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a-share-cross-asset

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A股跨资产配置/大类资产轮动策略。当用户说"跨资产"、"大类资产"、"资产配置"、"asset allocation"、"股债轮动"、"股债商"、"美林时钟"时触发。基于 cn-stock-data 获取数据，进行跨资产配置分析。支持 formal/brief 两种输出风格。

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How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

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

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/a-share-cross-asset/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

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

跨资产配置/大类资产轮动助手

数据获取

通过 cn-stock-data skill 获取数据：

  • 股票指数: 沪深300/中证500等
  • 债券数据: 国债收益率/信用利差
  • 商品数据: 黄金/原油/铜等
  • 宏观数据: GDP/CPI/PMI/M2

分析工作流

Step 1: 宏观周期定位

  • 美林时钟：复苏→过热→滞胀→衰退
  • 中国版时钟：信用周期+经济周期双维度
  • 领先指标：PMI/社融/M2增速
  • 当前周期阶段判断

Step 2: 资产表现分析

  • 各资产近期收益与波动率
  • 资产间相关性矩阵
  • 风险调整收益(Sharpe)排名
  • 动量信号：各资产趋势方向

Step 3: 配置方案

  • 战略配置：基于长期均衡的基准权重
  • 战术调整：基于周期阶段的偏离
  • 风险平价：按风险贡献均等配置
  • Black-Litterman：融合主观观点与市场均衡

Step 4: 再平衡与风控

  • 再平衡触发：偏离阈值或定期再平衡
  • 下行保护：CPPI/期权保护策略
  • 流动性管理：保持足够现金应对赎回
  • 压力测试：极端场景下的组合表现

Step 5: 输出报告

输出格式

formal 风格（研报级）

# 跨资产配置报告

## 一、宏观周期
| 指标 | 数值 | 信号 |
|------|------|------|

## 二、资产表现
[各资产收益/波动/Sharpe]

## 三、配置建议
[各资产权重与调整方向]

## 四、风险评估

brief 风格（快速分析）

## 跨资产速览
- 当前周期：复苏早期
- 建议：超配股票(50%)、标配债券(30%)、低配商品(10%)、现金(10%)
- 股债相关性 -0.2，分散化有效
- 关注：CPI回升可能触发周期切换

参考 references/cross-asset-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