a-share-autocorrelation

IndexedApache-2.0★ 261

Install

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-autocorrelation

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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About

A股自相关/序列相关性/收益率自相关结构分析。当用户说"自相关"、"autocorrelation"、"序列相关"、"收益率预测性"、"动量还是反转"、"自相关系数"、"ACF"、"PACF"、"Ljung-Box"、"收益率是否可预测"、"随机游走检验"时触发。MUST USE when user asks about return autocorrelation, serial correlation tests, or whether a stock's returns are predictable. 量化分析收益率的自相关结构（ACF/PACF、Ljung-Box检验、随机游走检验）。支持formal和brief风格。

Use cases: finance

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Permissions

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

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codeload.github.com · found in install command

Files it touches

~/.claude/skills/a-share-autocorrelation

What's inside

Skill name: a-share-autocorrelation
Description: A股自相关/序列相关性/收益率自相关结构分析。当用户说"自相关"、"autocorrelation"、"序列相关"、"收益率预测性"、"动量还是反转"、"自相关系数"、"ACF"、"PACF"、"Ljung-Box"、"收益率是否可预测"、"随机游走检验"时触发。MUST USE when user asks about return autocorrelation, serial correlation tests, or whether a stock's returns are predictable. 量化分析收益率的自相关结构（ACF/PACF、Ljung-Box检验、随机游走检验）。支持formal和brief风格。
License: Apache-2.0
Instructions: 47 lines in SKILL.md · view on GitHub

Files (2)

SKILL.md
autocorrelation-guide.md · references/

SKILL.md (first lines)

# A股自相关/序列相关性分析
## 数据源
```bash
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: 获取K线收益率序列
### Step 2: 计算自相关函数(ACF)
lag 1-20的自相关系数
### Step 3: 偏自相关函数(PACF)
### Step 4: Ljung-Box检验
检验序列是否存在显著自相关
### Step 5: 输出
| 维度 | formal | brief |
|------|--------|-------|
| ACF/PACF | 完整图表 | 关键lag |
| 检验 | LB统计量+p值 | 有无自相关 |
| 含义 | 动量/反转判断 | 交易含义 |
默认风格：brief。
## 关键规则
1. 正自相关=动量效应（涨了还会涨）
2. 负自相关=反转效应（涨了会跌回）
3. A股日频负自相关较明显（T+1导致的隔日反转）
4. 周频/月频正自相关更显著（中期动量）
5. 自相关结构是时间序列策略的基础

## 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.

Signals

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: GitHub github-graphql

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Cite this page

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