Discussing the article: "Neural Networks in Trading: Decomposition Instead of Scaling (Conclusion)"

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Check out the new article: Neural Networks in Trading: Decomposition Instead of Scaling (Conclusion).

We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data.

Finally, we tested the model on entirely new data — quotes for the period from January to March 2025. All parameters and settings remained unchanged. The results obtained provide an objective picture of the accuracy and practical reliability of the proposed approach. The test results are presented below.

 

The test results show moderately positive dynamics. Starting with a USD 100 deposit, the EA managed to earn USD 62.25, showing a small positive gap between total profit and total loss. At the same time, the profit factor is barely above 1, which indicates that the strategy is not very effective.

During the testing period, 898 trades were executed, with buy and sell trades split almost evenly. In both cases, the win rate remained around 50%, with a slight bias toward short positions. The average profitable trade yielded USD 1.97, while the average losing trade lost USD 1.82. The maximum profit reached USD 13.56, while the maximum loss was USD 12.42.

Overall, the EA demonstrates stable operation with accurate trade execution, but the strategy's profitability and robustness remain in question.

Author: Dmitriy Gizlyk