Discussing the article: "MetaTrader 5 Machine Learning Blueprint (Part 7): From Scattered Experiments to Reproducible Results"

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Check out the new article: MetaTrader 5 Machine Learning Blueprint (Part 7): From Scattered Experiments to Reproducible Results.

In the latest installment of this series, we move beyond individual machine learning techniques to address the "Research Chaos" that plagues many quantitative traders. This article focuses on the transition from ad-hoc notebook experiments to a principled, production-grade pipeline that ensures reproducibility, traceability, and efficiency.

Throughout this series, we've covered crucial components of machine learning for trading: data structures, labeling and meta-labeling, sample weighting and purged cross-validation. But these techniques, powerful as they are individually, reach their full potential only when integrated into a cohesive research system. In this article, I'll demonstrate how to assemble these building blocks into a production-grade pipeline that transforms ad-hoc experiments into reproducible, auditable research and builds on the caching architecture developed in my previous article.

The code we'll explore isn't just another example, it's a major aspect of the system I use for developing trading models. It handles everything from raw tick data to ONNX models ready for deployment in MetaTrader 5, with comprehensive logging, caching, and analysis reports generated automatically along the way. For the sake of clarity, I will ignore some critical topics for now, such as feature importance analysis, and the selection of optimal barriers for the triple-barrier method, among others. This article assumes that these preceding research steps have already occurred, and as such focuses on the creation of reproducible pipelines. What makes a research system "production-grade"?

• Reproducibility: Run the same code twice, get identical results
• Traceability: Know exactly what data trained each model
• Efficiency: Cache expensive computations, never repeat work
• Validation: Catch errors before they reach live trading
• Documentation: Automatic reports that explain every decision

Author: Patrick Murimi Njoroge