Discussing the article: "Measuring What Matters (Part 1) : Portfolio Risk Decomposition in MQL5"
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Check out the new article: Measuring What Matters (Part 1) : Portfolio Risk Decomposition in MQL5.
The article establishes a reproducible method to measure portfolio risk for multiple symbols using MQL5 matrices and OpenBLAS. It covers computing log returns, building a covariance matrix, and evaluating wᵀΣw instead of summing individual variances. A complete script prints naive versus true volatility and the cross‑term contribution, enabling you to detect when correlated instruments inflate exposure beyond single‑asset estimates.
Standard deviation is the backbone of almost every volatility measure in retail trading. It takes a single time series, measures how spread out the returns are around their average, and produces one number. That number is genuinely useful. It tells you how active or quiet an instrument has been. It forms the basis of ATR-based position sizing, Bollinger Bands width, and dozens of other tools traders depend on daily.
The limitation is not that standard deviation is wrong. It is that it is incomplete the moment you hold more than one position. Standard deviation is one-dimensional. It captures how much one thing moves. It says nothing about how two things move in relation to each other.
Consider EURUSD and GBPUSD. Both currency pairs react to the same underlying driver—the strength or weakness of the US Dollar Index. When the dollar surges, both pairs typically fall together. When the dollar weakens, both pairs typically rise together. Their movements are not identical, but they are far from independent. They share a common factor, and that shared factor means holding both simultaneously is not the same as holding two separate, uncorrelated risks.
Standard deviation will tell you EURUSD moved 0.42% per hour on average and GBPUSD moved 0.51% per hour. What it will never tell you is that in the hours when EURUSD fell sharply, GBPUSD fell in the same direction roughly 70% of the time. That tendency to co-move that 70% figure is the information that determines whether your two positions genuinely diversify each other or quietly amplify the same underlying risk. It is entirely outside what individual volatility can see.
The mathematical object that captures this relationship is the covariance matrix. Before we compute it, we need to understand it.
Author: Kayode Michael Oyetunde