Discussing the article: "Automated Parameter Optimization for Trading Strategies Using Python and MQL5"
Noted
Thankyou!
beautiful article, it is exactly what I was looking for to optimise parameters automatically while ea does its work.
However I downloaded the code and reproduced the same parameters but no operations are done in the test, it doesn't matter anyway, thank you very much for this information, I will soon implement the auto optimisation starting with the periods!
Emanuele Mastronardi #:
beautiful article, it is exactly what I was looking for to optimise parameters automatically while ea does its work.
beautiful article, it is exactly what I was looking for to optimise parameters automatically while ea does its work.
However I downloaded the code and reproduced the same parameters but no operations are done in the test, it doesn't matter anyway, thank you very much for this information, I will soon implement the auto optimisation starting with the periods!
Hi, thanks! Have you checked the number of days to reoptimize? (Settings->Reoptimize every 22 days-> use 16 (days) for the same example, sorry I have not explained well that setting option)
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Check out the new article: Automated Parameter Optimization for Trading Strategies Using Python and MQL5.
There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.
Imagine you have a trading bot developed with much effort. You're excited to see it in action but start without proper optimization. Initial positive results may mislead you into thinking everything is fine, but soon inconsistencies and losses appear.
An unoptimized bot lacks consistency and may respond to irrelevant data, leading to unpredictable profits and losses. It might make decisions based on false signals, not adapting to market changes, and assume unanticipated risks, causing significant losses. Optimization ensures better performance and reliability.
Readers will understand the importance of auto-optimization, different algorithms used, and see practical examples in Python and Expert Advisor (EA) scripts. They’ll learn how to set up auto-optimization, compare results, and properly configure parameter optimization, enhancing their trading strategy efficiency.
Self-optimization algorithms for trading strategies include parameter optimization, evolutionary algorithms, heuristic methods, gradient-based techniques, machine learning, and simulation-based optimization. Each has unique pros and cons, tailored for different trading needs and market conditions.
Author: Javier Santiago Gaston De Iriarte Cabrera