Discussing the article: "Seasonality Filtering and time period for Deep Learning ONNX models with python for EA"
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Check out the new article: Seasonality Filtering and time period for Deep Learning ONNX models with python for EA.
Can we benefit from seasonality when creating models for Deep Learning with python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.
When I read the article: Benefiting from Forex market seasonality, I thought of this to make this what I think is an interesting article. I could start comparing an EA with and without seasonality's and with to see if it can benefit.
I all ready new that markets depend on seasonality since I read that the Mark Zuckerberg got the money for Facebook, from an investor that got his money investing the one that they gave him in the Bar Mitzvah in stocks of petrol when the climate in the Caribbean there were going to have hurricanes. He first studied the climate and forecasted that there would be a bad weather during a period of time.
I'm very proud and interested in writing this article, since if the results are not better, even though I know that market and seasonality are good companions. I good approach to making this come true, would be to merge both EA's in one, but we already have an article about that, here's the link: An example on how to ensemble ONNX models in mql5.
First of all we will compare models with and without filtering with using an EA, to see how filtering data affects or not, and, after this, we will discuss seasoning with a graph, to end up with a real case of study, for February 2024, with and without seasoning. In the last part of the article (which I find very interesting), we will discuss other approaches to the EA we already have from the article: How to use ONNX models in MQL5 , and we will see if we can benffit of finetuning those EA's and ONNX models, and yes, the answer is that yes we can.)
Author: Javier Santiago Gaston De Iriarte Cabrera