detect-freight-led-inflation-turn

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Original

English

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Translation

Chinese

<essential_principles>

<principle name="cass_freight_index"> **CASS Freight Index 是最權威的貨運指標**

CASS Freight Index 由 Cass Information Systems 編制，追蹤北美地區的貨運出貨量與支出：

指標	說明	用途
Shipments Index	出貨量指數	衡量實體經濟需求強度
Expenditures Index	運費支出指數	衡量物流成本壓力
Shipments YoY	出貨量年增率	偵測週期轉折（主要分析指標）
Expenditures YoY	支出年增率	驗證成本傳導

數據來源：MacroMicro (透過 Highcharts 爬取) </principle>

<principle name="freight_leads_inflation"> **貨運量是通膨的領先指標**

核心邏輯：

貨運量 ≈ 實體經濟需求強度
出貨量下降 → 終端需求減弱 → 定價能力下降
歷史上 CASS 指標對 CPI 具有約 4-6 個月的領先性

關鍵訊號不是單月變化，而是「週期轉折」：

年增率轉負 (turned negative)
創週期新低 (new cycle low) </principle>

<principle name="signal_interpretation"> **訊號解讀：通膨緩解而非通縮**

當偵測到 CASS 週期轉折：

結論是「通膨壓力緩解」而非「通縮」
屬於 inflation easing / disinflation regime
支持市場對降息或政策轉向的預期

這是跨週期關係辨識：「物流需求動能 → 通膨方向」 </principle>

<principle name="multi_indicator"> **多指標交叉驗證**

建議同時觀察四個 CASS 指標：

Shipments YoY（主要）：需求端訊號
Expenditures YoY：成本端訊號
Shipments Index：絕對水準
Expenditures Index：運費壓力

當 Shipments 和 Expenditures 同時轉負，訊號更為可靠。 </principle>

</essential_principles>

<objective> 偵測 CASS Freight Index 的週期轉折，判斷通膨是否正在放緩。

輸出三層訊號：

Freight Status: CASS 各指標狀態與週期位置
Lead Alignment: 與 CPI YoY 的領先對齊分析
Signal Assessment: 通膨緩解訊號判斷與信心水準 </objective>

<quick_start>

最快的方式：使用 Chrome CDP 抓取數據

Step 1：安裝依賴

bash

pip install requests websocket-client pandas numpy

Step 2：啟動 Chrome 調試模式

bash

undefined

<essential_principles>

<principle name="cass_freight_index"> **CASS Freight Index is the most authoritative freight indicator**

The CASS Freight Index is compiled by Cass Information Systems, tracking freight shipments and expenditures in North America:

Indicator	Description	Purpose
Shipments Index	Shipment Volume Index	Measures the strength of real economic demand
Expenditures Index	Freight Expenditure Index	Measures logistics cost pressure
Shipments YoY	Shipment YoY Growth Rate	Detects cycle turning points (core analysis indicator)
Expenditures YoY	Expenditure YoY Growth Rate	Verifies cost pass-through

Data Source: MacroMicro (scraped via Highcharts) </principle>

<principle name="freight_leads_inflation"> **Freight volume is a leading indicator of inflation**

Core Logic:

Freight volume ≈ strength of real economic demand
Decline in shipments → weakening end demand → reduced pricing power
Historically, the CASS indicator has a lead time of approximately 4-6 months over CPI

The key signal is not monthly changes, but "cycle turning points":

Turned negative YoY
Reached a new cycle low </principle>

<principle name="signal_interpretation"> **Signal Interpretation: Inflation Easing, Not Deflation**

When a CASS cycle turning point is detected:

Conclusion is "inflation pressure is easing" rather than "deflation"
Belongs to the inflation easing / disinflation regime
Supports market expectations of interest rate cuts or policy shifts

This is cross-cycle relationship identification: "Logistics demand momentum → Inflation direction" </principle>

<principle name="multi_indicator"> **Cross-Validation with Multiple Indicators**

It is recommended to observe the four CASS indicators simultaneously:

Shipments YoY (primary): Demand-side signal
Expenditures YoY: Cost-side signal
Shipments Index: Absolute level
Expenditures Index: Freight cost pressure

The signal is more reliable when both Shipments and Expenditures turn negative YoY. </principle>

</essential_principles>

<objective> Detect cycle turning points of the CASS Freight Index to determine whether inflation is slowing down.

