Choosing Loss Functions for Machine Learning Models in Trading
Summary
The article surveys loss functions available in MQL5 and explains how their properties affect neural-network training. It contrasts magnitude-based measures such as mean squared error and mean absolute error with classification losses such as categorical and binary cross-entropy, and also discusses divergence and directional measures. The central point is to choose an objective that matches the model’s task and output, while distinguishing a training loss from a post-training regression metric.
It describes characteristics and limitations including MSE’s sensitivity to large errors, MAE’s less smooth gradients, cross-entropy’s use with probability outputs, and KL divergence’s asymmetry and sensitivity to zero probabilities. The examples use vector operations and later apply loss choices in a trading classifier workflow. The discussion is conceptual and implementation-focused; it does not establish that a particular loss improves trading returns, and the article notes that some built-in choices may not suit standard regression or classification setups.
Key ideas
- Loss functions quantify model error and should match the learning task and output representation.
- Mean squared error emphasizes large deviations, while mean absolute error is less sensitive to outliers.
- Cross-entropy losses are suited to classification outputs interpreted as probabilities.
- Kullback–Leibler divergence has asymmetry and zero-probability limitations.
- A loss used for training is distinct from a statistical metric used to assess a trained model.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.