forecast-sector-relative-return-from-yield-spread

Yield curve spread forecasting predicts sector relative performance by quantifying the lead-lag relationship between the 2Y-10Y US Treasury spread and sector ratios like QQQ vs XLV. It fetches FRED and Yahoo Finance data, applies regression, and outputs point estimates with confidence intervals.

The lead-lag relationship between yield curves and growth vs defensive sectors measures how current yield-curve shape predicts future relative performance. This Skill quantifies that timing delay using regression to support cross-asset allocation decisions between growth and defensive sector rotations.

Yes, you can use yfinance and pandas to predict QQQ vs XLV performance from Treasury yields. This Skill uses yfinance to fetch price data, pandas and numpy to compute the relative ratio, and scipy to perform the regression analysis on the FRED yield data.

To run a quick yield-curve spread analysis in Python, execute the script with default parameters using the command line interface. Navigate to the Skill directory and run the spread forecaster script with the quick flag to instantly view the current forecast and visualization outputs.

Analyzing yield-curve spreads and sector rotations requires US Treasury yield data from FRED and sector price data from Yahoo Finance. The process computes the 2Y-10Y spread and sector relative ratios, utilizing requests for data fetching and matplotlib for visualizing the regression results.

Yes, this yield-curve spread forecasting approach supports multiple lead times to quantify how current yield-curve shapes relate to future performance. It outputs structured JSON and Markdown results with point estimates, confidence intervals, and stability checks for informed decision making.