How does it calculate trend deviation?

It performs a linear regression on the logarithm of the asset price over time to fit an exponential curve, then calculates the percentage difference between the current price and that trend line.

Which assets can this skill analyze?

It is designed for any asset with a long-term exponential growth characteristic, including stock indices (S&P 500), commodities (Gold, Crude Oil), and cryptocurrencies.

What are macro regimes?

Regimes are classifications of the market environment (such as inflationary or growth-driven) determined by analyzing macro-economic factors alongside price action.

What dependencies are required to run this skill?

The skill requires Python with the pandas, numpy, yfinance, and statsmodels libraries installed to fetch data and perform statistical calculations.

Macro Trend Deviation Analyzer

Name: Macro Trend Deviation Analyzer
Author: fatfingererr

byfatfingererr

0•

Data Science & ML

Analyzes asset price deviations from long-term exponential trends to identify historical extremes and macro market regimes.

The Macro Trend Deviation Analyzer is a specialized tool designed to help financial analysts and investors identify when assets are overextended relative to their multi-decade growth paths. By applying log-linear regression to historical price data, the skill fits an exponential trend line and calculates the current deviation percentage. It provides context by comparing these figures to historical peaks and troughs, using macro-economic proxy indicators to classify the current market regime. This is particularly useful for long-term strategic positioning in assets like gold, equity indices, and commodities.

Key Features

01Visual reporting with annotated charts and peak-to-peak comparisons

02Automated data retrieval using yfinance and pandas-datareader

03Exponential trend fitting via log-linear regression for long-term assets

040 GitHub stars

05Historical quantile analysis to identify overbought or oversold extremes

06Macro factor decomposition for regime classification (e.g., 1970s-like vs 2000s-like)

Use Cases

01Evaluating if current gold or stock market prices are historically overextended

02Comparing current market 'bubbles' to specific historical dates like 1980 or 2011

03Classifying market conditions based on macro indicators to inform investment strategy

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fatfingererr/macro-skills evaluate-exponential-trend-deviation-regimes

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Visual reporting with annotated charts and peak-to-peak comparisons

02Automated data retrieval using yfinance and pandas-datareader

03Exponential trend fitting via log-linear regression for long-term assets

040 GitHub stars

05Historical quantile analysis to identify overbought or oversold extremes

06Macro factor decomposition for regime classification (e.g., 1970s-like vs 2000s-like)

Use Cases

01Evaluating if current gold or stock market prices are historically overextended

02Comparing current market 'bubbles' to specific historical dates like 1980 or 2011

03Classifying market conditions based on macro indicators to inform investment strategy

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add fatfingererr/macro-skills evaluate-exponential-trend-deviation-regimes

For use in Claude.ai and ChatGPT

Download Skill