analyze-copper-stock-resilience-dependency

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Chinese

<essential_principles>

<principle name="cross_asset_dependency"> **跨資產依賴核心邏輯**

銅價的關卡突破與回補，並非單純由銅本身決定，而是高度依賴股市韌性：

銅價走勢 = f(技術面關卡狀態) × f(股市韌性) × f(中國利率環境)

關鍵洞察：

股市韌性高時：銅突破關卡後「續航」機率更高
股市韌性低時：銅更容易出現「back-and-fill」回補到支撐區
中國10Y殖利率：作為風險壓力/政策寬鬆的雙面訊號 </principle>

<principle name="round_levels"> **心理關卡與趨勢狀態**

銅價的重要心理整數位（如 10,000 / 13,000 USD/ton）是判斷突破與回補的關鍵：

狀態	條件	含義
up	close > SMA(60) 且斜率為正	上升趨勢
down	close < SMA(60) 且斜率為負	下降趨勢
range	其他	區間整理

關卡判定：

```
near_resistance
```
: 接近上方關卡
```
near_support
```
: 接近下方支撐 </principle>

<principle name="equity_resilience_score"> **股市韌性評分（0-100）**

將「股市韌性」量化為可計算的分數：

因子	權重	計算方式
12個月動能	40%	12m 報酬分位數（vs 歷史）
均線位置	40%	是否站上 12 月均線（是=100，否=0）
近期回撤	20%	近 3m 回撤越小越好（反向計分）

評分解讀：

70-100：高韌性，銅突破關卡後續航機率較高
30-70：中性，需觀察其他因子
0-30：低韌性，回補風險顯著上升 </principle>

<principle name="rolling_beta"> **滾動迴歸：量化依賴強度**

計算銅價對股市與中國殖利率的滾動貝塔係數：

Δcopper ~ β1 × Δequity + β2 × Δchina_yield + ε

β1（股市貝塔）越大越正：銅越像風險資產，越依賴股市
β1 高分位：市場正在把銅當風險資產一起交易
β1 < 0（負相關）：銅與股市脫鉤，展現獨立邏輯（避險/供給/能源轉型敘事）
β2（殖利率貝塔）：正 = 殖利率上升利好銅（通膨敘事），負 = 反之 </principle>

<principle name="data_access"> **資料取得方式**

本 skill 使用以下公開數據來源：

數據	代碼/來源	取得方式
銅期貨價格	COMEX Copper (HG=F)	Yahoo Finance
全球股市市值	VT (Vanguard Total World Stock ETF)	Yahoo Finance
中國10Y殖利率	MacroMicro	Selenium + Highcharts

單位換算：

HG=F 為 $/lb，自動乘以 2204.62262 轉換為 $/ton
VT ETF 價格乘以係數轉換為全球市值估計（兆美元）
中國10Y 為百分比（%） </principle>

</essential_principles>

<objective> 實作銅價股市韌性依賴分析：

資料擷取：抓取銅價、全球股市、中國10Y殖利率
趨勢與關卡判定：計算 SMA、趨勢狀態、接近哪個關卡
股市韌性評分：計算 equity_resilience_score
依賴關係量化：滾動迴歸計算 β 係數
回補機率估計：歷史統計回補頻率（高韌性 vs 低韌性）
情境判讀：輸出當前是「續航」還是「回補」情境

輸出：當前狀態、依賴強度、回補機率、可執行警報旗標。 </objective>

<quick_start>

最快的方式：執行預設分析

bash

cd skills/analyze-copper-stock-resilience-dependency
pip install pandas numpy yfinance scipy statsmodels matplotlib  # 首次使用
python scripts/copper_stock_analyzer.py --quick

輸出範例：

json

{
  "as_of": "2026-01-22",
  "latest_state": {
    "copper_price_usd_per_ton": 12727,
    "copper_trend": "up",
    "equity_resilience_score": 83,
    "rolling_beta_equity_24m": -0.80
  },
  "diagnosis": {
    "narrative": "銅價上升趨勢中，接近 13,000 關卡，股市韌性高檔。"
  }
}

