Discussing the article: "Rough Volatility: Building a Roughness Index Feature from the RFSV Model for ML Trade Filtering"
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Check out the new article: Rough Volatility: Building a Roughness Index Feature from the RFSV Model for ML Trade Filtering.
This article builds a usable roughness index from rough volatility theory by fitting local H via a structure-function regression on blocked returns, all in native MQL5. We combine it with vol-of-vol features, train a gradient-boosted classifier in Python, export to ONNX, and call it from an EA. You will be able to compute H on every bar and use the model as a regime-aware entry filter.
Most volatility models you'll find in retail trading circles assume volatility itself moves smoothly — an EWMA here and a GARCH(1,1) there, both built on the idea that today's variance is a gentle blend of yesterday's variance and yesterday's shock. Actual market volatility does not behave that way. Since the 2014 work by Gatheral, Jaisson and Rosenbaum, which introduced what is now called rough volatility, a growing body of empirical evidence has shown that log-volatility itself looks like a fractional Brownian motion with a Hurst exponent far below the standard 0.5 — typically somewhere around 0.05 to 0.15. In plain terms: volatility is "rougher" than a random walk. It jitters and reverts far more locally than a smooth diffusion process would predict.
This article walks through turning that observation into something you can actually use inside an Expert Advisor. We estimate a rolling "roughness index" — a local Hurst exponent — directly from XAUUSD M5 price data using a generalized structure-function regression, implemented natively in MQL5 with no external DLLs or ALGLIB dependency. That roughness index, together with two complementary features, feeds a gradient-boosted classifier trained offline in Python and deployed through ONNX. The EA uses the classifier's output as a standalone directional filter: enter long when the model is confident the regime favors upside, short when it favors downside, and stay flat everywhere in between.
Author: Adedayo David Gbadebo