financial-data-analysis

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Original

English

🇨🇳

Translation

Chinese

📊 金融时间序列分析工具箱

📊 Financial Time Series Analysis Toolkit

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。

A comprehensive data analysis method toolkit covering stocks / commodity futures / cryptocurrencies / ETFs / foreign exchange / indices.

数据获取策略

Data Acquisition Strategy

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
。

Asset Type	Data Source	Method
A-share market/finance/index	Tushare MCP	`tushare_daily` , `tushare_income` and other MCP tools
A-share futures	Tushare MCP	`tushare_fut_daily` , `tushare_fut_holding` etc.
Hong Kong stocks/US stocks	Tushare MCP	`tushare_hk_daily` , `tushare_us_daily`
Macroeconomics	Tushare MCP	`tushare_cn_gdp` , `tushare_shibor` etc.
International commodity futures (WTI/gold etc.)	yfinance	`scripts/data_fetcher.py`
Cryptocurrency	yfinance	`scripts/data_fetcher.py`
Foreign exchange	Tushare MCP or yfinance	Depending on specific currency
Global indices	Tushare MCP or yfinance	`tushare_index_global` / yfinance

Rule: You must first load with
ToolSearch("+tushare <keyword>")
before calling the tushare MCP tool. The full index of tushare tools can be found in
reference/tool-index.md
of the
stock-tushare-pro-mcp
skill.

分析方法路由

Analysis Method Routing

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`	—

According to user intent, read the corresponding

references/methods/

documentation before performing analysis:

User Intent Keywords	Reference Document	Available Scripts
Stationarity, trend test, series decomposition, structural break, Hurst	`references/methods/01_time_series_fundamentals.md`	`scripts/analysis_toolkit.py`
Price forecast, ARIMA, Prophet, VAR	`references/methods/02_forecasting.md`	`scripts/analysis_toolkit.py`
Correlation, cointegration, causality, lead-lag	`references/methods/03_cross_asset_relationships.md`	`scripts/analysis_toolkit.py`
Volatility, GARCH, VaR, tail risk	`references/methods/04_volatility_and_risk.md`	`scripts/analysis_toolkit.py`
Portfolio optimization, factor analysis, risk parity, efficient frontier	`references/methods/05_portfolio_and_factor.md`	`scripts/analysis_toolkit.py`
Market state, regime, cycle, wavelet	`references/methods/06_regime_and_structure.md`	`scripts/analysis_toolkit.py`
Commodity seasonality, spread, term structure, contango	`references/methods/07_commodity_specific.md`	`scripts/analysis_toolkit.py`
Network analysis, information flow, clustering, MST	`references/methods/08_network_and_information.md`	`scripts/analysis_toolkit.py`
Technical indicators (MA/RSI/MACD/KDJ/Bollinger Bands)	`references/methods/01_time_series_fundamentals.md`	`scripts/indicators.py`
Chart drawing, visualization	`references/visualization_cookbook.md`	—
Report format	`references/output_templates.md`	—

执行流程

Execution Process

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

1. Identify user intent → Check the routing table above
2. Read the corresponding references/methods/ document → Select appropriate method
3. Acquire data: tushare MCP tool (priority) or scripts/data_fetcher.py
4. Perform analysis: scripts/analysis_toolkit.py or scripts/indicators.py
5. Generate charts: Refer to references/visualization_cookbook.md
6. Output report: Follow references/output_templates.md format

约束

Constraints

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

Mark data acquisition time and source (tushare / yfinance)
Attach a disclaimer to each report
Check series stationarity before analysis (when applicable)
Outlier marking and processing

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

❌ Give deterministic return commitments
❌ Forge or fabricate data
❌ Ignore risk warnings
❌ Guess key indicators when data is missing

输出存储规范

Output Storage Specification

输出目录

Output Directory

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）

The default root directory is

{output_dir}

(configured by input-variables, default

{workspace}/data/analysis/

{output_dir}/
├── reports/        # Analysis reports (.md)
├── charts/         # Chart files (.png)
├── datasets/       # Intermediate datasets (.csv)
└── temp/           # Temporary data (can be cleaned up)

文件命名

File Naming

{类型}_{标的}_{日期}.{格式}

示例：

```
report_CU_20260306.md
```
```
chart_AAPL_seasonal_20260306.png
```
```
dataset_corr_matrix_20260306.csv
```

{type}_{underlying}_{date}.{format}

Examples:

```
report_CU_20260306.md
```
```
chart_AAPL_seasonal_20260306.png
```
```
dataset_corr_matrix_20260306.csv
```

输出规则

Output Rules

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。

Data Volume	Processing Method
< 20 rows	Display directly in the conversation, no file storage
>= 20 rows	Store in `datasets/` , return file path + summary
Charts	Store in `charts/` , display embedded in the conversation
Analysis reports	Store in `reports/` , return full report
Temporary/intermediate data	Store in `temp/` , remind users it can be cleaned up

Collaboration with tushare skill: Original market data storage follows the
output-storage.md
specification of the tushare skill (
{workspace}/data/tushare/
), The
{output_dir}
of this skill only stores analysis results, not original data, to avoid duplication.

参数使用

Parameter Usage

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

undefined

All configurable parameters are declared through

input-variables

, and AI obtains values according to the following priority when executing:

Explicitly specified by the user in the conversation -> Highest priority
```
default
```
value in
```
input-variables
```
-> Fallback

undefined

用户说 "把分析结果存到 ~/Desktop/analysis"

User says "save the analysis results to ~/Desktop/analysis"

-> output_dir = ~/Desktop/analysis

用户说 "分析铜价"

User says "analyze copper prices"

-> output_dir = {workspace}/data/analysis (使用默认值) -> default_period = 1y (使用默认值)

undefined

-> output_dir = {workspace}/data/analysis (use default value) -> default_period = 1y (use default value)

undefined