Articles on manual and algorithmic trading in MetaTrader 5

The article explains how to establish a reproducible process for developing and testing trading systems in MetaTrader 5: from formalizing entry and exit rules and risk management to post-optimization validation. It is based on the "Pardo Method": splitting historical data into in-sample and out-of-sample periods, forward testing, multiple markets/timeframes, and selecting stable parameter "plateaus" instead of isolated peaks. Using the examples of PardoSystem and the PardoEA / Breakout_Bounce Expert Advisors (EAs), this article demonstrates a practical test plan for the MetaTrader 5 Strategy Tester.

This article implements the Maximal Information Coefficient (MINE) for MQL5, including the grid search with dynamic programming and the four MINE statistics. It explains why raw MIC has a nonzero noise floor and builds a permutation null to judge significance. The result is a verified library with a dependence scanner and a chart indicator, allowing you to test features and interpret scores consistently across relationship shapes.

This article delivers a native MQL5 MiniRocket: 84 fixed convolution kernels yield 9,996 features quickly and deterministically, requiring no training loop and no external runtime. We verify the port against sktime and a float64 reimplementation, then run a reproducible audit of seven classic setups; planted and coin‑flip controls confirm correctness, and a 25%‑flipped control sets the detection threshold.

This work delivers an MQL5 Expert Advisor that reads open-position symbols, builds return series, and computes a rolling Pearson correlation matrix, displayed as a CCanvas heatmap. Off-diagonal pairs that meet a warning threshold are bordered for quick scanning. The code details return processing, correlation arithmetic, and matrix indexing, with a verification script, helping you track shifting co-movements across your active book.

This article implements the Maximal Information Coefficient (MINE) for MQL5, including the grid search with dynamic programming and the four MINE statistics. It explains why raw MIC has a nonzero noise floor and builds a permutation null to judge significance. The result is a verified library with a dependence scanner and a chart indicator, allowing you to test features and interpret scores consistently across relationship shapes.

We demonstrate live forecast charting with Kronos in MQL5 and an evaluation pipeline designed to be look‑ahead‑safe. Deterministic dumps are scored per horizon against a random‑walk baseline (15,113 predictions), then fed to a simple Strategy Tester rule on EURUSD H1. The findings show no edge, and illustrate how a high threshold can yield a persuasive but low‑information backtest with very few trades.