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a-share-model-risk

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A股模型风险/回测过拟合分析。当用户说"模型风险"、"model risk"、"过拟合"、"overfitting"、"回测失真"、"样本外失效"、"模型验证"时触发。基于 cn-stock-data 获取数据，评估量化模型的过拟合与模型风险。支持 formal/brief 两种输出风格。

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模型风险/回测过拟合分析助手

数据获取

通过 cn-stock-data skill 获取数据：

  • 回测结果: 策略回测的绩效数据
  • 模型参数: 模型配置与超参数
  • 样本外数据: 未参与训练的数据

分析工作流

Step 1: 过拟合检测

  • 训练集vs测试集表现差距：差距>30%疑似过拟合
  • 参数敏感性：微调参数后表现大幅变化=过拟合
  • 策略复杂度：参数越多过拟合风险越大
  • 多重检验：测试了N个策略，需调整显著性水平

Step 2: 回测陷阱识别

  • 前视偏差(look-ahead bias)：使用了未来信息
  • 幸存者偏差(survivorship bias)：只用存活股票
  • 数据窥探(data snooping)：反复优化直到好看
  • 交易成本低估：未考虑滑点/冲击/流动性

Step 3: 模型验证方法

  • 样本外测试：严格的时间外推验证
  • Combinatorial Purged CV：Lopez de Prado方法
  • Walk-forward分析：滚动窗口前推验证
  • 蒙特卡洛排列检验：随机打乱标签的基准

Step 4: 模型风险管理

  • 模型多样化：不依赖单一模型
  • 定期重验证：每季度重新评估模型有效性
  • 模型衰减监控：IC/Sharpe的滚动趋势
  • 模型退役标准：连续N月表现低于阈值则停用

Step 5: 输出报告

输出格式

formal 风格（研报级）

# 模型风险评估报告

## 一、过拟合检测
| 指标 | 训练集 | 测试集 | 差距 |
|------|--------|--------|------|

## 二、回测陷阱
[各类偏差检查结果]

## 三、验证结果
[样本外/CV/排列检验]

## 四、风险管理建议

brief 风格（快速分析）

## 模型风险速览
- 训练Sharpe 3.2 vs 测试 1.8，差距44%
- 过拟合风险：中等
- 未发现前视偏差
- 建议：简化模型，减少参数数量

参考 references/model-risk-guide.md 获取详细方法论与 A股实证研究。