analyze-investment-clock-rotation

Investment clock rotation analysis maps macroeconomic data like earnings proxies against financial conditions to determine the current clock quadrant and hour for asset allocation decisions. This Skill pulls weekly data and quarterly earnings proxies from FRED to calculate rotation direction and magnitude.

Mapping earnings growth against financial conditions into an investment clock requires plotting coordinates to identify quadrants and compute the clock hour. This tool uses configurable axis mappings with FRED data sources like CP and NFCI, including optional smoothing for accurate quadrant positioning.

Calculating the current quadrant and clock hour involves analyzing macroeconomic coordinates to determine position within the investment clock. This Skill computes Q1-Q4 positions based on earnings growth and financial conditions, assessing rotation direction and comparing cycles to historic patterns.

FRED data integrates with pandas and numpy for quadrant analysis by pulling earnings proxies and financial conditions indices with adjustable frequency and alignment. This Skill requires Python 3.8 or higher with dependencies including pandas, numpy, requests, and optionally matplotlib for visualization.

Comparing current market cycles to historic investment clock patterns involves assessing rotation direction and magnitude against previous cycles. This Skill provides optional cycle comparison functionality, pulling weekly data and quarterly earnings proxies from FRED to generate contextual insights for asset allocation.

Investment clock rotation analysis requires Python 3.8 or higher and targets weekly data with quarterly earnings proxies from FRED. The Skill uses adjustable frequency and alignment for data integration, supporting configurable axis mappings and optional smoothing to refine the quadrant and clock hour calculations.