us-market-bubble-detector

Market bubble risk is evaluated by analyzing quantitative metrics like Put/Call ratios, VIX, margin debt, breadth, and IPO data using the revised Minsky/Kindleberger framework to generate a risk phase assessment.

The Minsky/Kindleberger framework is a methodology for bubble detection that analyzes quantitative data alongside strict qualitative criteria to identify market risk phases and guide investment decisions.

Yes, Python with pandas, numpy, matplotlib, and scikit-learn processes market data, applies the bubble detection framework, and visualizes risk phase assessments for investment decision-making.

Yes, this market analysis approach incorporates strict criteria for qualitative adjustments alongside quantitative metrics, preventing subjective bias from skewing the risk assessment results.

You need specific data sources providing VIX levels, margin debt figures, market breadth, Put/Call ratios, and IPO data to perform the quantitative risk assessment and calculate bubble phase scores.

You should not rely solely on quantitative metrics when market behavior shifts unpredictably, which is why strict qualitative criteria are applied alongside the data to adjust risk phase assessments accurately.