Handling Class Imbalance in Financial Market Models
Summary
The article examines how uneven bullish and bearish labels can bias financial classification models toward the more common class. It illustrates the issue with daily candle direction counts across several instruments and a USDJPY Random Forest example. Although the model’s aggregate accuracy appears reasonable, class-level precision, recall, and F1 scores reveal that it misses many examples from one class. The article argues that accuracy alone can conceal this weakness and recommends metrics that account for minority-class detection.
It discusses oversampling, undersampling, and hybrid approaches, and describes exporting trained models for use in a trading-platform expert advisor. The author compares resampling methods through model reports and strategy-tester runs, but the supplied text omits much of those results, so it does not establish that any technique consistently improves trading performance. Resampling can overfit repeated minority examples, discard useful observations, or add noise. Class balance and classification scores also do not by themselves demonstrate profitability or generalization across changing market regimes.
Key ideas
- Imbalanced directional labels can lead classifiers to favor the majority class and overlook minority cases.
- Aggregate accuracy can hide poor class-specific recall and precision.
- Precision, recall, and F1 provide more informative views of performance on imbalanced targets.
- Oversampling, undersampling, and hybrid resampling methods involve different risks to model quality.
- Trading performance requires separate evaluation beyond classification metrics.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.