Discussing the article: "Neural Networks in Trading: Generalizing Time Series Without Data-Specific Dependence (Conclusion)"
Результаты тестирования представлены ниже.
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Check out the new article: Neural Networks in Trading: Generalizing Time Series Without Data-Specific Dependence (Conclusion).
We used one-minute EURUSD quotes for the entire year of 2024 as the training set. To keep the experiment clean, final testing was conducted on historical data from January through March 2025 — a period that was not used in training. All other parameters remained unchanged to ensure that the evaluation of the strategy was objective and fair.
The test results are presented below.
It must be admitted that we are seeing a fairly high frequency of trades here. The average position holding time is just over 3 minutes. Overall, during the testing period, the model executed 2,677 trades, 1,240 of which were closed at a profit. Although the number of losing trades was slightly higher, the model managed to generate a profit over the testing period, and we can see fairly steady growth in the balance line. This can be partly explained by opening positions with a fairly tight stop-loss and then managing them. This assumption is supported by the small gap between the average losing trade and the largest losing trade. At the same time, the largest winning trade is nearly seven times greater than the average profit per trade.
Author: Dmitriy Gizlyk