Discussing the article: "Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence"

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All algorithmic trading strategies are difficult to set up and maintain, regardless of complexity—a challenge shared by beginners and experts alike. This article introduces an ensemble framework where supervised models and human intuition work together to overcome their shared limitations. By aligning a moving average channel strategy with a Ridge Regression model on the same indicators, we achieve centralized control, faster self-correction, and profitability from otherwise unprofitable systems.

All algorithmic trading strategies are difficult to set up and maintain, regardless of their complexity. This universal problem is shared by beginners and experts alike. Beginners struggle to keep tuning the periods of their moving average crossover strategies, while experts are just as restless adjusting the weights of their deep neural networks. There are material problems on either side of the fence.

Machine learning models are fragile and often fall apart in live trading environments. Their opaque and complex designs make them even harder to troubleshoot and diagnose for performance bottlenecks. On the otherhand, human strategies can be more resilient but often require manual configuration to get started—an intensive process depending on the approach. This article proposes an ensemble framework in which supervised models and human intuition build on each other to overcome their collective limitations in an accelerated way.

To attain this end, we designed our strategy and statistical model to share the same four technical indicators. We selected a moving average channel strategy and fit a Ridge Regression model on those same indicators. Doing this, allowed us to quickly identify a profitable configuration for the entire system.

Author: Gamuchirai Zororo Ndawana