Golden Gauss AI: A Predictive Machine Learning System for Probability-Based Trade Execution in Gold Markets
Description
The proliferation of "AI-powered" trading systems in retail markets has created a credibility crisis, with most systems exhibiting catastrophic failure when deployed on unseen data due to severe overfitting. This paper presents Golden Gauss AI, an Expert Advisor designed for the XAUUSD (Gold) market that employs instantaneous probability inference to make autonomous trade execution and management decisions.
Unlike reactive indicator-based systems, Golden Gauss AI processes 239 engineered features through a Gradient Boosting architecture to calculate real-time probability scores, using these probabilities to determine entry timing, position direction, and exit conditions. A novel "predictive labeling" methodology trains the model to recognize setup conditions preceding profitable moves, addressing the fundamental latency problem of traditional technical analysis.
The Expert Advisor incorporates sophisticated trade management logic that minimizes losses even when model predictions are suboptimal, providing robustness across varying market conditions. Deployed via ONNX for real-time inference within MetaTrader 5, the system maintains strict feature calculation parity between the Python training environment and the MQL5 execution engine.
The model was frozen on December 31, 2024, and subsequently validated through a rigorous 13-month historical out-of-sample walk-forward test on previously unseen data from January 2025 through January 2026. This out-of-sample forward test demonstrates approximately 83.67% directional accuracy at an 88% probability threshold, with controlled performance degradation from the training period indicating genuine pattern generalization rather than curve-fitting.
Availability: https://www.mql5.com/en/market/product/164091
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Golden_Gauss_AI_Paper.pdf
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Additional details
Related works
- Is supplement to
- Software: https://www.mql5.com/en/market/product/164091 (URL)
Dates
- Created
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2026-02-15
References
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