The article continues the topic of ready-made templates for using indicators in EAs. We have already considered the templates for connecting oscillators and volume and Bill Williams' indicators to EAs. Here we will look at connecting to EAs and using trend indicators. As in the previous articles, we will display the data received from indicators on the dashboard created in the first article of this series. The article will not differ in any way from ...
We discussed in the previous article how to use socket (websocket) to communicate between EA and python server to solve the backtesting problem, and also discussed why we adopted this technique. In this article, we will discuss how to use onnx, which is natively supported by mql5, to perform inference with our model, but this method has some limitations. If your model uses operators that are not supported by onnx, it may end in failure, so this method is not suitable for all models (of course, you ...
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...
We continue our look at category theory with one more take on functors. So far, we have seen applications of category theory in implementing custom instances of the Expert trailing class, and the Expert Signal class so we will consider applications in using the Expert Money class for this article. All these classes come with the Meta Editor IDE and are used with the MQL5 wizard in assembling expert advisors with minimal coding. In this article as we sum up our look at functors ...
Financial markets generate data with a huge amount of complex relationships. To analyze them, we need to use the most modern methods of applied mathematics. Successfully combining the high complexity of financial data with the simplicity and efficiency of analysis is a challenging task. ALGLIB is a high-performance library designed specifically for working with numerical methods and data analysis algorithms. It is a reliable assistant in the analysis of financial markets. more...
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