MQL5 Programs Market

An EA that passes CodeBase validation can still fail under execution delay and a freeze level. Using a tick scalper, the article reproduces four such failures in the Strategy Tester and shows each fix with its limits. An ablation under a fixed delay measures each change on its own. The burst-based stop distance removes most rejected modifications, while the weekend case behind the session buffer did not recur in those runs.

Migrating 3D bars from Python to native MQL5: instead of Plotly and a bridge to the terminal — a scene using CCanvas3D and DirectX 11 directly on the chart. Price, time, and tick volume are plotted along three axes; the geometry is assembled manually from vertices and triangles; and a mouse-controlled orbit camera allows interactive exploration without external dependencies.

This is, without a doubt, the article I've enjoyed writing the most so far. In it, I showed that we don't really need anything special to achieve a certain goal. We can achieve this in various ways, provided we have the necessary knowledge and are willing to learn and dedicate ourselves to the task. I would like to express my heartfelt thanks to everyone who helped me choose these functions. The material presented here is intended solely for educational purposes. Under no circumstances should this be viewed as intended for anything other than teaching and studying the concepts presented.

In this article, we'll see that things aren't quite what many people initially think they are. One of the most interesting aspects of programming is that we can guarantee that everything will always turn out exactly as we planned. So please read this article carefully to understand some of the most confusing concepts related to the use of subwindows. If you understand what is explained here, you'll be able to understand much of what we'll be doing next.

The article presents an adaptation of the SDformerFlow framework that achieves high adaptability by integrating spiking attention with multi-window convolution and weighted summation of query elements. The architecture allows each attention head to learn its own parameters, which improves the model's accuracy and sensitivity to the structure of the data being analyzed.