What statistical methods are used for shock detection?

The skill employs a dual-threshold approach using rolling z-scores (standard deviations from the mean) and daily price slopes to identify parabolic moves and price regimes.

How does this skill collect market data?

It uses a Python-based Chrome CDP (Chrome DevTools Protocol) scraper to automatically navigate TradingEconomics, extracting data directly from charts while bypassing common anti-scraping measures.

Can this skill predict future price movements?

No, this skill is designed for narrative verification. It tests if the data supports a specific causal hypothesis rather than providing speculative price forecasts.

What is the typical lead time identified by this tool?

The methodology searches for a lead-lag relationship within a window of 1 to 8 weeks (7-56 days), which is the standard historical window for energy-to-fertilizer cost transmission.

Gas-Fertilizer Shock Analyst

Name: Gas-Fertilizer Shock Analyst
Author: fatfingererr

byfatfingererr

数据科学与机器学习

Verifies causal narratives between natural gas price surges and fertilizer market disruptions using automated statistical analysis.

关于

The Gas-Fertilizer Shock Analyst is a specialized tool designed for macro analysts and commodity researchers to empirically test the 'energy-to-agriculture' price transmission narrative. It automates the extraction of daily price data via Chrome CDP, detects price regimes using rolling z-scores and slope thresholds, and performs lead-lag correlation tests. By providing a structured three-part verification process, it determines if market data supports the hypothesis that natural gas shocks lead to fertilizer supply constraints or price spikes, outputting professional-grade visualizations and quantitative confidence scores.

主要功能

Automated data scraping from TradingEconomics using headless Chrome CDP to bypass verification.
Statistical regime detection using rolling z-scores and volatility-adjusted price slopes.
0 GitHub stars
Three-part causal verification framework (Shock detection, Spike analysis, and Lead-Lag testing).
Lead-lag correlation analysis to identify the specific time delay between energy and fertilizer markets.
Generation of Bloomberg-style visualization charts highlighting key market turning points.

使用场景

Quantitative assessment of contract-shock narratives during global energy crises.
Macroeconomic research into the lag times between energy input costs and agricultural commodity outputs.
Validating news reports and social media claims about energy-driven fertilizer price spikes.

关于

主要功能

Automated data scraping from TradingEconomics using headless Chrome CDP to bypass verification.
Statistical regime detection using rolling z-scores and volatility-adjusted price slopes.
0 GitHub stars
Three-part causal verification framework (Shock detection, Spike analysis, and Lead-Lag testing).
Lead-lag correlation analysis to identify the specific time delay between energy and fertilizer markets.
Generation of Bloomberg-style visualization charts highlighting key market turning points.

使用场景

Quantitative assessment of contract-shock narratives during global energy crises.
Macroeconomic research into the lag times between energy input costs and agricultural commodity outputs.
Validating news reports and social media claims about energy-driven fertilizer price spikes.