skills.rest
fatfingererr
fatfingererrCOMMUNITY
Explore all skills by this author →
𝕏@fatfingererrGitHub

backsolve-miner-vs-metal-ratio-with-fundamentals

Decompose miner stock to metal price ratios into four fundamental factors.

3|1|Updated Jan 12, 2026
Search Tags:#fundamentals#analyze#AISC#dilution#miner ratio#EV/EBITDA
One-click install
npx skills add https://github.com/fatfingererr/macro-skills --skill backsolve-miner-vs-metal-ratio-with-fundamentals
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backsolve-miner-vs-metal-ratio-with-fundamentals
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/backsolve-miner-vs-metal-ratio-with-fundamentals
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill backsolve-miner-vs-metal-ratio-with-fundamentals

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, pandas, yfinance, matplotlib, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill explains movements in the miner stock to metal price ratio by decomposing it into four measurable drivers using real data, enabling users to validate the drivers behind ratio changes.

Core Features & Use Cases

  • Factor decomposition: compute the four fundamental factors (AISC/cost factor, leverage factor, EV/EBITDA, and dilution) from price data and disclosed financials and ops data.
  • Backsolve & scenario analysis: given a target ratio (e.g., 1.2 or 1.7), determine the required changes across factors to reach the target.
  • Data sources & outputs: integrates US/Canada disclosures and ETF holdings to estimate weights, producing structured JSON/Markdown reports for decision support.

Quick Start

Run a quick baseline using the default SIL/SI=F setup:

  • python scripts/fundamental_analyzer.py --quick

Frequently Asked Questions about backsolve-miner-vs-metal-ratio-with-fundamentals

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

FAQPage Schema
Can I run a quick baseline analysis for SIL ETF holdings and silver miners?▼

You can backsolve a target silver miner ratio by determining the required changes across AISC, leverage, EV/EBITDA, and dilution factors. This scenario analysis computes necessary adjustments from baseline data to reach target ratios like 1.2 or 1.7.

Does this analysis support backsolving specific target ratios like 1.2 or 1.7?▼

You need US and Canada financial disclosures and ETF holdings data to calculate AISC and EV/EBITDA. These sources provide the operational and financial metrics required to estimate portfolio weights and compute the four fundamental factors.

What financial metrics are needed to explain silver miner stock ratio changes?▼

You perform an event study on silver miner ratios by applying factor decomposition to historical price and filing data. This Python-based analysis uses pandas and yfinance to compute AISC, leverage, EV/EBITDA, and dilution, outputting structured JSON or Markdown reports.

Can I use yfinance and pandas to compute AISC and dilution for major silver miners?▼

Analysis is limited for miners outside US and Canada disclosure regimes because the factor computation relies on specific regional financial filings. Without these standardized disclosures, accurately estimating AISC, leverage, EV/EBITDA, and dilution becomes constrained.

Related Skills

Explore More Finance & Accounting Skills→
0xZKnw0xZKnw

edgar-sec-filings

Analyze SEC EDGAR filings to extract fundamental insights and risk signals.

Community
Advanced
Ritabrata-ChakrabortyRitabrata-Chakraborty

scientific-visualization

Generate publication-ready scientific figures with matplotlib, seaborn, and plotly.

Community
Advanced
santoosaraujosantoosaraujo

cross-market-strategy

Generate a unified signal engine for multi-market portfolios with pandas and numpy.

Community
Advanced
Liangwei-zhangLiangwei-zhang

valuation-model

Apply DCF, DDM, SOTP, and EV/EBITDA to derive intrinsic value.

Community
Advanced