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The Volatility Cone: A Quant's Tool for Mapping Price Uncertainty
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② One strategy in this book returned 2.3× the S&P 500 on a risk-adjusted basis over 5 years.
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
Install & Imports — upgrades Plotly and loads all dependencies (NumPy, Pandas, SciPy FFT, Matplotlib, Seaborn, Plotly)
PLTR Data Fetching — pulls 5 years of daily OHLCV data from the EODHD API with configurable symbol and date range
Mellin Transform Theory — markdown explanation of the 3-step causal Mellin algorithm with a parameter reference table for
σMellin Spectrum of Close Prices — log-time resampling + one-sided FFT applied to raw close prices, plotted as a magnitude vs. scale chart
Mellin Spectrum of Log Returns — same transform applied to absolute log returns, with interpretation notes on what each frequency peak represents
Low-Pass Filtering on Returns — the
mellin_lowpass_causalfunction with a 2×2 subplot grid comparing all four parameter combos (window sizes 64–512, cutoffs 0.05–0.30)Low-Pass Filtering on Raw Prices — the
mellin_lowpass_raw_pricefunction with another 2×2 comparison grid across different smoothing strengthsSliding Causal Spectrogram — the
sliding_causal_mellinfunction producing a time × scale heatmap rendered interactively with PlotlyFinal Combined Visualization — overlays the spectrogram heatmap (background) with raw price and Mellin-filtered price (dual y-axis) across the full 2021–2026 date range
Conclusions — Mellin vs. Moving Average comparison table, use cases, limitations, and full references list
Free readers – you already got the full breakdown and visuals in the article. Paid members – you get the actual tool.
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