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a-share-ml-stock-predict

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A股ML股价预测/收益率预测。当用户说"ML预测"、"机器学习预测"、"股价预测"、"收益率预测"、"预测模型"、"ML选股"时触发。基于 cn-stock-data 获取数据，构建ML预测模型。支持 formal/brief 两种输出风格。

QUICK START

How to use this skill

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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-ml-stock-predict/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

ML股价预测/收益率预测助手

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
  • 另类数据: 舆情/资金流等

分析工作流

Step 1: 特征工程

  • 技术特征：均线/MACD/RSI/布林带等50+指标
  • 基本面特征：PE/PB/ROE/营收增速等
  • 资金流特征：主力净流入/北向资金/融资余额
  • 时序特征：滞后收益率、波动率、换手率

Step 2: 模型训练

  • LightGBM/XGBoost：表格数据首选
  • 训练标签：下期N日收益率(回归)或涨跌方向(分类)
  • 时序交叉验证：Purged K-Fold避免前视偏差
  • 超参数优化：Optuna/Bayesian Optimization

Step 3: 模型评估

  • 回归：IC/ICIR/MSE/MAE
  • 分类：AUC/Precision/Recall/F1
  • 经济指标：多空收益/Sharpe/最大回撤
  • 样本外滚动测试：每月重训练

Step 4: 模型部署与监控

  • 预测信号生成：每日收盘后运行模型
  • 信号衰减监控：IC滚动均值是否下降
  • 模型漂移检测：特征分布变化预警
  • 定期重训练：月度/季度更新模型

Step 5: 输出报告

输出格式

formal 风格（研报级）

# ML收益率预测报告

## 一、模型概览
| 模型 | 特征数 | 训练期 |
|------|--------|--------|

## 二、预测表现
[IC/ICIR/多空收益]

## 三、当期预测
[Top/Bottom股票列表]

## 四、模型健康度
[漂移检测、信号衰减]

brief 风格（快速分析）

## ML预测速览
- LightGBM模型，128个特征
- 样本外IC=0.04, ICIR=1.5
- 本期Top10预测：[股票列表]
- 模型健康：信号稳定，无漂移

参考 references/ml-stock-predict-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