Multiple dynamic logistic regression first unsuccessful attempt
This tool for MetaTrader 5 is specifically engineered to streamline your trading operations. This library provides a collection of modular, reusable code. It is utilized by developers to organize common functions, allowing for the integration of complex logic across multiple Expert Advisors, indicators, or scripts without the need for code duplication.
How to Setup and Use Multiple dynamic logistic regression first unsuccessful attempt
1. Storage: Place library files in the MQL/Libraries directory to ensure they are accessible to your projects.
2. Implementation: Include the library in your code using the #import directive, ensuring you match the exact function names and parameters.
3. Compilation: Ensure the library is present in the directory before you compile your main EA or script, as the compiler links them during this phase.
4. Management: Keep libraries organized in sub-folders if you manage many custom functions to maintain a clean project structure.
Frequently Asked Questions
Q: What is a library file used for? A: Libraries store reusable code modules, allowing you to centralize common logic used by multiple EAs or indicators.
Q: Is a library executable? A: No, libraries are non-executable files containing functions; they must be imported into an EA, indicator, or script to function.
Q: Can I update a library while the platform is running? A: You should compile your EA or script after updating a library to ensure the latest code changes are integrated.
Description & Settings
For anyone who does not understand the code provided in this library read the article linked here
Multiple Dynamic Logistic Regression Challenge
The biggest challenge I faced when building both and logistic regression libraries in both articles is multiple dynamic regression functions where we could use them for multiple data columns without having to hard-code things up for every data that gets added to our model, in the previous Article I hardcoded two functions with the same name the only difference between them was the number of data each model could work with, one was able to work with two independent variables the other with four respectively
but, this method is inconvenient and it feels like a premature way of coding things up and it violates the rules of clean code and DRY (don't repeat yourself principles that OOP is trying to help us achieve)
Unlike python with flexible functions that could take a large number of functional arguments with the help of *args and **kwargs, In MQL5 this could be achieved using string only as far as I can think, I believe this is our starting point
the input x_columns represents all the independent variables columns that we will use in our library, these columns will require us to have multiple independent arrays for each of the columns but, there is no way we can create arrays dynamically, so the use of arrays fall flat here
We can create multiple CSV files dynamically and use them as arrays, for sure but this will make our programs more expensive when it comes to the use of computer resources compared to the use of arrays, especially when dealing with multiple data not to mention the while loops that we will frequently use to open the files will slow the whole process down, I'm not 100% sure so correct me if I'm wrong
thought we can still use the mentioned way,
I have discovered the way forward to use arrays we are going to store all the data from all the columns in one array then use the data separately from one that single Array
Inside the for loop, we can manipulate the data in the arrays and perform all the calculations for the model the way want for all the columns, I have tried this method but I'm still on the unsuccessful attempt, the reason I explained this hypothesis is to let everyone reading this article understand this challenge and I welcome all your opinions in the comment sections on how we can code this multiple dynamic logistic regression function
This is Just a hypothesis, it is aimed to inspire you into the process of coding the multiple dynamic regression Algorithm, as discussed in Data Science and Machine Learning Article series linked above