生成 Bloomberg 風格圖表：

bash

python scripts/visualize.py \
  --start 2015-01-01 \
  -o output/copper_resilience_2026-01-22.png

圖表包含：

銅價月線 + SMA60（右軸，橙紅/黃色）
全球股市市值（左軸，橙色面積圖）
中國 10Y 殖利率（左軸，黃線）
關卡線（10,000 / 13,000）

生成依賴度分析圖表（三面板綜合圖）：

bash

python scripts/plot_dependency_analysis.py \
  --start 2015-01-01 \
  -o ../../output/copper-dependency-analysis-2026-01-22.png

圖表包含三個面板：

銅價面板：銅價 + SMA60 + 趨勢背景色（綠=上升，紅=下降）+ 關卡線
β係數面板：滾動 β 時間序列 + ±1σ 區間 + 當前分位數 + 負值警示
韌性面板：股市韌性評分 + 高/低閾值線

完整分析：

bash

python scripts/copper_stock_analyzer.py \
  --start 2015-01-01 \
  --end 2026-01-22 \
  --copper HG=F \
  --equity ACWI \
  --output result.json

</quick_start>

<intake> 需要進行什麼操作？

快速檢查 - 查看目前銅價、股市韌性、關卡狀態
完整分析 - 分析時間區間內的依賴關係與回補機率
視覺化圖表 - 生成銅價與依賴因子的視覺化圖表
依賴度分析圖 - 生成三面板依賴度分析圖表（銅價+β係數+韌性）
方法論學習 - 了解跨資產依賴模型的邏輯

請選擇或直接提供分析參數。 </intake>

<routing> | Response | Action | |------------------------------|--------------------------------------------------------| | 1, "快速", "quick", "check" | 執行 `python scripts/copper_stock_analyzer.py --quick` | | 2, "完整", "分析", "full" | 閱讀 `workflows/analyze.md` 並執行 | | 3, "視覺化", "chart", "plot" | 閱讀 `workflows/visualize.md` 並執行 | | 4, "依賴度", "dependency" | 執行 `python scripts/plot_dependency_analysis.py` | | 5, "學習", "方法論", "why" | 閱讀 `references/methodology.md` | | 提供參數 (如日期範圍) | 閱讀 `workflows/analyze.md` 並使用參數執行 |

路由後，閱讀對應文件並執行。 </routing>

<directory_structure>

analyze-copper-stock-resilience-dependency/
├── SKILL.md                           # 本文件（路由器）
├── skill.yaml                         # 前端展示元數據
├── manifest.json                      # 技能元數據
├── workflows/
│   ├── analyze.md                     # 完整分析工作流
│   ├── quick-check.md                 # 快速檢查工作流
│   └── visualize.md                   # 視覺化工作流
├── references/
│   ├── methodology.md                 # 跨資產依賴方法論
│   ├── data-sources.md                # 數據來源與爬蟲說明
│   └── input-schema.md                # 完整輸入參數定義
├── templates/
│   ├── output-json.md                 # JSON 輸出模板
│   └── output-markdown.md             # Markdown 報告模板
├── scripts/
│   ├── copper_stock_analyzer.py       # 主分析腳本
│   ├── fetch_data.py                  # 數據抓取工具
│   ├── visualize.py                   # Bloomberg 風格圖表
│   └── plot_dependency_analysis.py    # 三面板依賴度分析圖表
├── data/
│   └── cache/                         # 數據快取目錄
└── examples/
    └── sample-output.json             # 範例輸出

