In the previous article, we used two ONNX models to arrange the voting classifier. The entire source text was organized as a single MQ5 file. The entire code was divided into functions. But what if we try to swap models? Or add another model? The original text will become even bigger. Let's try the object-oriented approach. more...
When we design artificial intelligence models, we often need to prepare data first. Good data quality will allow us to get twice the result with half the effort in model training and validation. But our foreign exchange or stock data is special, which contains complex market information and time information, and data labeling is difficult, but we can easily analyze the trend in historical data on the chart. This section introduces a method of making data sets with trend marks by EA ...
Without any prior programming experience, learning MQL5 can be difficult but not impossible. Understanding MQL5, a specialized language created for algorithmic trading, necessitates having both programming and financial market expertise. In this article, we will cover the following topics: Introduction to ProgrammingTypes of MQL5 ProgramsMetaEditor IDEMQL5 Language Basics more...
In the previous article, we discussed relational models which use attention mechanisms in their architecture. We used this model to create an Expert Advisor, and the resulting EA showed good results. However, we noticed that the model's learning rate was lower compared to our earlier experiments. This is due to the fact that the transformer block used in the model is a rather complex architectural solution performing a large number of operations. The number of these operations grows in a quadratic ...
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