Designing Reusable Data Preprocessing Pipelines in MQL5
Summary
This article proposes a modular MQL5 framework for preparing financial data before machine-learning models consume it. Its pipeline design chains separate preprocessing classes for median or mode imputation, standard scaling, robust scaling, min–max scaling, and one-hot encoding. Each step follows a fit-and-transform pattern: parameters are learned from training data, then reused to transform later datasets. The article argues that this structure can improve reuse, maintainability, and consistency across training, validation, testing, and deployment.
It explains why preprocessing matters, including differing feature scales, outliers, missing values, and categorical market sessions. Examples connect scaler choice to feature distributions and model activations. The source provides MQL5 code for the pipeline container and conceptual guidance, but the supplied text omits much of the individual transformer implementations and reports no empirical comparison or model results. It also stresses storing fitted parameters with the trained model so the same transformations can be reproduced in backtests and live use.
Key ideas
- A preprocessing pipeline chains reusable transformations before data reaches a model.
- Each transformer should learn parameters on training data and reuse them when transforming later data.
- Standard, robust, and min–max scaling address different feature distributions and modeling needs.
- Categorical market features need suitable encoding rather than arbitrary integer labels.
- Saving fitted transformation parameters supports consistent backtests, live trading, and retraining.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.