Discussing the article: "Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Conclusion)"

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Check out the new article: Neural Networks in Trading: Robust Trading Signals in Any Market Regime (Conclusion).

The article provides a detailed examination of the integration of the ST-Expert framework's approaches into the Extralonger architecture, which enables the simultaneous analysis of temporal and spatial representations of data. The results of testing on real historical data are presented, demonstrating the model's effectiveness and its robustness to market anomalies. The article describes the framework's modular structure, which ensures reproducibility, flexibility for research, and the ability to optimize components in stages.

The first stage — offline training — was conducted using historical data for the EURUSD currency pair on the H1 timeframe for the period from January 2024 to June 2025. This period turned out to be a real training ground for the model. The diverse environment allowed the model to hone its ability to recognize key signals, develop robust trading decisions, and maintain its bearings even in the most chaotic situations. At this stage, the model was learning not merely to repeat historical patterns, but to understand market regularities, adapting to price dynamics and trading volume. Just like an experienced trader who analyzes the market using both intuition and strategy at the same time.

After successfully completing the offline training, we moved on to the second stage — fine online fine-tuning in the MetaTrader 5 Strategy Tester. Here, data came in real time, one candlestick at a time, and the model learned to handle the market flow at real-world speeds. It learned to navigate the dynamics of actual price movements, remain stable amid market noise, adjust its actions during periods of low liquidity, and react instantly to sharp spikes. This stage served as a sort of refinement of the strategy. The underlying structure, based on historical data, remained unchanged, but the model learned to adapt to current market conditions, minimizing the risk of overfitting and improving its ability to make decisions 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, which allowed for an unbiased assessment of the model's generalization ability. The test results are presented below.


Author: Dmitriy Gizlyk

 

Can anyone advise on how to deal with this –

'Math' is not a class, struct or union VAE.mqh 93 8

'MathRandomNormal' – some operator expected VAE.mqh 93 14

Parameter conversion from type 'int[1]' to 'const uint[] &' is not permitted VAE.mqh 135 55

Incorrect number of parameters: 4 passed, but 5 are required VAE.mqh 135 15

could be one of 2 function(s) VAE.mqh 135 15

bool COpenCL::Execute(const int, const int, const uint&[], const uint&[]) OpenCL.mqh 82 22

bool COpenCL::Execute(const int, const int, const uint&[], const uint&[], const uint&[]) OpenCL.mqh 83 22

???


 
Вадим #:

Can anyone tell me how to deal with this –

'Math' is not a class, struct or union VAE.mqh 93 8

'MathRandomNormal' – some operator expected VAE.mqh 93 14

Parameter conversion from type 'int[1]' to 'const uint[] &' is not permitted VAE.mqh 135 55

Incorrect number of parameters: 4 were passed, but 5 are required VAE.mqh 135 15

could be one of 2 function(s) VAE.mqh 135 15

bool COpenCL::Execute(const int, const int, const uint&[], const uint&[]) OpenCL.mqh 82 22

bool COpenCL::Execute(const int, const int, const uint&[], const uint&[], const uint&[]) OpenCL.mqh 83 22

???


Are you using all the files from the library accompanying this article, or VAE.mqh from previous versions?
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