Output three levels of signals:

Freight Status: Status and cycle position of each CASS indicator
Lead Alignment: Lead alignment analysis with CPI YoY
Signal Assessment: Inflation easing signal judgment and confidence level </objective>

<quick_start>

Fastest Method: Use Chrome CDP to Fetch Data

Step 1: Install Dependencies

bash

pip install requests websocket-client pandas numpy

Step 2: Launch Chrome Debugging Mode

bash

undefined

Windows

"C:\Program Files\Google\Chrome\Application\chrome.exe" ^ --remote-debugging-port=9222 ^ --remote-allow-origins=* ^ --user-data-dir="%USERPROFILE%.chrome-debug-profile" ^ "https://www.macromicro.me/charts/46877/cass-freight-index"


**Step 3：等待頁面完全載入（圖表顯示），然後執行**
```bash
cd scripts
python fetch_cass_freight.py --cdp

Step 4：執行通膨訊號分析

bash

python freight_inflation_detector.py --quick

Step 5：生成視覺化圖表

bash

python visualize_freight_cpi.py \
  --cache cache/cass_freight_cdp.json \
  --output ../../output/freight_cpi_$(date +%Y-%m-%d).png \
  --start 1995-01-01

輸出範例：

JSON 分析結果：

json

{
  "signal": "inflation_easing",
  "confidence": "high",
  "freight_yoy": -7.46,
  "cycle_status": "negative",
  "indicator": "shipments_yoy",
  "macro_implication": "通膨壓力正在放緩，未來 CPI 下行風險上升"
}

視覺化圖表：
```
output/freight_cpi_2026-01-23.png
```

備選方法（Selenium）：

bash

pip install selenium webdriver-manager
python scripts/fetch_cass_freight.py --selenium --no-headless

</quick_start>

<intake> 需要進行什麼分析？

快速檢查 - 查看最新的 CASS 指標與通膨先行訊號
完整分析 - 執行完整的週期轉折偵測與領先性分析
方法論學習 - 了解 CASS 指標與通膨的領先關係

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/freight_inflation_detector.py --quick` | | 2, "完整", "full", "analyze" | 閱讀 `workflows/analyze.md` 並執行 | | 3, "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>

detect-freight-led-inflation-turn/
├── SKILL.md                           # 本文件（路由器）
├── skill.yaml                         # 前端展示元數據
├── manifest.json                      # 技能元資料
├── workflows/
│   ├── analyze.md                     # 完整分析工作流
│   └── quick-check.md                 # 快速檢查工作流
├── references/
│   ├── data-sources.md                # CASS 數據來源與爬蟲說明
│   ├── methodology.md                 # 領先性方法論解析
│   └── historical-episodes.md         # 歷史案例對照
├── templates/
│   ├── output-json.md                 # JSON 輸出模板
│   └── output-markdown.md             # Markdown 報告模板
├── scripts/
│   ├── fetch_cass_freight.py          # MacroMicro CASS 爬蟲
│   ├── fetch_via_cdp.py               # Chrome CDP 爬蟲模組
│   ├── freight_inflation_detector.py  # 主分析腳本
│   └── visualize_freight_cpi.py       # CASS vs CPI 領先性視覺化
└── examples/
    └── sample_output.json             # 範例輸出

</directory_structure>

<reference_index>

方法論: references/methodology.md

CASS Freight Index 與 CPI 的領先性關係
週期轉折偵測邏輯
訊號強度評估標準

資料來源: references/data-sources.md

MacroMicro Highcharts 爬蟲說明
CASS 四個指標定義
快取策略與更新頻率

歷史案例: references/historical-episodes.md

2008 金融危機前後
2020 疫情期間
2022 通膨高峰期

</reference_index>

<workflows_index>

Workflow	Purpose	使用時機
analyze.md	完整週期轉折分析	需要深度分析時
quick-check.md	快速檢查訊號	日常監控或快速回答
</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
fetch_cass_freight.py	`--cdp`	使用 CDP 爬取（推薦）
fetch_cass_freight.py	`--selenium --no-headless`	使用 Selenium 爬取（備選）
freight_inflation_detector.py	`--quick`	快速檢查最新訊號
freight_inflation_detector.py	`--start DATE --indicator X`	完整分析
visualize_freight_cpi.py	`--lead-months 6 --start DATE`	繪製 CASS vs CPI 領先圖
</scripts_index>

視覺化輸出：CASS vs CPI 領先性對比圖

核心特徵（參考 Bloomberg/Refinitiv 風格）：

CASS 6M Forward：將 CASS Freight Index 向前移動 6 個月，直觀展示領先關係
雙軸對比：CPI YoY（左軸藍線）vs CASS Shipments YoY（右軸灰線）
衰退區間標記：NBER 官方衰退期以淺色陰影標示
Bloomberg 深色風格：深藍背景、高對比度配色

快速繪圖：

bash

cd scripts
python visualize_freight_cpi.py \
  --cache cache/cass_freight_cdp.json \
  --output ../../output/freight_cpi_YYYY-MM-DD.png \
  --start 1995-01-01 \
  --lead-months 6

輸出路徑：

output/freight_cpi_YYYY-MM-DD.png

（根目錄）

圖表解讀：

當 CASS（灰線）先行轉負/創新低，而 CPI（藍線）仍在高位 → 通膨放緩訊號
當 CASS 與 CPI 走勢同步 → 領先關係暫時失效，需謹慎解讀

</visualization>

<input_schema>

<parameter name="start_date" required="true"> **Type**: string (ISO YYYY-MM-DD) **Description**: 分析起始日期 **Example**: "2010-01-01" </parameter> <parameter name="end_date" required="false" default="today"> **Type**: string (ISO YYYY-MM-DD) **Description**: 分析結束日期 </parameter> <parameter name="indicator" required="false" default="shipments_yoy"> **Type**: string **Options**: `shipments_index` | `expenditures_index` | `shipments_yoy` | `expenditures_yoy` **Description**: CASS 指標選擇 - `shipments_yoy`: 出貨量年增率（推薦，主要分析指標） - `expenditures_yoy`: 支出年增率 - `shipments_index`: 出貨量指數 - `expenditures_index`: 支出指數 </parameter> <parameter name="lead_months" required="false" default="6"> **Type**: integer **Description**: 領先 CPI 的月份數 **Range**: 3-12 </parameter> <parameter name="yoy_threshold" required="false" default="0.0"> **Type**: float **Description**: 年增率警戒門檻（如 0 表示轉負） </parameter>

</input_schema>

<output_schema> 參見

templates/output-json.md

的完整結構定義。

摘要：

json

{
  "signal": "inflation_easing | inflation_rising | neutral",
  "confidence": "high | medium | low",
  "freight_yoy": -2.9,
  "cycle_status": "new_cycle_low | negative | positive",
  "indicator": "shipments_yoy",
  "macro_implication": "通膨壓力正在放緩，未來 CPI 下行風險上升",
  "all_indicators": {
    "shipments_index": {...},
    "expenditures_index": {...},
    "shipments_yoy": {...},
    "expenditures_yoy": {...}
  }
}

</output_schema>

<success_criteria> 分析成功時應產出：

CASS 四個指標的最新數值
選定指標的 YoY 與週期狀態
與 CPI 的領先對齊驗證
通膨緩解訊號與信心水準
CASS vs CPI 領先性對比圖（output/freight_cpi_YYYY-MM-DD.png）
可操作的宏觀解讀
明確標註資料限制與假設 </success_criteria>


**Step 3: Wait for the page to fully load (chart displayed), then execute**
```bash
cd scripts
python fetch_cass_freight.py --cdp