</directory_structure>

<reference_index>

方法論: references/methodology.md

跨資產依賴概念與研究報告對照
股市韌性評分設計
滾動迴歸與貝塔解讀
Back-and-fill 回補判定邏輯

資料來源: references/data-sources.md

Yahoo Finance (yfinance) 使用說明
中國10Y殖利率爬蟲設計
數據頻率與對齊方法

輸入參數: references/input-schema.md

完整參數定義
預設值與建議範圍

</reference_index>

<workflows_index>

Workflow	Purpose	使用時機
analyze.md	完整分析	需要詳細依賴關係與回補分析
quick-check.md	快速檢查	只想看當前狀態
visualize.md	生成視覺化圖表	需要圖表展示
</workflows_index>

<templates_index>

Template	Purpose
output-json.md	JSON 輸出結構定義
output-markdown.md	Markdown 報告模板
</templates_index>

<scripts_index>

Script	Command	Purpose
copper_stock_analyzer.py	`--quick`	快速檢查當前狀態
copper_stock_analyzer.py	`--start DATE --end DATE`	完整分析
fetch_data.py	`--series HG=F,ACWI`	抓取市場數據
visualize.py	`--start 2015-01-01 -o output/chart.png`	生成 Bloomberg 風格圖表
plot_dependency_analysis.py	`--start 2015-01-01 -o output/chart.png`	生成三面板依賴度分析圖表
</scripts_index>

<input_schema_summary>

核心參數

參數	類型	預設值	說明
start_date	string	2020-01-01	分析起點
end_date	string	today	分析終點
freq	string	1mo	頻率（月）
copper_series	string	HG=F	銅價序列代碼
equity_proxy_series	string	ACWI	股市代理序列
china_10y_yield_series	string	爬取	中國10Y殖利率

模型參數

參數	類型	預設值	說明
ma_window	int	60	移動平均視窗
rolling_window	int	24	滾動迴歸視窗（月）
round_levels	list	[10000, 13000]	關卡位置
backfill_max_drawdown	float	0.25	回補幅度上限

完整參數定義見

references/input-schema.md

。

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "analyze-copper-stock-resilience-dependency",
  "as_of": "2026-01-22",
  "inputs": {
    "copper_series": "HG=F (converted to USD/ton)",
    "equity_proxy_series": "ACWI",
    "ma_window": 60,
    "rolling_window": 24
  },
  "latest_state": {
    "copper_price_usd_per_ton": 12727,
    "copper_sma_60": 9355,
    "copper_trend": "up",
    "near_resistance_levels": [13000],
    "near_support_levels": [],
    "equity_resilience_score": 91,
    "rolling_beta_equity_24m": -0.80,
    "rolling_beta_yield_24m": -0.05
  },
  "diagnosis": {
    "narrative": "銅價上升趨勢中，接近 13,000 關卡，股市韌性高檔。",
    "scenario": "續航機率較高",
    "dependency_status": "滾動 β 為負值 (-0.80)，銅與股市呈反向關係，脫離傳統風險資產模式"
  },
  "actionable_flags": [
    {
      "flag": "APPROACHING_RESISTANCE",
      "meaning": "接近重要阻力位，關注能否突破"
    },
    {
      "flag": "NEGATIVE_BETA_REGIME",
      "meaning": "銅與股市呈反向關係，脫離傳統風險資產模式"
    },
    {
      "flag": "LOW_BETA_ANOMALY",
      "meaning": "β 處於歷史極端低位，銅正展現獨立於股市的上漲邏輯"
    }
  ]
}

完整輸出結構見

templates/output-json.md

。 </output_schema_summary>

<success_criteria> 執行成功時應產出：

當前銅價與趨勢狀態（up/down/range）
接近的關卡位置（resistance/support）
股市韌性評分（0-100）
滾動貝塔係數（β_equity, β_yield）
回補機率估計（overall / high_resilience / low_resilience）
情境判讀敘述
可執行警報旗標
視覺化圖表（可選，輸出至
```
output/
```
目錄） </success_criteria>

<essential_principles>

<principle name="cross_asset_dependency"> **Core Logic of Cross-Asset Dependency**

