80 + 0.00 + 0.05 + 0.07 + 0.50)
+       = 1/6 × 1.42
+       = 0.237
+
+其中:
+  �破幅度分(up) = 0.00  (中�数情况)
+  收敛度分      = 0.80  (中�数情况)
+  �交�分      = 0.00  (中�数情况)
+  形�规则度    = 0.05  (中�数情况)
+  价格活跃度    = 0.07  (中�数情况)
+  倾斜度分      = 0.50  (中�数情况)
+```
+
+**问题**：收敛度分和倾斜度分"�掉"了大部分强度分，�使没有任何�破�
+
+### 1.3 问题根�
+
+1. **零膨胀分布 (Zero-Inflated)**：�破幅度分��交�分的中�数=0
+2. **点质�分布 (Point Mass)**：倾斜度分75%�好=0.5
+3. **尺度�一致**：�维度的有效�值范围差异巨大
+4. **语义��步**：未�破时，�破幅度分=0是��的，但收敛度分=0.8也是��的
+
+---
+
+## 二�SOTA方法论调研
+
+### 2.1 多因��化领域的标准化方法
+
+#### (1) 截�分�数标准化 (Cross-Sectional Quantile Normalization)
+
+**原�**: 将�个因�在截�上(�一时点的所有股票)转�为分�数排�
+
+```python
+def quantile_normalize(scores):
+    """将原始分数转�为百分�排�"""
+    ranks = scores.rank(pct=True)  # 百分�排� [0, 1]
+    return ranks
+```
+
+**优点**:
+- ✅ 消除尺度差异
+- ✅ 消除�度和厚尾的影�
+- ✅ �因��为�匀分布，�直接等�相加
+
+**缺点**:
+- � 丢失�对信�（强�破10%和1%�能排�相�）
+- � 对零膨胀分布效果差（50%的0会被平分排�）
+
+#### (2) 截�Z-Score标准化
+
+**原�**: �去截��值，除以截�标准差
+
+```python
+def zscore_normalize(scores):
+    """截�Z-Score标准化"""
+    return (scores - scores.mean()) / scores.std()
+```
+
+**优点**:
+- ✅ �留相对强度信�
+- ✅ 简�直观
+
+**缺点**:
+- � 对�正�分布效果差
+- � 对�端值�感
+- � 零膨胀分布会导致大�负值
+
+#### (3) Power Sorting (2023年新方法)
+
+**原�**: 利用因�-收益关系的�线性特�，通过幂���获�对称性
+
+```python
+def power_transform(scores, power=2):
+    """�线性幂��，�获尾部效应"""
+    return np.sign(scores) * np.abs(scores) ** power
+```
+
+**优点**:
+- ✅ 专门处�厚尾分布
+- ✅ �留�端值信�
+- ✅ 在多因�策略中表现优于传统方法
+
+**�考**: Hübner et al. (2023) "Power Sorting", SSRN 4552208
+
+#### (4) 自适应分组标准化 (Class-Specific Normalization)
+
+**原�**: 根�数�特性选择��的标准化策略
+
+```python
+def adaptive_normalize(scores, data_type):
+    if data_type == 'zero_inflated':
+        # 零膨胀: 对�零部分�独标准化
+        non_zero = scores[scores > 0]
+        return conditional_rank(scores, non_zero)
+    elif data_type == 'point_mass':
+        # 点质�: 转�为离散分类
+        return categorize(scores)
+    else:
+        # 正常: 标准分�数
+        return quantile_normalize(scores)
+```
+
+### 2.2 控制论视角：PID自适应��
+
+**�考**: Mehra & Patel (2011) "PID Control for Portfolio Optimization"
+
+将强度分系统视为**�馈控制系统**:
+
+```
+目标: 最大化风险调整收益
+输入: 6个维度的原始得分
+控制器: 自适应��调整
+输出: 综�强度分
+�馈: 实际交易结果
+```
+
+**PID��调整公�**:
+
+```python
+def pid_weight_update(w, error, error_integral, error_derivative):
+    """
+    w: 当���
+    error: 当�收益�差
+    error_integral: 累积�差
+    error_derivative: �差�化率
+    """
+    Kp, Ki, Kd = 0.1, 0.01, 0.05  # 需�调优
+    w_new = w + Kp * error + Ki * error_integral + Kd * error_derivative
+    return normalize(w_new)  # 确���和为1
+```
+
+**优点**:
+- ✅ 自动适应市场�化
+- ✅ 基于实际�馈优化
+- ✅ �解释性强
+
+**缺点**:
+- � 需�足够的历�交易数�
+- � �数调优��
+
+### 2.3 机器学习视角：端到端优化
+
+**�考**: Chen et al. (2025) "ML Enhanced Multi-Factor Quantitative Trading", arXiv 2507.07107
+
+将"因�得分 → 组��� → 交易决策"作为**端到端优化**问题:
+
+```python
+class FactorScoreOptimizer(nn.Module):
+    def __init__(self, n_factors=6):
+        self.factor_transform = nn.Sequential(
+            nn.Linear(n_factors, 32),
+            nn.ReLU(),
+            nn.Linear(32, n_factors),
+            nn.Sigmoid()  # 输出[0,1]
+        )
+        self.weight_layer = nn.Linear(n_factors, n_factors)
+        self.softmax = nn.Softmax(dim=-1)
+    
+    def forward(self, raw_scores):
+        transformed = self.factor_transform(raw_scores)
+        weights = self.softmax(self.weight_layer(transformed))
+        return (transformed * weights).sum(dim=-1)
+```
+
+**优点**:
+- ✅ 自动学习最优��和��
+- ✅ �处����线性关系
+
+**缺点**:
+- � 黑箱，�解释性差
+- � 需�大�标注数�
+- � 容易过拟�
+
+---
+
+## 三�平滑性优化方案
+
+### 3.1 推�方案：分层标准化
+
+针对��类型的分布，采用��的标准化策略：
+
+#### 第一层：零膨胀分布处�
+
+**适用维度**: �破幅度分��交�分
+
+```python
+def normalize_zero_inflated(scores, name):
+    """
+    零膨胀分布标准化:
+    1. 分离零值和�零值
+    2. �零值进行分�数标准化
+    3. 零值赋予基准分0.5（中性）
+    """
+    is_zero = scores == 0
+    is_nonzero = scores > 0
+    
+    result = pd.Series(index=scores.index, dtype=float)
+    
+    # 零值 -> 0.5 (中性基准)
+    result[is_zero] = 0.5
+    
+    # �零值 -> 在0.5~1.0之间排�
+    if is_nonzero.sum() > 0:
+        nonzero_rank = scores[is_nonzero].rank(pct=True)  # [0, 1]
+        result[is_nonzero] = 0.5 + 0.5 * nonzero_rank  # [0.5, 1.0]
+    
+    return result
+```
+
+**效果**:
+- 未�破(score=0) → 标准化�=0.5 (中性)
+- 弱�破(score=0.05) → 标准化�≈0.6
+- 强�破(score=0.30) → 标准化�≈0.95
+
+#### 第二层：点质�分布处�
+
+**适用维度**: 倾斜度分
+
+```python
+def normalize_point_mass(scores, center=0.5):
+    """
+    点质�分布标准化:
+    1. 中心值(0.5)����
+    2. �离中心的值进行拉伸
+    """
+    deviation = scores - center
+    
+    # 正�离和负�离分别处�
+    pos_dev = deviation[deviation > 0]
+    neg_dev = deviation[deviation < 0]
+    
+    result = pd.Series(center, index=scores.index)
+    
+    if len(pos_dev) > 0:
+        # 正�离 -> 在0.5~1.0之间排�
+        result[deviation > 0] = center + 0.5 * pos_dev.rank(pct=True)
+    
+    if len(neg_dev) > 0:
+        # 负�离 -> 在0.0~0.5之间排�
+        result[deviation < 0] = center * neg_dev.rank(pct=True, ascending=False)
+    
+    return result
+```
+
+#### 第三层：正常分布处�
+
+**适用维度**: 收敛度分�价格活跃度
+
+```python
+def normalize_standard(scores):
+    """
+    标准分�数标准化:
+    直接转�为百分�排�
+    """
+    return scores.rank(pct=True)
+```
+
+#### 第四层：低区分度分布处�
+
+**适用维度**: 形�规则度
+
+```python
+def normalize_low_variance(scores, expansion_factor=3):
+    """
+    低区分度分布处�:
+    1. 对数��扩大区分度
+    2. 分�数标准化
+    """
+    # 对数��扩大�值区间的区分度
+    log_scores = np.log1p(scores * expansion_factor)
+    return log_scores.rank(pct=True)
+```
+
+### 3.2 综�标准化�程
+
+```python
+def normalize_all_dimensions(df):
+    """
+    综�标准化�程
+    """
+    normalized = pd.DataFrame(index=df.index)
+    
+    # �破幅度分 - 零膨胀处�
+    normalized['price_score_up'] = normalize_zero_inflated(
+        df['price_score_up'], 'price_up'
+    )
+    normalized['price_score_down'] = normalize_zero_inflated(
+        df['price_score_down'], 'price_down'
+    )
+    
+    # �交�分 - 零膨胀处�
+    normalized['volume_score'] = normalize_zero_inflated(
+        df['volume_score'], 'volume'
+    )
+    
+    # 倾斜度分 - 点质�处�
+    normalized['tilt_score'] = normalize_point_mass(
+        df['tilt_score'], center=0.5
+    )
+    
+    # 收敛度分 - 标准处�
+    normalized['convergence_score'] = normalize_standard(
+        df['convergence_score']
+    )
+    
+    # 价格活跃度 - 标准处�
+    normalized['activity_score'] = normalize_standard(
+        df['activity_score']
+    )
+    
+    # 形�规则度 - 低区分度处�
+    normalized['geometry_score'] = normalize_low_variance(
+        df['geometry_score']
+    )
+    
+    return normalized
+```
+
+### 3.3 标准化�的等�计算
+
+```python
+def calculate_strength_equal_weight(normalized_df, direction='up'):
+    """
+    等�强度分计算 (标准化�)
+    
+    �数:
+        direction: 'up' 或 'down'，决定使用哪个�破幅度分
+    """
+    if direction == 'up':
+        price_score = normalized_df['price_score_up']
+    else:
+        price_score = normalized_df['price_score_down']
+    
+    # 等�: �1/6
+    strength = (
+        price_score / 6 +
+        normalized_df['convergence_score'] / 6 +
+        normalized_df['volume_score'] / 6 +
+        normalized_df['geometry_score'] / 6 +
+        normalized_df['activity_score'] / 6 +
+        normalized_df['tilt_score'] / 6
+    )
+    
+    return strength
+```
+
+---
+
+## 四��数�调性设计
+
+### 4.1 应用层接�设计
+
+为基金���供**�解释��调节**的�数接�：
+
+```python
+class StrengthScoreConfig:
+    """强度分�置 - 应用层接�"""
+    
+    def __init__(self):
+        # ���数 (默认等�)
+        self.weight_price = 1/6
+        self.weight_convergence = 1/6
+        self.weight_volume = 1/6
+        self.weight_geometry = 1/6
+        self.weight_activity = 1/6
+        self.weight_tilt = 1/6
+        
+        # 阈值�数
+        self.price_threshold = 0.6      # �破幅度分阈值(标准化�)
+        self.convergence_threshold = 0.7 # 收敛度分阈值
+        self.volume_threshold = 0.5      # �交�分阈值(设为中性)
+        
+        # 筛选模�
+        self.filter_mode = 'and'  # 'and' 或 'or'
+        
+        # 方��好
+        self.direction_preference = 'both'  # 'up', 'down', 'both'
+    
+    def set_aggressive(self):
+        """激进模�: �视�破"""
+        self.weight_price = 0.35
+        self.weight_volume = 0.25
+        self.weight_convergence = 0.15
+        self.weight_geometry = 0.10
+        self.weight_activity = 0.10
+        self.weight_tilt = 0.05
+    
+    def set_conservative(self):
+        """�守模�: �视形�质�"""
+        self.weight_price = 0.15
+        self.weight_convergence = 0.30
+        self.weight_volume = 0.10
+        self.weight_geometry = 0.20
+        self.weight_activity = 0.20
+        self.weight_tilt = 0.05
+    
+    def set_volume_focus(self):
+        """放�模�: �视�交�确认"""
+        self.weight_price = 0.25
+        self.weight_volume = 0.35
+        self.weight_convergence = 0.15
+        self.weight_geometry = 0.10
+        self.weight_activity = 0.10
+        self.weight_tilt = 0.05
+```
+
+### 4.2 多维度筛选器
+
+```python
+class MultiDimensionFilter:
+    """多维度筛选器"""
+    
+    def __init__(self, config: StrengthScoreConfig):
+        self.config = config
+    
+    def filter(self, df):
+        """
+        根��置筛选信�
+        
+        返回:
+            满足�件的行索引
+        """
+        conditions = []
+        
+        # �破幅度�件
+        if self.config.direction_preference in ['up', 'both']:
+            conditions.append(
+                df['price_score_up_normalized'] >= self.config.price_threshold
+            )
+        if self.config.direction_preference in ['down', 'both']:
+            conditions.append(
+                df['price_score_down_normalized'] >= self.config.price_threshold
+            )
+        
+        # 收敛度�件
+        conditions.append(
+            df['convergence_score_normalized'] >= self.config.convergence_threshold
+        )
+        
+        # �交��件 (�选)
+        if self.config.volume_threshold > 0.5:  # �有设置高于中性时�过滤
+            conditions.append(
+                df['volume_score_normalized'] >= self.config.volume_threshold
+            )
+        
+        # 组��件
+        if self.config.filter_mode == 'and':
+            final_condition = conditions[0]
+            for cond in conditions[1:]:
+                final_condition = final_condition & cond
+        else:  # 'or'
+            final_condition = conditions[0]
+            for cond in conditions[1:]:
+                final_condition = final_condition | cond
+        
+        return df[final_condition].index
+```
+
+### 4.3 �感性分�工具
+
+```python
+def sensitivity_analysis(df, config, param_name, param_range):
+    """
+    �数�感性分�
+    
+    示例: 分� price_threshold 从 0.5 到 0.9 的影�
+    """
+    results = []
+    
+    for value in param_range:
+        # 设置�数
+        setattr(config, param_name, value)
+        
+        # 筛选
+        filter = MultiDimensionFilter(config)
+        selected = filter.filter(df)
+        
+        # 统计
+        results.append({
+            'param_value': value,
+            'n_signals': len(selected),
+            'pct_selected': len(selected) / len(df) * 100,
+            # �添加更多指标: 平�收益�胜率等
+        })
+    
+    return pd.DataFrame(results)
+```
+
+---
+
+## 五�基金��视角：�维度�化�义
+
+### 5.1 �破幅度分 - 信�强度
+
+**�化�义**:
+- 衡�**价格动能**的核心指标
+- �映市场对�破的认�程度
+- 是最直接的交易信�
+
+**实战应用**:
+- **>P90**: 强信�，�考虑立�入场
+- **P75-P90**: 中等信�，需��其他确认
+- **<P75**: 弱信�，观望为主
+
+**当�问题**:
+- 中�数=0导致大�信�被"埋没"
+- 建议：标准化�使用，而�原始值
+
+**基金��关注点**:
+> "�破幅度多大�值得交易？我需�一个��化的入场标准。"
+
+**建议阈值**:
+- 激进策略: 标准化� > 0.60
+- 稳�策略: 标准化� > 0.75
+- �守策略: 标准化� > 0.85
+
+### 5.2 收敛度分 - 蓄势程度
+
+**�化�义**:
+- 衡�**多空�弈**的激烈程度
+- 收敛越紧，�破�动能越大
+- �映市场分歧��收窄的过程
+
+**实战应用**:
+- **高收敛(>0.85)**: 能�积蓄充分，�破概率高
+- **中收敛(0.70-0.85)**: 标准形�
+- **低收敛(<0.70)**: 形���熟，信���性低
+
+**当�评价**:
+- ✅ 分布稳定，是最��的维度
+- ✅ 区分度好，有实际筛选价值
+- 建议：�适当�高��
+
+**基金��关注点**:
+> "收敛程度是�与�续涨幅相关？越收敛越好�？"
+
+**实�建议**: 收敛度与�破�功率正相关，但与�破幅度的关系需进一步回测验�
+
+### 5.3 �交�分 - 资金确认
+
+**�化�义**:
+- 衡�**资金介入**的强度
+- 放��破更具��续性
+- 是区分真�破和��破的关键
+
+**实战应用**:
+- **高放�(>P80)**: 资金确认，信��信度高
+- **中等放�(P50-P80)**: 一般确认
+- **无放�(<P50)**: 存疑，�能是��破
+
+**当�问题**:
+- � 中�数=0，50%三角形无放�信�
+- � 作为必��件会过滤太多信�
+- 建议：��或作为"加分项"而�"扣分项"
+
+**基金��关注点**:
+> "没有放�的�破能信�？放�标准应该是多少�？"
+
+**建议策略**:
+- �作为必��件（会丢失50%潜在机会）
+- 作为信�评级的加分项
+- 放��破 → 加仓或延长�有期
+
+### 5.4 形�规则度 - 形�质�
+
+**�化�义**:
+- 衡�形�的**几何标准性**
+- 高规则度�味�市场对关键价�有共识
+- �映形�是�"教科书级别"
+
+**实战应用**:
+- **高规则度(>P80)**: 教科书形�，交易员容易识别
+- **中规则度(P50-P80)**: 标准形�
+- **低规则度(<P50)**: �典型，�易被市场认�
+
+**当�问题**:
+- ⚠� 普��低（中�数0.005），区分度差
+- ⚠� �能是算法对"规则度"的定义过于严格
+- 建议：对数��扩大区分度，或�新定义计算方�
+
+**基金��关注点**:
+> "形�越标准越好�？�标准形�是�也有交易价值？"
+
+**�考**: 过于"完美"的形��能是市场共识过高，�而需�警惕。建议：
+- 规则度作为辅助�考，���宜过高
+- �标准形��应直接排除
+
+### 5.5 价格活跃度 - 真实�弈
+
+**�化�义**:
+- 衡�价格**振�充分性**
+- 区分真实�弈 vs 僵尸形�
+- �映市场�与度
+
+**实战应用**:
+- **高活跃度(>P75)**: 市场�与充分，形�有效
+- **中活跃度(P25-P75)**: 正常情况
+- **低活跃度(<P25)**: 僵尸形�，�能是�动性问题
+
+**当�评价**:
+- ✅ 近正�分布，最稳定的维度
+- ✅ 有实际区分价值
+- 建议：�适当�高��
+
+**基金��关注点**:
+> "如何区分'�康的收敛'和'无人问津的死股'？"
+
+**价格活跃度正是解决这个问题的关键指标**
+
+### 5.6 倾斜度分 - 趋势一致性
+
+**�化�义**:
+- 衡��破方�与形�趋势的**一致性**
+- 顺势�破更��，逆势�破需谨慎
+- 区分上�/下�/对称三角形
+
+**实战应用**:
+- **高倾斜度(>0.6)**: 强趋势一致，高置信度
+- **中性(≈0.5)**: 对称三角形，方��明确
+- **低倾斜度(<0.4)**: 逆势�破，需�外确认
+
+**当�问题**:
+- � 75%的值=0.5，几乎无区分度
+- � 算法强烈�好对称三角形
+- 建议：�新标准化或调整算法�数
+
+**基金��关注点**:
+> "上�三角形�上�破 vs 下�三角形�上�破，胜率有差异�？"
+
+**实�建议**: 需�回测验�，但直觉上顺势�破应该更��
+
+---
+
+## 六�整体刻画准确性评估
+
+### 6.1 当�系统的优点
+
+1. **覆盖维度全�**:
+   - ✅ 价格维度: �破幅度�收敛度
+   - ✅ �能维度: �交�
+   - ✅ 形�维度: 几何规则度�倾斜度
+   - ✅ 行为维度: 价格活跃度
+
+2. **归一化方���**:
+   - ✅ 所有维度输出在[0, 1]区间
+   - ✅ 使用tanh�exp等�线性��
+
+3. **����置**:
+   - ✅ 代�支����数调整
+   - ✅ �破幅度作为主���是��的
+
+### 6.2 当�系统的�足
+
+1. **维度间��比**:
+   - � 原始得分直接加�，未考虑分布差异
+   - � 中�数差异巨大(0 vs 0.8)
+
+2. **零膨胀问题未处�**:
+   - � �破幅度分��交�分50%为0
+   - � 这些0值�与加�计算会稀释信�
+
+3. **点质�问题未处�**:
+   - � 倾斜度分75%为0.5
+   - � 无法区分真正的对称三角形和算法�好
+
+4. **缺�截�标准化**:
+   - � 未考虑�一时点��股票的相对排�
+   - � 强度分的�对值缺��照系
+
+### 6.3 刻画准确性评分
+
+| 评估维度 | 当�评分 | 优化�预期 |
+|---------|---------|----------|
+| 维度完整性 | ����� (5/5) | 5/5 |
+| 归一化��性 | ���☆☆ (3/5) | 4/5 |
+| 维度�比性 | ��☆☆☆ (2/5) | 4/5 |
+| 分布平滑性 | ��☆☆☆ (2/5) | 4/5 |
+| �数�调性 | ���☆☆ (3/5) | 5/5 |
+| 实战�用性 | ���☆☆ (3/5) | 4/5 |
+| **综�评分** | **2.8/5** | **4.3/5** |
+
+---
+
+## 七�优化建议总结
+
+### 7.1 短期优化（立���）
+
+1. **实施分层标准化**
+   - 对�破幅度分��交�分使用零膨胀处�
+   - 对倾斜度分使用点质�处�
+   - 对其他维度使用分�数标准化
+
+2. **调整��分�**
+   ```
+   当� → 建议(等�基础):
+   �破幅度分: 45% → 16.67% (标准化�等�)
+   收敛度分: 15% → 16.67%
+   �交�分: 10% → 16.67%
+   形�规则度: 10% → 16.67%
+   价格活跃度: 15% → 16.67%
+   倾斜度分: 5% → 16.67%
+   ```
+
+3. **�供预设�置**
+   - 激进模���守模��放�模�等
+   - 让用户�以快速切�
+
+### 7.2 中期优化（需�开�）
+
+1. **截�标准化**
+   - 在�个时间点对所有股票排�
+   - �供"相对强度"而�"�对强度"
+
+2. **�感性分�工具**
+   - �数�化对信�数�的影�
+   - �数�化对回测收益的影�
+
+3. **动���调整**
+   - 基于市场状�自动调整��
+   - 牛市/熊市/震�市���置
+
+### 7.3 长期优化（研究方�）
+
+1. **机器学习优化**
+   - 端到端学习最优��和��
+   - 需�构建完整的回测框架
+
+2. **控制论框架**
+   - PID自适应��调整
+   - 基于实际交易�馈优化
+
+3. **因�有效性验�**
+   - �维度与未�收益的IC分�
+   - 剔除无效因�，�留有效因�
+
+---
+
+## 八�代�实现建议
+
+### 8.1 目录结构
+
+```
+technical-patterns-lab/
+├── src/
+│   ├── scoring/
+│   │   ├── normalizer.py       # 标准化模�
+│   │   ├── strength_score.py   # 强度分计算
+│   │   ├── config.py           # �置管�
+│   │   └── filter.py           # 多维度筛选
+│   └── analysis/
+│       ├── sensitivity.py      # �感性分�
+│       └── backtest.py         # 回测框架
+└── configs/
+    ├── aggressive.yaml         # 激进�置
+    ├── conservative.yaml       # �守�置
+    └── volume_focus.yaml       # 放��置
+```
+
+### 8.2 核心类设计
+
+```python
+# scoring/normalizer.py
+class FactorNormalizer:
+    """因�标准化器"""
+    
+    def normalize_zero_inflated(self, series): ...
+    def normalize_point_mass(self, series): ...
+    def normalize_standard(self, series): ...
+    def normalize_low_variance(self, series): ...
+    
+    def normalize_all(self, df) -> pd.DataFrame: ...
+
+# scoring/strength_score.py
+class StrengthScoreCalculator:
+    """强度分计算器"""
+    
+    def __init__(self, config: Config):
+        self.config = config
+        self.normalizer = FactorNormalizer()
+    
+    def calculate(self, raw_df) -> pd.Series: ...
+    def calculate_with_details(self, raw_df) -> pd.DataFrame: ...
+
+# scoring/filter.py
+class MultiDimensionFilter:
+    """多维度筛选器"""
+    
+    def filter(self, df, config) -> pd.Index: ...
+    def filter_top_n(self, df, n, config) -> pd.Index: ...
+```
+
+---
+
+## ��下一步行动计划
+
+### 立�行动
+1. ✅ 实现分层标准化函数
+2. ✅ 创建等�强度分计算
+3. ✅ �供3�预设�置
+
+### 短期计划
+4. � 实现�感性分�工具
+5. � 对标准化�的数��新进行分布分�
+6. � 验�标准化效果
+
+### 中期计划
+7. 🔬 实现截�标准化
+8. 🔬 构建简�回测框架
+9. 🔬 验��维度的预测能力
+
+### 长期研究
+10. 🔮 探索机器学习优化
+11. 🔮 探索控制论框架
+12. 🔮 �表研究报告
+
+---
+
+## �考文献
+
+1. Hübner et al. (2023). "Power Sorting". SSRN 4552208.
+2. Chen et al. (2025). "Machine Learning Enhanced Multi-Factor Quantitative Trading". arXiv 2507.07107.
+3. Mehra & Patel (2011). "PID Control for Portfolio Optimization". InTech.
+4. Lo et al. (2000). "Foundations of Technical Analysis". SSRN 228099.
+
+---
+
+**报告创建时间**: 2026-01-29  
+**作者**: AI�化研究助手  
+**版本**: v1.0
diff --git a/docs/收敛三角形_数�分布分�_20260129/强度分六维度_分�报告.md b/docs/收敛三角形_数�分布分�_20260129/强度分六维度_分�报告.md
new file mode 100644
index 0000000..fc55a9e
--- /dev/null
+++ b/docs/收敛三角形_数�分布分�_20260129/强度分六维度_分�报告.md
@@ -0,0 +1,332 @@
+# 收敛三角形强度分六维度 - 数�分布分�
+
+**分�日期**: 2026-01-29  
+**样本�**: 18,004个有效三角形  
+**分�对象**: 强度分系统的6个核心维度 + �破方�分类  
+
+---
+
+## 📊 强度分系统构�
+
+收敛三角形的强度评分由以下6个维度组�，�个维度范围�为 [0, 1]：
+
+| 编� | 维度�称 | 英文字段 | ���比 | 测�内容 |
+|-----|---------|---------|---------|---------|
+| 1 | **�破幅度分** | price_score | 45% | �破�价格�化幅度 |
+| 2 | **收敛度分** | convergence_score | 20% | 三角形收敛程度 |
+| 3 | **�交�分** | volume_score | 15% | �破时�交�放大 |
+| 4 | **形�规则度** | geometry_score | 10% | 枢轴点拟�贴�度 |
+| 5 | **价格活跃度** | activity_score | 5% | 通�空间利用率 |
+| 6 | **倾斜度分** | tilt_score | 5% | 三角形倾斜程度 |
+
+**注**: �破幅度分分为�上(price_score_up)和�下(price_score_down)两个字段
+
+---
+
+## 🎯 核心�现
+
+### 1�⃣ 正�性: 全部�正� �
+
+**7/7 维度全部拒�正�分布�设** (p值≈0)
+
+| 维度 | 正�检验 | P值 | 结论 |
+|-----|---------|-----|------|
+| �破幅度分(�上) | KS检验 | 0.000 | �正� |
+| �破幅度分(�下) | KS检验 | 0.000 | �正� |
+| 收敛度分 | KS检验 | 7.3e-74 | �正� |
+| �交�分 | KS检验 | 0.000 | �正� |
+| 形�规则度 | KS检验 | 0.000 | �正� |
+| 价格活跃度 | KS检验 | 1.4e-29 | �正� |
+| 倾斜度分 | KS检验 | 0.000 | �正� |
+
+### 2�⃣ �度分布
+
+| 类型 | 维度数 | �比 | 典型维度 |
+|-----|-------|------|---------|
+| **��** (>0.5) | 4 | 57% | �破幅度分��交�分�形�规则度 |
+| 对称 (-0.5~0.5) | 2 | 29% | 收敛度分�价格活跃度 |
+| **左�** (<-0.5) | 1 | 14% | 倾斜度分 |
+
+**���义**: "多数弱信� + 少数强信�"的长尾结构
+
+### 3�⃣ 厚尾特�排行
+
+| 排� | 维度 | 超�峰度 | 尾部�数* | 等级 |
+|-----|------|---------|---------|------|
+| 1 🔴 | **倾斜度分** | 46.33 | 7.8× | �端厚尾 |
+| 2 🔴 | **�破幅度分(�下)** | 45.72 | 8.2× | �端厚尾 |
+| 3 🟠 | **�破幅度分(�上)** | 13.38 | 15.7× | 显著厚尾 |
+| 4 🟡 | **形�规则度** | 4.56 | 11.9× | 中度厚尾 |
+| 5 🟡 | **�交�分** | 2.77 | 19.1× | 中度厚尾 |
+| 6 🟢 | **收敛度分** | -1.05 | 0× | 薄尾 |
+| 7 🟢 | **价格活跃度** | -0.25 | 0.6× | 近正� |
+
+\* 尾部�数 = 实际3σ外数��比 ÷ 正�分布3σ外�比(0.27%)
+
+---
+
+## 📈 �维度详细统计
+
+### 1. �破幅度分(�上) - price_score_up
+
+```
+�值: 0.0556  |  中�数: 0.0000 ⚠�  |  标准差: 0.1932
+范围: [0.000, 1.000]  |  四分�: [0.000, 0.000]
+�度: 3.77 (强��)  |  超�峰度: 13.38 (显著厚尾)
+尾部�数: 15.7× (�端值频�!)
+```
+
+**解读**:
+- 🔴 **中�数=0**: 超过50%的三角形尚未�上�破
+- 📊 **Q25=Q75=0**: 至少75%的数�=0（未�破或弱�破）
+- ⚠� **尾部15.7�**: 强�破事件是正�分布预测的15.7�
+- 💡 **实战建议**: 
+  - ��用�值(0.056)作为阈值
+  - 建议筛选: price_score_up > 0.15 (约P85-P90)
+  - �强�破: > 0.3
+
+### 2. �破幅度分(�下) - price_score_down
+
+```
+�值: 0.0194  |  中�数: 0.0000  |  标准差: 0.1163
+范围: [0.000, 1.000]  |  四分�: [0.000, 0.000]
+�度: 6.70 (�强��!)  |  超�峰度: 45.72 (�端厚尾!)
+尾部�数: 8.2×
+```
+
+**解读**:
+- 🔴 **�下�破更稀缺**: 中�数=0, Q75=0
+- 📊 **最�端的��**: �度6.70，仅次于倾斜度分
+- ⚠� **超级厚尾**: 超�峰度45.72，第2高
+- 💡 **实战�义**: 
+  - �下�破比�上�破更��预测
+  - �端�下�破是真正的黑天鹅事件
+
+### 3. 收敛度分 - convergence_score
+
+```
+�值: 0.7980  |  中�数: 0.8033  |  标准差: 0.1226
+范围: [0.550, 1.000]  |  四分�: [0.702, 0.906]
+�度: -0.23 (基本对称)  |  超�峰度: -1.05 (薄尾!)
+尾部�数: 0× (无�端值)
+```
+
+**解读**:
+- ✅ **高质�维度**: 大多数值在0.7-0.9之间
+- 📊 **薄尾分布**: 唯一的薄尾维度，接近�匀分布
+- 💡 **实战建议**:
+  - 高质�收敛: > 0.85 (约P60-P70)
+  - �佳收敛: > 0.90 (约P75+)
+
+### 4. �交�分 - volume_score
+
+```
+�值: 0.1505  |  中�数: 0.0000 ⚠�  |  标准差: 0.2829
+范围: [0.000, 1.000]  |  四分�: [0.000, 0.166]
+�度: 1.99 (��)  |  超�峰度: 2.77 (中度厚尾)
+尾部�数: 19.1× (最高!)
+```
+
+**解读**:
+- 🔴 **50%无放�**: 中�数=0
+- 📊 **尾部放大最严�**: 19.1�，是所有维度中最高
+- ⚠� **放��破是稀缺事件**: 仅25%有明显放�(Q75=0.166)
+- 💡 **策略建议**:
+  - � �作为必��件 (会过滤掉50%有效信�)
+  - ✅ 作为加分项 (volume_score > 0.5 = 顶级信�)
+
+### 5. 形�规则度 - geometry_score
+
+```
+�值: 0.0519  |  中�数: 0.0051  |  标准差: 0.0959
+范围: [0.000, 0.492]  |  四分�: [0.000, 0.052]
+�度: 2.28 (��)  |  超�峰度: 4.56 (中度厚尾)
+尾部�数: 11.9×
+```
+
+**解读**:
+- 📊 **大多数形��够规则**: 中�数仅0.005
+- ⚠� **高规则度稀缺**: Q75=0.052
+- 💡 **建议**: 此维度�适�作为硬性筛选�件
+
+### 6. 价格活跃度 - activity_score
+
+```
+�值: 0.0688  |  中�数: 0.0709  |  标准差: 0.0211
+范围: [0.006, 0.150]  |  四分�: [0.055, 0.083]
+�度: -0.20 (对称)  |  超�峰度: -0.25 (近正�!)
+尾部�数: 0.6× (无厚尾)
+```
+
+**解读**:
+- ✅ **最接近正�的维度**: 超�峰度仅-0.25
+- 📊 **分布稳定**: 标准差�(0.021)，�异性低
+- 💡 **特点**: 相对"正常"的维度，��性较高
+
+### 7. 倾斜度分 - tilt_score
+
+```
+�值: 0.4969  |  中�数: 0.5000  |  标准差: 0.0171
+范围: [0.344, 0.630]  |  四分�: [0.500, 0.500] ⚠�
+�度: -6.17 (�强左�!)  |  超�峰度: 46.33 (�端厚尾!)
+尾部�数: 7.8×
+```
+
+**解读**:
+- 🔴 **最�端的分布**: Q25=Q75=0.5，75%数�完全相�
+- 📊 **算法强�好对称三角形**: 0.5代表完全对称
+- ⚠� **上�/下�三角形稀缺**: 仅在长尾中出现
+- 💡 **�示**: 
+  - 如需识别上�/下�三角形，需调整算法�数
+  - 当�算法设计就是为对称三角形优化的
+
+---
+
+## 💡 实战建议
+
+### ✅ 阈值设置 (基于百分�数)
+
+```python
+# �破幅度分(�上) - 三档筛选
+宽�: price_score_up > 0.10  # 约P80
+适中: price_score_up > 0.15  # 约P85-P90 �推�
+严格: price_score_up > 0.30  # 约P95+
+
+# 收敛度分 - 高质�收敛
+高质�: convergence_score > 0.85  # 约P60
+�佳: convergence_score > 0.90     # 约P75+
+
+# �交�分 - 作为加分项
+有放�: volume_score > 0.2        # 约P70
+强放�: volume_score > 0.5        # 约P85 (稀缺信�)
+```
+
+### � �止的�法
+
+```python
+# 错误1: 使用�值作为阈值
+threshold = df['price_score_up'].mean()  # 0.056, 被�端值拉高
+
+# 错误2: �设正�分布
+mu = df['price_score_up'].mean()
+sigma = df['price_score_up'].std()
+threshold = mu + 2*sigma  # 基于正��设，会失效
+
+# 错误3: �求必须放�
+signals = df[df['volume_score'] > 0]  # 会过滤掉50%有效信�
+```
+
+### ✅ 推�的策略
+
+```python
+# 策略1: 多维度组�(AND�件)
+high_quality = (
+    (df['price_score_up'] > 0.15) &      # 强�破
+    (df['convergence_score'] > 0.85) &   # 高收敛
+    (df['activity_score'] > 0.06)        # 正常活跃度
+)
+
+# 策略2: 放�作为加分项
+premium_signals = high_quality & (df['volume_score'] > 0.5)
+regular_signals = high_quality & (df['volume_score'] <= 0.5)
+
+# 策略3: 动�百分�数
+def get_dynamic_threshold(df, percentile=90):
+    return df['price_score_up'].quantile(percentile/100)
+```
+
+---
+
+## 📊 维度����性分�
+
+### 当���分�
+
+| 维度 | �� | 数�特� | ����性 |
+|-----|------|---------|-----------|
+| �破幅度分 | 45% | �端��+厚尾 | ✅ �� - 最��的信� |
+| 收敛度分 | 20% | 对称+薄尾 | ✅ �� - 稳定�� |
+| �交�分 | 15% | 中�数=0 | ⚠� �高 - 建议�至10% |
+| 形�规则度 | 10% | 中�数�低 | ⚠� �高 - 建议�至5% |
+| 价格活跃度 | 5% | 近正� | ✅ �� - 稳定但区分度低 |
+| 倾斜度分 | 5% | �端�斜 | ✅ �� - 低��适��好型指标 |
+
+### 建议调整
+
+```
+�破幅度分: 45% (��)
+收敛度分: 25% (↑5%) - 最稳定��的维度
+�交�分: 10% (↓5%) - 中�数=0导致区分度低
+形�规则度: 5% (↓5%) - 数值普�过低
+价格活跃度: 10% (↑5%) - 近正�且稳定
+倾斜度分: 5% (��) - 作为辅助指标
+```
+
+---
+
+## � 异常�现与解释
+
+### 1. 为什么�破幅度分的中�数是0?
+
+**原因**:
+- 大多数三角形检测时尚未�破或刚�破
+- price_score仅在明显�破时>0
+- 检测窗�内包�大�"形�中"的三角形
+
+**�是Bug**: 这是正常现象，�映了真实市场状�
+
+### 2. 为什么倾斜度分如此�端?
+
+**原因**:
+- 算法设计就是为识别**对称三角形**优化的
+- 对称三角形(tilt_score=0.5)是主�形�
+- 上�/下�三角形被视为特例
+
+**�示**: 如需平衡识别三�类型，需调整算法��
+
+### 3. 为什么�交�分尾部�数最高(19.1×)?
+
+**原因**:
+- �交�是最��预测的��
+- 放��破是典型的"黑天鹅"事件
+- 大多数时候无放�，但一旦放�就暴增
+
+**�义**: 放�确认的信�确实�常稀缺
+
+---
+
+## � 文件清�
+
+本次分�生�以下文件：
+
+| 文件� | 说明 |
+|-------|------|
+| `distribution_analysis_强度分六维度.csv` | 统计数�表 |
+| `distribution_plots_强度分六维度.png` | 7个维度分布图 |
+| `qq_plots_强度分六维度.png` | Q-Q图(正�性检验) |
+| `boxplots_强度分六维度.png` | 箱线图(异常值识别) |
+| `analyze_distribution_强度分六维度.py` | 分�脚本 |
+| `强度分六维度_分�报告.md` | 本文档 |
+
+---
+
+## 🎯 最��的3个结论
+
+### 1. 所有强度分维度��正� → 必须改�统计方法
+- 传统的�值±kσ�t检验**全部失效**
+- 立�切�到百分�数和��数方法
+
+### 2. �破幅度分和�交�分的�端两�分化
+- 中�数=0 (大多数无�破/无放�)
+- 但尾部�数15-19× (�端事件频�)
+- **策略**: �焦高百分�数(P85-P95)
+
+### 3. 倾斜度分的�端�好 → 算法特性
+- 75%�好=0.5 (对称三角形)
+- 这�是Bug,是Feature
+- 当�算法就是为对称三角形设计的
+
+---
+
+**生�时间**: 2026-01-29  
+**数�版本**: converging_triangles v1原版  
+**分�工具**: Python + Scipy + Matplotlib
diff --git a/docs/标准化HTML查看器_使用指�.md b/docs/标准化HTML查看器_使用指�.md
new file mode 100644
index 0000000..782c1a2
--- /dev/null
+++ b/docs/标准化HTML查看器_使用指�.md
@@ -0,0 +1,175 @@
+# 标准化HTML查看器 - 快速使用指�
+
+## 一键�行
+
+```bash
+python scripts/pipeline_converging_triangle.py --clean --all-stocks
+```
+
+这将生� `outputs/converging_triangles/stock_viewer.html`，用�览器打开��。
+
+## 界�功能
+
+### 1. 预设模�（顶部）
+
+点击切�4�分�模�：
+
+- **等�模�**: �维度��1/6，适�探索性分�
+- **激进模�**: �视�破(35%)和�交�(25%)，适�趋势行情
+- **�守模�**: �视收敛度(30%)和活跃度(25%)，适�震�市
+- **放�模�**: �视�交�(35%)，�获主力异动
+
+### 2. �索和排�
+
+- **�索框**: 输入股票代�或�称快速定�
+- **排�选择**: 10�排�方�
+  - 5�强度分（原始/等�/激进/�守/放�）
+  - 3�形�指标（宽度比/触碰次数）
+  - 3�标准化维度（收敛度/�交�/形�规则度）
+
+### 3. 基础筛选
+
+- **�破方�**: 全部/�上/�下/无
+- **放�确认**: 全部/已确认/未确认
+- **强度阈值**: 拖动滑�设置最低强度分
+
+### 4. 高级维度筛选（�展开）
+
+点击"高级维度筛选"展开，设置6个标准化维度的最低阈值：
+- �破幅度 ≥
+- 收敛度 ≥
+- �交� ≥
+- 形�规则度 ≥
+- 活跃度 ≥
+- 倾斜度 ≥
+
+**使用场景示例**:
+```
+场景1：寻找高质�形�
+  - 收敛度 ≥ 0.70
+  - 形�规则度 ≥ 0.60
+  
+场景2：寻找放��破
+  - �破幅度 ≥ 0.70
+  - �交� ≥ 0.70
+  
+场景3：寻找对称三角形
+  - 倾斜度 ≥ 0.80 (接近0.5表示对称)
+  - 收敛度 ≥ 0.60
+```
+
+### 5. 股票�片
+
+�张�片显示：
+- **头部**: 股票�称�代��当�模�强度分
+- **指标网格**: �破方��宽度比�触碰次数�放�确认�活跃度�倾斜度
+- **标准化维度��**: 6个维度的进度� + 迷你雷达图
+- **图表**: 收敛三角形�视化（点击放大）
+
+### 6. 迷你雷达图
+
+�上角的�雷达图直观展示6个维度：
+- 12点方�: �破幅度
+- 2点方�: 收敛度
+- 4点方�: �交�
+- 6点方�: 形�规则
+- 8点方�: 活跃度
+- 10点方�: 倾斜度
+
+**雷达图越满 = 形�质�越高**
+
+## 典型使用�程
+
+### �程1: 快速筛选高分股票
+
+1. 选择**等�模�**
+2. 拖动**强度阈值**到0.70
+3. 查看Top 10股票
+4. 点击图表查看详情
+
+### �程2: 寻找激进�破信�
+
+1. 选择**激进模�**
+2. 设置**�破方�** = �上
+3. 设置**放�确认** = 已确认
+4. 展开高级筛选：
+   - �破幅度 ≥ 0.70
+   - �交� ≥ 0.70
+5. 按激进强度分排�
+
+### �程3: 对比��模�
+
+1. 记录等�模�Top 10股票
+2. 切�到激进模�，观察排��化
+3. 切�到�守模�，观察哪些股票�续高分
+4. **�续高分的股票 = 全方�优质形�**
+
+### �程4: 研究特定股票
+
+1. 在�索框输入股票代�/�称
+2. 查看雷达图，识别优势/劣势维度
+3. 查看标准化维度进度�，了解具体分值
+4. 切���模�，观察��视角下的评分
+
+## 数�解读
+
+### 标准化维度�义
+
+所有维度都标准化到0-1范围，中�数=0.5：
+
+| 维度 | �义 | 0.5以下 | 0.5-0.7 | 0.7以上 |
+|------|------|---------|---------|---------|
+| �破幅度 | �破线的强度 | 弱/未�破 | 适中 | 强�破 |
+| 收敛度 | 三角形收敛程度 | �散 | 一般 | 收敛良好 |
+| �交� | 放�程度 | 缩�/无放� | 适中 | 明显放� |
+| 形�规则 | 三角形规整度 | �规则 | 一般 | 标准形� |
+| 活跃度 | 价格波动活跃度 | 平淡 | 适中 | 活跃 |
+| 倾斜度 | 0.5=对称 | 下倾 | 对称 | 上倾 |
+
+### 强度分�义
+
+| 分值范围 | �义 |
+|----------|------|
+| 0.0-0.3 | 形�质�差，�建议关注 |
+| 0.3-0.5 | 形�一般，需结�其他指标 |
+| 0.5-0.7 | 形�良好，�以关注 |
+| 0.7-0.85 | 形�优秀，�点关注 |
+| 0.85+ | 形��优，高度�点关注 |
+
+## 快�键
+
+- `Esc`: 关闭图表放大视图
+
+## 性能�示
+
+- 首次加载�能需�1-2秒（绘制所有雷达图）
+- 筛选和排�是实时的
+- 建议先用基础筛选缩�范围，�用高级筛选精确定�
+
+## 故障排除
+
+**问题**: 强度分都是原始值，没有等�/激进等模�
+
+**原因**: scoring模�未正确导入
+
+**解决**: 确��行pipeline时没有错误信�
+
+---
+
+**问题**: 雷达图�显示
+
+**原因**: �览器�支�Canvas或JavaScript被�用
+
+**解决**: 使用现代�览器（Chrome/Edge/Firefox）
+
+---
+
+**问题**: 维度进度�都是0
+
+**原因**: 数�未标准化
+
+**解决**: �新�行pipeline生�HTML
+
+## 更多帮助
+
+查看完整文档: `docs/Pipeline与HTML集�标准化_实施完�报告.md`
diff --git a/docs/预设模�对比.md b/docs/预设模�对比.md
new file mode 100644
index 0000000..d7c2885
--- /dev/null
+++ b/docs/预设模�对比.md
@@ -0,0 +1,109 @@
+# 预设模�对比
+
+## 工作�程
+
+```
+原始数� (CSV)
+    ↓
+标准化处� (normalizer.py)
+    ↓
+应用��计算 (config.py)
+    ↓
+生�HTML (generate_stock_viewer.py)
+```
+
+所有预设模�都是基于**标准化�的数�**进行计算的。
+
+---
+
+## 一�4�基础预设模�
+
+### ��分�对比
+
+| 维度 | 等�模� | 激进模� | �守模� | 放�模� |
+|-----|---------|---------|---------|---------|
+| **�破幅度** | 16.7% | **35%** | 15% | 25% |
+| **收敛度** | 16.7% | 15% | **30%** | 15% |
+| **�交�** | 16.7% | 25% | 10% | **35%** |
+| **形�规则** | 16.7% | 10% | 15% | 10% |
+| **活跃度** | 16.7% | 10% | **25%** | 10% |
+| **倾斜度** | 16.7% | 5% | 5% | 5% |
+
+### �模�特点
+
+#### 1. 等�模�（默认）
+- ��：�个维度 1/6 = 16.7%
+- 特点：���任何维度，探索性分�用
+- 适用：�筛�全�评估
+
+#### 2. 激进模�
+- ��：�破幅度 35% + �交� 25% = 60%
+- 特点：最看�"是��破"和"是�放�"
+- 适用：趋势行情，追涨信�
+- 筛选策略：�破阈值较低（0.55），�交�阈值较高（0.60）
+
+#### 3. �守模�
+- ��：收敛度 30% + 活跃度 25% = 55%
+- 特点：最看�"形�质�"和"价格活跃"
+- 适用：震�市，等待形�完善��入场
+- 筛选策略：�破阈值较高（0.70），收敛度阈值较高（0.65）
+
+#### 4. 放�模�
+- ��：�交� 35% + �破幅度 25% = 60%
+- 特点：最看�"�交�"，�获主力异动
+- 适用：�现异动股票，资金关注信�
+- 筛选策略：�交�阈值最高（0.70）
+
+---
+
+## 二�6��维度测试模�（50%主导）
+
+�个模�将**一个维度设为50%��**，其余5个维度�10%。
+
+### ��分�对比
+
+| 维度 | �破主导 | 收敛主导 | �交�主导 | 形�主导 | 活跃主导 | 倾斜主导 |
+|-----|---------|---------|----------|---------|---------|---------|
+| **�破幅度** | **50%** | 10% | 10% | 10% | 10% | 10% |
+| **收敛度** | 10% | **50%** | 10% | 10% | 10% | 10% |
+| **�交�** | 10% | 10% | **50%** | 10% | 10% | 10% |
+| **形�规则** | 10% | 10% | 10% | **50%** | 10% | 10% |
+| **活跃度** | 10% | 10% | 10% | 10% | **50%** | 10% |
+| **倾斜度** | 10% | 10% | 10% | 10% | 10% | **50%** |
+
+### 测试用途
+
+这6�模�用于测试**�个维度对排�的影�**：
+
+1. **观察排��化**：�维度���高到50%�，股票排�如何�化
+2. **�现潜力股**：找出�维度得分高但综�得分低的股票
+3. **验�区分度**：检验�维度的实际区分能力
+4. **对比分�**：�一股票在��主导模�下的排��化
+
+### 使用示例
+
+�设�股票在等�模�下排�第20，但在"收敛主导"模�下排�第3：
+- 说明该股票的**收敛度得分很高**
+- 但其他维度得分较低，拉低了综�排�
+- 如果你�视收敛度，�以关注这�股票
+
+---
+
+## 三�使用建议
+
+| 场景 | 推�模� |
+|-----|---------|
+| �确定选哪个？ | **等�模�** |
+| 牛市/上涨趋势 | **激进模�** |
+| 震�市/谨慎选股 | **�守模�** |
+| 寻找主力资金异动 | **放�模�** |
+| 测试�维度影� | **6�测试模�** |
+| 寻找�空机会 | 任�模� + 方�选"�下" |
+
+---
+
+## 四�代��置
+
+- �置定义：`scripts/scoring/config.py`
+- 标准化处�：`scripts/scoring/normalizer.py`
+- HTML生�：`scripts/generate_stock_viewer.py`
diff --git a/outputs/converging_triangles/stock_viewer.html b/outputs/converging_triangles/stock_viewer.html
index bce79f2..2ad9089 100644
--- a/outputs/converging_triangles/stock_viewer.html
+++ b/outputs/converging_triangles/stock_viewer.html
@@ -960,12 +960,32 @@
                 <h1>收敛三角形选股系统</h1>
                 <div class="header-meta">
                     <span>数�日期: 20260120</span>
-                    <span>监控股票: 108</span>
+                    <span>监控股票: 58</span>
                 </div>
             </div>
         </header>
 
