This Expert Advisor monitors the Stochastic oscillator to automatically close positions based on user-selected conditions: crossovers above overbought or below oversold, entry into overbought/oversold zones, or exit from those zones. Positions are filtered for closure by profit state (loss, profit
This topic is created in order to have a guide and discussion about multi strategy EAs. I have seen this topic appeared in some topics but the discussion tends to die after some comments. So the intention is to have the topic in here and don't let it die
I've thinking the past days about this mix in an EA. I think that finding an strategy that workds and then adding filters is a very strong combo. What you guys think about this
When facing the strategy tester , I think the most fundamental thing is maximizing the Sharpe ratio and minimizing drawdown. Since I don't want to get into an echo chamber, I'd like to ask you guys: what do you think about this
The strategy that I am currently developing performs very well in the strategy tester , but when I do forward testing it fails. How can I shorteh the gap between strategy tester data and forward testing
1-2 months ago my strategy tester didn't take to much to start simulations, but now, with the same strategies, and takes a lot of time, in the past was almost instantly, but now takes 5-10 minutes
The option popped, but I closed since I didn't want to update it at the moment, but now that I want to I don't find how to do it
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For me, is the moon strategy: it uses lunar phases (especially new moons and full moons) to predict market movements and make buy/sell decisions
This isn't my first MQL5 account, I been in the forum for several months now, posting 1-2 times a week and reading, as well as coding and testing ideas frequently. I have coded multiple ideas and posted them into de codebase (in the other account) as well as 1 article (very enriching, tbh, and one
Forward testing is very common when you want to train and test your strategy by dividing your data into two parts: training on one set and testing on the other. Cross-validation, on the other hand, divides the data into equal parts (folds) and trains/tests the model across multiple iterations. Let's