MQL5 Programs Market

A native MQL5 pipeline computes L-moments from probability‑weighted moments and compares their shape ratios to conventional Pearson estimators on the same rolling window. A single-pass walk-forward validator flags L‑kurtosis (tau4) and excess kurtosis spikes against adaptive baselines and scores them with precision, recall and F1 on real FX history. The script is diagnostic, input-driven, and avoids the Strategy Tester to provide reproducible, data-first results.

In the second part, the cellular automaton is transferred from a grid to a graph. The features become vertices in a graph with local and long-range small-world connections, and the cells become agents that interact not only with their geometric neighbors but also with their semantic neighbors. The article discusses graph-based feature filtering, the construction of a neighbor graph, updated coherence-based voting, and the Graph Coherence and Graph Health metrics. This reduces the impact of isolated outliers and accelerates the propagation of market regimes while maintaining full compatibility with MQL5.

CTradePreflight checks a trade request before OrderSend(), so an EA finds an off-step volume, a wrong-side stop, missing margin or a closed session instead of getting a server rejection. The class repairs only safe cases, reads the OrderCheck() retcode, and counts every refusal by reason. Unit tests and a random-delay stress EA show that levels placed very close to the price can still fail, and an optional delay buffer stops those rejections at the cost of blocking some valid requests.

This article explains why and how the gradient is used in training a perceptron, based on the least squares method and the chain rule for computing partial derivatives. We will demonstrate how to implement gradient calculation in the C_Neuron class using the MQL5 language and test it using examples with 1 and 2 inputs. You'll learn how to adjust the weights and bias using gradient descent and prepare for forward propagation and backpropagation.