Discussing the article: "Neural Networks in Trading: Adaptive Periodic Segmentation (Conclusion)"

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Check out the new article: Neural Networks in Trading: Adaptive Periodic Segmentation (Conclusion).

We invite you to dive into the exciting world of LightGTS — a lightweight yet powerful framework for time-series forecasting, where adaptive convolution and RoPE encoding are combined with innovative attention mechanisms. In our article, you will find a detailed description of all components — from creating patches to the complex mixture of experts in the decoder — ready for integration into MQL5 projects. Discover how LightGTS takes automated trading to a whole new level!

We then moved on to the second phase — online fine-tuning of the model using historical data from 2024. Here, the training was conducted under conditions that closely resembled real-time trading: the model interacted with the market on a candle-by-candle basis, encountering market noise, random fluctuations, and temporal distortions. This made it possible not only to fine-tune the model, but also to adapt its behavior to the real-time dynamics of the market, adjust the strategy, and improve its resilience in the face of uncertainty.

After training was complete, we conducted full-scale testing on new data — quotes for January–March 2025. All settings were fixed in advance and remained unchanged throughout the test. This ensured the objectivity and transparency of the evaluation, ruling out any form of curve-fitting or intervention. The test results are shown below.


Author: Dmitriy Gizlyk

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