Discussing the article: "Neural Networks in Trading: Adaptive Detection of Market Anomalies (Final Part)"

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Check out the new article: Neural Networks in Trading: Adaptive Detection of Market Anomalies (Final Part).

We continue to build the algorithms that form the basis of the DADA framework, which is an advanced tool for detecting anomalies in time series. This approach enables effective distinguishing random fluctuations from significant deviations. Unlike classical methods, DADA dynamically adapts to different data types, choosing the optimal compression level in each specific case.

For training, we generated a dataset of random runs using the MetaTrader 5 strategy tester on historical EURUSD data (M1 timeframe) for 2024. The data was collected using standard indicator settings to ensure a clean experiment and eliminate external influences.

The trained models were then tested on historical data from January–February 2025. All experimental parameters were kept unchanged to ensure an objective evaluation of the Actor's learned behavior. Testing on data not used during training is a crucial validation step, as it reflects how the model performs under near-real conditions.

The testing results are presented below.


Author: Dmitriy Gizlyk

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