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a-share-xgboost-screen

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A股XGBoost选股/梯度提升模型。当用户说"XGBoost"、"GBDT"、"梯度提升"、"LightGBM选股"、"树模型选股"、"XGB"、"机器学习选股"时触发。基于 cn-stock-data 获取数据，构建XGBoost/LightGBM选股模型。支持 formal/brief 两种输出风格。

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How to use this skill

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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-xgboost-screen/SKILL.md

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

XGBoost选股/梯度提升模型助手

数据获取

通过 cn-stock-data skill 获取数据：

  • K线数据: 日线+技术指标
  • 财务数据: 季度财务指标
  • 因子数据: 预计算因子库

分析工作流

Step 1: 特征准备

  • 量价特征：收益率/波动率/换手率/动量等
  • 基本面特征：PE/PB/ROE/营收增速/现金流等
  • 技术特征：MACD/RSI/布林带/均线偏离等
  • 特征预处理：缺失值填充/极值处理/标准化

Step 2: 模型训练

  • LightGBM vs XGBoost：LightGBM更快，效果相当
  • 标签定义：下期N日超额收益(行业中性化后)
  • Purged K-Fold交叉验证：防止前视偏差
  • 超参数：max_depth=6, num_leaves=63, lr=0.05

Step 3: 特征重要性分析

  • Gain重要性：特征对损失函数的贡献
  • SHAP值：每个特征对每个预测的边际贡献
  • 特征交互：哪些特征组合产生非线性效应
  • 特征筛选：去除低重要性特征简化模型

Step 4: 选股信号生成

  • 模型预测→截面排序→选股信号
  • Top N选股：选预测收益最高的N只
  • 行业中性：每个行业内选Top K
  • 信号衰减监控：滚动IC是否下降

Step 5: 输出报告

输出格式

formal 风格（研报级）

# XGBoost选股报告

## 一、模型概览
| 参数 | 设置 |
|------|------|

## 二、特征重要性
[Top10特征及SHAP值]

## 三、选股表现
[IC/多空收益/分组单调性]

## 四、本期选股
[Top股票列表与预测分数]

brief 风格（快速分析）

## XGBoost选股速览
- LightGBM，85个特征
- IC=0.042, ICIR=1.6
- Top特征：20日动量、ROE变化、换手率
- 本期Top10：[股票列表]

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

使用示例

示例 1: 基本使用

# 调用 skill
result = run_skill({
    "param1": "value1",
    "param2": "value2"
})

示例 2: 命令行使用

python scripts/run_skill.py --input data.json