Discussing the article: "Neural Networks in Trading: Training Deep Spiking Models (Conclusion)"

New comment

Check out the new article: Neural Networks in Trading: Training Deep Spiking Models (Conclusion).

The article demonstrates a practical implementation of the SEW ResNet framework using MQL5, with a focus on its applied use in trading. The Double Bottleneck makes it possible to simultaneously analyze univariate streams and inter-channel dependencies without losing gradients during training. Spiking activations with adaptive thresholds and gates improve robustness to noise and sensitivity to market novelty. The text provides details on the implementation and test results.

We have completed a major effort to integrate the key components of the SEW ResNeXt framework, and now it is time to evaluate the effectiveness of the solutions we have implemented. Before entrusting the model with real funds, it is necessary to thoroughly test the strategy using historical and simulated data, as if honing the skills of an experienced trader and verifying its ability to perform under a wide variety of market conditions. Each stage simulates a live market, allowing the model to gradually gain experience, learn to distinguish signals from noise, and develop robust behavioral algorithms necessary for operating in a real trading environment.

The first stage — offline training — is based on historical data for the EURUSD currency pair on the H1 timeframe for the period from January 2024 through June 2025. This period served as a real training ground. The model was trained to reproduce historical patterns, understand price dynamics and trade volumes, and identify regularities and relationships between key features. It could be said that the model developed a trader's intuition combined with precise strategic judgment. It gradually developed the ability to anticipate market movements and assess the risk of each potential trade.

The next stage — online fine-tuning in the MetaTrader 5 Strategy Tester — will allow the model to work with real-time data streams, candle by candle. Here, it masters the dynamics of the real market, learns to remain stable amid market noise, adjust its actions under low liquidity, and react instantly to sharp price spikes. This stage served as a sort of final refinement of the strategy. The base structure formed on historical data remained unchanged, but the model learned to respond flexibly to current market conditions, minimizing the risk of overfitting and improving forecast accuracy even in a volatile environment.

The final test was conducted using data from July–September 2025 — data that was entirely new and had not been used before. All parameters obtained in the previous stages were loaded without modification, which ensured a fair assessment of the model's generalization ability. Test results confirm that a step-by-step approach to training and adaptation enables the model to analyze the market and generate a forecast of the upcoming movement of key market features, which can be relied upon when making real trading decisions.


Author: Dmitriy Gizlyk

New comment