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a-share-graph-network

A股图神经网络/关联图谱量化。当用户说"图神经网络"、"GNN"、"graph network"、"知识图谱"、"关联图谱"、"股票关系网络"、"供应链图谱"时触发。基于 cn-stock-data 获取数据，构建股票关系图谱与GNN模型。支持 formal/brief 两种输出风格。

Skill by aifinlab

IndexedApache-2.0★ 261
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/aifinlab-a-share-graph-network

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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A股图神经网络/关联图谱量化。当用户说"图神经网络"、"GNN"、"graph network"、"知识图谱"、"关联图谱"、"股票关系网络"、"供应链图谱"时触发。基于 cn-stock-data 获取数据，构建股票关系图谱与GNN模型。支持 formal/brief 两种输出风格。

Use cases: data, finance

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What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

Tools
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Install scripts
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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/a-share-graph-network

What's inside

Skill name
a-share-graph-network
Description
A股图神经网络/关联图谱量化。当用户说"图神经网络"、"GNN"、"graph network"、"知识图谱"、"关联图谱"、"股票关系网络"、"供应链图谱"时触发。基于 cn-stock-data 获取数据，构建股票关系图谱与GNN模型。支持 formal/brief 两种输出风格。
License
Apache-2.0
Instructions
88 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • graph-network-guide.md · references/

SKILL.md (first lines)

# 图神经网络/关联图谱量化助手

## 数据获取

通过 cn-stock-data skill 获取数据：
- **K线数据**: 日线历史数据
- **关系数据**: 行业/供应链/股东关系
- **因子数据**: 节点特征

## 分析工作流

### Step 1: 图结构构建
- 行业关系图：同行业股票相连
- 供应链图：上下游企业关系
- 相关性图：收益率相关性>阈值则连边
- 股东关系图：共同股东/交叉持股

### Step 2: GNN模型设计
- GCN：图卷积网络，聚合邻居信息
- GAT：图注意力网络，自适应邻居权重
- GraphSAGE：采样聚合，适合大规模图
- 时序GNN：结合时序信息的动态图网络

### Step 3: 图特征提取
- 节点嵌入：GNN学习的股票向量表示
- 图结构特征：度中心性/介数中心性/聚类系数
- 社区发现：识别股票群组/板块
- 信息传播：利好/利空在图上的扩散路径

### Step 4: 图谱量化应用
- 关联选股：基于图邻居的协同过滤
- 风险传染：识别系统性风险传播路径
- 领先滞后：图结构中的信息传导时差
- 组合分散化：基于图距离的分散化投资

### Step 5: 输出报告

## 输出格式

### formal 风格（研报级）
```
# 图神经网络量化报告

## 一、图结构概览
| 指标 | 数值 |
|------|------|
| 节点数 | 4,500 |
| 边数 | 28,000 |

## 二、GNN模型表现
[IC/多空收益]

## 三、图谱洞察
[关键社区、传播路径]

## 四、应用建议
```

### brief 风格（快速分析）
```
## GNN量化速览
- GAT模型，4,500节点，28K边
- 图增强IC=0.048 vs 无图0.040
- 发现3个强关联社区
- 供应链传导延迟约2-3天
```

参考 `references/graph-network-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

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

### 示例 2: 命令行使用

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

From aifinlab/FinClaw, Apache-2.0.

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Stars
261
Forks
39
Open issues
2
Last commit (this folder)
Mar 26, 2026 (7 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
Repository
github.com/aifinlab/FinClaw
First seen
Oct 24, 2026
Last checked
Oct 24, 2026
Source
GitHubgithub-graphql

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a-share-graph-network skill. Agent Mart. https://agentmart.dev/skills/aifinlab-a-share-graph-network (last updated 2026-10-24)