MQL5 Programs Market

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.

Build and deploy a Neural Additive Model natively in MQL5: one small network per feature, additive output, and no post-hoc explainability. The article provides a PyTorch trainer, two‑output ONNX export (signal and contributions), an include wrapper using OnnxCreateFromBuffer/OnnxRun, and an EA that prints real per‑feature contributions each bar. It covers identical feature math for training/export, threshold measurement, Strategy Tester results, and edge cases such as NaNs and out‑of‑distribution inputs.

In this article, theory meets practice. We implement the key modules of the TMA framework — MPE and MPA. Here, data takes on meaning, and cross-attention becomes a tool for precise analysis of market dynamics. Minimal unnecessary operations, maximum efficiency — a step toward next-generation intelligent trading.