financial-data-analysis

Q: How do I install financial-data-analysis?

Run npx skills add https://github.com/kirkluokun/awesome-a-stock-openclawskills --skill financial-data-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/financial-data-analysis/ (personal) or .claude/skills/financial-data-analysis/ (project).

by kirkluokunBusiness & Finance

金融时间序列分析方法工具箱。覆盖股票 / 商品期货 / 加密货币 / ETF / 外汇 / 指数，从数据获取到高阶分析全流程。内置 70+ 分析方法，涵盖时间序列检验、预测建模、跨资产关系、波动率风险、组合优化、状态识别、商品专项和网络分析 8 大方法域。数据获取优先使用 tushare MCP tool（A股/港股/美股/期货/基金/宏观），商品期货(CL=F)、加密(BTC-USD)等 tushare 未覆盖资产用 yfinance 脚本补充。

Installs: 284
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/kirkluokun/awesome-a-stock-openclawskills --skill financial-data-analysis

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads financial-data-analysis automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

📊 金融时间序列分析工具箱

覆盖股票 / 商品期货 / 加密货币 / ETF / 外汇 / 指数的综合数据分析方法工具箱。

数据获取策略

资产类型	数据源	方式
A股行情/财务/指数	Tushare MCP	`tushare_daily`, `tushare_income` 等 MCP tool
A股期货	Tushare MCP	`tushare_fut_daily`, `tushare_fut_holding` 等
港股/美股	Tushare MCP	`tushare_hk_daily`, `tushare_us_daily`
宏观经济	Tushare MCP	`tushare_cn_gdp`, `tushare_shibor` 等
国际商品期货(WTI/黄金等)	yfinance	`scripts/data_fetcher.py`
加密货币	yfinance	`scripts/data_fetcher.py`
外汇	Tushare MCP 或 yfinance	视具体币种
全球指数	Tushare MCP 或 yfinance	`tushare_index_global` / yfinance

规则：调用 tushare MCP tool 前必须先 ToolSearch("+tushare <关键词>") 加载。 tushare tool 完整索引见 stock-tushare-pro-mcp skill 的 reference/tool-index.md。

分析方法路由

根据用户意图，阅读对应 references/methods/ 文档后执行分析：

用户意图关键词	参考文档	可用脚本
平稳性、趋势检验、序列分解、结构断裂、Hurst	`references/methods/01_time_series_fundamentals.md`	`scripts/analysis_toolkit.py`
价格预测、ARIMA、Prophet、VAR	`references/methods/02_forecasting.md`	`scripts/analysis_toolkit.py`
相关性、协整、因果关系、领先滞后	`references/methods/03_cross_asset_relationships.md`	`scripts/analysis_toolkit.py`
波动率、GARCH、VaR、尾部风险	`references/methods/04_volatility_and_risk.md`	`scripts/analysis_toolkit.py`
组合优化、因子分析、风险平价、有效前沿	`references/methods/05_portfolio_and_factor.md`	`scripts/analysis_toolkit.py`
市场状态、regime、周期、小波	`references/methods/06_regime_and_structure.md`	`scripts/analysis_toolkit.py`
商品季节性、价差、期限结构、contango	`references/methods/07_commodity_specific.md`	`scripts/analysis_toolkit.py`
网络分析、信息流、聚类、MST	`references/methods/08_network_and_information.md`	`scripts/analysis_toolkit.py`
技术指标（MA/RSI/MACD/KDJ/布林带）	`references/methods/01_time_series_fundamentals.md`	`scripts/indicators.py`
图表绘制、可视化	`references/visualization_cookbook.md`	—
报告格式	`references/output_templates.md`	—

执行流程

1. 识别用户意图 → 查上方路由表
2. 读取对应 references/methods/ 文档 → 选择合适方法
3. 获取数据：tushare MCP tool（优先）或 scripts/data_fetcher.py
4. 执行分析：scripts/analysis_toolkit.py 或 scripts/indicators.py
5. 生成图表：参照 references/visualization_cookbook.md
6. 输出报告：按 references/output_templates.md 格式

约束

MUST

标注数据获取时间和来源（tushare / yfinance）
每份报告附免责声明
分析前检查序列平稳性（适用时）
异常值标注和处理

MUST NOT

❌ 给出确定性收益承诺
❌ 伪造或编造数据
❌ 忽略风险提示
❌ 数据缺失时猜测关键指标

输出存储规范

输出目录

默认根目录为 {output_dir}（由 input-variables 配置，默认 {workspace}/data/analysis/）。

{output_dir}/
├── reports/        # 分析报告 (.md)
├── charts/         # 图表文件 (.png)
├── datasets/       # 中间数据集 (.csv)
└── temp/           # 临时数据（可清理）

文件命名

{类型}_{标的}_{日期}.{格式}

示例：

report_CU_20260306.md
chart_AAPL_seasonal_20260306.png
dataset_corr_matrix_20260306.csv

输出规则

数据量	处理方式
< 20 行	直接在对话中展示，不存文件
>= 20 行	存入 `datasets/`，返回文件路径 + 摘要
图表	存入 `charts/`，在对话中内嵌展示
分析报告	存入 `reports/`，返回完整报告
临时/中间数据	存入 `temp/`，提醒用户可清理

与 tushare skill 协作：原始行情数据存储遵循 tushare skill 的 output-storage.md 规范（{workspace}/data/tushare/），本 skill 的 {output_dir} 只存分析结果，不存原始数据，避免重复。

参数使用

所有可配置参数通过 input-variables 声明，AI 在执行时按如下优先级获取值：

用户在对话中明确指定 -> 最高优先
input-variables 中的 default 值 -> 兜底

# 用户说 "把分析结果存到 ~/Desktop/analysis"
-> output_dir = ~/Desktop/analysis

# 用户说 "分析铜价"
-> output_dir = {workspace}/data/analysis (使用默认值)
-> default_period = 1y (使用默认值)

FAQ

What does the financial-data-analysis skill do?

How do I install financial-data-analysis?

Run npx skills add https://github.com/kirkluokun/awesome-a-stock-openclawskills --skill financial-data-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/financial-data-analysis/ (personal) or .claude/skills/financial-data-analysis/ (project).

Which AI agents can use financial-data-analysis?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is financial-data-analysis safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains financial-data-analysis?

It is published by kirkluokun in kirkluokun/awesome-a-stock-openclawskills. It has 284 recorded installs.

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financial-data-analysis

Install

Install

Describe the task

Review the result

SKILL.md

📊 金融时间序列分析工具箱

数据获取策略

分析方法路由

执行流程

约束

MUST

MUST NOT

输出存储规范

输出目录

文件命名

输出规则

参数使用

FAQ

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