Discussing the article: "Neural Networks in Trading: Hybrid Graph Sequence Models (Final Part)"

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Check out the new article: Neural Networks in Trading: Hybrid Graph Sequence Models (Final Part).

We continue exploring hybrid graph sequence models (GSM++), which integrate the advantages of different architectures, providing high analysis accuracy and efficient distribution of computing resources. These models effectively identify hidden patterns, reducing the impact of market noise and improving forecasting quality.

For a fair comparison, both models were trained on the same dataset previously used for Hidformer training. Recall that:

  • The training set consists of historical EURUSD M1 data for the entire 2024 calendar year.
  • All analyzed indicator parameters remain at default values, without additional optimization, eliminating external factor influence.
  • Testing of the trained model was conducted on January 2025 historical data, keeping all other parameters unchanged to ensure objective comparison.

The testing results are presented below.

During the test period, the model executed 15 trades, which is relatively low for high-frequency trading on the M1 timeframe. This figure is even below that achieved by the baseline Hidformer model. Only 7 trades were profitable, representing 46.67%, This is also lower than the baseline 62.07%. Here we see reduced accuracy of short positions. However, there was a slight decrease in loss size alongside a relative increase in profitable trade sizes.

If the baseline model’s ratio of average profitable to losing trades was 1.6, in the new model this ratio exceeds 4. This nearly doubled overall profit for the test period, with a corresponding increase in the profit factor. This suggests that the new architecture prioritizes loss minimization and profit maximization for successful trades. This may lead to more stable financial results over the long term. However, the short test period and small number of trades prevent conclusions about long-term model performance.


Author: Dmitriy Gizlyk

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