Discussing the article: "Data label for time series mining (Part 6)：Apply and Test in EA Using ONNX"
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Check out the new article: Data label for time series mining (Part 6)：Apply and Test in EA Using ONNX.
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
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 can also add operators to support your model, but it requires a lot of time and effort). This is why I spent a lot of space in the previous article to introduce the socket method and recommend it to you.
Of course, converting a general model to onnx format is very convenient, and it provides us with effective support for cross-platform operations. This article mainly involves some basic operations of operating ONNX models in mql5, including how to match the input and output of torch models and ONNX models, and how to convert suitable data formats for ONNX models. Of course, it also includes EA order management. I will explain it in detail for you. Now let’s start the main topic of this article!
Author: Yuqiang Pan