detect-freight-led-inflation-turn

<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

# 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: 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>

detect-freight-led-inflation-turn

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