analyze-investment-clock-rotation

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Chinese

<essential_principles>

<principle name="quadrant_model"> **四象限模型核心**

投資時鐘將市場狀態簡化為四個象限：

          金融環境支持（寬鬆）
                ↑
       Q3      │      Q1
     修復過渡   │   理想象限
  ────────────┼────────────→ 獲利成長
       Q4      │      Q2
     最差象限   │   好壞混合
                │
          金融環境不支持（緊縮）

象限	獲利	金融環境	含義	配置建議
Q1 理想象限	↑	支持↑	風險資產友善	偏多、順風配置
Q2 好壞混合	↑	不支持↓	估值壓力、波動	波動管理、估值敏感
Q3 修復過渡	↓	支持↑	寬鬆救市、基本面未回	勿誤判為全面牛市
Q4 最差象限	↓	不支持↓	風險資產易受傷	風險控管、降槓桿

</principle> <principle name="axis_convention"> **軸向約定（重要！）**

不同來源的投資時鐘圖可能有不同的軸向定義。本 skill 預設：

X 軸：金融環境（Financial Conditions）
- 左側 = 寬鬆（支持性高）
- 右側 = 緊縮（支持性低）
Y 軸：獲利成長（Earnings Growth）
- 上方 = 正成長
- 下方 = 負成長

若你的圖表定義不同，請在輸入參數中調整

axis_mapping

和

clock_convention

。 </principle>

<principle name="clock_hour"> **時鐘點位計算**

透過

atan2(y, x)

計算角度，再轉換成 12 小時制：

12 點：正上方（獲利最高、金融環境中性）
3 點：右側（金融環境最緊）
6 點：正下方（獲利最低）
9 點：左側（金融環境最寬鬆）

旋轉方向：

順時針：典型景氣循環路徑（Q1 → Q2 → Q4 → Q3 → Q1）
逆時針：政策干預或非典型事件 </principle>

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

本 skill 使用無需 API key 的資料來源：

FRED CSV:
```
https://fred.stlouisfed.org/graph/fredgraph.csv?id={SERIES_ID}
```
- 金融環境：NFCI（Chicago Fed）、STLFSI4（St. Louis Fed）
- 獲利代理：CP（企業利潤）、GDP 相關指標

腳本位於

scripts/

目錄，可直接執行。 </principle>

</essential_principles>

<objective> 實作投資時鐘分析：

建構座標：從 FRED 數據計算獲利成長與金融環境 Z-score
判定象限：識別當前落在哪個象限
計算點位：轉換為 12 小時制時鐘點位
分析旋轉：判斷旋轉方向與幅度
循環比較：與前一輪循環比較（可選）

輸出：當前象限、時鐘點位、旋轉摘要、配置建議。 </objective>

<quick_start>

最快的方式：執行預設分析

bash

cd skills/analyze-investment-clock-rotation
pip install pandas numpy requests  # 首次使用
python scripts/investment_clock.py --quick

輸出範例：

json

{
  "as_of": "2026-01-15",
  "current_position": {
    "clock_hour": 10,
    "quadrant": "Q1_ideal",
    "earnings_growth": 0.052,
    "financial_conditions_zscore": -0.35
  },
  "interpretation": "理想象限，風險資產相對順風"
}

完整分析：

bash

python scripts/investment_clock.py \
  --start 2022-01-01 \
  --end 2026-01-19 \
  --compare-cycle 2020-01-01 2022-12-31 \
  --output result.json

</quick_start>

<intake> 需要進行什麼操作？

快速檢查 - 查看目前的投資時鐘位置與象限
完整分析 - 分析時間區間內的旋轉路徑與方向
循環比較 - 與前一輪循環比較旋轉特徵
視覺化圖表 - 生成投資時鐘視覺化圖表
方法論學習 - 了解投資時鐘模型的邏輯

