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evaluate-exponential-trend-deviation-regimes

Quantify asset price distance from a long-run exponential growth trend.

3|1|Updated Jan 12, 2026
Search Tags:#analyze#macro-analysis#gold#trend-deviation#exponential-growth#historical-peaks
One-click install
npx skills add https://github.com/fatfingererr/macro-skills --skill evaluate-exponential-trend-deviation-regimes
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: evaluate-exponential-trend-deviation-regimes
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/evaluate-exponential-trend-deviation-regimes
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill evaluate-exponential-trend-deviation-regimes

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yfinance, pandas, numpy, pandas_datareader, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill quantifies how far current prices are from a long-run exponential growth trend, enabling objective assessment of whether markets sit in historically extreme zones and informing macro analyses.

Core Features & Use Cases

  • Trend deviation analysis: compute percent distance from an exponential trend for any asset with sufficient history.
  • Historical peaks & percentile: compare current deviation against peak moments (e.g., 2011, 1980) and report historical percentile.
  • Macro regime exploration: optionally decompose deviations using macro proxies (real rates, inflation, USD, geopolitical risk) to classify market regime.

Quick Start

  • Install required Python packages: pip install pandas numpy yfinance pandas-datareader statsmodels
  • Quick detect for gold: python scripts/trend_deviation.py --symbol GC=F --start 1970-01-01 --quick
  • Analyze the S&P 500: python scripts/trend_deviation.py --symbol ^GSPC --start 1950-01-01
  • Full analysis with macro factors: python scripts/trend_deviation.py --symbol GC=F --start 1970-01-01 --include-macro

Frequently Asked Questions about evaluate-exponential-trend-deviation-regimes

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I quantify asset price deviation from a long-term exponential trend?▼

To quantify price deviation from an exponential trend, fit historical asset prices to an exponential growth model and calculate the percent distance of current prices from that fitted trend line.

Can I compare current price deviations against historical peaks like 1980 or 2011?▼

Yes, comparing current price deviations against historical peaks involves ranking the current deviation percentile against extreme market zones from moments like 1980 or 2011.

How do I analyze macro factors like real rates and inflation for trend deviation?▼

To analyze macro factors for trend deviation, optionally decompose price distances using real rates, inflation, USD, and geopolitical proxies to classify the current market regime.

Does this trend deviation analysis work with yfinance and pandas?▼

Yes, this trend deviation analysis works with yfinance and pandas, utilizing them alongside numpy and pandas_datareader to fetch historical prices and compute exponential trend fits.

What assets support exponential trend deviation analysis?▼

Assets supporting exponential trend deviation analysis include gold, stock indices, commodities, and cryptocurrencies, provided they have sufficient historical price data to fit a reliable long-term trend.

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