MQL5 Programs Market

This article uses practical examples to explain how to calculate and apply gradients of the weights and bias (intercept) in a linear neuron implemented in MQL5, and also discusses a stochastic variant of gradient descent. We analyze stopping criteria, iteration limits, and the effects of training on data subsets. The results are displayed in the MetaTrader 5 terminal, where a simple chart is also plotted. Readers are invited to modify the training set and analyze the model's behavior.

Introducing the TMA framework — an intelligent system capable of forecasting market dynamics with sufficient accuracy. In this article, we have integrated all the components into a single architecture and transformed it into a fully-fledged trading agent that analyzes the market and makes decisions in real time.

The article focuses on label-auditing for meta-labeling, not on a new entry signal. It presents a native MQL5 implementation of confident learning with out-of-fold scoring and calibrated pruning, plus an EA switch that trains the meta-filter on raw or cleaned datasets. A controlled Strategy Tester comparison helps you quantify noise and choose a pruning level that preserves generalization.

The article continues the series on AI Hedge Fund and addresses three limitations of v4: analysts' reputation scores are now stored persistently in SQLite, the EA has been removed from the critical execution path, and signals from the council of fifteen are distributed to multiple brokers via the TradeMux REST API. The logic behind the council and risk management has remained the same: Python retrieves data via the MetaTrader 5 SDK and executes orders directly. The result is resilience to restarts and scalability across multiple terminals.

This article introduces the foundations of connecting MetaTrader 5 to a cryptocurrency exchange using MQL5 and the MEXC Futures API. It covers terminal configuration, JSON response processing, WebRequest() execution and validation, and conversion of API data into native MQL5 types. Practical examples demonstrate retrieving and processing exchange data. These components establish the basis for a reusable MEXC Futures client, developed progressively throughout the series. The series focuses exclusively on exchange integration; how readers use the retrieved data remains outside its scope.

Build Constant Proportion Portfolio Insurance natively in MQL5. The article details the floor and cushion formulas, a rebalance engine that operates safely on both netting and hedging accounts, and a price‑only reference indicator that reuses the same math. A real Strategy Tester run on XAUUSD M5 demonstrates configuration, order sizing, and stability under a rebalance band.