         <section class="control-panel">
+            <!-- Preset Modes Row -->
+            <div class="filter-section" style="margin-bottom: 24px; padding-bottom: 20px; border-bottom: 1px solid var(--border-subtle);">
+                <label class="filter-label">预设模�</label>
+                <div class="filter-chips" id="presetModes">
+                    <div class="filter-chip active" data-mode="equal" title="�维度等�1/6">等�模�</div>
+                    <div class="filter-chip" data-mode="aggressive" title="�视�破35%+�交�25%">激进模�</div>
+                    <div class="filter-chip" data-mode="conservative" title="�视收敛30%+活跃25%">�守模�</div>
+                    <div class="filter-chip" data-mode="volume" title="�视�交�35%">放�模�</div>
+                </div>
+                <label class="filter-label" style="margin-top: 12px;">�维度测试（50%主导）</label>
+                <div class="filter-chips" id="presetModes2">
+                    <div class="filter-chip" data-mode="test_price" title="�破50%+其余�10%">�破主导</div>
+                    <div class="filter-chip" data-mode="test_convergence" title="收敛50%+其余�10%">收敛主导</div>
+                    <div class="filter-chip" data-mode="test_volume" title="�交�50%+其余�10%">�交�主导</div>
+                    <div class="filter-chip" data-mode="test_geometry" title="形�50%+其余�10%">形�主导</div>
+                    <div class="filter-chip" data-mode="test_activity" title="活跃50%+其余�10%">活跃主导</div>
+                    <div class="filter-chip" data-mode="test_tilt" title="倾斜50%+其余�10%">倾斜主导</div>
+                </div>
+            </div>
+
             <!-- Top Filter Row: Search, Sort, Reset -->
             <div class="filter-row">
                 <div class="search-box">
@@ -975,9 +995,12 @@
                 <div class="sort-wrapper">
                     <span class="sort-label">排�</span>
                     <select class="sort-select" id="sortSelect">
-                        <option value="strength">按强度分</option>
+                        <option value="current_mode">按当�模�强度分</option>
+                        <option value="strength">按原始强度分</option>
                         <option value="widthRatio">按宽度比</option>
                         <option value="touches">按触碰次数</option>
+                        <option value="convergenceNorm">按收敛度</option>
+                        <option value="volumeNorm">按�交�</option>
                     </select>
                     <button class="sort-toggle active" id="sortOrder" title="切�排�顺�">↓</button>
                 </div>
@@ -1010,6 +1033,40 @@
                 </div>
             </div>
 
