evaluate-exponential-trend-deviation-regimes

<essential_principles> Asset Trend Deviation Analysis Core Principles

1. Single-Asset Detection - Calculate the trend deviation and historical percentile of a single asset
2. Historical Comparative Analysis - Compare the current deviation with a user-specified historical date or automatically identified extreme values
3. Macro Factor Decomposition - Break down in detail the contribution of each macro proxy indicator to the market regime determination (applicable to supported asset classes)

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After reading the workflow, follow its steps completely.

<quick_start> Quick Start

# 安裝依賴
pip install pandas numpy yfinance pandas-datareader statsmodels

# 快速偵測（範例：黃金期貨）
cd skills/evaluate-exponential-trend-deviation-regimes
python scripts/trend_deviation.py --symbol GC=F --quick

# 分析其他資產（範例：S&P 500）
python scripts/trend_deviation.py --symbol ^GSPC --start 1950-01-01

# 完整分析（含宏觀因子，目前支援黃金）
python scripts/trend_deviation.py --symbol GC=F --start 1970-01-01 --include-macro

# 指定歷史參考日期
python scripts/trend_deviation.py --symbol GC=F --compare-peaks "2011-09-06,2020-08-07"

# 生成視覺化圖表（輸出 PNG + JSON）
python scripts/generate_chart.py --output ./output/

</quick_start>

<reference_index> Reference Documents (references/)

Document	Content
input-schema.md	Detailed definition of input parameters and validation rules
methodology.md	Methodology for exponential trend fitting and deviation calculation
regime-rules.md	1970s-like vs 2000s-like regime determination rules
data-sources.md	Data sources and alternative options
</reference_index>

<workflows_index>

Workflow	Purpose
detect.md	Single-asset trend deviation detection and regime determination
compare.md	Detailed comparative analysis of historical peaks
macro.md	Macro factor decomposition and contribution analysis
</workflows_index>

<templates_index>

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

<scripts_index>

Script	Purpose
trend_deviation.py	Main analysis script: trend fitting, deviation calculation, regime determination
generate_chart.py	Visualization chart generation: historical deviation charts with peak annotations
</scripts_index>

<examples_index> Example Output (examples/)

Document	Content
gold-deviation-2026.json	2026 Gold Trend Deviation Analysis Example
</examples_index>

<success_criteria> When the Skill executes successfully:

• Successfully fit the exponential trendline of gold prices
• Calculate the current deviation percentage and historical percentile
• Effectively compare with the 2011/1980 peaks
• Synthesize macro proxy indicators to derive the regime determination (1970s-like / 2000s-like)
• Output a complete JSON or Markdown report
• (Optional) Generate a visualization chart (PNG) annotating historical peaks and the current position </success_criteria>