analyze-silver-miner-metal-ratio

You can perform silver miner relative valuation by computing the miner-to-metal ratio using yfinance proxies like SIL and SI=F. The analysis calculates percentile ranks to identify bottom or top zones and applies optional smoothing to reveal trend signals.

The best way to test for a silver miner valuation bottom is by computing percentile ranks of the miner-to-metal ratio against historical distributions. You can then simulate forward performance after bottom signals using customized forward horizons like 52, 104, or 156 weeks.

Yes, you can use yfinance to run scenario analysis on silver mining stocks by fetching historical proxies. The framework quantifies how much miners must rise or silver must fall to reach top valuations, supporting weekly and monthly data intervals.

You generate forward-looking signals for silver miners using Python with pandas and numpy to evaluate historical analogs and forward returns. Specifying custom forward horizons and start dates projects future performance scenarios based on historical ratio patterns.

The silver miner ratio analysis requires Python with pandas, numpy, and yfinance for core ratio computations and scenario analysis. Scipy and matplotlib are optional dependencies used specifically for generating visual plots and applying advanced statistical smoothing.

Using SIL and SI=F proxies for miner-to-metal ratio analysis limits you to the historical data ranges available through yfinance. While it supports customizable start dates and weekly or monthly intervals, scenario planning and divergence detection depend on these specific proxy assets.