Discussing the article: "Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes"
Thank you for you clear and well written article, It is exactly what I was trying to understand and was working away to check correlations myself . Thanks also for the python file as it makes an easy template for me to adapt . Hopefully after some analysis I will say thanks for opening my eyes to what is possible
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Check out the new article: Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes.
In this article, we dive deep into the crucial aspects of choosing the most relevant and high-quality Forex data to enhance the performance of AI models.
With all the trading data and information such as indicators (there are more than 36 built-in indicators in MetaTrader 5), symbol pairs (there are more than 100 symbols) that can also be used as data for correlation strategies, there are also news which are valuable data for traders, etc. The point I'm trying to raise is that there is abundant information for traders to use in manual trading or when trying to build Artificial Intelligence models to help us make smart trading decisions in our trading robots.
Out of all the information we have at hand, there has to be some bad information (that is just common sense). Not all indicators, data, strategy, etc. are useful for a particular trading symbol, strategy, or situation. How do we determine the right information for trading and machine learning models for maximum efficiency and profitability? This is where feature selection comes into play.
Author: Omega J Msigwa