Breakouts and back-and-fill pullbacks of copper prices are not solely determined by copper itself, but highly dependent on stock market resilience:

Copper Price Trend = f(Technical Level Status) × f(Stock Market Resilience) × f(Chinese Interest Rate Environment)

Key Insights:

High stock market resilience: Higher probability of copper sustaining its breakout after breaking key levels
Low stock market resilience: Copper is more likely to experience a "back-and-fill" pullback to support zones
China 10Y Yield: Serves as a dual signal of risk pressure/policy easing </principle>

<principle name="round_levels"> **Psychological Levels and Trend Status**

Important psychological integer levels for copper prices (e.g., 10,000 / 13,000 USD/ton) are key to judging breakouts and back-and-fill pullbacks:

State	Condition	Meaning
up	close > SMA(60) with positive slope	Uptrend
down	close < SMA(60) with negative slope	Downtrend
range	Otherwise	Sideways trading

Level Determination:

```
near_resistance
```
: Approaching upper resistance level
```
near_support
```
: Approaching lower support level </principle>

<principle name="equity_resilience_score"> **Equity Resilience Score (0-100)**

Quantify "stock market resilience" into a calculable score:

Factor	Weight	Calculation Method
12-month Momentum	40%	12m return quantile (vs. historical data)
Moving Average Position	40%	100 if above 12-month MA, 0 otherwise
Recent Drawdown	20%	Smaller 3m drawdown = higher score (reverse scoring)

Score Interpretation:

70-100: High resilience, higher probability of copper sustaining breakout
30-70: Neutral, need to observe other factors
0-30: Low resilience, significantly increased back-and-fill risk </principle>

<principle name="rolling_beta"> **Rolling Regression: Quantify Dependency Strength**

Calculate rolling beta coefficients of copper prices to stock market and Chinese yields:

Δcopper ~ β1 × Δequity + β2 × Δchina_yield + ε

Larger positive β1 (equity beta): Copper behaves more like a risk asset, more dependent on stock market
High quantile of β1: Market is trading copper as a risk asset alongside stocks
β1 < 0 (negative correlation): Copper decouples from stock market, showing independent logic (safe-haven/supply/energy transition narrative)
β2 (yield beta): Positive = rising yields are bullish for copper (inflation narrative), Negative = opposite </principle>

<principle name="data_access"> **Data Acquisition Methods**

This skill uses the following public data sources:

Data Type	Code/Source	Acquisition Method
Copper Futures Price	COMEX Copper (HG=F)	Yahoo Finance
Global Stock Market Cap	VT (Vanguard Total World Stock ETF)	Yahoo Finance
China 10Y Yield	MacroMicro	Selenium + Highcharts

Unit Conversion:

HG=F is in $/lb, automatically multiplied by 2204.62262 to convert to $/ton
VT ETF price multiplied by coefficient to estimate global market cap (trillion USD)
China 10Y Yield is in percentage (%) </principle>

</essential_principles>

<objective> Implement copper price-stock market resilience dependency analysis:

Data Extraction: Fetch copper prices, global stock market data, China 10Y yield
Trend and Level Determination: Calculate SMA, trend status, and which levels are being approached
Equity Resilience Score: Compute the equity_resilience_score
Dependency Quantification: Calculate β coefficients via rolling regression
Back-and-Fill Probability Estimation: Historical statistics of back-and-fill frequency (high vs. low resilience)
Scenario Interpretation: Output current "sustain breakout" or "back-and-fill" scenario

Output: Current status, dependency strength, back-and-fill probability, actionable alert flags. </objective>

<quick_start>

Fastest Way: Run Default Analysis

bash

cd skills/analyze-copper-stock-resilience-dependency
pip install pandas numpy yfinance scipy statsmodels matplotlib  # First-time use
python scripts/copper_stock_analyzer.py --quick

