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The Volatility Cone: A Quant's Tool for Mapping Price Uncertainty
Here’s your lifeline.
Another headline. Another client pays late. The next 10 days shift. You open your bank app before walking into the office.
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Fully coded in Python. Yours to run today.
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Premium Members – Your Full Notebook Is Ready
The complete Google Colab notebook from today’s article (with live data, full Hidden Markov Model, interactive charts, statistics, and one-click CSV export) is waiting for you.
Preview of what you’ll get:
Inside the Strategy Lab
📥 Auto-fetches cross-asset market data — Pulls SPY, IWM, HYG, LQD, and VIX price history from Yahoo Finance and aligns everything into one unified dataframe.
🧮 Feature engineering pipeline — Builds credit spread, daily/21D/63D/126D returns, relative SPY vs IWM returns, realized volatility, VIX transformations, and drawdown features.
⚖️ Volatility context logic — Compares implied volatility to realized volatility using VIX-to-SPX volatility ratios and rolling VIX averages to help spot regime shifts.
🧼 Data cleaning step — Removes rows with missing or infinite values before modeling so the clustering and PCA steps run on clean input only.
📐 Standardization + PCA — Scales all numeric features, then compresses them with PCA while keeping enough components to explain 95% of the variance.
🔍 Silhouette-based cluster search — Tests multiple K-Means cluster counts and picks the best number using silhouette score on both raw and PCA-reduced features.
🧠 Regime classification output — Assigns a regime label to each valid row and stores it back into the main dataframe as a nullable integer column.
📊 Price and cluster visualization — Generates an OHLCV chart for SPY and a PCA scatter plot colored by detected regime.
📈 Model quality reporting — Prints the number of rows used, best silhouette scores, chosen representation, and final regime distribution.
Free readers – you already got the full breakdown and visuals in the article. Paid members – you get the actual tool.
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