Discussing the article: "Build Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis"
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Check out the new article: Build Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis.
How best can we combine multiple strategies to create a powerful ensemble strategy? Join us in this discussion as we look to fit together three different strategies into our trading application. Traders often employ specialized strategies for opening and closing positions, and we want to know if our machines can perform this task better. For our opening discussion, we will get familiar with the faculties of the strategy tester and the principles of OOP we will need for this task.
There are many challenges we face as algorithmic traders, which we have already discussed in this series. For example, we have noticed that our statistical models find it easier to predict future technical indicator readings than to predict future price levels.
We also looked at the benefits of a trading system that models the relationship between the strategy it follows and the market where it applies that strategy.
Our models consistently performed better when we replaced the classical task of direct price prediction with these alternative tasks. Direct price prediction is difficult, but by changing how we frame the problem, we can outperform models stuck on the classical task while still using the same statistical tools.
Today, we will explore a new potential strategy that builds on our previous findings. What if we create an application that knows three different trading strategies? Can this application learn to choose one strategy at a time, periodically switching to the most profitable one instead of following all three simultaneously? If the application can change strategies periodically, can it profitably select the best one from the three it knows?
Such an application could be more useful than a fixed trading algorithm that follows all three strategies or a combination of them.
Author: Gamuchirai Zororo Ndawana