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Check out the new article: Neural Networks in Trading: An Intelligent Forecast Pipeline (Conclusion).
The model training process was divided into two stages. This approach made it possible to build the system in a consistent, reliable, and unhurried way.
First comes offline training. We used 15 years of history for the EURUSD pair on the M1 timeframe. This provided the model with a large volume of diverse market situations. The Encoder learned to recognize regularities, identify significant patterns, and encode the market state into a compact, information-rich feature vector. This vector serves as the foundation for all decisions made by the agent. During training, the Actor learns a behavioral strategy by receiving signals from the Critic and the Director.
Then comes online fine-tuning. It is run in the MetaTrader 5 Strategy Tester. Here, the model interacts with historical data in a realistic mode: candlestick by candlestick, with market noise, random fluctuations, and instability. This helps adapt the agent's behavior to live market dynamics and adjust its strategy under near-real conditions.
After training, the model was tested on new data — quotes for January 2025. All settings were fixed in advance and remained unchanged. This ensures that the evaluation is objective and transparent. The test results are shown below.
Author: Dmitriy Gizlyk