Sample Output:

json

{
  "as_of": "2026-01-22",
  "latest_state": {
    "copper_price_usd_per_ton": 12727,
    "copper_trend": "up",
    "equity_resilience_score": 83,
    "rolling_beta_equity_24m": -0.80
  },
  "diagnosis": {
    "narrative": "Copper is in an uptrend, approaching the 13,000 level, with high stock market resilience."
  }
}

Generate Bloomberg-Style Charts:

bash

python scripts/visualize.py \
  --start 2015-01-01 \
  -o output/copper_resilience_2026-01-22.png

Charts Include:

Copper monthly price + SMA60 (right axis, orange/red-yellow)
Global stock market cap (left axis, orange area chart)
China 10Y Yield (left axis, yellow line)
Level lines (10,000 / 13,000)

Generate Dependency Analysis Chart (Three-Panel Composite Chart):

bash

python scripts/plot_dependency_analysis.py \
  --start 2015-01-01 \
  -o ../../output/copper-dependency-analysis-2026-01-22.png

Charts Include Three Panels:

Copper Price Panel: Copper price + SMA60 + trend background color (green=uptrend, red=downtrend) + level lines
β Coefficient Panel: Rolling β time series + ±1σ interval + current quantile + negative value alert
Resilience Panel: Equity resilience score + high/low threshold lines

Full Analysis:

bash

python scripts/copper_stock_analyzer.py \
  --start 2015-01-01 \
  --end 2026-01-22 \
  --copper HG=F \
  --equity ACWI \
  --output result.json

</quick_start>

<intake> What operation do you need to perform?

Quick Check - View current copper price, stock market resilience, and level status
Full Analysis - Analyze dependency relationships and back-and-fill probability over a time period
Visualization Charts - Generate visual charts of copper prices and dependency factors
Dependency Analysis Chart - Generate three-panel dependency analysis chart (copper price + β coefficient + resilience)
Methodology Learning - Understand the logic of the cross-asset dependency model

Please select or directly provide analysis parameters. </intake>

<routing> | Response | Action | |------------------------------|--------------------------------------------------------| | 1, "quick", "check" | Execute `python scripts/copper_stock_analyzer.py --quick` | | 2, "full", "analyze" | Read `workflows/analyze.md` and execute | | 3, "visualize", "chart", "plot" | Read `workflows/visualize.md` and execute | | 4, "dependency" | Execute `python scripts/plot_dependency_analysis.py` | | 5, "learn", "methodology", "why" | Read `references/methodology.md` | | Provide parameters (e.g., date range) | Read `workflows/analyze.md` and execute with parameters |

After routing, read the corresponding document and execute. </routing>

<directory_structure>

analyze-copper-stock-resilience-dependency/
├── SKILL.md                           # This document (router)
├── skill.yaml                         # Frontend display metadata
├── manifest.json                      # Skill metadata
├── workflows/
│   ├── analyze.md                     # Full analysis workflow
│   ├── quick-check.md                 # Quick check workflow
│   └── visualize.md                   # Visualization workflow
├── references/
│   ├── methodology.md                 # Cross-asset dependency methodology
│   ├── data-sources.md                # Data sources and web scraping instructions
│   └── input-schema.md                # Complete input parameter definition
├── templates/
│   ├── output-json.md                 # JSON output template
│   └── output-markdown.md             # Markdown report template
├── scripts/
│   ├── copper_stock_analyzer.py       # Main analysis script
│   ├── fetch_data.py                  # Data fetching tool
│   ├── visualize.py                   # Bloomberg-style chart generator
│   └── plot_dependency_analysis.py    # Three-panel dependency analysis chart generator
├── data/
│   └── cache/                         # Data cache directory
└── examples/
    └── sample-output.json             # Sample output

</directory_structure>

<reference_index>

Methodology: references/methodology.md

Cross-asset dependency concept and research report alignment
Equity resilience score design
Rolling regression and beta interpretation
Back-and-fill pullback determination logic

