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a-share-multi-strategy

Business
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A股多策略组合/策略配置分析。当用户说"多策略"、"multi strategy"、"策略组合"、"策略配置"、"怎么组合策略"、"策略相关性"时触发。基于 cn-stock-data 获取数据，量化分析多策略组合的协同效应。支持研报风格（formal）和快速分析风格（brief）。

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How to use this skill

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  3. Review the proposed files and risks before you approve installation.
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Source SKILL.md: https://github.com/aifinlab/FinClaw/blob/HEAD/skills/a-share-multi-strategy/SKILL.md

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A股多策略组合/策略配置分析

数据源

SCRIPTS="$SKILLS_ROOT/cn-stock-data/scripts"
python "$SCRIPTS/cn_stock_data.py" kline --code [CODE] --freq daily --start [日期]
python "$SCRIPTS/cn_stock_data.py" quote --code [CODE]
python "$SCRIPTS/cn_stock_data.py" finance --code [CODE]

Workflow

Step 1: 策略池定义

列出候选策略（动量/价值/均值回归/事件驱动等），明确各策略逻辑。

Step 2: 单策略回测

分别回测各策略的收益率序列、夏普比率、最大回撤。

Step 3: 策略相关性分析

计算策略间收益率相关矩阵，识别低相关/负相关策略组合。

Step 4: 策略权重优化

  • 等权配置
  • 风险平价（按波动率倒数加权）
  • 最大夏普比率优化
  • 最小相关性组合

Step 5: 输出

维度formalbrief
策略表现各策略完整回测夏普/回撤
相关矩阵完整相关性热力图平均相关系数
组合效果多种配置方案对比推荐配置

默认风格：brief。

关键规则

  1. 低相关性策略组合才有分散化价值
  2. 策略相关性在极端行情下会趋同（尾部相关性上升）
  3. A 股策略容量有限——小盘策略尤其需要考虑冲击成本
  4. 定期再平衡（月度/季度）优于漂移不管
  5. 策略失效检测：滚动夏普比率跌破阈值时降权

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