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/investment_clock.py --quick` | | 2, "完整", "分析", "full" | 閱讀 `workflows/analyze.md` 並執行 | | 3, "比較", "循環", "compare" | 閱讀 `workflows/compare-cycle.md` 並執行 | | 4, "視覺化", "chart", "plot" | 閱讀 `workflows/visualize.md` 並執行 | | 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-investment-clock-rotation/
├── 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
│   ├── compare-cycle.md               # 循環比較工作流
│   └── visualize.md                   # 視覺化工作流
├── references/
│   ├── methodology.md                 # 投資時鐘方法論
│   ├── data-sources.md                # FRED 系列代碼與資料來源
│   └── 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/
    ├── investment_clock.py            # 主分析腳本
    ├── fetch_data.py                  # 數據抓取工具
    └── visualize.py                   # 視覺化繪圖工具

</directory_structure>

<reference_index>

方法論: references/methodology.md

投資時鐘概念與歷史
四象限定義與配置含義
旋轉方向解讀

資料來源: references/data-sources.md

FRED 系列代碼（金融環境/獲利代理）
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	需要詳細象限與旋轉分析
compare-cycle.md	循環比較	比較不同循環的特徵
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
investment_clock.py	`--quick`	快速檢查當前位置
investment_clock.py	`--start DATE --end DATE`	完整分析
investment_clock.py	`--compare-cycle START END`	循環比較
fetch_data.py	`--series NFCI,CP`	抓取 FRED 資料
visualize.py	`-i result.json -o chart.png`	生成視覺化圖表
</scripts_index>

<input_schema_summary>

核心參數

參數	類型	預設值	說明
market	string	US_EQUITY	分析標的
start_date	string	2022-01-01	分析起點
end_date	string	today	分析終點
freq	string	weekly	頻率（weekly/monthly）

資料來源參數

參數	類型	說明
earnings_series.source	string	fred / api / csv / manual
earnings_series.series_id	string	FRED 序列 ID（如 CP）
earnings_series.growth_method	string	yoy / qoq_annualized
financial_conditions_series.source	string	fred / api / csv
financial_conditions_series.series_id	string	NFCI / STLFSI4
financial_conditions_series.transform	string	level / zscore / inverse

軸向參數

參數	類型	預設值	說明
axis_mapping.x	string	financial_conditions	X 軸定義
axis_mapping.y	string	earnings_growth	Y 軸定義
clock_convention.financial_loose_is_left	bool	true	寬鬆在左側

完整參數定義見

references/input-schema.md

。

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "analyze-investment-clock-rotation",
  "as_of": "2026-01-19",
  "market": "US_EQUITY",
  "current_state": {
    "clock_hour": 10,
    "quadrant": "Q1_ideal",
    "quadrant_name": "理想象限",
    "x_value": -0.35,
    "y_value": 0.052
  },
  "rotation_summary": {
    "from_hour": 2,
    "to_hour": 10,
    "direction": "clockwise",
    "magnitude_degrees": 240
  },
  "interpretation": "獲利成長為正，金融環境偏支持，屬於風險資產相對順風的象限"
}

完整輸出結構見

templates/output-json.md

。 </output_schema_summary>

<success_criteria> 執行成功時應產出：

當前象限（Q1/Q2/Q3/Q4）
時鐘點位（1-12 點）
獲利成長值與金融環境 Z-score
旋轉方向（順時針/逆時針）
旋轉幅度（度數）
配置建議（依象限）
循環比較摘要（若有啟用）
視覺化圖表（可選，輸出至
```
output/
```
目錄） </success_criteria>

<essential_principles>

<principle name="quadrant_model"> **Core of the Four-Quadrant Model**

The Investment Clock simplifies market conditions into four quadrants:

          Supportive Financial Conditions (Loose)
                ↑
       Q3      │      Q1
     Recovery Transition │   Ideal Quadrant
  ────────────┼────────────→ Profit Growth
       Q4      │      Q2
     Worst Quadrant │   Mixed Prospects
                │
          Unsupportive Financial Conditions (Tight)

