Discussing the article: "Neural Networks in Trading: Actor—Director—Critic (Final Part)"

MetaQuotes 2026.06.16 13:18

Check out the new article: Neural Networks in Trading: Actor—Director—Critic (Final Part).

The Actor–Director–Critic framework is an evolution of the classic agent learning architecture. The article presents practical experience of its implementation and adaptation to financial market conditions.

The training dataset was generated using random agent runs in the MetaTrader 5 Strategy Tester, allowing us to collect a broad spectrum of behavioral scenarios. The dataset includes historical EURUSD M1 data for the entire year of 2024.

Initial model training was performed offline without updating the training dataset until prediction errors stabilized. We then switched to the MetaTrader 5 Strategy Tester and continued fine-tuning the model parameters until stable performance was achieved.

An objective assessment of the learned trading policy can only be obtained by evaluating the trained models on data outside the training sample. To test the performance, we used historical data from January through March 2025. Since this period was not used during training, the risk of overfitting is eliminated, giving the results genuine practical significance.

All other parameters, including the market environment, timeframe, execution simulation model, and terminal settings, were left unchanged. This ensured a clean evaluation of the learned strategy itself, without interference from external factors.

The testing results are presented below and provide a clear illustration of the agent's behavioral model.

Testing results