Discussing the article: "Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR"
Awesome , another excellent walk through . Thank you for the workbook , we now have a template to test our own correlations. Much appreciated
linfo2 #:
Awesome , another excellent walk through . Thank you for the workbook , we now have a template to test our own correlations. Much appreciated
The pleasure is mine Neil, I remember you once told me that you had an idea that involves searching for correlation between the indicators, feel free to share how your findings in that project may help us here, and hopefully we can cook 💯🔥
Awesome , another excellent walk through . Thank you for the workbook , we now have a template to test our own correlations. Much appreciated
In the script you aren't closing the file opened to write the date, both in the article and in fetchData.mq5
Carl Schreiber #:
In the script you aren't closing the file opened to write the date, both in the article and in fetchData.mq5
Thank you for highlighting that, I'll correct myself in future
In the script you aren't closing the file opened to write the date, both in the article and in fetchData.mq5

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Check out the new article: Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR.
In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.
For us to assess the relationship, we exported all our market data from our MetaTrader 5 terminal using a script written in MQL5. We trained various models using 2 groups of possible inputs for the models:
From the data collected, it appears that oil has stronger correlation levels with the UDZAR currency pair than gold.
Since our data were on different scales, we standardized and normalized the data before training. We performed 10-fold cross validation without random shuffling to compare our accuracy across the different sets of inputs.
Author: Gamuchirai Zororo Ndawana