Quadrant	Profit	Financial Conditions	Implication	Allocation Recommendation
Q1 Ideal Quadrant	↑	Supportive ↑	Risk Asset Friendly	Overweight, Tailwind Allocation
Q2 Mixed Prospects	↑	Unsupportive ↓	Valuation Pressure, Volatility	Volatility Management, Valuation Sensitivity
Q3 Recovery Transition	↓	Supportive ↑	Loose Policy Rescue, Fundamentals Unrecovered	Do Not Misjudge as a Full Bull Market
Q4 Worst Quadrant	↓	Unsupportive ↓	Risk Assets Vulnerable	Risk Control, Deleveraging

</principle> <principle name="axis_convention"> **Axis Convention (Important!)**

Investment Clock diagrams from different sources may have different axis definitions. This skill defaults to:

X-axis: Financial Conditions
- Left = Loose (Highly Supportive)
- Right = Tight (Low Support)
Y-axis: Earnings Growth
- Top = Positive Growth
- Bottom = Negative Growth

If your diagram uses a different definition, adjust

axis_mapping

and

clock_convention

in the input parameters. </principle>

<principle name="clock_hour"> **Clock Position Calculation**

Calculate the angle using

atan2(y, x)

, then convert to a 12-hour format:

12 o'clock: Top (Highest Profit, Neutral Financial Conditions)
3 o'clock: Right (Tightest Financial Conditions)
6 o'clock: Bottom (Lowest Profit)
9 o'clock: Left (Loosest Financial Conditions)

Rotation Direction:

Clockwise: Typical Business Cycle Path (Q1 → Q2 → Q4 → Q3 → Q1)
Counterclockwise: Policy Intervention or Atypical Events </principle>

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

This skill uses data sources that do not require an API key:

FRED CSV:
```
https://fred.stlouisfed.org/graph/fredgraph.csv?id={SERIES_ID}
```
- Financial Conditions: NFCI (Chicago Fed), STLFSI4 (St. Louis Fed)
- Profit Proxies: CP (Corporate Profits), GDP-related Indicators

Scripts are located in the

scripts/

directory and can be executed directly. </principle>

</essential_principles>

<objective> Implement Investment Clock Analysis:

Construct Coordinates: Calculate Z-scores for Earnings Growth and Financial Conditions from FRED data
Determine Quadrant: Identify the current quadrant
Calculate Position: Convert to 12-hour clock position
Analyze Rotation: Determine rotation direction and magnitude
Cycle Comparison: Compare with the previous cycle (Optional)

Output: Current Quadrant, Clock Position, Rotation Summary, Allocation Recommendation. </objective>

<quick_start>

Fastest Way: Run the Default Analysis

bash

cd skills/analyze-investment-clock-rotation
pip install pandas numpy requests  # First-time use
python scripts/investment_clock.py --quick

Sample Output:

json

{
  "as_of": "2026-01-15",
  "current_position": {
    "clock_hour": 10,
    "quadrant": "Q1_ideal",
    "earnings_growth": 0.052,
    "financial_conditions_zscore": -0.35
  },
  "interpretation": "Ideal Quadrant, Risk Assets in a Relative Tailwind"
}

Full Analysis:

bash

python scripts/investment_clock.py \
  --start 2022-01-01 \
  --end 2026-01-19 \
  --compare-cycle 2020-01-01 2022-12-31 \
  --output result.json

</quick_start>

<intake> What operation do you need to perform?

Quick Check - View the current Investment Clock position and quadrant
Full Analysis - Analyze rotation path and direction within a time interval
Cycle Comparison - Compare rotation characteristics with the previous cycle
Visualization Chart - Generate Investment Clock visualization chart
Methodology Learning - Understand the logic of the Investment Clock model

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

<routing> | Response | Action | |--------------------------------|----------------------------------------------------| | 1, "快速", "quick", "check" | Execute `python scripts/investment_clock.py --quick` | | 2, "完整", "分析", "full" | Read `workflows/analyze.md` and execute | | 3, "比較", "循環", "compare" | Read `workflows/compare-cycle.md` and execute | | 4, "視覺化", "chart", "plot" | Read `workflows/visualize.md` and execute | | 5, "學習", "方法論", "why" | Read `references/methodology.md` | | Provide parameters (e.g., date range) | Read `workflows/analyze.md` and execute with parameters |

