Articles on the MQL5 programming and use of trading robots

We introduce a Partial Information Decomposition library for MQL5 that decomposes two sources about a target into four atoms: unique to each, shared, and synergy. The implementation uses quantile binning, tabulated logarithms, and a maximum-entropy fit (for I_ccs), and it pairs results with a block-permutation null because atoms sit above zero on finite samples. Use it to screen indicator pairs and judge significance, including family-wise correction.

In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in the MQL5 environment and laying the foundation for reliable predictive analysis.

We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data.