detect-shanghai-silver-stock-drain

Quantify Shanghai silver stock drain from exchange inventories using Z-score standardization.

3|1|Updated Jan 12, 2026

Search Tags:#drain #stock-analytics #Shanghai #SHFE #Z-score #three-dimensional

Ready to run

npx skills add https://github.com/fatfingererr/macro-skills --skill detect-shanghai-silver-stock-drain

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: detect-shanghai-silver-stock-drain
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/detect-shanghai-silver-stock-drain
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill detect-shanghai-silver-stock-drain

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, selenium, webdriver-manager, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill quantifies the decline of Shanghai silver stock from public exchange inventories and converts it into actionable supply signals, reducing guesswork about market tightness.

Core Features & Use Cases

Three-dimensional analysis: direction, velocity, and acceleration to detect late-stage supply signals.
Z-score standardization and cross-checks with market indicators (e.g., COMEX, ETFs) to validate signals.
Outputs include a structured signal level, historical stock levels, and narrative interpretation for decision support.

Quick Start

Fast start: run python scripts/drain_detector.py --quick to get an immediate signal. Full analysis: run python drain_detector.py --start 2020-01-01 --end 2026-01-16 --output result.json Data sources: ensure SGE and SHFE data are available in data/ (or fetched via scripts/fetch_shfe_stock.py and scripts/fetch_sge_stock.py)

Frequently Asked Questions about detect-shanghai-silver-stock-drain

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect Shanghai silver stock drain signals from SHFE inventory data?▼

Shanghai silver stock drain signals are detected by applying a three-dimensional framework measuring direction, velocity, and acceleration, then using Z-score standardization to quantify supply tightness from public exchange inventory data.

What is the three-dimensional framework for analyzing silver inventory drain?▼

The three-dimensional drain framework analyzes inventory changes across direction, velocity, and acceleration to identify late-stage supply signals, transforming raw public exchange stock data into standardized, actionable market tightness indicators.

How do I run a quick silver stock drain analysis using Python and pandas?▼

Run `python scripts/drain_detector.py --quick` in your Python environment with pandas, numpy, selenium, and webdriver-manager installed to get an immediate silver stock drain signal without specifying custom date ranges.

Can I cross-check SHFE silver stock signals with COMEX and ETF data?▼

Yes, you can cross-check SHFE silver stock drain signals with COMEX and ETF market indicators to validate the detected supply tightness, ensuring the Z-score standardized signals reflect broader market conditions.

Do I need selenium and webdriver-manager to fetch Shanghai silver stock data?▼

Yes, selenium and webdriver-manager are required dependencies for fetching Shanghai silver stock data via the included scripts, alongside numpy and pandas for processing the inventory data into structured JSON or Markdown outputs.

What are the limitations of using Z-score standardization for silver stock drain detection?▼

Z-score standardization for silver stock drain detection relies on historical data availability and clean public exchange inputs; missing SGE or SHFE inventory data in the data directory can prevent accurate signal generation.