Building a Model-Based Trading Robot with XGBoost and Drawdown Controls

Summary

This installment describes connecting a trained classifier to MetaTrader 5 for live order execution. Predictions above or below a threshold trigger long or short trades with preset stop-loss and take-profit levels, while a cap on open positions limits new entries. The article also outlines a pipeline that retrieves, augments, labels, and engineers features before fitting an XGBoost classifier.

Its risk-management proposal reduces trade volume after daily account drawdowns and restores the original volume after the balance exceeds its prior peak. The article reports roughly 60% test accuracy, but provides no detailed live-trading results or validation of the proposed drawdown logic. Its sample implementation has operational limitations, and the promised quantum learning, reinforcement learning, and swarm-based stop selection are future ideas rather than demonstrated methods.

Key ideas

• A classifier's directional signal is used to open long or short trades through MetaTrader 5.
• Each order receives preset stop-loss and take-profit levels, and new entries stop when the open-position cap is reached.
• The proposed risk control reduces trade size during drawdowns and restores it after the account balance recovers above its previous peak.
• The article reports about 60% test accuracy but gives limited evidence about live performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.