analyze-gas-fertilizer-contract-shock

To test whether a natural gas shock causes fertilizer price spikes, you analyze daily price series using a three-part causal framework identifying gas shocks, fertilizer spikes, and lead-lag relations. The tool compares shock regimes to deliver structured assessments and visualizations.

Detecting lead-lag relations between gas and fertilizer prices involves cross-correlation analysis on daily price series. This Skill applies shock detection to label regime start and end dates, then measures the timing and magnitude of price movements across the two series.

Fetch daily price data by running the fetch script with specified symbols like natural-gas and urea. Then execute the analyzer script with the CSV file paths, date range, and output parameters to generate structured JSON results for macro research.

Yes, you can use pandas and scipy alongside matplotlib to visualize natural gas shock regimes for risk analysis. The Skill generates Bloomberg-style visualizations labeling shock start and end dates, magnitudes, and durations suitable for contract-hedging narratives.

Yes, this shock detection approach works for academic analysis of energy and agriculture price linkages. It provides a transparent causal framework comparing shock regimes and lead-lag relations, suitable for teaching causality in time-series and validating public narratives.

Using cross-correlation for narrative verification in commodity markets limits causal inference to temporal timing rather than structural economic modeling. This approach detects regime timing and magnitude but should be supplemented with domain knowledge for comprehensive risk assessment.