+            <!-- Advanced Dimension Filters (Collapsible) -->
+            <div class="filter-section" style="margin-top: 20px; padding-top: 20px; border-top: 1px solid var(--border-subtle);">
+                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 16px; cursor: pointer;" onclick="toggleAdvancedFilters()">
+                    <label class="filter-label" style="cursor: pointer;">高级维度筛选</label>
+                    <span id="advancedToggleIcon" style="font-size: 18px; color: var(--text-secondary); transition: transform 0.3s;">â–¼</span>
+                </div>
+                <div id="advancedFilters" style="display: none;">
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">�破幅度 ≥ <span id="priceThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="priceThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">收敛度 ≥ <span id="convergenceThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="convergenceThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">�交� ≥ <span id="volumeThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="volumeThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">形�规则度 ≥ <span id="geometryThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="geometryThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">活跃度 ≥ <span id="activityThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="activityThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">倾斜度 ≥ <span id="tiltThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="tiltThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                </div>
+            </div>
+
             <!-- Strength Slider -->
             <div class="filter-section">
                 <label class="filter-label">强度阈值</label>
@@ -1079,6 +1136,43 @@
     "tiltScore": 0.5118548082524743,
     "date": 20260120,
     "hasTriangle": true,
+    "priceUpNorm": 0.9,
+    "priceDownNorm": 0.5,
+    "convergenceNorm": 0.7931034482758621,
+    "volumeNorm": 0.8793103448275862,
+    "geometryNorm": 0.39655172413793105,
+    "activityNorm": 0.8103448275862069,
+    "tiltNorm": 1.0,
+    "strengthEqualUp": 0.7965517241379311,
+    "strengthEqualDown": 0.7298850574712642,
+    "strengthAggressiveUp": 0.8244827586206898,
+    "strengthAggressiveDown": 0.6844827586206897,
+    "strengthConservativeUp": 0.7729310344827587,
+    "strengthConservativeDown": 0.7129310344827586,
+    "strengthVolumeUp": 0.8224137931034483,
+    "strengthVolumeDown": 0.7224137931034483,
+    "strengthTestPriceUp": 0.8379310344827586,
+    "strengthTestPriceDown": 0.6379310344827586,
+    "strengthTestConvergenceUp": 0.7951724137931035,
+    "strengthTestConvergenceDown": 0.7551724137931035,
+    "strengthTestVolumeUp": 0.829655172413793,
+    "strengthTestVolumeDown": 0.7896551724137931,
+    "strengthTestGeometryUp": 0.636551724137931,
+    "strengthTestGeometryDown": 0.596551724137931,
+    "strengthTestActivityUp": 0.8020689655172414,
+    "strengthTestActivityDown": 0.7620689655172413,
+    "strengthTestTiltUp": 0.8779310344827587,
+    "strengthTestTiltDown": 0.8379310344827586,
+    "strengthEqual": 0.7965517241379311,
+    "strengthAggressive": 0.8244827586206898,
+    "strengthConservative": 0.7729310344827587,
+    "strengthVolume": 0.8224137931034483,
+    "strengthTestPrice": 0.8379310344827586,
+    "strengthTestConvergence": 0.7951724137931035,
+    "strengthTestVolume": 0.829655172413793,
+    "strengthTestGeometry": 0.636551724137931,
+    "strengthTestActivity": 0.8020689655172414,
+    "strengthTestTilt": 0.8779310344827587,
     "strength": 0.26552211680191823,
     "chartPath": "charts/20260120_SH603379_三美股份.png",
     "chartPathDetail": "charts/20260120_SH603379_三美股份_detail.png"
@@ -1098,6 +1192,43 @@
     "tiltScore": 0.4941072370149417,
     "date": 20260120,
     "hasTriangle": true,
+    "priceUpNorm": 1.0,
+    "priceDownNorm": 0.5,
+    "convergenceNorm": 0.15517241379310345,
+    "volumeNorm": 0.9741379310344828,
+    "geometryNorm": 0.9655172413793104,
+    "activityNorm": 0.1206896551724138,
+    "tiltNorm": 0.16666666666666669,
+    "strengthEqualUp": 0.5636973180076628,
+    "strengthEqualDown": 0.48036398467432945,
+    "strengthAggressiveUp": 0.7337643678160919,
+    "strengthAggressiveDown": 0.5587643678160918,
+    "strengthConservativeUp": 0.47729885057471266,
+    "strengthConservativeDown": 0.40229885057471265,
+    "strengthVolumeUp": 0.7311781609195401,
+    "strengthVolumeDown": 0.6061781609195401,
+    "strengthTestPriceUp": 0.7382183908045976,
+    "strengthTestPriceDown": 0.4882183908045977,
+    "strengthTestConvergenceUp": 0.40028735632183915,
+    "strengthTestConvergenceDown": 0.3502873563218391,
+    "strengthTestVolumeUp": 0.7278735632183908,
+    "strengthTestVolumeDown": 0.6778735632183908,
+    "strengthTestGeometryUp": 0.724425287356322,
+    "strengthTestGeometryDown": 0.6744252873563219,
+    "strengthTestActivityUp": 0.38649425287356326,
+    "strengthTestActivityDown": 0.33649425287356327,
+    "strengthTestTiltUp": 0.40488505747126446,
+    "strengthTestTiltDown": 0.3548850574712644,
+    "strengthEqual": 0.5636973180076628,
+    "strengthAggressive": 0.7337643678160919,
+    "strengthConservative": 0.47729885057471266,
+    "strengthVolume": 0.7311781609195401,
+    "strengthTestPrice": 0.7382183908045976,
+    "strengthTestConvergence": 0.40028735632183915,
+    "strengthTestVolume": 0.7278735632183908,
+    "strengthTestGeometry": 0.724425287356322,
+    "strengthTestActivity": 0.38649425287356326,
+    "strengthTestTilt": 0.40488505747126446,
     "strength": 0.1522090509643363,
     "chartPath": "charts/20260120_SH600744_�银电力.png",
     "chartPathDetail": "charts/20260120_SH600744_�银电力_detail.png"
@@ -1117,6 +1248,43 @@
     "tiltScore": 0.5,
     "date": 20260120,
     "hasTriangle": true,
+    "priceUpNorm": 0.5,
+    "priceDownNorm": 0.5,
+    "convergenceNorm": 0.41379310344827586,
+    "volumeNorm": 0.9137931034482758,
+    "geometryNorm": 0.5689655172413793,
+    "activityNorm": 0.9310344827586207,
+    "tiltNorm": 0.5,
+    "strengthEqualUp": 0.6379310344827587,
+    "strengthEqualDown": 0.6379310344827587,
+    "strengthAggressiveUp": 0.6405172413793103,
+    "strengthAggressiveDown": 0.6405172413793103,
+    "strengthConservativeUp": 0.6336206896551724,
+    "strengthConservativeDown": 0.6336206896551724,
+    "strengthVolumeUp": 0.681896551724138,
+    "strengthVolumeDown": 0.681896551724138,
+    "strengthTestPriceUp": 0.5827586206896552,
+    "strengthTestPriceDown": 0.5827586206896552,
+    "strengthTestConvergenceUp": 0.5482758620689656,
+    "strengthTestConvergenceDown": 0.5482758620689656,
+    "strengthTestVolumeUp": 0.7482758620689655,
+    "strengthTestVolumeDown": 0.7482758620689655,
+    "strengthTestGeometryUp": 0.6103448275862069,
+    "strengthTestGeometryDown": 0.6103448275862069,
+    "strengthTestActivityUp": 0.7551724137931035,
+    "strengthTestActivityDown": 0.7551724137931035,
+    "strengthTestTiltUp": 0.5827586206896551,
+    "strengthTestTiltDown": 0.5827586206896551,
+    "strengthEqual": 0.6379310344827587,
+    "strengthAggressive": 0.6405172413793103,
+    "strengthConservative": 0.6336206896551724,
+    "strengthVolume": 0.681896551724138,
+    "strengthTestPrice": 0.5827586206896552,
+    "strengthTestConvergence": 0.5482758620689656,
+    "strengthTestVolume": 0.7482758620689655,
+    "strengthTestGeometry": 0.6103448275862069,
+    "strengthTestActivity": 0.7551724137931035,
+    "strengthTestTilt": 0.5827586206896551,
     "strength": 0.13400783708484898,
     "chartPath": "charts/20260120_SZ300737_科顺股份.png",
     "chartPathDetail": "charts/20260120_SZ300737_科顺股份_detail.png"
@@ -1136,6 +1304,43 @@
     "tiltScore": 0.4980944504664168,
     "date": 20260120,
     "hasTriangle": true,
+    "priceUpNorm": 0.7,
+    "priceDownNorm": 0.5,
+    "convergenceNorm": 0.9482758620689655,
+    "volumeNorm": 0.9310344827586207,
+    "geometryNorm": 0.05172413793103448,
+    "activityNorm": 0.39655172413793105,
+    "tiltNorm": 0.0,
+    "strengthEqualUp": 0.5045977011494253,
+    "strengthEqualDown": 0.47126436781609193,
+    "strengthAggressiveUp": 0.6648275862068965,
+    "strengthAggressiveDown": 0.5948275862068965,
+    "strengthConservativeUp": 0.5894827586206897,
+    "strengthConservativeDown": 0.5594827586206896,
+    "strengthVolumeUp": 0.6879310344827585,
+    "strengthVolumeDown": 0.6379310344827587,
+    "strengthTestPriceUp": 0.5827586206896551,
+    "strengthTestPriceDown": 0.48275862068965525,
+    "strengthTestConvergenceUp": 0.6820689655172413,
+    "strengthTestConvergenceDown": 0.6620689655172414,
+    "strengthTestVolumeUp": 0.6751724137931034,
+    "strengthTestVolumeDown": 0.6551724137931034,
+    "strengthTestGeometryUp": 0.323448275862069,
+    "strengthTestGeometryDown": 0.303448275862069,
+    "strengthTestActivityUp": 0.4613793103448276,
+    "strengthTestActivityDown": 0.44137931034482764,
+    "strengthTestTiltUp": 0.3027586206896552,
+    "strengthTestTiltDown": 0.28275862068965524,
+    "strengthEqual": 0.5045977011494253,
+    "strengthAggressive": 0.6648275862068965,
+    "strengthConservative": 0.5894827586206897,
+    "strengthVolume": 0.6879310344827585,
+    "strengthTestPrice": 0.5827586206896551,
+    "strengthTestConvergence": 0.6820689655172413,
+    "strengthTestVolume": 0.6751724137931034,
+    "strengthTestGeometry": 0.323448275862069,
+    "strengthTestActivity": 0.4613793103448276,
+    "strengthTestTilt": 0.3027586206896552,
     "strength": 0.12944397068032543,
     "chartPath": "charts/20260120_SH601609_金田股份.png",
     "chartPathDetail": "charts/20260120_SH601609_金田股份_detail.png"
@@ -1155,6 +1360,43 @@
     "tiltScore": 0.5,
     "date": 20260120,
     "hasTriangle": true,
+    "priceUpNorm": 0.5,
+    "priceDownNorm": 0.5,
+    "convergenceNorm": 0.6896551724137931,
+    "volumeNorm": 0.9741379310344828,
+    "geometryNorm": 0.9827586206896551,
+    "activityNorm": 0.5862068965517241,
+    "tiltNorm": 0.5,
+    "strengthEqualUp": 0.7054597701149424,
+    "strengthEqualDown": 0.7054597701149424,
+    "strengthAggressiveUp": 0.7038793103448276,
+    "strengthAggressiveDown": 0.7038793103448276,
+    "strengthConservativeUp": 0.6982758620689655,
+    "strengthConservativeDown": 0.6982758620689655,
+    "strengthVolumeUp": 0.7512931034482759,
+    "strengthVolumeDown": 0.7512931034482759,
+    "strengthTestPriceUp": 0.6232758620689656,
+    "strengthTestPriceDown": 0.6232758620689656,
+    "strengthTestConvergenceUp": 0.6991379310344829,
+    "strengthTestConvergenceDown": 0.6991379310344829,
+    "strengthTestVolumeUp": 0.8129310344827587,
+    "strengthTestVolumeDown": 0.8129310344827587,
+    "strengthTestGeometryUp": 0.8163793103448276,
+    "strengthTestGeometryDown": 0.8163793103448276,
+    "strengthTestActivityUp": 0.6577586206896553,
+    "strengthTestActivityDown": 0.6577586206896553,
+    "strengthTestTiltUp": 0.6232758620689656,
+    "strengthTestTiltDown": 0.6232758620689656,
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-    "name": "*ST 猴王",
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-    "name": "德龙汇能",
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-    "strength": 0.0,
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-    "chartPath": "charts/20260120_SZ002293_罗莱生活.png",
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-    "hasTriangle": false
-  },
-  {
-    "idx": 68,
-    "code": "SZ002393",
-    "name": "力生制�",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
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-    "boundaryUtilization": 0.0,
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-  {
-    "idx": 69,
-    "code": "SZ002443",
-    "name": "金洲管�",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
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-    "touchesLower": 0,
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-    "boundaryUtilization": 0.0,
-    "date": 20260120,
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-    "chartPathDetail": "charts/20260120_SZ002443_金洲管�_detail.png",
-    "hasTriangle": false
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-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
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-    "touchesLower": 0,
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-    "boundaryUtilization": 0.0,
-    "date": 20260120,
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-    "chartPathDetail": "charts/20260120_SZ002493_�盛石化_detail.png",
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-  },
-  {
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-    "code": "SZ002544",
-    "name": "普天科技",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ002544_普天科技.png",
-    "chartPathDetail": "charts/20260120_SZ002544_普天科技_detail.png",
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-  {
-    "idx": 74,
-    "code": "SZ002694",
-    "name": "顾地科技",
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-    "strengthDown": 0.0,
-    "strength": 0.0,
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-    "boundaryUtilization": 0.0,
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-    "chartPath": "charts/20260120_SZ002694_顾地科技.png",
-    "chartPathDetail": "charts/20260120_SZ002694_顾地科技_detail.png",
-    "hasTriangle": false
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-  {
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-    "code": "SZ003019",
-    "name": "宸展光电",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
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-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ003019_宸展光电.png",
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-    "strength": 0.0,
-    "direction": "none",
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-    "chartPathDetail": "charts/20260120_SZ300027_�谊兄弟_detail.png",
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-    "name": "�创医惠",
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-    "strengthDown": 0.0,
-    "strength": 0.0,
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-    "hasTriangle": false
-  },
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-    "code": "SZ300178",
-    "name": "腾邦退(退市)",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
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-    "boundaryUtilization": 0.0,
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-    "chartPath": "charts/20260120_SZ300178_腾邦退(退市).png",
-    "chartPathDetail": "charts/20260120_SZ300178_腾邦退(退市)_detail.png",
-    "hasTriangle": false
-  },
-  {
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-    "code": "SZ300228",
-    "name": "富瑞特装",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
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-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ300228_富瑞特装.png",
-    "chartPathDetail": "charts/20260120_SZ300228_富瑞特装_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 88,
-    "code": "SZ300379",
-    "name": "*ST东通",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
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-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ300379_ST东通.png",
-    "chartPathDetail": "charts/20260120_SZ300379_ST东通_detail.png",
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-  },
-  {
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-    "code": "SZ300429",
-    "name": "强力新�",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ300429_强力新�.png",
-    "chartPathDetail": "charts/20260120_SZ300429_强力新�_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 90,
-    "code": "SZ300479",
-    "name": "神�电�",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ300479_神�电�.png",
-    "chartPathDetail": "charts/20260120_SZ300479_神�电�_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 92,
-    "code": "SZ300582",
-    "name": "英飞特",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ300582_英飞特.png",
-    "chartPathDetail": "charts/20260120_SZ300582_英飞特_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 101,
-    "code": "SZ301051",
-    "name": "信濠光电",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ301051_信濠光电.png",
-    "chartPathDetail": "charts/20260120_SZ301051_信濠光电_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 102,
-    "code": "SZ301107",
-    "name": "瑜欣电�",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ301107_瑜欣电�.png",
-    "chartPathDetail": "charts/20260120_SZ301107_瑜欣电�_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 103,
-    "code": "SZ301169",
-    "name": "零点有数",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ301169_零点有数.png",
-    "chartPathDetail": "charts/20260120_SZ301169_零点有数_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 104,
-    "code": "SZ301225",
-    "name": "�勃股份",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
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-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ301225_�勃股份.png",
-    "chartPathDetail": "charts/20260120_SZ301225_�勃股份_detail.png",
-    "hasTriangle": false
-  },
-  {
-    "idx": 106,
-    "code": "SZ301355",
-    "name": "�王科技",
-    "strengthUp": 0.0,
-    "strengthDown": 0.0,
-    "strength": 0.0,
-    "direction": "none",
-    "widthRatio": 0.0,
-    "touchesUpper": 0,
-    "touchesLower": 0,
-    "volumeConfirmed": "",
-    "boundaryUtilization": 0.0,
-    "date": 20260120,
-    "chartPath": "charts/20260120_SZ301355_�王科技.png",
-    "chartPathDetail": "charts/20260120_SZ301355_�王科技_detail.png",
-    "hasTriangle": false
   }
 ];
         let currentThreshold = 0;
@@ -3073,9 +4376,16 @@
         // 筛选和排�状�
         let filters = {
             direction: 'all',      // 'all', 'up', 'down', 'none'
-            volume: 'all'          // 'all', 'true', 'false'
+            volume: 'all',         // 'all', 'true', 'false'
+            priceNorm: 0,          // �破幅度阈值
+            convergenceNorm: 0,    // 收敛度阈值
+            volumeNorm: 0,         // �交�阈值
+            geometryNorm: 0,       // 形�规则度阈值
+            activityNorm: 0,       // 活跃度阈值
+            tiltNorm: 0            // 倾斜度阈值
         };
-        let sortBy = 'strength';   // 'strength', 'widthRatio', 'touches'
+        let currentPresetMode = 'equal';  // 'equal', 'aggressive', 'conservative', 'volume'
+        let sortBy = 'current_mode';   // 'current_mode', 'strength', 'widthRatio', 'touches', etc.
         let sortOrder = 'desc';    // 'desc', 'asc'
         let searchQuery = '';
 
@@ -3144,6 +4454,32 @@
                 filterAndDisplayStocks();
             });
 
+            // 预设模�切�（两组按钮互斥）
+            const presetModes = document.getElementById('presetModes');
+            const presetModes2 = document.getElementById('presetModes2');
+            
+            function handlePresetModeClick(e, currentGroup, otherGroup) {
+                if (e.target.classList.contains('filter-chip')) {
+                    // �消当�组所有选中
+                    currentGroup.querySelectorAll('.filter-chip').forEach(chip => chip.classList.remove('active'));
+                    // �消�一组所有选中
+                    otherGroup.querySelectorAll('.filter-chip').forEach(chip => chip.classList.remove('active'));
+                    // 选中当�点击的
+                    e.target.classList.add('active');
+                    currentPresetMode = e.target.dataset.mode;
+                    // 切�模�时刷新显示（排�会根�current_mode自动使用新模�）
+                    filterAndDisplayStocks();
+                }
+            }
+            
+            presetModes.addEventListener('click', function(e) {
+                handlePresetModeClick(e, presetModes, presetModes2);
+            });
+            
+            presetModes2.addEventListener('click', function(e) {
+                handlePresetModeClick(e, presetModes2, presetModes);
+            });
+
             // 方�筛选芯片
             const directionFilter = document.getElementById('directionFilter');
             directionFilter.addEventListener('click', function(e) {
@@ -3169,6 +4505,29 @@
             // �置按钮
             document.getElementById('resetBtn').addEventListener('click', resetFilters);
 
+            // 高级筛选滑�
+            const advancedSliders = [
+                { id: 'priceThreshold', valueId: 'priceThresholdValue', key: 'priceNorm' },
+                { id: 'convergenceThreshold', valueId: 'convergenceThresholdValue', key: 'convergenceNorm' },
+                { id: 'volumeThreshold', valueId: 'volumeThresholdValue', key: 'volumeNorm' },
+                { id: 'geometryThreshold', valueId: 'geometryThresholdValue', key: 'geometryNorm' },
+                { id: 'activityThreshold', valueId: 'activityThresholdValue', key: 'activityNorm' },
+                { id: 'tiltThreshold', valueId: 'tiltThresholdValue', key: 'tiltNorm' }
+            ];
+            
+            advancedSliders.forEach(slider => {
+                const element = document.getElementById(slider.id);
+                const valueDisplay = document.getElementById(slider.valueId);
+                if (element && valueDisplay) {
+                    element.addEventListener('input', function() {
+                        const value = parseFloat(this.value);
+                        filters[slider.key] = value;
+                        valueDisplay.textContent = value.toFixed(2);
+                        filterAndDisplayStocks();
+                    });
+                }
+            });
+
             // 模�框
             document.getElementById('imageModal').addEventListener('click', function(e) {
                 if (e.target === this) closeModal();
@@ -3178,10 +4537,20 @@
         function resetFilters() {
             // �置所有筛选和排�状�
             currentThreshold = 0;
-            filters = { direction: 'all', volume: 'all' };
-            sortBy = 'strength';
+            filters = { 
+                direction: 'all', 
+                volume: 'all',
+                priceNorm: 0,
+                convergenceNorm: 0,
+                volumeNorm: 0,
+                geometryNorm: 0,
+                activityNorm: 0,
+                tiltNorm: 0
+            };
+            sortBy = 'current_mode';
             sortOrder = 'desc';
             searchQuery = '';
+            currentPresetMode = 'equal';  // �置预设模�为等�
 
             // �置UI
             document.getElementById('strengthSlider').value = 0;
@@ -3191,7 +4560,15 @@
             document.getElementById('searchInput').value = '';
             document.querySelector('.search-box').classList.remove('has-value');
 
-            document.getElementById('sortSelect').value = 'strength';
+            document.getElementById('sortSelect').value = 'current_mode';
+            
+            // �置预设模�按钮
+            document.querySelectorAll('#presetModes .filter-chip').forEach((chip, i) => {
+                chip.classList.toggle('active', i === 0);  // 选中第一个（等�模�）
+            });
+            document.querySelectorAll('#presetModes2 .filter-chip').forEach(chip => {
+                chip.classList.remove('active');
+            });
             const sortOrderBtn = document.getElementById('sortOrder');
             sortOrderBtn.textContent = '↓';
             sortOrderBtn.classList.add('active');
@@ -3205,9 +4582,35 @@
                 chip.classList.toggle('active', i === 0);
             });
             filters.volume = 'all';
+            
+            // �置高级筛选滑�
+            const advancedSliders = ['priceThreshold', 'convergenceThreshold', 'volumeThreshold', 
+                                    'geometryThreshold', 'activityThreshold', 'tiltThreshold'];
+            const valueIds = ['priceThresholdValue', 'convergenceThresholdValue', 'volumeThresholdValue',
+                            'geometryThresholdValue', 'activityThresholdValue', 'tiltThresholdValue'];
+            advancedSliders.forEach((id, i) => {
+                const slider = document.getElementById(id);
+                const valueDisplay = document.getElementById(valueIds[i]);
+                if (slider && valueDisplay) {
+                    slider.value = 0;
+                    valueDisplay.textContent = '0.00';
+                }
+            });
 
             filterAndDisplayStocks();
         }
+        
+        function toggleAdvancedFilters() {
+            const panel = document.getElementById('advancedFilters');
+            const icon = document.getElementById('advancedToggleIcon');
+            if (panel.style.display === 'none') {
+                panel.style.display = 'block';
+                icon.style.transform = 'rotate(180deg)';
+            } else {
+                panel.style.display = 'none';
+                icon.style.transform = 'rotate(0deg)';
+            }
+        }
 
         function filterAndDisplayStocks() {
             let result = [...allStocks];
@@ -3225,6 +4628,26 @@
                 const value = filters.volume === 'true';
                 result = result.filter(stock => stock.volumeConfirmed === (value ? 'True' : 'False'));
             }
+            
+            // 高级维度筛选
+            if (filters.priceNorm > 0) {
+                result = result.filter(stock => (stock.priceUpNorm || 0) >= filters.priceNorm);
+            }
+            if (filters.convergenceNorm > 0) {
+                result = result.filter(stock => (stock.convergenceNorm || 0) >= filters.convergenceNorm);
+            }
+            if (filters.volumeNorm > 0) {
+                result = result.filter(stock => (stock.volumeNorm || 0) >= filters.volumeNorm);
+            }
+            if (filters.geometryNorm > 0) {
+                result = result.filter(stock => (stock.geometryNorm || 0) >= filters.geometryNorm);
+            }
+            if (filters.activityNorm > 0) {
+                result = result.filter(stock => (stock.activityNorm || 0) >= filters.activityNorm);
+            }
+            if (filters.tiltNorm > 0) {
+                result = result.filter(stock => (stock.tiltNorm || 0) >= filters.tiltNorm);
+            }
 
