analyze-gas-fertilizer-contract-shock

What problem does it solve?

This Skill analyzes daily price data to determine whether a natural gas price shock leads to fertilizer price spikes, providing a quantitative, narrative-verification framework instead of guesswork.

Core Features & Use Cases

• Narrative verification: test the claim that gas shocks cause fertilizer spikes using a transparent three-part causal framework (A: gas shock; B: fert spike; C: lead-lag).
• Regime detection & visualization: detect and label gas shock regimes and fertilizer spike regimes with start/end dates, magnitudes and durations, plus Bloomberg-style visualizations.
• Decision support: outputs structured results (JSON/Markdown-ready) for macro research, risk assessment, or contract-hedging narratives.
• Use cases include validating public narratives for policy or industry risk, academic analysis of energy–agriculture linkages, and teaching causality in time-series.

Quick Start

• Install prerequisites: pip install pandas numpy requests websocket-client matplotlib scipy
• Fetch data: python scripts/fetch_te_data.py --symbol natural-gas --symbol urea
• Run analysis: python scripts/gas_fertilizer_analyzer.py --gas-file data/cache/natural-gas.csv --fert-file data/cache/urea.csv --start 2025-08-01 --end 2026-02-01 --output data/analysis_result.json
• Generate visualization: python scripts/visualize_shock_regimes.py --data data/analysis_result.json --output output/gas_fert_shock.png