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

<directory_structure>

analyze-investment-clock-rotation/
├── SKILL.md                           # This file (Router)
├── skill.yaml                         # Frontend Display Metadata
├── manifest.json                      # Skill Metadata
├── workflows/
│   ├── analyze.md                     # Full Analysis Workflow
│   ├── compare-cycle.md               # Cycle Comparison Workflow
│   └── visualize.md                   # Visualization Workflow
├── references/
│   ├── methodology.md                 # Investment Clock Methodology
│   ├── data-sources.md                # FRED Series Codes and Data Sources
│   └── input-schema.md                # Complete Input Parameter Definitions
├── templates/
│   ├── output-json.md                 # JSON Output Template
│   └── output-markdown.md             # Markdown Report Template
└── scripts/
    ├── investment_clock.py            # Main Analysis Script
    ├── fetch_data.py                  # Data Fetching Tool
    └── visualize.py                   # Visualization Plotting Tool

</directory_structure>

<reference_index>

Methodology: references/methodology.md

Investment Clock Concepts and History
Four-Quadrant Definitions and Allocation Implications
Rotation Direction Interpretation

Data Sources: references/data-sources.md

FRED Series Codes (Financial Conditions/Profit Proxies)
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	Requires detailed quadrant and rotation analysis
compare-cycle.md	Cycle Comparison	Compare characteristics of different cycles
visualize.md	Generate Visualization Chart	Needs chart display
</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
investment_clock.py	`--quick`	Quickly check current position
investment_clock.py	`--start DATE --end DATE`	Full Analysis
investment_clock.py	`--compare-cycle START END`	Cycle Comparison
fetch_data.py	`--series NFCI,CP`	Fetch FRED Data
visualize.py	`-i result.json -o chart.png`	Generate Visualization Chart
</scripts_index>

<input_schema_summary>

Core Parameters

Parameter	Type	Default Value	Description
market	string	US_EQUITY	Analysis Target
start_date	string	2022-01-01	Analysis Start Date
end_date	string	today	Analysis End Date
freq	string	weekly	Frequency (weekly/monthly)

Data Source Parameters

Parameter	Type	Description
earnings_series.source	string	fred / api / csv / manual
earnings_series.series_id	string	FRED Series ID (e.g., CP)
earnings_series.growth_method	string	yoy / qoq_annualized
financial_conditions_series.source	string	fred / api / csv
financial_conditions_series.series_id	string	NFCI / STLFSI4
financial_conditions_series.transform	string	level / zscore / inverse

Axis Parameters

Parameter	Type	Default Value	Description
axis_mapping.x	string	financial_conditions	X-axis Definition
axis_mapping.y	string	earnings_growth	Y-axis Definition
clock_convention.financial_loose_is_left	bool	true	Loose Conditions on Left

Complete parameter definitions can be found in

references/input-schema.md

</input_schema_summary>

<output_schema_summary>

json

{
  "skill": "analyze-investment-clock-rotation",
  "as_of": "2026-01-19",
  "market": "US_EQUITY",
  "current_state": {
    "clock_hour": 10,
    "quadrant": "Q1_ideal",
    "quadrant_name": "Ideal Quadrant",
    "x_value": -0.35,
    "y_value": 0.052
  },
  "rotation_summary": {
    "from_hour": 2,
    "to_hour": 10,
    "direction": "clockwise",
    "magnitude_degrees": 240
  },
  "interpretation": "Earnings growth is positive, financial conditions are relatively supportive, belonging to a quadrant favorable for risk assets"
}

Complete output structure can be found in

templates/output-json.md

. </output_schema_summary>

<success_criteria> Successful execution should produce:

Current Quadrant (Q1/Q2/Q3/Q4)
Clock Position (1-12 o'clock)
Earnings Growth Value and Financial Conditions Z-score
Rotation Direction (Clockwise/Counterclockwise)
Rotation Magnitude (Degrees)
Allocation Recommendation (Based on Quadrant)
Cycle Comparison Summary (If Enabled)
Visualization Chart (Optional, Output to
```
output/
```
Directory) </success_criteria>