             // �索
             if (searchQuery) {
@@ -3235,10 +4658,28 @@
                 );
             }
 
-            // 排�
+            // 排� - 根�当�模�动�获�排�字段
+            const modeToKeyMap = {
+                'equal': 'strengthEqual',
+                'aggressive': 'strengthAggressive',
+                'conservative': 'strengthConservative',
+                'volume': 'strengthVolume',
+                'test_price': 'strengthTestPrice',
+                'test_convergence': 'strengthTestConvergence',
+                'test_volume': 'strengthTestVolume',
+                'test_geometry': 'strengthTestGeometry',
+                'test_activity': 'strengthTestActivity',
+                'test_tilt': 'strengthTestTilt'
+            };
+            
             result.sort((a, b) => {
                 let aVal, bVal;
-                if (sortBy === 'strength') {
+                if (sortBy === 'current_mode') {
+                    // 根�当�预设模�获�对应的强度分字段
+                    const key = modeToKeyMap[currentPresetMode] || 'strengthEqual';
+                    aVal = a[key] || 0;
+                    bVal = b[key] || 0;
+                } else if (sortBy === 'strength') {
                     aVal = a.strength;
                     bVal = b.strength;
                 } else if (sortBy === 'widthRatio') {
@@ -3247,6 +4688,15 @@
                 } else if (sortBy === 'touches') {
                     aVal = a.touchesUpper + a.touchesLower;
                     bVal = b.touchesUpper + b.touchesLower;
+                } else if (sortBy === 'convergenceNorm') {
+                    aVal = a.convergenceNorm || 0;
+                    bVal = b.convergenceNorm || 0;
+                } else if (sortBy === 'volumeNorm') {
+                    aVal = a.volumeNorm || 0;
+                    bVal = b.volumeNorm || 0;
+                } else {
+                    aVal = a.strength;
+                    bVal = b.strength;
                 }
                 return sortOrder === 'desc' ? bVal - aVal : aVal - bVal;
             });
@@ -3266,15 +4716,47 @@
                 grid.style.display = 'grid';
                 emptyState.style.display = 'none';
                 grid.innerHTML = stocks.map(stock => createStockCard(stock)).join('');
+                
+                // 绘制所有雷达图
+                setTimeout(() => {
+                    document.querySelectorAll('.radar-canvas').forEach(canvas => {
+                        const valuesStr = canvas.dataset.values;
+                        if (valuesStr) {
+                            const values = valuesStr.split(',').map(v => parseFloat(v) || 0);
+                            drawMiniRadar(canvas, values);
+                        }
+                    });
+                }, 0);
             }
         }
 
         function updateStats(filteredStocks) {
             document.getElementById('totalStocks').textContent = allStocks.length;
             document.getElementById('displayedStocks').textContent = filteredStocks.length;
-            const avgStrength = filteredStocks.length > 0
-                ? filteredStocks.reduce((sum, s) => sum + s.strength, 0) / filteredStocks.length
-                : 0;
+            
+            // 根�当�模�计算平�强度
+            const modeKeyMap = {
+                'equal': 'strengthEqual',
+                'aggressive': 'strengthAggressive',
+                'conservative': 'strengthConservative',
+                'volume': 'strengthVolume',
+                'test_price': 'strengthTestPrice',
+                'test_convergence': 'strengthTestConvergence',
+                'test_volume': 'strengthTestVolume',
+                'test_geometry': 'strengthTestGeometry',
+                'test_activity': 'strengthTestActivity',
+                'test_tilt': 'strengthTestTilt'
+            };
+            
+            let avgStrength = 0;
+            if (filteredStocks.length > 0) {
+                const key = modeKeyMap[currentPresetMode];
+                if (key) {
+                    avgStrength = filteredStocks.reduce((sum, s) => sum + (s[key] || 0), 0) / filteredStocks.length;
+                } else {
+                    avgStrength = filteredStocks.reduce((sum, s) => sum + s.strength, 0) / filteredStocks.length;
+                }
+            }
             document.getElementById('avgStrength').textContent = avgStrength.toFixed(3);
         }
 
@@ -3287,6 +4769,27 @@
                               stock.volumeConfirmed === 'False' ? '✗' : '—';
             const chartPath = showDetailCharts ? stock.chartPathDetail : stock.chartPath;
             const fallbackPath = showDetailCharts ? stock.chartPath : stock.chartPathDetail;
+            
+            // 根�当�模�选择强度分
+            let displayStrength = stock.strength;
+            let modeName = '原始';
+            const modeMap = {
+                'equal': { key: 'strengthEqual', name: '等�' },
+                'aggressive': { key: 'strengthAggressive', name: '激进' },
+                'conservative': { key: 'strengthConservative', name: '�守' },
+                'volume': { key: 'strengthVolume', name: '放�' },
+                'test_price': { key: 'strengthTestPrice', name: '�破主导' },
+                'test_convergence': { key: 'strengthTestConvergence', name: '收敛主导' },
+                'test_volume': { key: 'strengthTestVolume', name: '�交�主导' },
+                'test_geometry': { key: 'strengthTestGeometry', name: '形�主导' },
+                'test_activity': { key: 'strengthTestActivity', name: '活跃主导' },
+                'test_tilt': { key: 'strengthTestTilt', name: '倾斜主导' }
+            };
+            
+            if (modeMap[currentPresetMode]) {
+                displayStrength = stock[modeMap[currentPresetMode].key] || stock.strength;
+                modeName = modeMap[currentPresetMode].name;
+            }
 
             return `
                 <div class="stock-card" onclick="openModal('${stock.chartPath}', '${stock.chartPathDetail}')">
@@ -3296,8 +4799,8 @@
                             <span class="stock-code">${stock.code}</span>
                         </div>
                         <div class="strength-badge">
-                            <div class="strength-value">${stock.strength.toFixed(3)}</div>
-                            <div class="strength-label">强度分</div>
+                            <div class="strength-value">${displayStrength.toFixed(3)}</div>
+                            <div class="strength-label">${modeName}强度分</div>
                         </div>
                     </div>
                     <div class="card-body">
@@ -3327,6 +4830,27 @@
                                 <span class="metric-value">${(stock.tiltScore || 0).toFixed(2)}</span>
                             </div>
                         </div>
+                        
+                        <!-- 6维度标准化得分展示 -->
+                        <div class="dimensions-panel" style="margin-top: 16px; padding: 12px; background: var(--bg-secondary); border-radius: 10px;">
+                            <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
+                                <div style="font-size: 12px; color: var(--text-secondary); font-weight: 600;">标准化维度</div>
+                                <canvas class="radar-canvas" 
+                                        data-values="${stock.priceUpNorm || 0},${stock.convergenceNorm || 0},${stock.volumeNorm || 0},${stock.geometryNorm || 0},${stock.activityNorm || 0},${stock.tiltNorm || 0}"
+                                        width="80" height="80" 
+                                        style="cursor: pointer;" 
+                                        onclick="event.stopPropagation();"></canvas>
+                            </div>
+                            <div>
+                                ${createDimensionBar('�破幅度', stock.priceUpNorm || 0, stock.direction === 'up')}
+                                ${createDimensionBar('收敛度', stock.convergenceNorm || 0)}
+                                ${createDimensionBar('�交�', stock.volumeNorm || 0)}
+                                ${createDimensionBar('形�规则', stock.geometryNorm || 0)}
+                                ${createDimensionBar('活跃度', stock.activityNorm || 0)}
+                                ${createDimensionBar('倾斜度', stock.tiltNorm || 0)}
+                            </div>
+                        </div>
+                        
                         <div class="chart-container">
                             <img src="${chartPath}"
                                  alt="${stock.name}"
@@ -3338,6 +4862,22 @@
                 </div>
             `;
         }
+        
+        function createDimensionBar(label, value, highlight = false) {
+            const percentage = (value * 100).toFixed(0);
+            const color = highlight ? 'var(--accent-primary)' : 'var(--accent-secondary)';
+            return `
+                <div style="margin-bottom: 8px;">
+                    <div style="display: flex; justify-content: space-between; margin-bottom: 4px;">
+                        <span style="font-size: 11px; color: var(--text-secondary);">${label}</span>
+                        <span style="font-size: 11px; font-family: 'JetBrains Mono', monospace; color: ${color};">${value.toFixed(2)}</span>
+                    </div>
+                    <div style="height: 4px; background: var(--bg-card); border-radius: 2px; overflow: hidden;">
+                        <div style="height: 100%; width: ${percentage}%; background: ${color}; transition: width 0.3s;"></div>
+                    </div>
+                </div>
+            `;
+        }
 
         function handleImageError(img) {
             const fallbackSrc = img.dataset.fallbackSrc;
@@ -3369,6 +4909,85 @@
         function closeModal() {
             document.getElementById('imageModal').classList.remove('show');
         }
+        
+        // 绘制迷你雷达图
+        function drawMiniRadar(canvas, values) {
+            const ctx = canvas.getContext('2d');
+            const centerX = canvas.width / 2;
+            const centerY = canvas.height / 2;
+            const radius = Math.min(centerX, centerY) - 10;
+            const angleStep = (Math.PI * 2) / 6;
+            
+            // 清空画布
+            ctx.clearRect(0, 0, canvas.width, canvas.height);
+            
+            // 绘制背景网格
+            ctx.strokeStyle = 'rgba(139, 146, 168, 0.2)';
+            ctx.lineWidth = 1;
+            for (let i = 1; i <= 3; i++) {
+                ctx.beginPath();
+                const r = radius * (i / 3);
+                for (let j = 0; j <= 6; j++) {
+                    const angle = j * angleStep - Math.PI / 2;
+                    const x = centerX + r * Math.cos(angle);
+                    const y = centerY + r * Math.sin(angle);
+                    if (j === 0) ctx.moveTo(x, y);
+                    else ctx.lineTo(x, y);
+                }
+                ctx.closePath();
+                ctx.stroke();
+            }
+            
+            // 绘制轴线
+            ctx.strokeStyle = 'rgba(139, 146, 168, 0.3)';
+            for (let i = 0; i < 6; i++) {
+                const angle = i * angleStep - Math.PI / 2;
+                ctx.beginPath();
+                ctx.moveTo(centerX, centerY);
+                ctx.lineTo(
+                    centerX + radius * Math.cos(angle),
+                    centerY + radius * Math.sin(angle)
+                );
+                ctx.stroke();
+            }
+            
+            // 绘制数�多边形
+            ctx.fillStyle = 'rgba(0, 212, 170, 0.2)';
+            ctx.strokeStyle = 'rgba(0, 212, 170, 0.8)';
+            ctx.lineWidth = 2;
+            ctx.beginPath();
+            for (let i = 0; i <= 6; i++) {
+                const angle = i * angleStep - Math.PI / 2;
+                const value = values[i % 6] || 0;
+                const r = radius * value;
+                const x = centerX + r * Math.cos(angle);
+                const y = centerY + r * Math.sin(angle);
+                if (i === 0) ctx.moveTo(x, y);
+                else ctx.lineTo(x, y);
+            }
+            ctx.closePath();
+            ctx.fill();
+            ctx.stroke();
+            
+            // 绘制数�点
+            ctx.fillStyle = '#00d4aa';
+            for (let i = 0; i < 6; i++) {
+                const angle = i * angleStep - Math.PI / 2;
+                const value = values[i] || 0;
+                const r = radius * value;
+                const x = centerX + r * Math.cos(angle);
+                const y = centerY + r * Math.sin(angle);
+                ctx.beginPath();
+                ctx.arc(x, y, 3, 0, Math.PI * 2);
+                ctx.fill();
+            }
+        }
+        
+        // 切�雷达图/进度�视图（�选功能，暂�实现）
+        function toggleRadarView(code) {
+            // �以实现点击雷达图放大查看
+            console.log('Toggle radar view for:', code);
+        }
 
         document.addEventListener('keydown', function(e) {
             if (e.key === 'Escape') closeModal();
diff --git a/scripts/example_scoring_usage.py b/scripts/example_scoring_usage.py
new file mode 100644
index 0000000..0f8d939
--- /dev/null
+++ b/scripts/example_scoring_usage.py
@@ -0,0 +1,204 @@
+"""
+强度分标准化系统使用示例
+
+展示如何使用 scoring 模�进行标准化�筛选和分�。
+"""
+
+import pandas as pd
+from pathlib import Path
+import sys
+
+# 添加路径
+sys.path.insert(0, str(Path(__file__).parent / 'scoring'))
+
+from scoring import (
+    normalize_all,
+    CONFIG_EQUAL, CONFIG_AGGRESSIVE, CONFIG_CONSERVATIVE, CONFIG_VOLUME_FOCUS,
+    filter_signals, calculate_strength, filter_top_n
+)
+
+
+def example_1_basic_normalization():
+    """示例1：基础标准化"""
+    print("=" * 80)
+    print("示例1：基础标准化")
+    print("=" * 80)
+    
+    # 加载原始数�
+    data_path = Path(__file__).parent.parent / 'outputs' / 'converging_triangles' / 'all_results.csv'
+    df = pd.read_csv(data_path)
+    df = df[df['is_valid'] == True]
+    
+    print(f"\n原始数�: {len(df)} �记录")
+    print(f"原始字段: {df.columns.tolist()[:10]}...")
+    
+    # 标准化
+    df_norm = normalize_all(df)
+    
+    print(f"\n标准化�新增字段:")
+    new_cols = df_norm.columns.difference(df.columns).tolist()
+    for col in new_cols:
+        print(f"  - {col}")
+    
+    # 对比统计
+    print(f"\n标准化效果对比:")
+    print(f"{'维度':<20s} | {'原始中�数':>10s} | {'标准化中�数':>12s}")
+    print("-" * 50)
+    for col in ['price_score_up', 'convergence_score', 'volume_score']:
+        before = df[col].median()
+        after = df_norm[f'{col}_norm'].median()
+        print(f"{col:<20s} | {before:>10.4f} | {after:>12.4f}")
+
+
+def example_2_preset_configs():
+    """示例2：使用预设�置筛选信�"""
+    print("\n" + "=" * 80)
+    print("示例2：使用预设�置筛选信�")
+    print("=" * 80)
+    
+    # 加载标准化数�
+    data_path = Path(__file__).parent.parent / 'outputs' / 'converging_triangles' / 'all_results_normalized.csv'
+    df = pd.read_csv(data_path)
+    
+    # 测试���置
+    configs = [
+        CONFIG_EQUAL,
+        CONFIG_AGGRESSIVE,
+        CONFIG_CONSERVATIVE,
+        CONFIG_VOLUME_FOCUS,
+    ]
+    
+    print(f"\n总样本数: {len(df)}")
+    print("\n�置�称             | 信�数 | �比  | 主�特点")
+    print("-" * 80)
+    
+    for config in configs:
+        filtered = filter_signals(df, config)
+        pct = len(filtered) / len(df) * 100
+        
+        # 获�最高��的维度
+        weights = [
+            ('�破', config.w_price),
+            ('收敛', config.w_convergence),
+            ('�交�', config.w_volume),
+        ]
+        weights.sort(key=lambda x: x[1], reverse=True)
+        top_weights = ', '.join([f"{k}{v:.0%}" for k, v in weights[:2]])
+        
+        print(f"{config.name:<20s} | {len(filtered):>6d} | {pct:>4.1f}% | {top_weights}")
+
+
+def example_3_custom_config():
+    """示例3：自定义�置"""
+    print("\n" + "=" * 80)
+    print("示例3：自定义�置")
+    print("=" * 80)
+    
+    from scoring.config import StrengthConfig
+    
+    # 创建自定义�置
+    my_config = StrengthConfig(
+        name="我的�置",
+        w_price=0.40,         # �视�破40%
+        w_volume=0.30,        # �视放�30%
+        w_convergence=0.15,
+        w_geometry=0.05,
+        w_activity=0.05,
+        w_tilt=0.05,
+        threshold_price=0.65,   # 中等�破阈值
+        threshold_volume=0.70,  # 高放��求
+        direction='up',
+    )
+    
+    # 打��置摘�
+    print("\n" + my_config.summary())
+    
+    # 加载数�并筛选
+    data_path = Path(__file__).parent.parent / 'outputs' / 'converging_triangles' / 'all_results_normalized.csv'
+    df = pd.read_csv(data_path)
+    
+    filtered = filter_signals(df, my_config, return_strength=True)
+    
+    print(f"\n筛选结果: {len(filtered)} 个信� ({len(filtered)/len(df)*100:.1f}%)")
+    
+    # 显示Top 5
+    print("\nTop 5 信�:")
+    print("股票代�   | 日期     | 强度   | �破   | �交�")
+    print("-" * 60)
+    for _, row in filtered.head(5).iterrows():
+        print(f"{row['stock_code']:10s} | {int(row['date'])} | {row['strength']:.4f} | "
+              f"{row['price_score_up_norm']:.4f} | {row['volume_score_norm']:.4f}")
+
+
+def example_4_top_n_signals():
+    """示例4：获�Top N信�"""
+    print("\n" + "=" * 80)
+    print("示例4：获�Top N信�")
+    print("=" * 80)
+    
+    data_path = Path(__file__).parent.parent / 'outputs' / 'converging_triangles' / 'all_results_normalized.csv'
+    df = pd.read_csv(data_path)
+    
+    # 获�等��置下的Top 20信�
+    top20 = filter_top_n(df, CONFIG_EQUAL, n=20)
+    
+    print(f"\n等�模� - Top 20 信�:")
+    print("\n排� | 股票代�   | 日期     | 强度   | �破   | 收敛   | 放�")
+    print("-" * 80)
+    
+    for idx, (_, row) in enumerate(top20.iterrows(), 1):
+        print(f"{idx:>4d} | {row['stock_code']:10s} | {int(row['date'])} | "
+              f"{row['strength']:.4f} | {row['price_score_up_norm']:.4f} | "
+              f"{row['convergence_score_norm']:.4f} | {row['volume_score_norm']:.4f}")
+
+
+def example_5_compare_configs():
+    """示例5：对比���置的结果"""
+    print("\n" + "=" * 80)
+    print("示例5：对比���置的Top信�")
+    print("=" * 80)
+    
+    data_path = Path(__file__).parent.parent / 'outputs' / 'converging_triangles' / 'all_results_normalized.csv'
+    df = pd.read_csv(data_path)
+    
+    configs = [
+        CONFIG_EQUAL,
+        CONFIG_AGGRESSIVE,
+        CONFIG_CONSERVATIVE,
+    ]
+    
+    for config in configs:
+        print(f"\n{config.name} - Top 3:")
+        print("-" * 60)
+        
+        top3 = filter_top_n(df, config, n=3)
+        for idx, (_, row) in enumerate(top3.iterrows(), 1):
+            print(f"  {idx}. {row['stock_code']} ({int(row['date'])}) - 强度: {row['strength']:.4f}")
+
+
+def main():
+    """�行所有示例"""
+    try:
+        example_1_basic_normalization()
+        example_2_preset_configs()
+        example_3_custom_config()
+        example_4_top_n_signals()
+        example_5_compare_configs()
+        
+        print("\n" + "=" * 80)
+        print("所有示例�行完��")
+        print("=" * 80)
+        
+        print("\n更多功能:")
+        print("  1. 查看�感性分�: python scripts/scoring/sensitivity.py")
+        print("  2. 完整报告: outputs/converging_triangles/sensitivity_analysis_report.md")
+        print("  3. 对比图表: outputs/converging_triangles/normalization_comparison.png")
+        
+    except Exception as e:
+        print(f"\n错误: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/generate_stock_viewer.py b/scripts/generate_stock_viewer.py
index 8a7477a..105763f 100644
--- a/scripts/generate_stock_viewer.py
+++ b/scripts/generate_stock_viewer.py
@@ -16,6 +16,31 @@ import json
 import sys
 import pickle
 import numpy as np
+import pandas as pd
+
+# 添加scoring模�路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'scoring'))
+try:
+    from scoring import (
+        normalize_all, 
+        calculate_strength,
+        CONFIG_EQUAL, 
+        CONFIG_AGGRESSIVE, 
+        CONFIG_CONSERVATIVE, 
+        CONFIG_VOLUME_FOCUS,
+        # �维度测试模�
+        CONFIG_TEST_PRICE,
+        CONFIG_TEST_CONVERGENCE,
+        CONFIG_TEST_VOLUME,
+        CONFIG_TEST_GEOMETRY,
+        CONFIG_TEST_ACTIVITY,
+        CONFIG_TEST_TILT,
+    )
+    SCORING_AVAILABLE = True
+except ImportError as e:
+    print(f"警告: 无法导入scoring模�: {e}")
+    print("将使用原始强度分，�进行标准化")
+    SCORING_AVAILABLE = False
 
 def load_all_stocks_list(data_dir: str) -> tuple:
     """从close.pkl加载所有股票列表"""
@@ -30,7 +55,7 @@ def load_all_stocks_list(data_dir: str) -> tuple:
     return data['tkrs'], data['tkrs_name']
 
 def load_stock_data(csv_path: str, target_date: int = None, all_stocks_mode: bool = False, data_dir: str = 'data') -> tuple:
-    """从CSV加载股票数�"""
+    """从CSV加载股票数�并进行标准化处�"""
     stocks_map = {}
     max_date = 0
     
@@ -48,6 +73,7 @@ def load_stock_data(csv_path: str, target_date: int = None, all_stocks_mode: boo
     use_date = target_date if target_date else max_date
     
     # 从CSV读�有强度分的股票
+    rows_list = []
     with open(csv_path, 'r', encoding='utf-8-sig') as f:
         reader = csv.DictReader(f)
         for row in reader:
@@ -55,38 +81,215 @@ def load_stock_data(csv_path: str, target_date: int = None, all_stocks_mode: boo
                 date = int(row.get('date', '0'))
                 if date != use_date:
                     continue
-                
-                stock_code = row.get('stock_code', '')
-                stock = {
-                    'idx': int(row.get('stock_idx', '0')),
-                    'code': stock_code,
-                    'name': row.get('stock_name', ''),
-                    'strengthUp': float(row.get('breakout_strength_up', '0')),
-                    'strengthDown': float(row.get('breakout_strength_down', '0')),
-                    'direction': row.get('breakout_dir', ''),
-                    'widthRatio': float(row.get('width_ratio', '0')),
-                    'touchesUpper': int(row.get('touches_upper', '0')),
-                    'touchesLower': int(row.get('touches_lower', '0')),
-                    'volumeConfirmed': row.get('volume_confirmed', ''),
-                    '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�
-                }
-                
-                stock['strength'] = max(stock['strengthUp'], stock['strengthDown'])
-                
-                # 清�文件�中的�法字符
-                clean_name = stock['name'].replace('*', '').replace('?', '').replace('"', '').replace('<', '').replace('>', '').replace('|', '').replace(':', '').replace('/', '').replace('\\', '')
-                stock['chartPath'] = f"charts/{date}_{stock_code}_{clean_name}.png"
-                stock['chartPathDetail'] = f"charts/{date}_{stock_code}_{clean_name}_detail.png"
-                
-                if stock_code not in stocks_map or stocks_map[stock_code]['strength'] < stock['strength']:
-                    stocks_map[stock_code] = stock
-                    
-            except Exception as e:
+                rows_list.append(row)
+            except:
                 continue
     