Step 4: Execute Inflation Signal Analysis

bash

python freight_inflation_detector.py --quick

Step 5: Generate Visualization Chart

bash

python visualize_freight_cpi.py \
  --cache cache/cass_freight_cdp.json \
  --output ../../output/freight_cpi_$(date +%Y-%m-%d).png \
  --start 1995-01-01

Output Example:

JSON Analysis Result:

json

{
  "signal": "inflation_easing",
  "confidence": "high",
  "freight_yoy": -7.46,
  "cycle_status": "negative",
  "indicator": "shipments_yoy",
  "macro_implication": "Inflation pressure is slowing down, and the downside risk of CPI will rise in the future"
}

Visualization Chart:
```
output/freight_cpi_2026-01-23.png
```

Alternative Method (Selenium):

bash

pip install selenium webdriver-manager
python scripts/fetch_cass_freight.py --selenium --no-headless

</quick_start>

<intake> What analysis do you need?

Quick Check - View the latest CASS indicators and leading inflation signals
Full Analysis - Execute complete cycle turning point detection and lead analysis
Methodology Learning - Understand the leading relationship between CASS indicators and inflation

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

<routing> | Response | Action | |------------------------------|-------------------------------------------------------------| | 1, "快速", "quick", "check" | Execute `python scripts/freight_inflation_detector.py --quick` | | 2, "完整", "full", "analyze" | Read `workflows/analyze.md` and execute | | 3, "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 document and execute. </routing>

<directory_structure>

detect-freight-led-inflation-turn/
├── 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
├── references/
│   ├── data-sources.md                # CASS data sources and scraping instructions
│   ├── methodology.md                 # Leading relationship methodology analysis
│   └── historical-episodes.md         # Historical case comparisons
├── templates/
│   ├── output-json.md                 # JSON output template
│   └── output-markdown.md             # Markdown report template
├── scripts/
│   ├── fetch_cass_freight.py          # MacroMicro CASS scraper
│   ├── fetch_via_cdp.py               # Chrome CDP scraping module
│   ├── freight_inflation_detector.py  # Main analysis script
│   └── visualize_freight_cpi.py       # CASS vs CPI leading relationship visualization
└── examples/
    └── sample_output.json             # Sample output

