analyze-copper-stock-resilience-dependency

To analyze copper price resilience, calculate equity resilience scores and rolling beta coefficients against stock market trends. This Skill computes SMA trends and cross-asset signals using pandas and yfinance to quantify breakout probability.

Calculating copper backfill probability requires integrating copper price dynamics with global stock-market resilience and China 10Y yield. This Skill uses statsmodels and scipy to output scenario narratives and actionable risk flags for monthly horizons.

Yes, you can use yfinance and statsmodels for cross-asset breakout analysis. This Skill fetches copper price and global market data with yfinance, then applies statsmodels to compute rolling beta values and resilience scores for breakout risk framing.

Yes, copper resilience analysis outputs JSON reports and visualizations. Using matplotlib and pandas, the Skill generates charts alongside a JSON report containing backfill probabilities, resilience scores, and scenario narratives for portfolio risk managers.

Rolling beta coefficients measure copper's sensitivity to global stock-market resilience over time. By tracking these coefficients alongside SMA trends, the Skill quantifies whether copper is likely to break major levels or revert to support.

When analyzing copper dependency, web scraping via selenium and bs4 may face rate limits or site structure changes affecting data retrieval. The core analysis relies on yfinance for price data, while scraping supplements cross-asset signals like China 10Y yield.