Discussing the article: "Markowitz Mean-Variance Optimization for MQL5: A Native Efficient-Frontier Portfolio Builder"

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Check out the new article: Markowitz Mean-Variance Optimization for MQL5: A Native Efficient-Frontier Portfolio Builder.

This article shows how to implement a native MQL5 portfolio optimizer for multi-symbol baskets. It derives a covariance matrix from bar log-returns, inverts it via Gauss–Jordan, and solves the global minimum-variance, tangency, and target-return portfolios without external libraries. You will build an EA that rebalances positions long-only with clip-and-renormalize and a read‑only dashboard that tracks drift versus targets, plus learn Strategy Tester setup and common pitfalls.

Most Expert Advisors decide when to buy or sell a single instrument. This one decides how much of your equity to put into each instrument in a basket, using the same weight formula Harry Markowitz published in 1952. If you have never built a multi-symbol EA before, the mental shift is bigger than the math: there is no "signal" here, no crossover, no oscillator. There is a covariance matrix, three closed-form solvers, and a rebalance loop that nudges live positions toward whatever the matrix says is optimal this week.

Everything below is native MQL5: no external DLLs, no Python bridge, and no imported matrix library. The Gauss-Jordan inversion, the covariance estimation, and the Lagrangian frontier solve are all hand-written in MarkowitzCore.mqh, and I'll walk through the actual functions instead of talking around them. Wherever I show a formula, the next block is the code that implements it, so you can check one against the other line by line.

Author: Adewumi Babatunde Gbadebo