Trained on the Past, Blind to the Present: Why AI Trading Models Collapse When Emerging Market Central Banks Move
Machine learning models built on historical price data carry a structural flaw that most US traders never confront until it is too late: they cannot anticipate regime-breaking central bank interventions in emerging markets. When policy authorities in Istanbul, Nairobi, or Buenos Aires move without warning, algorithmic strategies trained on years of calm data can unravel in minutes. This analysis examines why the backtesting process itself manufactures false confidence and what practitioners can