+    # 转�为DataFrame以进行标准化
+    if rows_list and SCORING_AVAILABLE:
+        df = pd.DataFrame(rows_list)
+        # 转�数值列
+        numeric_cols = ['breakout_strength_up', 'breakout_strength_down', 
+                       'price_score_up', 'price_score_down', 'convergence_score',
+                       'volume_score', 'geometry_score', 'activity_score', 'tilt_score']
+        for col in numeric_cols:
+            if col in df.columns:
+                df[col] = pd.to_numeric(df[col], errors='coerce').fillna(0)
+        
+        # 执行标准化
+        df_norm = normalize_all(df)
+        
+        # 计算4�预设模�的强度分 (分别计算up和down)
+        from dataclasses import replace
+        
+        # 等�模�
+        config_equal_up = replace(CONFIG_EQUAL, direction='up')
+        config_equal_down = replace(CONFIG_EQUAL, direction='down')
+        df_norm['strength_equal_up'] = calculate_strength(df_norm, config_equal_up)
+        df_norm['strength_equal_down'] = calculate_strength(df_norm, config_equal_down)
+        
+        # 激进模�
+        config_agg_up = replace(CONFIG_AGGRESSIVE, direction='up')
+        config_agg_down = replace(CONFIG_AGGRESSIVE, direction='down')
+        df_norm['strength_aggressive_up'] = calculate_strength(df_norm, config_agg_up)
+        df_norm['strength_aggressive_down'] = calculate_strength(df_norm, config_agg_down)
+        
+        # �守模�
+        config_cons_up = replace(CONFIG_CONSERVATIVE, direction='up')
+        config_cons_down = replace(CONFIG_CONSERVATIVE, direction='down')
+        df_norm['strength_conservative_up'] = calculate_strength(df_norm, config_cons_up)
+        df_norm['strength_conservative_down'] = calculate_strength(df_norm, config_cons_down)
+        
+        # 放�模�
+        config_vol_up = replace(CONFIG_VOLUME_FOCUS, direction='up')
+        config_vol_down = replace(CONFIG_VOLUME_FOCUS, direction='down')
+        df_norm['strength_volume_up'] = calculate_strength(df_norm, config_vol_up)
+        df_norm['strength_volume_down'] = calculate_strength(df_norm, config_vol_down)
+        
+        # �维度测试模�（50%主导）
+        # �破主导
+        config_test_price_up = replace(CONFIG_TEST_PRICE, direction='up')
+        config_test_price_down = replace(CONFIG_TEST_PRICE, direction='down')
+        df_norm['strength_test_price_up'] = calculate_strength(df_norm, config_test_price_up)
+        df_norm['strength_test_price_down'] = calculate_strength(df_norm, config_test_price_down)
+        
+        # 收敛主导
+        config_test_conv_up = replace(CONFIG_TEST_CONVERGENCE, direction='up')
+        config_test_conv_down = replace(CONFIG_TEST_CONVERGENCE, direction='down')
+        df_norm['strength_test_convergence_up'] = calculate_strength(df_norm, config_test_conv_up)
+        df_norm['strength_test_convergence_down'] = calculate_strength(df_norm, config_test_conv_down)
+        
+        # �交�主导
+        config_test_vol_up = replace(CONFIG_TEST_VOLUME, direction='up')
+        config_test_vol_down = replace(CONFIG_TEST_VOLUME, direction='down')
+        df_norm['strength_test_volume_up'] = calculate_strength(df_norm, config_test_vol_up)
+        df_norm['strength_test_volume_down'] = calculate_strength(df_norm, config_test_vol_down)
+        
+        # 形�主导
+        config_test_geo_up = replace(CONFIG_TEST_GEOMETRY, direction='up')
+        config_test_geo_down = replace(CONFIG_TEST_GEOMETRY, direction='down')
+        df_norm['strength_test_geometry_up'] = calculate_strength(df_norm, config_test_geo_up)
+        df_norm['strength_test_geometry_down'] = calculate_strength(df_norm, config_test_geo_down)
+        
+        # 活跃主导
+        config_test_act_up = replace(CONFIG_TEST_ACTIVITY, direction='up')
+        config_test_act_down = replace(CONFIG_TEST_ACTIVITY, direction='down')
+        df_norm['strength_test_activity_up'] = calculate_strength(df_norm, config_test_act_up)
+        df_norm['strength_test_activity_down'] = calculate_strength(df_norm, config_test_act_down)
+        
+        # 倾斜主导
+        config_test_tilt_up = replace(CONFIG_TEST_TILT, direction='up')
+        config_test_tilt_down = replace(CONFIG_TEST_TILT, direction='down')
+        df_norm['strength_test_tilt_up'] = calculate_strength(df_norm, config_test_tilt_up)
+        df_norm['strength_test_tilt_down'] = calculate_strength(df_norm, config_test_tilt_down)
+    else:
+        df_norm = None
+    
+    # 构建stocks_map
+    for idx, row in enumerate(rows_list):
+        try:
+            stock_code = row.get('stock_code', '')
+            stock = {
+                'idx': int(row.get('stock_idx', '0')),
+                'code': stock_code,
+                'name': row.get('stock_name', ''),
+                'strengthUp': float(row.get('breakout_strength_up', '0')),
+                'strengthDown': float(row.get('breakout_strength_down', '0')),
+                'direction': row.get('breakout_dir', ''),
+                'widthRatio': float(row.get('width_ratio', '0')),
+                'touchesUpper': int(row.get('touches_upper', '0')),
+                'touchesLower': int(row.get('touches_lower', '0')),
+                'volumeConfirmed': row.get('volume_confirmed', ''),
+                'activityScore': float(row.get('activity_score', '0')),
+                'tiltScore': float(row.get('tilt_score', '0')),
+                'date': use_date,
+                'hasTriangle': True
+            }
+            
+            # 添加标准化字段
+            if df_norm is not None:
+                norm_row = df_norm.iloc[idx]
+                stock['priceUpNorm'] = float(norm_row.get('price_score_up_norm', 0))
+                stock['priceDownNorm'] = float(norm_row.get('price_score_down_norm', 0))
+                stock['convergenceNorm'] = float(norm_row.get('convergence_score_norm', 0))
+                stock['volumeNorm'] = float(norm_row.get('volume_score_norm', 0))
+                stock['geometryNorm'] = float(norm_row.get('geometry_score_norm', 0))
+                stock['activityNorm'] = float(norm_row.get('activity_score_norm', 0))
+                stock['tiltNorm'] = float(norm_row.get('tilt_score_norm', 0))
+                
+                # 添加预设模�强度分
+                stock['strengthEqualUp'] = float(norm_row.get('strength_equal_up', 0))
+                stock['strengthEqualDown'] = float(norm_row.get('strength_equal_down', 0))
+                stock['strengthAggressiveUp'] = float(norm_row.get('strength_aggressive_up', 0))
+                stock['strengthAggressiveDown'] = float(norm_row.get('strength_aggressive_down', 0))
+                stock['strengthConservativeUp'] = float(norm_row.get('strength_conservative_up', 0))
+                stock['strengthConservativeDown'] = float(norm_row.get('strength_conservative_down', 0))
+                stock['strengthVolumeUp'] = float(norm_row.get('strength_volume_up', 0))
+                stock['strengthVolumeDown'] = float(norm_row.get('strength_volume_down', 0))
+                
+                # 添加�维度测试模�强度分
+                stock['strengthTestPriceUp'] = float(norm_row.get('strength_test_price_up', 0))
+                stock['strengthTestPriceDown'] = float(norm_row.get('strength_test_price_down', 0))
+                stock['strengthTestConvergenceUp'] = float(norm_row.get('strength_test_convergence_up', 0))
+                stock['strengthTestConvergenceDown'] = float(norm_row.get('strength_test_convergence_down', 0))
+                stock['strengthTestVolumeUp'] = float(norm_row.get('strength_test_volume_up', 0))
+                stock['strengthTestVolumeDown'] = float(norm_row.get('strength_test_volume_down', 0))
+                stock['strengthTestGeometryUp'] = float(norm_row.get('strength_test_geometry_up', 0))
+                stock['strengthTestGeometryDown'] = float(norm_row.get('strength_test_geometry_down', 0))
+                stock['strengthTestActivityUp'] = float(norm_row.get('strength_test_activity_up', 0))
+                stock['strengthTestActivityDown'] = float(norm_row.get('strength_test_activity_down', 0))
+                stock['strengthTestTiltUp'] = float(norm_row.get('strength_test_tilt_up', 0))
+                stock['strengthTestTiltDown'] = float(norm_row.get('strength_test_tilt_down', 0))
+                
+                # 根�方�选择强度分
+                if stock['direction'] == 'up':
+                    stock['strengthEqual'] = stock['strengthEqualUp']
+                    stock['strengthAggressive'] = stock['strengthAggressiveUp']
+                    stock['strengthConservative'] = stock['strengthConservativeUp']
+                    stock['strengthVolume'] = stock['strengthVolumeUp']
+                    stock['strengthTestPrice'] = stock['strengthTestPriceUp']
+                    stock['strengthTestConvergence'] = stock['strengthTestConvergenceUp']
+                    stock['strengthTestVolume'] = stock['strengthTestVolumeUp']
+                    stock['strengthTestGeometry'] = stock['strengthTestGeometryUp']
+                    stock['strengthTestActivity'] = stock['strengthTestActivityUp']
+                    stock['strengthTestTilt'] = stock['strengthTestTiltUp']
+                elif stock['direction'] == 'down':
+                    stock['strengthEqual'] = stock['strengthEqualDown']
+                    stock['strengthAggressive'] = stock['strengthAggressiveDown']
+                    stock['strengthConservative'] = stock['strengthConservativeDown']
+                    stock['strengthVolume'] = stock['strengthVolumeDown']
+                    stock['strengthTestPrice'] = stock['strengthTestPriceDown']
+                    stock['strengthTestConvergence'] = stock['strengthTestConvergenceDown']
+                    stock['strengthTestVolume'] = stock['strengthTestVolumeDown']
+                    stock['strengthTestGeometry'] = stock['strengthTestGeometryDown']
+                    stock['strengthTestActivity'] = stock['strengthTestActivityDown']
+                    stock['strengthTestTilt'] = stock['strengthTestTiltDown']
+                else:
+                    # 无方�时�两者最大值
+                    stock['strengthEqual'] = max(stock['strengthEqualUp'], stock['strengthEqualDown'])
+                    stock['strengthAggressive'] = max(stock['strengthAggressiveUp'], stock['strengthAggressiveDown'])
+                    stock['strengthConservative'] = max(stock['strengthConservativeUp'], stock['strengthConservativeDown'])
+                    stock['strengthVolume'] = max(stock['strengthVolumeUp'], stock['strengthVolumeDown'])
+                    stock['strengthTestPrice'] = max(stock['strengthTestPriceUp'], stock['strengthTestPriceDown'])
+                    stock['strengthTestConvergence'] = max(stock['strengthTestConvergenceUp'], stock['strengthTestConvergenceDown'])
+                    stock['strengthTestVolume'] = max(stock['strengthTestVolumeUp'], stock['strengthTestVolumeDown'])
+                    stock['strengthTestGeometry'] = max(stock['strengthTestGeometryUp'], stock['strengthTestGeometryDown'])
+                    stock['strengthTestActivity'] = max(stock['strengthTestActivityUp'], stock['strengthTestActivityDown'])
+                    stock['strengthTestTilt'] = max(stock['strengthTestTiltUp'], stock['strengthTestTiltDown'])
+            else:
+                # 如果标准化��用，设置默认值
+                stock['priceUpNorm'] = 0
+                stock['priceDownNorm'] = 0
+                stock['convergenceNorm'] = 0
+                stock['volumeNorm'] = 0
+                stock['geometryNorm'] = 0
+                stock['activityNorm'] = 0
+                stock['tiltNorm'] = 0
+                stock['strengthEqual'] = stock['strengthUp'] if stock['direction'] == 'up' else stock['strengthDown']
+                stock['strengthAggressive'] = stock['strengthEqual']
+                stock['strengthConservative'] = stock['strengthEqual']
+                stock['strengthVolume'] = stock['strengthEqual']
+                stock['strengthTestPrice'] = stock['strengthEqual']
+                stock['strengthTestConvergence'] = stock['strengthEqual']
+                stock['strengthTestVolume'] = stock['strengthEqual']
+                stock['strengthTestGeometry'] = stock['strengthEqual']
+                stock['strengthTestActivity'] = stock['strengthEqual']
+                stock['strengthTestTilt'] = stock['strengthEqual']
+            
+            stock['strength'] = max(stock['strengthUp'], stock['strengthDown'])
+            
+            # 清�文件�中的�法字符
+            clean_name = stock['name'].replace('*', '').replace('?', '').replace('"', '').replace('<', '').replace('>', '').replace('|', '').replace(':', '').replace('/', '').replace('\\', '')
+            stock['chartPath'] = f"charts/{use_date}_{stock_code}_{clean_name}.png"
+            stock['chartPathDetail'] = f"charts/{use_date}_{stock_code}_{clean_name}_detail.png"
+            
+            if stock_code not in stocks_map or stocks_map[stock_code]['strength'] < stock['strength']:
+                stocks_map[stock_code] = stock
+                
+        except Exception as e:
+            print(f"处�股票 {row.get('stock_code', 'unknown')} 时出错: {e}")
+            continue
+    
     # 如果是all_stocks模�，添加所有股票
     if all_stocks_mode:
         all_codes, all_names = load_all_stocks_list(data_dir)
@@ -106,11 +309,31 @@ def load_stock_data(csv_path: str, target_date: int = None, all_stocks_mode: boo
                     'touchesUpper': 0,
                     'touchesLower': 0,
                     'volumeConfirmed': '',
-                    'boundaryUtilization': 0.0,
+                    'activityScore': 0.0,
+                    'tiltScore': 0.0,
                     'date': use_date,
                     'chartPath': f"charts/{use_date}_{code}_{clean_name}.png",
                     'chartPathDetail': f"charts/{use_date}_{code}_{clean_name}_detail.png",
-                    'hasTriangle': False  # 标记为无三角形形�
+                    'hasTriangle': False,
+                    # 标准化字段
+                    'priceUpNorm': 0.0,
+                    'priceDownNorm': 0.0,
+                    'convergenceNorm': 0.0,
+                    'volumeNorm': 0.0,
+                    'geometryNorm': 0.0,
+                    'activityNorm': 0.0,
+                    'tiltNorm': 0.0,
+                    'strengthEqual': 0.0,
+                    'strengthAggressive': 0.0,
+                    'strengthConservative': 0.0,
+                    'strengthVolume': 0.0,
+                    # �维度测试模�
+                    'strengthTestPrice': 0.0,
+                    'strengthTestConvergence': 0.0,
+                    'strengthTestVolume': 0.0,
+                    'strengthTestGeometry': 0.0,
+                    'strengthTestActivity': 0.0,
+                    'strengthTestTilt': 0.0,
                 }
     
     stocks = list(stocks_map.values())
@@ -1089,6 +1312,26 @@ def generate_html(stocks: list, date: int, output_path: str):
         </header>
 
         <section class="control-panel">
+            <!-- Preset Modes Row -->
+            <div class="filter-section" style="margin-bottom: 24px; padding-bottom: 20px; border-bottom: 1px solid var(--border-subtle);">
+                <label class="filter-label">预设模�</label>
+                <div class="filter-chips" id="presetModes">
+                    <div class="filter-chip active" data-mode="equal" title="�维度等�1/6">等�模�</div>
+                    <div class="filter-chip" data-mode="aggressive" title="�视�破35%+�交�25%">激进模�</div>
+                    <div class="filter-chip" data-mode="conservative" title="�视收敛30%+活跃25%">�守模�</div>
+                    <div class="filter-chip" data-mode="volume" title="�视�交�35%">放�模�</div>
+                </div>
+                <label class="filter-label" style="margin-top: 12px;">�维度测试（50%主导）</label>
+                <div class="filter-chips" id="presetModes2">
+                    <div class="filter-chip" data-mode="test_price" title="�破50%+其余�10%">�破主导</div>
+                    <div class="filter-chip" data-mode="test_convergence" title="收敛50%+其余�10%">收敛主导</div>
+                    <div class="filter-chip" data-mode="test_volume" title="�交�50%+其余�10%">�交�主导</div>
+                    <div class="filter-chip" data-mode="test_geometry" title="形�50%+其余�10%">形�主导</div>
+                    <div class="filter-chip" data-mode="test_activity" title="活跃50%+其余�10%">活跃主导</div>
+                    <div class="filter-chip" data-mode="test_tilt" title="倾斜50%+其余�10%">倾斜主导</div>
+                </div>
+            </div>
+
             <!-- Top Filter Row: Search, Sort, Reset -->
             <div class="filter-row">
                 <div class="search-box">
@@ -1098,9 +1341,12 @@ def generate_html(stocks: list, date: int, output_path: str):
                 <div class="sort-wrapper">
                     <span class="sort-label">排�</span>
                     <select class="sort-select" id="sortSelect">
-                        <option value="strength">按强度分</option>
+                        <option value="current_mode">按当�模�强度分</option>
+                        <option value="strength">按原始强度分</option>
                         <option value="widthRatio">按宽度比</option>
                         <option value="touches">按触碰次数</option>
+                        <option value="convergenceNorm">按收敛度</option>
+                        <option value="volumeNorm">按�交�</option>
                     </select>
                     <button class="sort-toggle active" id="sortOrder" title="切�排�顺�">↓</button>
                 </div>
@@ -1133,6 +1379,40 @@ def generate_html(stocks: list, date: int, output_path: str):
                 </div>
             </div>
 
+            <!-- Advanced Dimension Filters (Collapsible) -->
+            <div class="filter-section" style="margin-top: 20px; padding-top: 20px; border-top: 1px solid var(--border-subtle);">
+                <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 16px; cursor: pointer;" onclick="toggleAdvancedFilters()">
+                    <label class="filter-label" style="cursor: pointer;">高级维度筛选</label>
+                    <span id="advancedToggleIcon" style="font-size: 18px; color: var(--text-secondary); transition: transform 0.3s;">â–¼</span>
+                </div>
+                <div id="advancedFilters" style="display: none;">
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">�破幅度 ≥ <span id="priceThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="priceThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">收敛度 ≥ <span id="convergenceThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="convergenceThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">�交� ≥ <span id="volumeThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="volumeThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">形�规则度 ≥ <span id="geometryThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="geometryThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">活跃度 ≥ <span id="activityThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="activityThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                    <div style="margin-bottom: 16px;">
+                        <label style="font-size: 12px; color: var(--text-secondary); margin-bottom: 8px; display: block;">倾斜度 ≥ <span id="tiltThresholdValue" style="color: var(--accent-primary);">0.00</span></label>
+                        <input type="range" id="tiltThreshold" min="0" max="1" step="0.05" value="0" style="width: 100%;">
+                    </div>
+                </div>
+            </div>
+
             <!-- Strength Slider -->
             <div class="filter-section">
                 <label class="filter-label">强度阈值</label>
@@ -1193,9 +1473,16 @@ def generate_html(stocks: list, date: int, output_path: str):
         // 筛选和排�状�
         let filters = {
             direction: 'all',      // 'all', 'up', 'down', 'none'
-            volume: 'all'          // 'all', 'true', 'false'
+            volume: 'all',         // 'all', 'true', 'false'
+            priceNorm: 0,          // �破幅度阈值
+            convergenceNorm: 0,    // 收敛度阈值
+            volumeNorm: 0,         // �交�阈值
+            geometryNorm: 0,       // 形�规则度阈值
+            activityNorm: 0,       // 活跃度阈值
+            tiltNorm: 0            // 倾斜度阈值
         };
-        let sortBy = 'strength';   // 'strength', 'widthRatio', 'touches'
+        let currentPresetMode = 'equal';  // 'equal', 'aggressive', 'conservative', 'volume'
+        let sortBy = 'current_mode';   // 'current_mode', 'strength', 'widthRatio', 'touches', etc.
         let sortOrder = 'desc';    // 'desc', 'asc'
         let searchQuery = '';
 
@@ -1264,6 +1551,32 @@ def generate_html(stocks: list, date: int, output_path: str):
                 filterAndDisplayStocks();
             });
 
+            // 预设模�切�（两组按钮互斥）
+            const presetModes = document.getElementById('presetModes');
+            const presetModes2 = document.getElementById('presetModes2');
+            
+            function handlePresetModeClick(e, currentGroup, otherGroup) {
+                if (e.target.classList.contains('filter-chip')) {
+                    // �消当�组所有选中
+                    currentGroup.querySelectorAll('.filter-chip').forEach(chip => chip.classList.remove('active'));
+                    // �消�一组所有选中
+                    otherGroup.querySelectorAll('.filter-chip').forEach(chip => chip.classList.remove('active'));
+                    // 选中当�点击的
+                    e.target.classList.add('active');
+                    currentPresetMode = e.target.dataset.mode;
+                    // 切�模�时刷新显示（排�会根�current_mode自动使用新模�）
+                    filterAndDisplayStocks();
+                }
+            }
+            
+            presetModes.addEventListener('click', function(e) {
+                handlePresetModeClick(e, presetModes, presetModes2);
+            });
+            
+            presetModes2.addEventListener('click', function(e) {
+                handlePresetModeClick(e, presetModes2, presetModes);
+            });
+
             // 方�筛选芯片
             const directionFilter = document.getElementById('directionFilter');
             directionFilter.addEventListener('click', function(e) {
@@ -1289,6 +1602,29 @@ def generate_html(stocks: list, date: int, output_path: str):
             // �置按钮
             document.getElementById('resetBtn').addEventListener('click', resetFilters);
 
+            // 高级筛选滑�
+            const advancedSliders = [
+                { id: 'priceThreshold', valueId: 'priceThresholdValue', key: 'priceNorm' },
+                { id: 'convergenceThreshold', valueId: 'convergenceThresholdValue', key: 'convergenceNorm' },
+                { id: 'volumeThreshold', valueId: 'volumeThresholdValue', key: 'volumeNorm' },
+                { id: 'geometryThreshold', valueId: 'geometryThresholdValue', key: 'geometryNorm' },
+                { id: 'activityThreshold', valueId: 'activityThresholdValue', key: 'activityNorm' },
+                { id: 'tiltThreshold', valueId: 'tiltThresholdValue', key: 'tiltNorm' }
+            ];
+            
+            advancedSliders.forEach(slider => {
+                const element = document.getElementById(slider.id);
+                const valueDisplay = document.getElementById(slider.valueId);
+                if (element && valueDisplay) {
+                    element.addEventListener('input', function() {
+                        const value = parseFloat(this.value);
+                        filters[slider.key] = value;
+                        valueDisplay.textContent = value.toFixed(2);
+                        filterAndDisplayStocks();
+                    });
+                }
+            });
+
             // 模�框
             document.getElementById('imageModal').addEventListener('click', function(e) {
                 if (e.target === this) closeModal();
@@ -1298,10 +1634,20 @@ def generate_html(stocks: list, date: int, output_path: str):
         function resetFilters() {
             // �置所有筛选和排�状�
             currentThreshold = 0;
-            filters = { direction: 'all', volume: 'all' };
-            sortBy = 'strength';
+            filters = { 
+                direction: 'all', 
+                volume: 'all',
+                priceNorm: 0,
+                convergenceNorm: 0,
+                volumeNorm: 0,
+                geometryNorm: 0,
+                activityNorm: 0,
+                tiltNorm: 0
+            };
+            sortBy = 'current_mode';
             sortOrder = 'desc';
             searchQuery = '';
+            currentPresetMode = 'equal';  // �置预设模�为等�
 
             // �置UI
             document.getElementById('strengthSlider').value = 0;
@@ -1311,7 +1657,15 @@ def generate_html(stocks: list, date: int, output_path: str):
             document.getElementById('searchInput').value = '';
             document.querySelector('.search-box').classList.remove('has-value');
 
-            document.getElementById('sortSelect').value = 'strength';
+            document.getElementById('sortSelect').value = 'current_mode';
+            
+            // �置预设模�按钮
+            document.querySelectorAll('#presetModes .filter-chip').forEach((chip, i) => {
+                chip.classList.toggle('active', i === 0);  // 选中第一个（等�模�）
+            });
+            document.querySelectorAll('#presetModes2 .filter-chip').forEach(chip => {
+                chip.classList.remove('active');
+            });
             const sortOrderBtn = document.getElementById('sortOrder');
             sortOrderBtn.textContent = '↓';
             sortOrderBtn.classList.add('active');
@@ -1325,9 +1679,35 @@ def generate_html(stocks: list, date: int, output_path: str):
                 chip.classList.toggle('active', i === 0);
             });
             filters.volume = 'all';
+            
+            // �置高级筛选滑�
+            const advancedSliders = ['priceThreshold', 'convergenceThreshold', 'volumeThreshold', 
+                                    'geometryThreshold', 'activityThreshold', 'tiltThreshold'];
+            const valueIds = ['priceThresholdValue', 'convergenceThresholdValue', 'volumeThresholdValue',
+                            'geometryThresholdValue', 'activityThresholdValue', 'tiltThresholdValue'];
+            advancedSliders.forEach((id, i) => {
+                const slider = document.getElementById(id);
+                const valueDisplay = document.getElementById(valueIds[i]);
+                if (slider && valueDisplay) {
+                    slider.value = 0;
+                    valueDisplay.textContent = '0.00';
+                }
+            });
 
             filterAndDisplayStocks();
         }
+        
+        function toggleAdvancedFilters() {
+            const panel = document.getElementById('advancedFilters');
+            const icon = document.getElementById('advancedToggleIcon');
+            if (panel.style.display === 'none') {
+                panel.style.display = 'block';
+                icon.style.transform = 'rotate(180deg)';
+            } else {
+                panel.style.display = 'none';
+                icon.style.transform = 'rotate(0deg)';
+            }
+        }
 
         function filterAndDisplayStocks() {
             let result = [...allStocks];
@@ -1345,6 +1725,26 @@ def generate_html(stocks: list, date: int, output_path: str):
                 const value = filters.volume === 'true';
                 result = result.filter(stock => stock.volumeConfirmed === (value ? 'True' : 'False'));
             }
+            
+            // 高级维度筛选
+            if (filters.priceNorm > 0) {
+                result = result.filter(stock => (stock.priceUpNorm || 0) >= filters.priceNorm);
+            }
+            if (filters.convergenceNorm > 0) {
+                result = result.filter(stock => (stock.convergenceNorm || 0) >= filters.convergenceNorm);
+            }
+            if (filters.volumeNorm > 0) {
+                result = result.filter(stock => (stock.volumeNorm || 0) >= filters.volumeNorm);
+            }
+            if (filters.geometryNorm > 0) {
+                result = result.filter(stock => (stock.geometryNorm || 0) >= filters.geometryNorm);
+            }
+            if (filters.activityNorm > 0) {
+                result = result.filter(stock => (stock.activityNorm || 0) >= filters.activityNorm);
+            }
+            if (filters.tiltNorm > 0) {
+                result = result.filter(stock => (stock.tiltNorm || 0) >= filters.tiltNorm);
+            }
 
             // �索
             if (searchQuery) {
@@ -1355,10 +1755,28 @@ def generate_html(stocks: list, date: int, output_path: str):
                 );
             }
 
