analyze-copper-inventory-rebuild-signal

Copper price turning points are identified by combining SHFE inventory rebound z-scores with COMEX inventory levels, creating a dual-stock signal that flags short-term caution when inventory rebuilds rapidly while prices sit at high historical percentiles.

You can fetch SHFE and COMEX copper inventory data by running the fetch_copper_data.py Python script, which connects to the MacroMicro CDP API and Yahoo Finance, caches the results locally, and prepares datasets for z-score computation.

Yes, running the copper inventory signal analyzer requires Python 3.9+ and installing pandas, numpy, requests, websocket-client, yfinance, and matplotlib to support data fetching, statistical computation, and visualization.

The best way to visualize copper inventory signals is by running the visualize_inventory_signal.py script, which uses matplotlib to plot SHFE z-scores, COMEX inventory levels, and 10-year copper price percentiles from cached data.

The 10-year price percentile assesses long-term copper valuation by comparing current copper futures prices against the past decade of data, categorizing the market as cheap, fair, or rich to contextualize short-term inventory signals.

The copper inventory signal analyzer outputs decision support reports in Markdown and JSON formats, containing computed z-scores, price percentiles, and dual-source caution signals saved in the output directory.