Discussing the article: "Online Machine Learning for Trade Signal Filtering in MQL5 (Part 1)"

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Check out the new article: Online Machine Learning for Trade Signal Filtering in MQL5 (Part 1).

This article implements an online logistic‑regression trade filter in native MQL5 and integrates it into an EMA‑crossover EA with a closed‑trade feedback loop. It details the shared class, features, SGD update, persistence, and a read‑only probability view. Synthetic experiments cover multi‑seed separation, calibration, feature ablation, regime‑shift baselines, and hyperparameter sweeps. You get reproducible scripts and a walk‑forward protocol to validate the filter on your own instrument.

Take a plain EMA(12)/EMA(26) crossover and run it on any liquid pair for a year: it catches real trends, but it also fires constantly into chop, because it only knows two numbers just touched, nothing about the market condition around them. Hand-tuned filters, an ADX threshold here, an RSI band there, fix this only until the market moves past the slice of history they were tuned on. Nothing in that pipeline was learning; it was fitting a fixed rule to fixed data.

Machine learning is the obvious next step, but most articles that reach for it train a model once offline, freeze the weights, and export them into the EA. That inherits the same blind spot: the model is a snapshot, and the relationship it learned is not guaranteed to still hold once the market's character changes, with no mechanism to notice it hasn't.

This article builds a filter with that missing mechanism included: an online logistic regression whose weights update themselves after every closed trade, using nothing but native MQL5 arrays and a five-line SGD step. No external training script, no ONNX export, no retraining schedule to remember.

Author: Adeolu Kayode Gbadebo