Discussing the article: "Neural Networks in Trading: From Transformers to Spiking Neurons (Conclusion)"
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Check out the new article: Neural Networks in Trading: From Transformers to Spiking Neurons (Conclusion).
The first stage — offline training — was conducted using historical data for the EURUSD currency pair on the H1 timeframe, covering the period from January 2024 to June 2025. This period served as a real training ground. The model was trained to reproduce historical patterns, understand market dynamics, identify patterns in price movement and trading volume, and adapt to market volatility. It could be said that it developed a trader's intuition combined with precise strategic judgment.
The next stage — online tuning in the MetaTrader 5 Strategy Tester — allowed the model to process real-time data streams, candle by candle. Here, it mastered the dynamics of the real market, learned to remain stable amid market noise, adjust its actions under low-liquidity conditions, and react instantly to sharp price spikes. This stage became the strategy fine-tuning phase: the basic structure, formed using historical data, remained unchanged, but the model learned to adapt flexibly to current market conditions, minimizing the risk of overfitting and improving forecast accuracy in an unpredictable environment.
The final test was conducted using data from July–August 2025 — data that was entirely new and had not been used before. All parameters obtained in the previous stages were loaded without modification, ensuring an unbiased assessment of the model’s ability to generalize and of its robustness to new market conditions. The test results are presented below, demonstrating the effectiveness of a step-by-step approach to training the model and adapting it to real-world financial market conditions.
Author: Dmitriy Gizlyk