</directory_structure>

<reference_index>

Methodology: references/methodology.md

Leading relationship between CASS Freight Index and CPI
Cycle turning point detection logic
Signal strength evaluation criteria

Data Sources: references/data-sources.md

MacroMicro Highcharts scraping instructions
Definitions of the four CASS indicators
Caching strategy and update frequency

Historical Cases: references/historical-episodes.md

Before and after the 2008 financial crisis
During the 2020 pandemic
The 2022 inflation peak period

</reference_index>

<workflows_index>

Workflow	Purpose	Usage Scenario
analyze.md	Complete cycle turning point analysis	When in-depth analysis is required
quick-check.md	Quick signal check	Daily monitoring or quick answers
</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
fetch_cass_freight.py	`--cdp`	Scrape via CDP (recommended)
fetch_cass_freight.py	`--selenium --no-headless`	Scrape via Selenium (alternative)
freight_inflation_detector.py	`--quick`	Quick check of the latest signals
freight_inflation_detector.py	`--start DATE --indicator X`	Full analysis
visualize_freight_cpi.py	`--lead-months 6 --start DATE`	Plot CASS vs CPI leading relationship chart
</scripts_index>

Visualization Output: CASS vs CPI Leading Relationship Comparison Chart

Core Features (refer to Bloomberg/Refinitiv style):

CASS 6M Forward: Shift the CASS Freight Index forward by 6 months to intuitively display the leading relationship
Dual-Axis Comparison: CPI YoY (blue line on left axis) vs CASS Shipments YoY (gray line on right axis)
Recession Interval Marking: NBER official recession periods marked with light shading
Bloomberg Dark Style: Dark blue background, high-contrast color scheme

Quick Plotting:

bash

cd scripts
python visualize_freight_cpi.py \
  --cache cache/cass_freight_cdp.json \
  --output ../../output/freight_cpi_YYYY-MM-DD.png \
  --start 1995-01-01 \
  --lead-months 6

Output Path:

output/freight_cpi_YYYY-MM-DD.png

(root directory)

Chart Interpretation:

When CASS (gray line) turns negative/reaches a new low ahead, while CPI (blue line) remains high → Inflation easing signal
When CASS and CPI trends are synchronized → Leading relationship temporarily invalid, interpretation requires caution

</visualization>

<input_schema>

<parameter name="start_date" required="true"> **Type**: string (ISO YYYY-MM-DD) **Description**: Analysis start date **Example**: "2010-01-01" </parameter> <parameter name="end_date" required="false" default="today"> **Type**: string (ISO YYYY-MM-DD) **Description**: Analysis end date </parameter> <parameter name="indicator" required="false" default="shipments_yoy"> **Type**: string **Options**: `shipments_index` | `expenditures_index` | `shipments_yoy` | `expenditures_yoy` **Description**: CASS indicator selection - `shipments_yoy`: Shipment YoY growth rate (recommended, core analysis indicator) - `expenditures_yoy`: Expenditure YoY growth rate - `shipments_index`: Shipment volume index - `expenditures_index`: Freight expenditure index </parameter> <parameter name="lead_months" required="false" default="6"> **Type**: integer **Description**: Number of months the indicator leads CPI **Range**: 3-12 </parameter> <parameter name="yoy_threshold" required="false" default="0.0"> **Type**: float **Description**: YoY warning threshold (e.g., 0 means turning negative) </parameter>

</input_schema>

<output_schema> Refer to the complete structure definition in

templates/output-json.md

Summary:

json

{
  "signal": "inflation_easing | inflation_rising | neutral",
  "confidence": "high | medium | low",
  "freight_yoy": -2.9,
  "cycle_status": "new_cycle_low | negative | positive",
  "indicator": "shipments_yoy",
  "macro_implication": "Inflation pressure is slowing down, and the downside risk of CPI will rise in the future",
  "all_indicators": {
    "shipments_index": {...},
    "expenditures_index": {...},
    "shipments_yoy": {...},
    "expenditures_yoy": {...}
  }
}

</output_schema>

<success_criteria> When the analysis is successful, it should produce:

Latest values of the four CASS indicators
YoY growth rate and cycle status of the selected indicator
Lead alignment verification with CPI
Inflation easing signal and confidence level
CASS vs CPI Leading Relationship Comparison Chart (output/freight_cpi_YYYY-MM-DD.png)
Actionable macroeconomic interpretations
Clear annotation of data limitations and assumptions </success_criteria>