Subject: Request for Detailed Clarification on Data Utilization in MetaForecast

Dear Vahidreza,

I hope this message finds you well. I've been diligently trying to optimize the use of the MetaForecast indicator, particularly understanding how the training data is structured and utilized, especially in relation to the Past Size and Layers settings.

I have configured the indicator with a Past Size of 10,000 bars and a Layers setting of [1000, 2000, 100]. From our previous discussions, it is my understanding that MetaForecast uses Past Size plus Input Size amount of data, totaling 11,000 bars in this setup. Could you please clarify exactly how these bars are utilized for training the neural network?

To ensure clarity in our discussion, let's define each bar on the chart as follows:

• The current live bar (which has not yet closed) is labeled as shift = 0 .
• The most recently closed bar is shift = 1 .
• The bar before that is shift = 2 , and so on, increasing the shift count as we go further back in history.

Given this naming convention, could you specify which exact bars are used for training? For instance, does the training use data from shift = 1 to shift = 10,000 ?

Additionally, you mentioned that MetaForecast does not know the future of the last 1,000 bars. Could you specify which bars these are? Would they be from shift = 10,000 to shift = 11,000 ? How are these bars handled during the training process? Are they used as a form of out-of-sample testing to gauge the model’s predictive accuracy, or do they serve another purpose?

Understanding these details will greatly enhance my ability to use MetaForecast effectively and contribute a well-informed positive review. I am genuinely enthusiastic about the capabilities of your tool and eager to fully grasp its operational mechanics.

Thank you very much for your time and assistance. 😊👍

Best regards,

Lev