Discussing the article: "Neural Networks in Trading: From Transformers to Spiking Neurons (Key Components)"
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Hello. I would just like to mention that when I tried to compile Neuronet.mqh, I encountered errors regarding an incorrect number of parameters in the Math.mqh file. In a previous article, you replied that we needed to change `MathPow` to `::MathPow`; this is where I am confused. As a non-programmer, I am unsure whether I should change the `MathPow` functions in `Neuronet.mqh` or `Math.mqh`. I would be grateful if you could clarify this for me. The article "Neural Networks in Trading: From Transformers to Spiking Neurons (Basic Components)" has been published:
Author: Dmitry Gizlyk
Kind regards.
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Check out the new article: Neural Networks in Trading: From Transformers to Spiking Neurons (Key Components).
Here, it is worth taking a closer look at an important element of the architecture — the mechanism for encoding signals into spikes. For financial time series, this task is particularly challenging: it is not enough simply to record price or volume values; they must also be transformed into a format in which each spike reflects a significant change in the data. The authors propose several encoding strategies. From a simple threshold-based approach, where a spike occurs when a critical value is exceeded, to more complex schemes that take into account the rate of change and the context of previous events. This wide variety of methods makes the model versatile. It can perform equally well with high-frequency data, where every millisecond counts, and with quieter daily charts, where the overall trend is what matters most.
The next key block is the integrators. They are responsible for accumulating and summing incoming impulses, determining the moment when a neuron becomes active. From the perspective of financial markets, this is similar to a noise-filtering mechanism: many small fluctuations can be ignored, but their cumulative effect, once it reaches a certain threshold, triggers a response from the model. This principle allows the model to maintain a balance between sensitivity and stability, which is particularly valuable in the face of market noise.
The framework’s authors proposed a model in which the hybrid organization of layers plays a central role. Some of them specialize in identifying short-term patterns, capturing rapid market fluctuations. Others focus on integrating signals over a longer time horizon, which makes it possible to generate more stable forecasts. This combination gives the system a kind of dual vision. It is capable of both capturing the finest details of the present moment and maintaining a strategic perspective. For financial markets, this is akin to being able to see both the movement of a stopwatch’s second hand and the overall rhythm of the pendulum that determines the passage of time.
Author: Dmitriy Gizlyk