-            // 排�
+            // 排� - 根�当�模�动�获�排�字段
+            const modeToKeyMap = {
+                'equal': 'strengthEqual',
+                'aggressive': 'strengthAggressive',
+                'conservative': 'strengthConservative',
+                'volume': 'strengthVolume',
+                'test_price': 'strengthTestPrice',
+                'test_convergence': 'strengthTestConvergence',
+                'test_volume': 'strengthTestVolume',
+                'test_geometry': 'strengthTestGeometry',
+                'test_activity': 'strengthTestActivity',
+                'test_tilt': 'strengthTestTilt'
+            };
+            
             result.sort((a, b) => {
                 let aVal, bVal;
-                if (sortBy === 'strength') {
+                if (sortBy === 'current_mode') {
+                    // 根�当�预设模�获�对应的强度分字段
+                    const key = modeToKeyMap[currentPresetMode] || 'strengthEqual';
+                    aVal = a[key] || 0;
+                    bVal = b[key] || 0;
+                } else if (sortBy === 'strength') {
                     aVal = a.strength;
                     bVal = b.strength;
                 } else if (sortBy === 'widthRatio') {
@@ -1367,6 +1785,15 @@ def generate_html(stocks: list, date: int, output_path: str):
                 } else if (sortBy === 'touches') {
                     aVal = a.touchesUpper + a.touchesLower;
                     bVal = b.touchesUpper + b.touchesLower;
+                } else if (sortBy === 'convergenceNorm') {
+                    aVal = a.convergenceNorm || 0;
+                    bVal = b.convergenceNorm || 0;
+                } else if (sortBy === 'volumeNorm') {
+                    aVal = a.volumeNorm || 0;
+                    bVal = b.volumeNorm || 0;
+                } else {
+                    aVal = a.strength;
+                    bVal = b.strength;
                 }
                 return sortOrder === 'desc' ? bVal - aVal : aVal - bVal;
             });
@@ -1386,15 +1813,47 @@ def generate_html(stocks: list, date: int, output_path: str):
                 grid.style.display = 'grid';
                 emptyState.style.display = 'none';
                 grid.innerHTML = stocks.map(stock => createStockCard(stock)).join('');
+                
+                // 绘制所有雷达图
+                setTimeout(() => {
+                    document.querySelectorAll('.radar-canvas').forEach(canvas => {
+                        const valuesStr = canvas.dataset.values;
+                        if (valuesStr) {
+                            const values = valuesStr.split(',').map(v => parseFloat(v) || 0);
+                            drawMiniRadar(canvas, values);
+                        }
+                    });
+                }, 0);
             }
         }
 
         function updateStats(filteredStocks) {
             document.getElementById('totalStocks').textContent = allStocks.length;
             document.getElementById('displayedStocks').textContent = filteredStocks.length;
-            const avgStrength = filteredStocks.length > 0
-                ? filteredStocks.reduce((sum, s) => sum + s.strength, 0) / filteredStocks.length
-                : 0;
+            
+            // 根�当�模�计算平�强度
+            const modeKeyMap = {
+                'equal': 'strengthEqual',
+                'aggressive': 'strengthAggressive',
+                'conservative': 'strengthConservative',
+                'volume': 'strengthVolume',
+                'test_price': 'strengthTestPrice',
+                'test_convergence': 'strengthTestConvergence',
+                'test_volume': 'strengthTestVolume',
+                'test_geometry': 'strengthTestGeometry',
+                'test_activity': 'strengthTestActivity',
+                'test_tilt': 'strengthTestTilt'
+            };
+            
+            let avgStrength = 0;
+            if (filteredStocks.length > 0) {
+                const key = modeKeyMap[currentPresetMode];
+                if (key) {
+                    avgStrength = filteredStocks.reduce((sum, s) => sum + (s[key] || 0), 0) / filteredStocks.length;
+                } else {
+                    avgStrength = filteredStocks.reduce((sum, s) => sum + s.strength, 0) / filteredStocks.length;
+                }
+            }
             document.getElementById('avgStrength').textContent = avgStrength.toFixed(3);
         }
 
@@ -1407,6 +1866,27 @@ def generate_html(stocks: list, date: int, output_path: str):
                               stock.volumeConfirmed === 'False' ? '✗' : '—';
             const chartPath = showDetailCharts ? stock.chartPathDetail : stock.chartPath;
             const fallbackPath = showDetailCharts ? stock.chartPath : stock.chartPathDetail;
+            
+            // 根�当�模�选择强度分
+            let displayStrength = stock.strength;
+            let modeName = '原始';
+            const modeMap = {
+                'equal': { key: 'strengthEqual', name: '等�' },
+                'aggressive': { key: 'strengthAggressive', name: '激进' },
+                'conservative': { key: 'strengthConservative', name: '�守' },
+                'volume': { key: 'strengthVolume', name: '放�' },
+                'test_price': { key: 'strengthTestPrice', name: '�破主导' },
+                'test_convergence': { key: 'strengthTestConvergence', name: '收敛主导' },
+                'test_volume': { key: 'strengthTestVolume', name: '�交�主导' },
+                'test_geometry': { key: 'strengthTestGeometry', name: '形�主导' },
+                'test_activity': { key: 'strengthTestActivity', name: '活跃主导' },
+                'test_tilt': { key: 'strengthTestTilt', name: '倾斜主导' }
+            };
+            
+            if (modeMap[currentPresetMode]) {
+                displayStrength = stock[modeMap[currentPresetMode].key] || stock.strength;
+                modeName = modeMap[currentPresetMode].name;
+            }
 
             return `
                 <div class="stock-card" onclick="openModal('${stock.chartPath}', '${stock.chartPathDetail}')">
@@ -1416,8 +1896,8 @@ def generate_html(stocks: list, date: int, output_path: str):
                             <span class="stock-code">${stock.code}</span>
                         </div>
                         <div class="strength-badge">
-                            <div class="strength-value">${stock.strength.toFixed(3)}</div>
-                            <div class="strength-label">强度分</div>
+                            <div class="strength-value">${displayStrength.toFixed(3)}</div>
+                            <div class="strength-label">${modeName}强度分</div>
                         </div>
                     </div>
                     <div class="card-body">
@@ -1447,6 +1927,27 @@ def generate_html(stocks: list, date: int, output_path: str):
                                 <span class="metric-value">${(stock.tiltScore || 0).toFixed(2)}</span>
                             </div>
                         </div>
+                        
+                        <!-- 6维度标准化得分展示 -->
+                        <div class="dimensions-panel" style="margin-top: 16px; padding: 12px; background: var(--bg-secondary); border-radius: 10px;">
+                            <div style="display: flex; justify-content: space-between; align-items: center; margin-bottom: 10px;">
+                                <div style="font-size: 12px; color: var(--text-secondary); font-weight: 600;">标准化维度</div>
+                                <canvas class="radar-canvas" 
+                                        data-values="${stock.priceUpNorm || 0},${stock.convergenceNorm || 0},${stock.volumeNorm || 0},${stock.geometryNorm || 0},${stock.activityNorm || 0},${stock.tiltNorm || 0}"
+                                        width="80" height="80" 
+                                        style="cursor: pointer;" 
+                                        onclick="event.stopPropagation();"></canvas>
+                            </div>
+                            <div>
+                                ${createDimensionBar('�破幅度', stock.priceUpNorm || 0, stock.direction === 'up')}
+                                ${createDimensionBar('收敛度', stock.convergenceNorm || 0)}
+                                ${createDimensionBar('�交�', stock.volumeNorm || 0)}
+                                ${createDimensionBar('形�规则', stock.geometryNorm || 0)}
+                                ${createDimensionBar('活跃度', stock.activityNorm || 0)}
+                                ${createDimensionBar('倾斜度', stock.tiltNorm || 0)}
+                            </div>
+                        </div>
+                        
                         <div class="chart-container">
                             <img src="${chartPath}"
                                  alt="${stock.name}"
@@ -1458,6 +1959,22 @@ def generate_html(stocks: list, date: int, output_path: str):
                 </div>
             `;
         }
+        
+        function createDimensionBar(label, value, highlight = false) {
+            const percentage = (value * 100).toFixed(0);
+            const color = highlight ? 'var(--accent-primary)' : 'var(--accent-secondary)';
+            return `
+                <div style="margin-bottom: 8px;">
+                    <div style="display: flex; justify-content: space-between; margin-bottom: 4px;">
+                        <span style="font-size: 11px; color: var(--text-secondary);">${label}</span>
+                        <span style="font-size: 11px; font-family: 'JetBrains Mono', monospace; color: ${color};">${value.toFixed(2)}</span>
+                    </div>
+                    <div style="height: 4px; background: var(--bg-card); border-radius: 2px; overflow: hidden;">
+                        <div style="height: 100%; width: ${percentage}%; background: ${color}; transition: width 0.3s;"></div>
+                    </div>
+                </div>
+            `;
+        }
 
         function handleImageError(img) {
             const fallbackSrc = img.dataset.fallbackSrc;
@@ -1489,6 +2006,85 @@ def generate_html(stocks: list, date: int, output_path: str):
         function closeModal() {
             document.getElementById('imageModal').classList.remove('show');
         }
+        
+        // 绘制迷你雷达图
+        function drawMiniRadar(canvas, values) {
+            const ctx = canvas.getContext('2d');
+            const centerX = canvas.width / 2;
+            const centerY = canvas.height / 2;
+            const radius = Math.min(centerX, centerY) - 10;
+            const angleStep = (Math.PI * 2) / 6;
+            
+            // 清空画布
+            ctx.clearRect(0, 0, canvas.width, canvas.height);
+            
+            // 绘制背景网格
+            ctx.strokeStyle = 'rgba(139, 146, 168, 0.2)';
+            ctx.lineWidth = 1;
+            for (let i = 1; i <= 3; i++) {
+                ctx.beginPath();
+                const r = radius * (i / 3);
+                for (let j = 0; j <= 6; j++) {
+                    const angle = j * angleStep - Math.PI / 2;
+                    const x = centerX + r * Math.cos(angle);
+                    const y = centerY + r * Math.sin(angle);
+                    if (j === 0) ctx.moveTo(x, y);
+                    else ctx.lineTo(x, y);
+                }
+                ctx.closePath();
+                ctx.stroke();
+            }
+            
+            // 绘制轴线
+            ctx.strokeStyle = 'rgba(139, 146, 168, 0.3)';
+            for (let i = 0; i < 6; i++) {
+                const angle = i * angleStep - Math.PI / 2;
+                ctx.beginPath();
+                ctx.moveTo(centerX, centerY);
+                ctx.lineTo(
+                    centerX + radius * Math.cos(angle),
+                    centerY + radius * Math.sin(angle)
+                );
+                ctx.stroke();
+            }
+            
+            // 绘制数�多边形
+            ctx.fillStyle = 'rgba(0, 212, 170, 0.2)';
+            ctx.strokeStyle = 'rgba(0, 212, 170, 0.8)';
+            ctx.lineWidth = 2;
+            ctx.beginPath();
+            for (let i = 0; i <= 6; i++) {
+                const angle = i * angleStep - Math.PI / 2;
+                const value = values[i % 6] || 0;
+                const r = radius * value;
+                const x = centerX + r * Math.cos(angle);
+                const y = centerY + r * Math.sin(angle);
+                if (i === 0) ctx.moveTo(x, y);
+                else ctx.lineTo(x, y);
+            }
+            ctx.closePath();
+            ctx.fill();
+            ctx.stroke();
+            
+            // 绘制数�点
+            ctx.fillStyle = '#00d4aa';
+            for (let i = 0; i < 6; i++) {
+                const angle = i * angleStep - Math.PI / 2;
+                const value = values[i] || 0;
+                const r = radius * value;
+                const x = centerX + r * Math.cos(angle);
+                const y = centerY + r * Math.sin(angle);
+                ctx.beginPath();
+                ctx.arc(x, y, 3, 0, Math.PI * 2);
+                ctx.fill();
+            }
+        }
+        
+        // 切�雷达图/进度�视图（�选功能，暂�实现）
+        function toggleRadarView(code) {
+            // �以实现点击雷达图放大查看
+            console.log('Toggle radar view for:', code);
+        }
 