Data Sources: references/data-sources.md

Yahoo Finance (yfinance) usage instructions
China 10Y Yield web scraping design
Data frequency and alignment methods

Input Parameters: references/input-schema.md

Complete parameter definitions
Default values and recommended ranges

</reference_index>

<workflows_index>

Workflow	Purpose	Usage Scenario
analyze.md	Full Analysis	Need detailed dependency and back-and-fill analysis
quick-check.md	Quick Check	Only want to view current status
visualize.md	Generate Visualizations	Need chart presentations
</workflows_index>

<templates_index>

Template	Purpose
output-json.md	JSON output structure definition
output-markdown.md	Markdown report template
</templates_index>

<scripts_index>

Script	Command	Purpose
copper_stock_analyzer.py	`--quick`	Quick check of current status
copper_stock_analyzer.py	`--start DATE --end DATE`	Full analysis
fetch_data.py	`--series HG=F,ACWI`	Fetch market data
visualize.py	`--start 2015-01-01 -o output/chart.png`	Generate Bloomberg-style charts
plot_dependency_analysis.py	`--start 2015-01-01 -o output/chart.png`	Generate three-panel dependency analysis chart
</scripts_index>

<input_schema_summary>

Core Parameters

Parameter	Type	Default Value	Description
start_date	string	2020-01-01	Analysis start date
end_date	string	today	Analysis end date
freq	string	1mo	Frequency (monthly)
copper_series	string	HG=F	Copper price series code
equity_proxy_series	string	ACWI	Equity proxy series code
china_10y_yield_series	string	Scraped	China 10Y Yield series

Model Parameters

Parameter	Type	Default Value	Description
ma_window	int	60	Moving average window
rolling_window	int	24	Rolling regression window (months)
round_levels	list	[10000, 13000]	Key level positions
backfill_max_drawdown	float	0.25	Maximum back-and-fill drawdown

Complete parameter definitions can be found in

references/input-schema.md

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "analyze-copper-stock-resilience-dependency",
  "as_of": "2026-01-22",
  "inputs": {
    "copper_series": "HG=F (converted to USD/ton)",
    "equity_proxy_series": "ACWI",
    "ma_window": 60,
    "rolling_window": 24
  },
  "latest_state": {
    "copper_price_usd_per_ton": 12727,
    "copper_sma_60": 9355,
    "copper_trend": "up",
    "near_resistance_levels": [13000],
    "near_support_levels": [],
    "equity_resilience_score": 91,
    "rolling_beta_equity_24m": -0.80,
    "rolling_beta_yield_24m": -0.05
  },
  "diagnosis": {
    "narrative": "Copper is in an uptrend, approaching the 13,000 level, with high stock market resilience.",
    "scenario": "Higher probability of sustaining breakout",
    "dependency_status": "Rolling β is negative (-0.80), copper has an inverse relationship with the stock market, deviating from traditional risk asset patterns"
  },
  "actionable_flags": [
    {
      "flag": "APPROACHING_RESISTANCE",
      "meaning": "Approaching key resistance level, focus on breakout potential"
    },
    {
      "flag": "NEGATIVE_BETA_REGIME",
      "meaning": "Copper has an inverse relationship with the stock market, deviating from traditional risk asset patterns"
    },
    {
      "flag": "LOW_BETA_ANOMALY",
      "meaning": "β is at an extreme historical low, copper is showing independent bullish logic decoupled from the stock market"
    }
  ]
}

Complete output structure can be found in

templates/output-json.md

. </output_schema_summary>

<success_criteria> Successful execution should produce:

Current copper price and trend status (up/down/range)
Approached key levels (resistance/support)
Equity resilience score (0-100)
Rolling beta coefficients (β_equity, β_yield)
Back-and-fill probability estimate (overall / high_resilience / low_resilience)
Scenario interpretation narrative
Actionable alert flags
Visualization charts (optional, output to
```
output/
```
directory) </success_criteria>