MQL5 Programs Market

Neural networks are already changing the way we analyze markets, and new architectures are opening up even more possibilities. In this article, we wrap up our work with the SpikingBrain framework, which opens up new possibilities for us.

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 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 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.

We implement visible bookmark annotations and a hand-off mechanism for cross-symbol and cross-timeframe recall. The navigator stores the target in terminal Global Variables, waits for stable bars after reinitialization, and rebuilds only matching markers with safe cleanup. Traders can inspect saved events in context and navigate back to them without reconstructing chart settings.

Learn how to bring advanced plotting and data visualization to the MetaTrader 5 terminal by leveraging Matplotlib, one of Python's most powerful and widely used visualization libraries.

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.