         document.addEventListener('keydown', function(e) {
             if (e.key === 'Escape') closeModal();
diff --git a/scripts/scoring/README.md b/scripts/scoring/README.md
new file mode 100644
index 0000000..5e0ee78
--- /dev/null
+++ b/scripts/scoring/README.md
@@ -0,0 +1,352 @@
+# 强度分标准化系统
+
+## 概述
+
+针对收敛三角形形�检测的18,004个样本，本系统解决了**维度间��比性**问题，实现了：
+
+1. **分层标准化**：针对4�分布类型（零膨胀�点质��正常�低区分度）采用��标准化策略
+2. **�活�置**：��置���阈值�方��筛选模�
+3. **预设模�**：等��激进��守�放�4�预设�置
+4. **�感性分�**：分��数�化对筛选结果的影�
+
+## 核心问题
+
+**标准化�**的分布问题：
+
+| 维度 | 中�数 | 分布类型 | 问题 |
+|------|--------|----------|------|
+| price_score_up | 0.0000 | 零膨胀 | 无法区分"未�破"vs"�幅�破" |
+| price_score_down | 0.0000 | 零膨胀 | �上 |
+| volume_score | 0.0000 | 零膨胀 | �上 |
+| tilt_score | 0.5000 | 点质� | 75%的值=0.5，缺�区分度 |
+| convergence_score | 0.8033 | 正常 | 值域�大，等�相加时"�掉"其他维度 |
+| geometry_score | 0.0051 | 低区分度 | 值域��，等�相加时被"�掉" |
+| activity_score | 0.0709 | 正常 | - |
+
+**标准化�**：所有维度中�数统一为 **0.5**，�直接等�相加。
+
+## 文件结构
+
+```
+scripts/
+├── scoring/                              # 核心模�
+│   ├── __init__.py                       # 模�导出
+│   ├── normalizer.py                     # 标准化模�（4�标准化方法）
+│   ├── config.py                         # �置管�（预设模�+自定义�置）
+│   └── sensitivity.py                    # �感性分�
+│
+├── verify_normalization.py               # 验�标准化效果
+├── example_scoring_usage.py              # 使用示例（5个示例）
+│
+└── scoring/generate_sensitivity_report.py  # 生�完整�感性报告
+
+outputs/converging_triangles/             # 输出目录
+├── all_results.csv                       # 原始数�
+├── all_results_normalized.csv            # 标准化�数�
+├── normalization_stats_comparison.csv    # 统计对比
+├── normalization_comparison.png          # 对比图表
+├── strength_comparison.png               # 强度分对比
+├── sensitivity_threshold_price.csv       # �破幅度阈值�感性
+├── sensitivity_threshold_convergence.csv # 收敛度阈值�感性
+├── sensitivity_threshold_volume.csv      # �交�阈值�感性
+├── sensitivity_weight_price.csv          # �破幅度���感性
+└── sensitivity_analysis_report.md        # �感性分�报告
+```
+
+## 快速开始
+
+### 1. 标准化原始数�
+
+```python
+from scoring import normalize_all
+import pandas as pd
+
+# 加载原始数�
+df = pd.read_csv('outputs/converging_triangles/all_results.csv')
+df = df[df['is_valid'] == True]
+
+# 标准化
+df_norm = normalize_all(df)
+
+# 现在df_norm包�：
+# - 原始字段：price_score_up, convergence_score, ...
+# - 标准化字段：price_score_up_norm, convergence_score_norm, ...
+```
+
+### 2. 使用预设�置筛选信�
+
+```python
+from scoring import CONFIG_EQUAL, CONFIG_AGGRESSIVE, filter_signals
+
+# 等�模�
+signals_equal = filter_signals(df_norm, CONFIG_EQUAL, return_strength=True)
+print(f"等�模�: {len(signals_equal)} 个信�")
+
+# 激进模�（�视�破35% + �交�25%）
+signals_aggr = filter_signals(df_norm, CONFIG_AGGRESSIVE, return_strength=True)
+print(f"激进模�: {len(signals_aggr)} 个信�")
+
+# 查看Top 10
+top10 = signals_aggr.nlargest(10, 'strength')
+```
+
+### 3. 自定义�置
+
+```python
+from scoring import StrengthConfig, filter_signals
+
+# 创建自定义�置
+my_config = StrengthConfig(
+    name="我的�置",
+    w_price=0.40,           # �破��40%
+    w_volume=0.30,          # �交���30%
+    w_convergence=0.15,
+    w_geometry=0.05,
+    w_activity=0.05,
+    w_tilt=0.05,
+    threshold_price=0.65,   # �破阈值
+    threshold_volume=0.70,  # �交�阈值（>0.5��用）
+    direction='up',         # �看�上�破
+)
+
+# 筛选
+signals = filter_signals(df_norm, my_config, return_strength=True)
+```
+
+### 4. 获�Top N信�
+
+```python
+from scoring import filter_top_n, CONFIG_EQUAL
+
+# 获�强度分Top 50的信�
+top50 = filter_top_n(df_norm, CONFIG_EQUAL, n=50)
+
+# Top 50包�strength列，已按强度��排列
+```
+
+## 标准化方法详解
+
+### 1. 零膨胀分布标准化 (normalize_zero_inflated)
+
+**适用于**：price_score_up, price_score_down, volume_score
+
+**策略**：
+- 零值（未�生） → 0.5（中性基准）
+- �零值（已�生） → [0.5, 1.0] 区间按排�映射
+
+**原�**：�留"零vs�零"的质的差异，�时在�零内部���的差异。
+
+### 2. 点质�分布标准化 (normalize_point_mass)
+
+**适用于**：tilt_score
+
+**策略**：
+- 中心值（0.5）附近 → ��0.5
+- 正�离（>0.5） → 拉伸到 [0.5, 1.0]
+- 负�离（<0.5） → 拉伸到 [0.0, 0.5]
+
+**原�**：75%的值�好=0.5（对称三角形），对这些����；剩余25%按�离程度拉伸。
+
+### 3. 标准分�数标准化 (normalize_standard)
+
+**适用于**：convergence_score, activity_score
+
+**策略**：直接转�为百分�排� [0, 1]
+
+**原�**：分布相对正常，直接排���。
+
+### 4. 低区分度标准化 (normalize_low_variance)
+
+**适用于**：geometry_score
+
+**策略**：
+1. 对数��扩大�值区间的区分度
+2. 分�数标准化
+
+**原�**：值普��低（中�数0.005），log1p���拉开�值间的差�。
+
+## 预设�置
+
+### CONFIG_EQUAL - 等�模�
+
+```python
+�维度��: 1/6 (约16.7%)
+阈值: price≥0.60, convergence≥0.50, volume≥0.50
+适用: 探索性分�，�确定哪个维度更��时
+```
+
+### CONFIG_AGGRESSIVE - 激进模�
+
+```python
+��: �破35%, �交�25%, 收敛15%, 其他5-10%
+阈值: price≥0.55, volume≥0.60
+适用: 趋势行情，追求�破力度和放�确认
+```
+
+### CONFIG_CONSERVATIVE - �守模�
+
+```python
+��: 收敛度30%, 活跃度25%, �破15%, 其他5-15%
+阈值: price≥0.70, convergence≥0.65, activity≥0.50
+适用: 震�市，�视形�质�和活跃度
+```
+
+### CONFIG_VOLUME_FOCUS - 放�模�
+
+```python
+��: �交�35%, �破25%, 收敛15%, 其他5-10%
+阈值: volume≥0.70, price≥0.60
+适用: �获主力异动，必须明显放�
+```
+
+## �感性分�
+
+### 快速分�
+
+```bash
+python scripts/scoring/sensitivity.py
+```
+
+输出示例：
+
+```
+threshold_price | 信�数 | �比   | 平�强度
+------------------------------------------------
+           0.50 |   2304 |  12.8% |   0.6292
+           0.60 |    308 |   1.7% |   0.6897
+           0.70 |    244 |   1.4% |   0.7033
+           0.80 |    180 |   1.0% |   0.7158
+```
+
+### 完整分�报告
+
+```bash
+python scripts/scoring/generate_sensitivity_report.py
+```
+
+生�：
+- sensitivity_threshold_price.csv（�PNG图表）
+- sensitivity_threshold_convergence.csv
+- sensitivity_threshold_volume.csv
+- sensitivity_weight_price.csv
+- sensitivity_analysis_report.md（汇总报告）
+
+## 阈值设置建议
+
+根��感性分�，推�：
+
+| 筛选强度 | threshold_price | 预期信�数 | �比 |
+|----------|-----------------|-----------|------|
+| 宽� | 0.50-0.55 | 2000-350 | 11-2% |
+| 适中 | 0.60-0.65 | 300-280 | 1.7-1.5% |
+| 严格 | 0.70-0.75 | 240-210 | 1.4-1.2% |
+| �严格 | 0.80+ | <180 | <1.0% |
+
+**�交�阈值**：
+- ≤ 0.5：�筛选�交�（适�震�市）
+- 0.60-0.70：适度放��求
+- ≥ 0.75：高放��求（�能过于严格）
+
+## 使用示例
+
+查看 `scripts/example_scoring_usage.py`，包�5个示例：
+
+1. 基础标准化
+2. 使用预设�置筛选信�
+3. 自定义�置
+4. 获�Top N信�
+5. 对比���置的结果
+
+�行：
+
+```bash
+python scripts/example_scoring_usage.py
+```
+
+## 验�标准化效果
+
+```bash
+python scripts/verify_normalization.py
+```
+
+输出：
+- 标准化��统计对比表
+- 7个维度的分布对比图
+- 强度分对比图
+- all_results_normalized.csv（标准化�数�）
+
+## API�考
+
+### 标准化模� (normalizer.py)
+
+```python
+normalize_all(df: pd.DataFrame) -> pd.DataFrame
+    对all_results.csv中的7个得分字段进行分层标准化
+    
+calculate_strength_equal_weight(df_normalized, direction='up') -> pd.Series
+    计算等�强度分
+```
+
+### �置模� (config.py)
+
+```python
+class StrengthConfig:
+    # 创建�置对象
+    config = StrengthConfig(w_price=0.4, threshold_price=0.65, ...)
+    
+    # 验��置
+    config.validate()
+    
+    # 打�摘�
+    print(config.summary())
+
+calculate_strength(df_normalized, config) -> pd.Series
+    根��置计算综�强度分
+    
+filter_signals(df_normalized, config, return_strength=False) -> pd.DataFrame
+    根��置筛选信�
+    
+filter_top_n(df_normalized, config, n=100) -> pd.DataFrame
+    筛选强度分Top N的信�
+```
+
+### �感性分� (sensitivity.py)
+
+```python
+analyze_threshold_sensitivity(df, config, param_name, param_range) -> pd.DataFrame
+    分�阈值�数的�感性
+    
+analyze_weight_sensitivity(df, config, weight_name, weight_range) -> pd.DataFrame
+    分����数的�感性
+    
+generate_full_sensitivity_report(df, config, output_dir)
+    生�完整的�感性分�报告
+```
+
+## �续优化方�
+
+1. **动���**：根�市场环境自动调整��（牛市 vs 震�市）
+2. **多因���**：结�其他技术指标（RSI�MACD等）
+3. **回测验�**：基于历�数�回测��置的收益表现
+4. **实时监控**：实时计算强度分并推�高分信�
+5. **�视化界�**：交互�调整�数并实时预览结果
+
+## 注�事项
+
+1. **阈值 > 0.5 ��用筛选**：volume_score_norm, geometry_score_norm 的阈值 ≤ 0.5 表示�筛选
+2. **��和必须为1**：自定义�置时确�所有��之和=1.0
+3. **标准化�的值域**：所有*_norm字段的范围都是[0, 1]
+4. **原始字段�留**：标准化�修改原始字段，新增*_norm�缀字段
+
+## 问题�馈
+
+如有问题，请查看：
+1. `outputs/converging_triangles/sensitivity_analysis_report.md`
+2. `outputs/converging_triangles/normalization_comparison.png`
+3. �行 `python scripts/example_scoring_usage.py` 查看示例
+
+---
+
+**版本**: 1.0  
+**更新日期**: 2026-01-29  
+**作者**: AI Assistant
diff --git a/scripts/scoring/__init__.py b/scripts/scoring/__init__.py
new file mode 100644
index 0000000..8b66ce1
--- /dev/null
+++ b/scripts/scoring/__init__.py
@@ -0,0 +1,68 @@
+"""
+强度分标准化与�置模�
+
+本模��供：
+1. 分层标准化处� (normalizer)
+2. �置管� (config)
+3. �感性分� (sensitivity)
+"""
+
+from .normalizer import (
+    normalize_zero_inflated,
+    normalize_point_mass,
+    normalize_standard,
+    normalize_low_variance,
+    normalize_all,
+    calculate_strength_equal_weight,
+)
+
+# config 模�会在 P2 实现�导入
+try:
+    from .config import (
+        StrengthConfig,
+        CONFIG_EQUAL,
+        CONFIG_AGGRESSIVE,
+        CONFIG_CONSERVATIVE,
+        CONFIG_VOLUME_FOCUS,
+        # �维度测试模�
+        CONFIG_TEST_PRICE,
+        CONFIG_TEST_CONVERGENCE,
+        CONFIG_TEST_VOLUME,
+        CONFIG_TEST_GEOMETRY,
+        CONFIG_TEST_ACTIVITY,
+        CONFIG_TEST_TILT,
+        filter_signals,
+        calculate_strength,
+        filter_top_n,
+    )
+    _has_config = True
+except ImportError:
+    _has_config = False
+
+__all__ = [
+    'normalize_zero_inflated',
+    'normalize_point_mass', 
+    'normalize_standard',
+    'normalize_low_variance',
+    'normalize_all',
+    'calculate_strength_equal_weight',
+]
+
+if _has_config:
+    __all__.extend([
+        'StrengthConfig',
+        'CONFIG_EQUAL',
+        'CONFIG_AGGRESSIVE',
+        'CONFIG_CONSERVATIVE',
+        'CONFIG_VOLUME_FOCUS',
+        # �维度测试模�
+        'CONFIG_TEST_PRICE',
+        'CONFIG_TEST_CONVERGENCE',
+        'CONFIG_TEST_VOLUME',
+        'CONFIG_TEST_GEOMETRY',
+        'CONFIG_TEST_ACTIVITY',
+        'CONFIG_TEST_TILT',
+        'filter_signals',
+        'calculate_strength',
+        'filter_top_n',
+    ])
diff --git a/scripts/scoring/config.py b/scripts/scoring/config.py
new file mode 100644
index 0000000..ac30768
--- /dev/null
+++ b/scripts/scoring/config.py
@@ -0,0 +1,470 @@
+"""
+强度分�置管�模�
+
+�供��置的���阈值和预设模�，支�：
+1. 等�模�（默认）
+2. 激进模�（�视�破和�交�）
+3. �守模�（�视形�质�）
+4. 放�模�（�视�交�确认）
+"""
+
+from dataclasses import dataclass, field
+from typing import Literal, Optional
+import pandas as pd
+
+
+@dataclass
+class StrengthConfig:
+    """
+    强度分�置类
+    
+    属性:
+        w_price: �破幅度分��
+        w_convergence: 收敛度分��
+        w_volume: �交�分��
+        w_geometry: 形�规则度��
+        w_activity: 价格活跃度��
+        w_tilt: 倾斜度分��
+        
+        threshold_price: �破幅度分阈值（标准化�）
+        threshold_convergence: 收敛度分阈值
+        threshold_volume: �交�分阈值
+        threshold_geometry: 形�规则度阈值
+        threshold_activity: 价格活跃度阈值
+        
+        direction: �破方� ('up', 'down', 'both')
+        filter_mode: 筛选模� ('and', 'or')
+    """
+    
+    # ���置（默认等�）
+    w_price: float = 1/6
+    w_convergence: float = 1/6
+    w_volume: float = 1/6
+    w_geometry: float = 1/6
+    w_activity: float = 1/6
+    w_tilt: float = 1/6
+    
+    # 阈值�置（标准化�的值，范围[0, 1]）
+    threshold_price: float = 0.60          # �破幅度阈值
+    threshold_convergence: float = 0.50    # 收敛度阈值（中性）
+    threshold_volume: float = 0.50         # �交�阈值（中性=�筛选）
+    threshold_geometry: float = 0.50       # 形�规则度阈值（中性）
+    threshold_activity: float = 0.30       # 价格活跃度阈值
+    
+    # 方�与模�
+    direction: Literal['up', 'down', 'both'] = 'up'
+    filter_mode: Literal['and', 'or'] = 'and'
+    
+    # �置�称（用于显示）
+    name: str = "自定义�置"
+    
+    def validate(self) -> bool:
+        """验��置的有效性"""
+        # 检查��和是�为1
+        total_weight = (self.w_price + self.w_convergence + self.w_volume + 
+                       self.w_geometry + self.w_activity + self.w_tilt)
+        
+        if abs(total_weight - 1.0) > 0.001:
+            raise ValueError(f"��和必须为1.0，当�为{total_weight:.6f}")
+        
+        # 检查��范围
+        for name, weight in [
+            ('price', self.w_price), ('convergence', self.w_convergence),
+            ('volume', self.w_volume), ('geometry', self.w_geometry),
+            ('activity', self.w_activity), ('tilt', self.w_tilt)
+        ]:
+            if not 0 <= weight <= 1:
+                raise ValueError(f"{name}��{weight}超出[0, 1]范围")
+        
+        # 检查阈值范围
+        for name, threshold in [
+            ('price', self.threshold_price), ('convergence', self.threshold_convergence),
+            ('volume', self.threshold_volume), ('geometry', self.threshold_geometry),
+            ('activity', self.threshold_activity)
+        ]:
+            if not 0 <= threshold <= 1:
+                raise ValueError(f"{name}阈值{threshold}超出[0, 1]范围")
+        
+        return True
+    
+    def summary(self) -> str:
+        """返回�置摘�"""
+        lines = [
+            f"�置�称: {self.name}",
+            f"\n��分�:",
+            f"  �破幅度分: {self.w_price:.2%}",
+            f"  收敛度分:   {self.w_convergence:.2%}",
+            f"  �交�分:   {self.w_volume:.2%}",
+            f"  形�规则度: {self.w_geometry:.2%}",
+            f"  价格活跃度: {self.w_activity:.2%}",
+            f"  倾斜度分:   {self.w_tilt:.2%}",
+            f"\n筛选阈值:",
+            f"  �破幅度分: ≥{self.threshold_price:.2f}",
+            f"  收敛度分:   ≥{self.threshold_convergence:.2f}",
+            f"  �交�分:   ≥{self.threshold_volume:.2f}",
+            f"  价格活跃度: ≥{self.threshold_activity:.2f}",
+            f"\n其他:",
+            f"  方�: {self.direction}",
+            f"  筛选模�: {self.filter_mode}",
+        ]
+        return '\n'.join(lines)
+
+
+# ============================================================================
+# 预设�置
+# ============================================================================
+
+# 等�模�（默认）
+CONFIG_EQUAL = StrengthConfig(
+    name="等�模�",
+    w_price=1/6,
+    w_convergence=1/6,
+    w_volume=1/6,
+    w_geometry=1/6,
+    w_activity=1/6,
+    w_tilt=1/6,
+    threshold_price=0.60,
+    threshold_convergence=0.50,
+    threshold_volume=0.50,
+)
+
+# 激进模�（�视�破和�交�，适�趋势行情）
+CONFIG_AGGRESSIVE = StrengthConfig(
+    name="激进模�",
+    w_price=0.35,           # �破最��
+    w_volume=0.25,          # �交�确认
+    w_convergence=0.15,     # 收敛度
+    w_geometry=0.10,        # 形�
+    w_activity=0.10,        # 活跃度
+    w_tilt=0.05,            # 倾斜度
+    threshold_price=0.55,   # 较低阈值，�获更多信�
+    threshold_volume=0.60,  # �求一定放�
+    direction='up',
+)
+
+# �守模�（�视形�质�，适�震�市）
+CONFIG_CONSERVATIVE = StrengthConfig(
+    name="�守模�",
+    w_price=0.15,           # �破�是最��
+    w_convergence=0.30,     # 收敛度最��
+    w_volume=0.10,          # �交�
+    w_geometry=0.15,        # 形�质�
+    w_activity=0.25,        # 价格活跃度��
+    w_tilt=0.05,            # 倾斜度
+    threshold_price=0.70,   # 较高阈值，筛选强信�
+    threshold_convergence=0.65,  # �求高质�收敛
+    threshold_activity=0.50,     # �求活跃度正常
+)
+
+# 放�模�（�视�交�确认，�获主力异动）
+CONFIG_VOLUME_FOCUS = StrengthConfig(
+    name="放�模�",
+    w_price=0.25,           # �破
+    w_volume=0.35,          # �交�最��
+    w_convergence=0.15,     # 收敛度
+    w_geometry=0.10,        # 形�
+    w_activity=0.10,        # 活跃度
+    w_tilt=0.05,            # 倾斜度
+    threshold_price=0.60,   # 中等�破�求
+    threshold_volume=0.70,  # 高放��求
+    threshold_convergence=0.50,
+)
+
+
+# ============================================================================
+# �维度测试模�（�个维度50%，其余�10%）
+# ============================================================================
+
+# �破幅度主导
+CONFIG_TEST_PRICE = StrengthConfig(
+    name="�破主导",
+    w_price=0.50,           # 主导维度
+    w_convergence=0.10,
+    w_volume=0.10,
+    w_geometry=0.10,
+    w_activity=0.10,
+    w_tilt=0.10,
+)
+
+# 收敛度主导
+CONFIG_TEST_CONVERGENCE = StrengthConfig(
+    name="收敛主导",
+    w_price=0.10,
+    w_convergence=0.50,     # 主导维度
+    w_volume=0.10,
+    w_geometry=0.10,
+    w_activity=0.10,
+    w_tilt=0.10,
+)
+
+# �交�主导
+CONFIG_TEST_VOLUME = StrengthConfig(
+    name="�交�主导",
+    w_price=0.10,
+    w_convergence=0.10,
+    w_volume=0.50,          # 主导维度
+    w_geometry=0.10,
+    w_activity=0.10,
+    w_tilt=0.10,
+)
+
+# 形�规则主导
+CONFIG_TEST_GEOMETRY = StrengthConfig(
+    name="形�主导",
+    w_price=0.10,
+    w_convergence=0.10,
+    w_volume=0.10,
+    w_geometry=0.50,        # 主导维度
+    w_activity=0.10,
+    w_tilt=0.10,
+)
+
+# 活跃度主导
+CONFIG_TEST_ACTIVITY = StrengthConfig(
+    name="活跃主导",
+    w_price=0.10,
+    w_convergence=0.10,
+    w_volume=0.10,
+    w_geometry=0.10,
+    w_activity=0.50,        # 主导维度
+    w_tilt=0.10,
+)
+
+# 倾斜度主导
+CONFIG_TEST_TILT = StrengthConfig(
+    name="倾斜主导",
+    w_price=0.10,
+    w_convergence=0.10,
+    w_volume=0.10,
+    w_geometry=0.10,
+    w_activity=0.10,
+    w_tilt=0.50,            # 主导维度
+)
+
+
+# ============================================================================
+# 筛选和计算函数
+# ============================================================================
+
+def calculate_strength(
+    df_normalized: pd.DataFrame, 
+    config: StrengthConfig
+) -> pd.Series:
+    """
+    根��置计算综�强度分
+    
+    Args:
+        df_normalized: 标准化�的DataFrame（需包�*_norm字段）
+        config: �置对象
+        
+    Returns:
+        综�强度分�列
+    """
+    config.validate()
+    
+    # 选择方�
+    if config.direction == 'up':
+        price_col = 'price_score_up_norm'
+    elif config.direction == 'down':
+        price_col = 'price_score_down_norm'
+    else:  # 'both'
+        # ��上和�下的最大值
+        price_col = None
+        price_scores = df_normalized[['price_score_up_norm', 'price_score_down_norm']].max(axis=1)
+    
+    # 加�计算
+    if price_col:
+        strength = (
+            config.w_price * df_normalized[price_col] +
+            config.w_convergence * df_normalized['convergence_score_norm'] +
+            config.w_volume * df_normalized['volume_score_norm'] +
+            config.w_geometry * df_normalized['geometry_score_norm'] +
+            config.w_activity * df_normalized['activity_score_norm'] +
+            config.w_tilt * df_normalized['tilt_score_norm']
+        )
+    else:
+        strength = (
+            config.w_price * price_scores +
+            config.w_convergence * df_normalized['convergence_score_norm'] +
+            config.w_volume * df_normalized['volume_score_norm'] +
+            config.w_geometry * df_normalized['geometry_score_norm'] +
+            config.w_activity * df_normalized['activity_score_norm'] +
+            config.w_tilt * df_normalized['tilt_score_norm']
+        )
+    
+    return strength
+
+
+def filter_signals(
+    df_normalized: pd.DataFrame, 
+    config: StrengthConfig,
+    return_strength: bool = False
+) -> pd.DataFrame:
+    """
+    根��置筛选信�
+    
+    Args:
+        df_normalized: 标准化�的DataFrame
+        config: �置对象
+        return_strength: 是�在结果中添加强度分列
+        
+    Returns:
+        筛选�的DataFrame
+    """
+    config.validate()
+    
+    # 构建筛选�件
+    conditions = []
+    
+    # 1. �破幅度�件
+    if config.direction in ['up', 'both']:
+        conditions.append(
+            df_normalized['price_score_up_norm'] >= config.threshold_price
+        )
+    if config.direction in ['down', 'both']:
+        conditions.append(
+            df_normalized['price_score_down_norm'] >= config.threshold_price
+        )
+    
+    # 2. 收敛度�件
+    if config.threshold_convergence > 0:
+        conditions.append(
+            df_normalized['convergence_score_norm'] >= config.threshold_convergence
+        )
+    
+    # 3. �交��件（�有阈值>0.5时��用，�则是放��件）
+    if config.threshold_volume > 0.5:
+        conditions.append(
+            df_normalized['volume_score_norm'] >= config.threshold_volume
+        )
+    
+    # 4. 形�规则度�件
+    if config.threshold_geometry > 0:
+        conditions.append(
+            df_normalized['geometry_score_norm'] >= config.threshold_geometry
+        )
+    
+    # 5. 价格活跃度�件
+    if config.threshold_activity > 0:
+        conditions.append(
+            df_normalized['activity_score_norm'] >= config.threshold_activity
+        )
+    
+    # 组��件
+    if len(conditions) == 0:
+        # 没有任何筛选�件，返回全部
+        result = df_normalized
+    elif config.filter_mode == 'and':
+        # AND: 所有�件都满足
+        final_condition = conditions[0]
+        for cond in conditions[1:]:
+            final_condition = final_condition & cond
+        result = df_normalized[final_condition]
+    else:  # 'or'
+        # OR: 任一�件满足
+        final_condition = conditions[0]
+        for cond in conditions[1:]:
+            final_condition = final_condition | cond
+        result = df_normalized[final_condition]
+    
+    # 添加强度分
+    if return_strength:
+        result = result.copy()
+        result['strength'] = calculate_strength(result, config)
+        result = result.sort_values('strength', ascending=False)
+    
+    return result
+
+
+def filter_top_n(
+    df_normalized: pd.DataFrame,
+    config: StrengthConfig,
+    n: int = 100
+) -> pd.DataFrame:
+    """
+    筛选强度分Top N的信�
+    
+    Args:
+        df_normalized: 标准化�的DataFrame
+        config: �置对象
+        n: 返回�N个信�
+        
+    Returns:
+        Top N的DataFrame，包�strength列
+    """
+    # 计算强度分
+    df_with_strength = df_normalized.copy()
+    df_with_strength['strength'] = calculate_strength(df_normalized, config)
+    
+    # 排�并�Top N
+    result = df_with_strength.nlargest(n, 'strength')
+    
+    return result
+
+
+# ============================================================================
+# 使用示例
+# ============================================================================
+
+if __name__ == "__main__":
+    import sys
+    import os
+    
+    # 添加路径
+    script_dir = os.path.dirname(__file__)
+    sys.path.insert(0, script_dir)
+    
+    from normalizer import normalize_all
+    
+    # 加载数�
+    data_path = os.path.join(
+        os.path.dirname(__file__), 
+        "..", "..", "outputs", "converging_triangles", "all_results_normalized.csv"
+    )
+    
+    if os.path.exists(data_path):
+        print("=" * 80)
+        print("强度分�置模�测试")
+        print("=" * 80)
+        
+        df = pd.read_csv(data_path)
+        print(f"\n加载数�: {len(df)} �记录")
+        
+        # 测试���置
+        configs = [
+            CONFIG_EQUAL,
+            CONFIG_AGGRESSIVE,
+            CONFIG_CONSERVATIVE,
+            CONFIG_VOLUME_FOCUS,
+        ]
+        
+        print("\n" + "=" * 80)
+        print("��置筛选结果对比")
+        print("=" * 80)
+        
+        for config in configs:
+            filtered = filter_signals(df, config, return_strength=False)
+            print(f"\n{config.name}:")
+            print(f"  信�数�: {len(filtered)} ({len(filtered)/len(df)*100:.1f}%)")
+            print(f"  ��: P{config.w_price:.0%}/C{config.w_convergence:.0%}/V{config.w_volume:.0%}")
+            print(f"  阈值: price≥{config.threshold_price:.2f}, vol≥{config.threshold_volume:.2f}")
+        
+        # 测试Top N
+        print("\n" + "=" * 80)
+        print("Top 10 信�（等�模�）")
+        print("=" * 80)
+        
+        top10 = filter_top_n(df, CONFIG_EQUAL, n=10)
+        print("\nstock_code | date     | strength | price_up | convergence | volume")
+        print("-" * 80)
+        for _, row in top10.iterrows():
+            print(f"{row['stock_code']:10s} | {int(row['date'])} | "
+                  f"{row['strength']:.4f} | "
+                  f"{row['price_score_up_norm']:.4f} | "
+                  f"{row['convergence_score_norm']:.4f} | "
+                  f"{row['volume_score_norm']:.4f}")
+        
+        print("\n测试通过!")
+    else:
+        print(f"数�文件�存在: {data_path}")
+        print("请先�行 verify_normalization.py 生�标准化数�")
diff --git a/scripts/scoring/generate_sensitivity_report.py b/scripts/scoring/generate_sensitivity_report.py
new file mode 100644
index 0000000..c8ec5ec
--- /dev/null
+++ b/scripts/scoring/generate_sensitivity_report.py
@@ -0,0 +1,41 @@
+"""
+生�完整的�感性分�报告
+"""
+
+import sys
+import os
+from pathlib import Path
+
+# 添加路径
+script_dir = Path(__file__).parent
+sys.path.insert(0, str(script_dir))
+
+from sensitivity import generate_full_sensitivity_report
+from config import CONFIG_EQUAL
+import pandas as pd
+
+
+def main():
+    # 加载标准化数�
+    data_path = script_dir.parent.parent / 'outputs' / 'converging_triangles' / 'all_results_normalized.csv'
+    
+    if not data_path.exists():
+        print(f"数�文件�存在: {data_path}")
+        print("请先�行 verify_normalization.py")
+        return
+    
+    df = pd.read_csv(data_path)
+    print(f"加载数�: {len(df)} �记录")
+    
+    # 输出目录
+    output_dir = script_dir.parent.parent / 'outputs' / 'converging_triangles'
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # 生�报告
+    generate_full_sensitivity_report(df, CONFIG_EQUAL, output_dir)
+    
+    print("\n所有报告已生�完毕�")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/scoring/normalizer.py b/scripts/scoring/normalizer.py
new file mode 100644
index 0000000..2f8f49a
--- /dev/null
+++ b/scripts/scoring/normalizer.py
@@ -0,0 +1,285 @@
+"""
+强度分标准化模�
+
+针对��分布类型的得分字段，采用��的标准化策略，
+使得所有维度在标准化�具有�比性，能够进行等�相加。
+
+核心问题：
+1. 零膨胀分布（�破幅度分��交�分）：中�数=0
+2. 点质�分布（倾斜度分）：75%的值=0.5
+3. 低区分度（形�规则度）：中�数�低
+"""
+
+import pandas as pd
+import numpy as np
+from typing import Literal
+
+
+def normalize_zero_inflated(series: pd.Series) -> pd.Series:
+    """
+    零膨胀分布标准化
+    
+    适用于：�破幅度分(up/down)��交�分
+    
+    策略：
+    - 零值 -> 0.5（中性基准）
+    - �零值 -> 在[0.5, 1.0]区间内按排�映射
+    
+    原�：
+    - 零值代表"未�生"（未�破/无放�），赋予中性分0.5
+    - �零值代表"已�生"，根�强度排�在0.5-1.0之间分�
+    - 这样既�留了"零vs�零"的质的差异，�在�零内部��了�的差异
+    
+    Args:
+        series: 原始得分�列
+        
+    Returns:
+        标准化�的�列，范围[0.5, 1.0]
+    """
+    result = pd.Series(0.5, index=series.index, dtype=float)
+    
+    # 找出�零值
+    nonzero_mask = series > 1e-6  # 使用�容差��浮点误差
+    
+    if nonzero_mask.sum() > 0:
+        # �零值按百分�排�
+        ranks = series[nonzero_mask].rank(pct=True)  # [0, 1]
+        # 映射到[0.5, 1.0]
+        result[nonzero_mask] = 0.5 + 0.5 * ranks
+    
+    return result
+
+
+def normalize_point_mass(series: pd.Series, center: float = 0.5, tol: float = 0.001) -> pd.Series:
+    """
+    点质�分布标准化
+    
+    适用于：倾斜度分
+    
+    策略：
+    - 中心值（0.5）附近的����
+    - �离中心的值按�离程度拉伸
+    
+    原�：
+    - 75%的值�好=0.5（对称三角形），这些��0.5
+    - 剩余25%�离0.5的值，分别�两侧拉伸
+    - 正�离（>0.5）拉伸到[0.5, 1.0]
+    - 负�离（<0.5）拉伸到[0.0, 0.5]
+    
+    Args:
+        series: 原始得分�列
+        center: 中心值（默认0.5）
+        tol: 容差，在center±tol内的都视为中心值
+        
+    Returns:
+        标准化�的�列，范围[0, 1]
+    """
+    result = pd.Series(center, index=series.index, dtype=float)
+    deviation = series - center
+    
+    # 正�离：> center + tol
+    pos_mask = deviation > tol
+    if pos_mask.sum() > 0:
+        pos_dev = deviation[pos_mask]
+        ranks = pos_dev.rank(pct=True)  # [0, 1]
+        result[pos_mask] = center + 0.5 * ranks  # [center, 1.0]
+    
+    # 负�离：< center - tol
+    neg_mask = deviation < -tol
+    if neg_mask.sum() > 0:
+        neg_dev = deviation[neg_mask].abs()
+        ranks = neg_dev.rank(pct=True, ascending=False)  # 越�离越�
+        result[neg_mask] = center * (1 - ranks)  # [0.0, center]
+    
+    return result
+
+
+def normalize_standard(series: pd.Series) -> pd.Series:
+    """
+    标准分�数标准化
+    
+    适用于：收敛度分�价格活跃度
+    
+    策略：
+    - 直接转�为百分�排�
+    
+    原�：
+    - 这些维度分布相对正常（近�匀或近正�）
+    - 直接排���，最�值->0，最大值->1
+    
+    Args:
+        series: 原始得分�列
+        
+    Returns:
+        标准化�的�列，范围[0, 1]
+    """
+    return series.rank(pct=True)
+
+
+def normalize_low_variance(series: pd.Series, expansion_factor: float = 10.0) -> pd.Series:
+    """
+    低区分度分布标准化
+    
+    适用于：形�规则度
+    
+    策略：
+    - 对数��扩大�值区间的区分度
+    - 然�进行分�数标准化
+    
+    原�：
+    - 形�规则度普��低（中�数0.005），直接排�区分度差
+    - log1p���以拉开�值之间的差�
+    - 0.001 -> log1p(0.01) = 0.0099
+    - 0.010 -> log1p(0.10) = 0.0953 (差�放大9�)
+    
+    Args:
+        series: 原始得分�列
+        expansion_factor: 放大因�（先乘以此因���对数）
+        
+    Returns:
+        标准化�的�列，范围[0, 1]
+    """
+    # 对数��扩大区分度
+    log_transformed = np.log1p(series * expansion_factor)
+    # 分�数标准化
+    return log_transformed.rank(pct=True)
+
+
+def normalize_all(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    对all_results.csv中的所有得分字段进行分层标准化
+    
+    处�映射：
+    - price_score_up, price_score_down, volume_score: 零膨胀标准化
+    - tilt_score: 点质�标准化
+    - convergence_score, activity_score: 标准分�数标准化
+    - geometry_score: 低区分度标准化
+    
+    Args:
+        df: 原始数�DataFrame，需包�以下字段：
+            - price_score_up, price_score_down
+            - convergence_score, volume_score
+            - geometry_score, activity_score, tilt_score
+            
+    Returns:
+        标准化�的DataFrame，新增带_norm�缀的字段
+    """
+    result = df.copy()
+    
+    # 1. 零膨胀分布：�破幅度分��交�分
+    for col in ['price_score_up', 'price_score_down', 'volume_score']:
+        if col in df.columns:
+            result[f'{col}_norm'] = normalize_zero_inflated(df[col])
+    
+    # 2. 点质�分布：倾斜度分
+    if 'tilt_score' in df.columns:
+        result['tilt_score_norm'] = normalize_point_mass(df['tilt_score'], center=0.5)
+    
+    # 3. 标准分�数：收敛度分�价格活跃度
+    for col in ['convergence_score', 'activity_score']:
+        if col in df.columns:
+            result[f'{col}_norm'] = normalize_standard(df[col])
+    
+    # 4. 低区分度：形�规则度
+    if 'geometry_score' in df.columns:
+        result['geometry_score_norm'] = normalize_low_variance(df['geometry_score'])
+    
+    return result
+
+
+def calculate_strength_equal_weight(
+    df_normalized: pd.DataFrame, 
+    direction: Literal['up', 'down'] = 'up'
+) -> pd.Series:
+    """
+    等�强度分计算（基于标准化�的数�）
+    
+    Args:
+        df_normalized: 标准化�的DataFrame，需包�*_norm字段
+        direction: �破方�，'up'或'down'
+        
+    Returns:
+        等�强度分�列，范围[0, 1]
+    """
+    # 选择对应方�的�破幅度分
+    price_col = f'price_score_{direction}_norm'
+    
+    # 等�计算：�1/6
+    strength = (
+        df_normalized[price_col] +
+        df_normalized['convergence_score_norm'] +
+        df_normalized['volume_score_norm'] +
+        df_normalized['geometry_score_norm'] +
+        df_normalized['activity_score_norm'] +
+        df_normalized['tilt_score_norm']
+    ) / 6.0
+    
+    return strength
+
+
+def normalize_and_score(
+    df: pd.DataFrame, 
+    direction: Literal['up', 'down'] = 'up'
+) -> pd.DataFrame:
+    """
+    一站�：标准化 + 等�计算
+    
+    Args:
+        df: 原始数�DataFrame
+        direction: �破方�
+        
+    Returns:
+        包�标准化字段和等�强度分的DataFrame
+    """
+    # 标准化
+    result = normalize_all(df)
+    
+    # 计算等�强度分
+    result[f'strength_{direction}_equal'] = calculate_strength_equal_weight(
+        result, direction=direction
+    )
+    
+    return result
+
+
+if __name__ == "__main__":
+    """
+    测试和演示
+    """
+    import sys
+    import os
+    
+    # 添加路径
+    sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
+    
+    # 读�数�
+    data_path = os.path.join(
+        os.path.dirname(__file__), 
+        "..", "..", "outputs", "converging_triangles", "all_results.csv"
+    )
+    
+    if os.path.exists(data_path):
+        print("=" * 80)
+        print("强度分标准化模�测试")
+        print("=" * 80)
+        
+        df = pd.read_csv(data_path)
+        print(f"\n加载数�: {len(df)} �记录")
+        
+        # 标准化
+        df_norm = normalize_all(df)
+        print(f"标准化完�: 新增 {df_norm.columns.difference(df.columns).tolist()} 字段")
+        
+        # 统计
+        print("\n标准化�中�数对比:")
+        for col in ['price_score_up', 'price_score_down', 'convergence_score', 
+                    'volume_score', 'geometry_score', 'activity_score', 'tilt_score']:
+            if col in df.columns:
+                before = df[col].median()
+                after = df_norm[f'{col}_norm'].median()
+                print(f"  {col:20s}: {before:.4f} -> {after:.4f}")
+        
+        print("\n测试通过!")
+    else:
+        print(f"数�文件�存在: {data_path}")
+        print("请先�行检测脚本生�数�")
diff --git a/scripts/scoring/sensitivity.py b/scripts/scoring/sensitivity.py
new file mode 100644
index 0000000..bd7b5ea
--- /dev/null
+++ b/scripts/scoring/sensitivity.py
@@ -0,0 +1,385 @@
+"""
+�感性分�工具
+
+分��数�化对筛选结果的影�，帮助用户优化�数设置。
+
+主�功能：
+1. 阈值�感性分�
+2. ���感性分�
+3. 生�完整的�感性分�报告
+"""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+from typing import List, Dict, Any
+import sys
+import os
+
+# 添加路径
+script_dir = os.path.dirname(__file__)
+sys.path.insert(0, script_dir)
+
+from config import StrengthConfig, filter_signals, calculate_strength
+from normalizer import normalize_all
+
+# 设置中文字体
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+
+def analyze_threshold_sensitivity(
+    df_normalized: pd.DataFrame,
+    config: StrengthConfig,
+    param_name: str,
+    param_range: List[float]
+) -> pd.DataFrame:
+    """
+    分�阈值�数的�感性
+    
+    Args:
+        df_normalized: 标准化�的DataFrame
+        config: 基础�置
+        param_name: �数�（如'threshold_price'）
+        param_range: �数�值范围列表
+        
+    Returns:
+        �感性分�结果DataFrame
+    """
+    results = []
+    
+    for value in param_range:
+        # �制�置并修改�数
+        test_config = StrengthConfig(**config.__dict__)
+        setattr(test_config, param_name, value)
+        
+        # 筛选
+        try:
+            filtered = filter_signals(df_normalized, test_config)
+            n_signals = len(filtered)
+            pct_selected = n_signals / len(df_normalized) * 100
+            
+            # 计算筛选�的平�强度分
+            if n_signals > 0:
+                strength = calculate_strength(filtered, test_config)
+                avg_strength = strength.mean()
+                min_strength = strength.min()
+                max_strength = strength.max()
+            else:
+                avg_strength = 0.0
+                min_strength = 0.0
+                max_strength = 0.0
+            
+            results.append({
+                '�数值': value,
+                '信�数�': n_signals,
+                '�比%': pct_selected,
+                '平�强度': avg_strength,
+                '最�强度': min_strength,
+                '最大强度': max_strength,
+            })
+        except Exception as e:
+            results.append({
+                '�数值': value,
+                '信�数�': 0,
+                '�比%': 0.0,
+                '平�强度': 0.0,
+                '最�强度': 0.0,
+                '最大强度': 0.0,
+                '错误': str(e)
+            })
+    
+    return pd.DataFrame(results)
+
+
+def analyze_weight_sensitivity(
+    df_normalized: pd.DataFrame,
+    config: StrengthConfig,
+    weight_name: str,
+    weight_range: List[float]
+) -> pd.DataFrame:
+    """
+    分����数的�感性
+    
+    Args:
+        df_normalized: 标准化�的DataFrame
+        config: 基础�置
+        weight_name: ���数�（如'w_price'）
+        weight_range: ���值范围列表（注�：其他��会自动调整以��和为1）
+        
+    Returns:
+        �感性分�结果DataFrame
+    """
+    results = []
+    
+    # 获�所有���数
+    weight_params = ['w_price', 'w_convergence', 'w_volume', 
+                    'w_geometry', 'w_activity', 'w_tilt']
+    
+    for value in weight_range:
+        # �制�置
+        test_config = StrengthConfig(**config.__dict__)
+        
+        # 设置目标��
+        setattr(test_config, weight_name, value)
+        
+        # 调整其他��以��和为1
+        other_weights = [w for w in weight_params if w != weight_name]
+        remaining_weight = 1.0 - value
+        
+        if remaining_weight < 0:
+            continue  # 跳过无效�置
+        
+        # 等比例分�剩余��
+        for w in other_weights:
+            original_value = getattr(config, w)
+            original_sum = sum(getattr(config, w) for w in other_weights)
+            if original_sum > 0:
+                new_value = (original_value / original_sum) * remaining_weight
+                setattr(test_config, w, new_value)
+        
+        try:
+            test_config.validate()
+            
+            # 计算强度分
+            strength = calculate_strength(df_normalized, test_config)
+            
+            results.append({
+                '��值': value,
+                '平�强度': strength.mean(),
+                '中�数强度': strength.median(),
+                '标准差': strength.std(),
+                'P90': strength.quantile(0.90),
+                'P95': strength.quantile(0.95),
+            })
+        except Exception as e:
+            continue
+    
+    return pd.DataFrame(results)
+
+
+def plot_threshold_sensitivity(
+    sensitivity_df: pd.DataFrame,
+    param_name: str,
+    output_path: Path
+):
+    """绘制阈值�感性图表"""
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6))
+    
+    # 左图：信�数��化
+    ax1 = axes[0]
+    ax1.plot(sensitivity_df['�数值'], sensitivity_df['信�数�'], 
+            marker='o', linewidth=2, markersize=8, color='steelblue')
+    ax1.set_xlabel(f'{param_name} 阈值', fontsize=12)
+    ax1.set_ylabel('信�数�', fontsize=12, color='steelblue')
+    ax1.tick_params(axis='y', labelcolor='steelblue')
+    ax1.grid(True, alpha=0.3)
+    ax1.set_title(f'{param_name} 阈值�感性分�', fontsize=14, fontweight='bold')
+    
+    # �轴：�比
+    ax1_twin = ax1.twinx()
+    ax1_twin.plot(sensitivity_df['�数值'], sensitivity_df['�比%'], 
+                 marker='s', linewidth=2, markersize=8, color='coral', alpha=0.7)
+    ax1_twin.set_ylabel('�比 (%)', fontsize=12, color='coral')
+    ax1_twin.tick_params(axis='y', labelcolor='coral')
+    
+    # �图：平�强度�化
+    ax2 = axes[1]
+    ax2.plot(sensitivity_df['�数值'], sensitivity_df['平�强度'], 
+            marker='o', linewidth=2, markersize=8, color='forestgreen')
+    ax2.set_xlabel(f'{param_name} 阈值', fontsize=12)
+    ax2.set_ylabel('筛选�平�强度', fontsize=12, color='forestgreen')
+    ax2.tick_params(axis='y', labelcolor='forestgreen')
+    ax2.grid(True, alpha=0.3)
+    ax2.set_title(f'阈值对强度分的影�', fontsize=14, fontweight='bold')
+    
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=150, bbox_inches='tight')
+    plt.close()
+
+
+def generate_full_sensitivity_report(
+    df_normalized: pd.DataFrame,
+    base_config: StrengthConfig,
+    output_dir: Path
+):
+    """
+    生�完整的�感性分�报告
+    
+    Args:
+        df_normalized: 标准化�的DataFrame
+        base_config: 基础�置
+        output_dir: 输出目录
+    """
+    print("=" * 80)
+    print("生�完整�感性分�报告")
+    print("=" * 80)
+    
+    # 1. �破幅度阈值�感性
+    print("\n[1] 分� threshold_price �感性...")
+    price_range = np.arange(0.50, 0.91, 0.05)
+    price_sens = analyze_threshold_sensitivity(
+        df_normalized, base_config, 'threshold_price', price_range.tolist()
+    )
+    price_sens_path = output_dir / 'sensitivity_threshold_price.csv'
+    price_sens.to_csv(price_sens_path, index=False, encoding='utf-8-sig')
+    print(f"    已�存: {price_sens_path}")
+    
+    # 绘图
+    plot_path = output_dir / 'sensitivity_threshold_price.png'
+    plot_threshold_sensitivity(price_sens, 'threshold_price', plot_path)
+    print(f"    图表已�存: {plot_path}")
+    
+    # 2. 收敛度阈值�感性
+    print("\n[2] 分� threshold_convergence �感性...")
+    conv_range = np.arange(0.30, 0.81, 0.05)
+    conv_sens = analyze_threshold_sensitivity(
+        df_normalized, base_config, 'threshold_convergence', conv_range.tolist()
+    )
+    conv_sens_path = output_dir / 'sensitivity_threshold_convergence.csv'
+    conv_sens.to_csv(conv_sens_path, index=False, encoding='utf-8-sig')
+    print(f"    已�存: {conv_sens_path}")
+    
+    # 3. �交�阈值�感性
+    print("\n[3] 分� threshold_volume �感性...")
+    vol_range = np.arange(0.50, 0.91, 0.05)
+    vol_sens = analyze_threshold_sensitivity(
+        df_normalized, base_config, 'threshold_volume', vol_range.tolist()
+    )
+    vol_sens_path = output_dir / 'sensitivity_threshold_volume.csv'
+    vol_sens.to_csv(vol_sens_path, index=False, encoding='utf-8-sig')
+    print(f"    已�存: {vol_sens_path}")
+    
+    # 4. �破幅度���感性
+    print("\n[4] 分� w_price ���感性...")
+    price_weight_range = np.arange(0.10, 0.51, 0.05)
+    price_weight_sens = analyze_weight_sensitivity(
+        df_normalized, base_config, 'w_price', price_weight_range.tolist()
+    )
+    price_weight_path = output_dir / 'sensitivity_weight_price.csv'
+    price_weight_sens.to_csv(price_weight_path, index=False, encoding='utf-8-sig')
+    print(f"    已�存: {price_weight_path}")
+    
+    # 5. 生�汇总报告
+    print("\n[5] 生�汇总报告...")
+    
+    # 辅助函数：将DataFrame转为markdown表格
+    def df_to_markdown(df):
+        lines = []
+        # 表头
+        lines.append('| ' + ' | '.join(df.columns) + ' |')
+        lines.append('|' + '|'.join(['---' for _ in df.columns]) + '|')
+        # 数�行
+        for _, row in df.iterrows():
+            values = []
+            for v in row:
+                if isinstance(v, float):
+                    values.append(f"{v:.4f}")
+                elif isinstance(v, int):
+                    values.append(str(v))
+                else:
+                    values.append(str(v))
+            lines.append('| ' + ' | '.join(values) + ' |')
+        return '\n'.join(lines)
+    
+    summary_lines = [
+        "# �感性分�汇总报告",
+        "",
+        f"基础�置: {base_config.name}",
+        f"样本数�: {len(df_normalized):,}",
+        "",
+        "## 1. �破幅度阈值�感性",
+        "",
+        df_to_markdown(price_sens),
+        "",
+        "### 建议:",
+        f"- 宽�筛选 (10%+信�): threshold_price ≈ 0.60",
+        f"- 适中筛选 (5%信�): threshold_price ≈ 0.70",
+        f"- 严格筛选 (1-2%信�): threshold_price ≈ 0.80",
+        "",
+        "## 2. 收敛度阈值�感性",
+        "",
+        df_to_markdown(conv_sens),
+        "",
+        "## 3. �交�阈值�感性",
+        "",
+        df_to_markdown(vol_sens),
+        "",
+        "### 注�:",
+        "�交�阈值 > 0.5 时��用筛选，≤ 0.5 表示��制",
+        "",
+        "## 4. �破幅度���感性",
+        "",
+        df_to_markdown(price_weight_sens),
+        "",
+    ]
+    
+    summary_path = output_dir / 'sensitivity_analysis_report.md'
+    with open(summary_path, 'w', encoding='utf-8') as f:
+        f.write('\n'.join(summary_lines))
+    print(f"    汇总报告已�存: {summary_path}")
+    
+    print("\n" + "=" * 80)
+    print("�感性分�完��")
+    print("=" * 80)
+
+
+def quick_analysis():
+    """快速�感性分�（仅关键�数）"""
+    # 加载数�
+    data_path = Path(__file__).parent.parent.parent / 'outputs' / 'converging_triangles' / 'all_results_normalized.csv'
+    
+    if not data_path.exists():
+        print(f"标准化数��存在: {data_path}")
+        print("请先�行 verify_normalization.py")
+        return
+    
+    print("=" * 80)
+    print("快速�感性分�")
+    print("=" * 80)
+    
+    df = pd.read_csv(data_path)
+    print(f"\n加载数�: {len(df)} �记录")
+    
+    # 使用等��置作为基础
+    from config import CONFIG_EQUAL
+    
+    # 分��破幅度阈值
+    print("\n[1] �破幅度阈值�感性")
+    print("-" * 80)
+    price_range = [0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90]
+    price_sens = analyze_threshold_sensitivity(df, CONFIG_EQUAL, 'threshold_price', price_range)
+    
+    print("\nthreshold_price | 信�数 | �比   | 平�强度")
+    print("-" * 60)
+    for _, row in price_sens.iterrows():
+        print(f"{row['�数值']:15.2f} | {row['信�数�']:6.0f} | {row['�比%']:5.1f}% | {row['平�强度']:8.4f}")
+    
+    # 分��交�阈值
+    print("\n[2] �交�阈值�感性")
+    print("-" * 80)
+    vol_range = [0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80]
+    vol_sens = analyze_threshold_sensitivity(df, CONFIG_EQUAL, 'threshold_volume', vol_range)
+    
+    print("\nthreshold_volume | 信�数 | �比   | 平�强度")
+    print("-" * 60)
+    for _, row in vol_sens.iterrows():
+        print(f"{row['�数值']:16.2f} | {row['信�数�']:6.0f} | {row['�比%']:5.1f}% | {row['平�强度']:8.4f}")
+    
+    # 找出最佳阈值建议
+    print("\n" + "=" * 80)
+    print("阈值设置建议")
+    print("=" * 80)
+    
+    # 根�信�数�给出建议
+    for target_pct, desc in [(10.0, "宽�"), (5.0, "适中"), (2.0, "严格"), (1.0, "�严格")]:
+        closest = price_sens.iloc[(price_sens['�比%'] - target_pct).abs().argsort()[:1]]
+        if len(closest) > 0:
+            row = closest.iloc[0]
+            print(f"{desc:6s} (目标{target_pct:4.1f}%信�): threshold_price ≈ {row['�数值']:.2f} "
+                  f"(实际{row['�比%']:.1f}%, {int(row['信�数�'])}个信�)")
+
+
+if __name__ == "__main__":
+    quick_analysis()
diff --git a/scripts/verify_normalization.py b/scripts/verify_normalization.py
new file mode 100644
index 0000000..928c49e
--- /dev/null
+++ b/scripts/verify_normalization.py
@@ -0,0 +1,233 @@
+"""
+验�标准化效果
+
+对比标准化��的统计特�和分布形�，确�标准化达到预期效果。
+"""
+
+import pandas as pd
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+from pathlib import Path
+import sys
+import os
+
+# 添加路径
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+from scoring.normalizer import normalize_all, calculate_strength_equal_weight
+
+# 设置中文字体
+plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
+plt.rcParams['axes.unicode_minus'] = False
+
+
+def load_data():
+    """加载数�"""
+    data_path = Path(__file__).parent.parent / 'outputs' / 'converging_triangles' / 'all_results.csv'
+    if not data_path.exists():
+        raise FileNotFoundError(f"数�文件�存在: {data_path}")
+    
+    df = pd.read_csv(data_path)
+    df_valid = df[df['is_valid'] == True].copy()
+    return df_valid
+
+
+def compare_statistics(df_before, df_after):
+    """对比标准化��的统计特�"""
+    score_cols = [
+        'price_score_up', 'price_score_down', 'convergence_score',
+        'volume_score', 'geometry_score', 'activity_score', 'tilt_score'
+    ]
+    
+    results = []
+    for col in score_cols:
+        if col not in df_before.columns:
+            continue
+        
+        before = df_before[col]
+        after = df_after[f'{col}_norm']
+        
+        result = {
+            '维度': col.replace('_score', '').replace('_', ' '),
+            '原始-�值': before.mean(),
+            '原始-中�数': before.median(),
+            '原始-标准差': before.std(),
+            '原始-�度': stats.skew(before),
+            '原始-超�峰度': stats.kurtosis(before, fisher=True),
+            '标准化-�值': after.mean(),
+            '标准化-中�数': after.median(),
+            '标准化-标准差': after.std(),
+            '标准化-�度': stats.skew(after),
+            '标准化-超�峰度': stats.kurtosis(after, fisher=True),
+        }
+        results.append(result)
+    
+    return pd.DataFrame(results)
+
+
+def plot_before_after_comparison(df_before, df_after, output_dir):
+    """绘制标准化��对比图"""
+    score_cols = [
+        ('�破幅度分(上)', 'price_score_up'),
+        ('�破幅度分(下)', 'price_score_down'),
+        ('收敛度分', 'convergence_score'),
+        ('�交�分', 'volume_score'),
+        ('形�规则度', 'geometry_score'),
+        ('价格活跃度', 'activity_score'),
+        ('倾斜度分', 'tilt_score'),
+    ]
+    
+    # 创建对比图
+    fig, axes = plt.subplots(7, 2, figsize=(16, 24))
+    
+    for idx, (name, col) in enumerate(score_cols):
+        if col not in df_before.columns:
+            continue
+        
+        before = df_before[col].dropna()
+        after = df_after[f'{col}_norm'].dropna()
+        
+        # 左图：标准化�
+        ax_before = axes[idx, 0]
+        ax_before.hist(before, bins=50, alpha=0.7, color='lightcoral', edgecolor='black')
+        ax_before.axvline(before.median(), color='red', linestyle='--', linewidth=2, 
+                         label=f'中�数={before.median():.3f}')
+        ax_before.axvline(before.mean(), color='darkred', linestyle=':', linewidth=2,
+                         label=f'�值={before.mean():.3f}')
+        ax_before.set_title(f"{name} - 标准化�", fontsize=12, fontweight='bold')
+        ax_before.set_xlabel('原始值')
+        ax_before.set_ylabel('频数')
+        ax_before.legend()
+        ax_before.grid(True, alpha=0.3)
+        
+        # �图：标准化�
+        ax_after = axes[idx, 1]
+        ax_after.hist(after, bins=50, alpha=0.7, color='lightblue', edgecolor='black')
+        ax_after.axvline(after.median(), color='blue', linestyle='--', linewidth=2,
+                        label=f'中�数={after.median():.3f}')
+        ax_after.axvline(after.mean(), color='darkblue', linestyle=':', linewidth=2,
+                        label=f'�值={after.mean():.3f}')
+        ax_after.set_title(f"{name} - 标准化�", fontsize=12, fontweight='bold')
+        ax_after.set_xlabel('标准化值 [0, 1]')
+        ax_after.set_ylabel('频数')
+        ax_after.legend()
+        ax_after.grid(True, alpha=0.3)
+        ax_after.set_xlim([0, 1])
+    
+    plt.tight_layout()
+    plot_path = output_dir / 'normalization_comparison.png'
+    plt.savefig(plot_path, dpi=150, bbox_inches='tight')
+    print(f"对比图已�存: {plot_path}")
+    plt.close()
+
+
+def plot_strength_comparison(df_before, df_after, output_dir):
+    """对比原始强度分和等�标准化强度分"""
+    fig, axes = plt.subplots(1, 2, figsize=(16, 6))
+    
+    # 原始强度分（使用当���）
+    W_PRICE = 0.45
+    W_CONVERGENCE = 0.15
+    W_VOLUME = 0.10
+    W_GEOMETRY = 0.10
+    W_ACTIVITY = 0.15
+    W_TILT = 0.05
+    
+    strength_before_up = (
+        W_PRICE * df_before['price_score_up'] +
+        W_CONVERGENCE * df_before['convergence_score'] +
+        W_VOLUME * df_before['volume_score'] +
+        W_GEOMETRY * df_before['geometry_score'] +
+        W_ACTIVITY * df_before['activity_score'] +
+        W_TILT * df_before['tilt_score']
+    )
+    
+    # 标准化�等�强度分
+    strength_after_up = calculate_strength_equal_weight(df_after, direction='up')
+    
+    # 绘图
+    ax1 = axes[0]
+    ax1.hist(strength_before_up, bins=50, alpha=0.7, color='lightcoral', edgecolor='black')
+    ax1.axvline(strength_before_up.median(), color='red', linestyle='--', linewidth=2,
+               label=f'中�数={strength_before_up.median():.3f}')
+    ax1.set_title('原始强度分（当���45/15/10/10/15/5）', fontsize=12, fontweight='bold')
+    ax1.set_xlabel('强度分')
+    ax1.set_ylabel('频数')
+    ax1.legend()
+    ax1.grid(True, alpha=0.3)
+    
+    ax2 = axes[1]
+    ax2.hist(strength_after_up, bins=50, alpha=0.7, color='lightblue', edgecolor='black')
+    ax2.axvline(strength_after_up.median(), color='blue', linestyle='--', linewidth=2,
+               label=f'中�数={strength_after_up.median():.3f}')
+    ax2.set_title('标准化�等�强度分（�1/6）', fontsize=12, fontweight='bold')
+    ax2.set_xlabel('强度分')
+    ax2.set_ylabel('频数')
+    ax2.legend()
+    ax2.grid(True, alpha=0.3)
+    
+    plt.tight_layout()
+    plot_path = output_dir / 'strength_comparison.png'
+    plt.savefig(plot_path, dpi=150, bbox_inches='tight')
+    print(f"强度分对比图已�存: {plot_path}")
+    plt.close()
+
+
+def main():
+    """主函数"""
+    print("=" * 80)
+    print("强度分标准化效果验�")
+    print("=" * 80)
+    
+    # 加载数�
+    print("\n[1] 加载数�...")
+    df = load_data()
+    print(f"    样本数: {len(df):,}")
+    
+    # 标准化
+    print("\n[2] 执行标准化...")
+    df_normalized = normalize_all(df)
+    print(f"    新增字段: {df_normalized.columns.difference(df.columns).tolist()}")
+    
+    # 统计对比
+    print("\n[3] 统计对比...")
+    stats_df = compare_statistics(df, df_normalized)
+    
+    # �存统计表
+    output_dir = Path(__file__).parent.parent / 'outputs' / 'converging_triangles'
+    output_dir.mkdir(parents=True, exist_ok=True)
+    stats_path = output_dir / 'normalization_stats_comparison.csv'
+    stats_df.to_csv(stats_path, index=False, encoding='utf-8-sig')
+    print(f"    统计对比表已�存: {stats_path}")
+    
+    # 打�关键统计
+    print("\n" + "=" * 80)
+    print("标准化��对比")
+    print("=" * 80)
+    print("\n维度�称             | 原始-中�数 | 标准化-中�数 | 原始-�度 | 标准化-�度")
+    print("-" * 80)
+    for _, row in stats_df.iterrows():
+        print(f"{row['维度']:20s} | {row['原始-中�数']:10.4f} | {row['标准化-中�数']:12.4f} | "
+              f"{row['原始-�度']:8.2f} | {row['标准化-�度']:10.2f}")
+    
+    # 生��视化
+    print("\n[4] 生�对比图表...")
+    plot_before_after_comparison(df, df_normalized, output_dir)
+    plot_strength_comparison(df, df_normalized, output_dir)
+    
+    # �存标准化�数�（�选）
+    normalized_path = output_dir / 'all_results_normalized.csv'
+    df_normalized.to_csv(normalized_path, index=False, encoding='utf-8-sig')
+    print(f"\n[5] 标准化�数�已�存: {normalized_path}")
+    
+    print("\n" + "=" * 80)
+    print("验�完��")
+    print("=" * 80)
+    print("\n关键改善:")
+    print("  - 所有维度中�数统一为 0.5")
+    print("  - 维度间�以直接等�相加")
+    print("  - �度显著�低（分布更�匀）")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/test_integration.py b/test_integration.py
new file mode 100644
index 0000000..204f5eb
--- /dev/null
+++ b/test_integration.py
@@ -0,0 +1,65 @@
+# -*- coding: utf-8 -*-
+"""
+测试generate_stock_viewer.py集�的标准化功能
+"""
+import sys
+import os
+
+# 添加路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'scripts'))
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'scripts', 'scoring'))
+
+from scripts.generate_stock_viewer import load_stock_data
+
+print("=" * 80)
+print("测试标准化集�")
+print("=" * 80)
+
+# 测试加载数�
+csv_path = "outputs/converging_triangles/all_results.csv"
+if not os.path.exists(csv_path):
+    print(f"错误: 文件�存在 {csv_path}")
+    print("请先�行: python scripts/pipeline_converging_triangle.py")
+    sys.exit(1)
+
+print(f"\n加载数�: {csv_path}")
+try:
+    stocks, date = load_stock_data(csv_path, target_date=None, all_stocks_mode=False)
+    print(f"[OK] �功加载 {len(stocks)} �股票")
+    print(f"[OK] 数�日期: {date}")
+    
+    # 检查标准化字段
+    if stocks:
+        sample = stocks[0]
+        print(f"\n示例股票: {sample['name']} ({sample['code']})")
+        print(f"  原始强度分: {sample['strength']:.4f}")
+        
+        if 'strengthEqual' in sample:
+            print(f"  等�强度分: {sample['strengthEqual']:.4f}")
+            print(f"  激进强度分: {sample['strengthAggressive']:.4f}")
+            print(f"  �守强度分: {sample['strengthConservative']:.4f}")
+            print(f"  放�强度分: {sample['strengthVolume']:.4f}")
+            
+            print(f"\n标准化维度:")
+            print(f"  �破幅度: {sample['priceUpNorm']:.4f}")
+            print(f"  收敛度: {sample['convergenceNorm']:.4f}")
+            print(f"  �交�: {sample['volumeNorm']:.4f}")
+            print(f"  形�规则: {sample['geometryNorm']:.4f}")
+            print(f"  活跃度: {sample['activityNorm']:.4f}")
+            print(f"  倾斜度: {sample['tiltNorm']:.4f}")
+        else:
+            print("  [WARNING] 没有标准化字段 - scoring模��能未安装")
+        
+        # 统计有标准化字段的股票数�
+        has_norm = sum(1 for s in stocks if 'strengthEqual' in s)
+        print(f"\n[OK] {has_norm}/{len(stocks)} �股票包�标准化字段")
+        
+    print("\n" + "=" * 80)
+    print("测试通过!")
+    print("=" * 80)
+    
+except Exception as e:
+    print(f"\n[ERROR] 错误: {e}")
+    import traceback
+    traceback.print_exc()
+    sys.exit(1)