Why I Chose Classic Patterns Over Pure ML for Trading
Machine learning is powerful, but in low-signal environments, interpretability and engineering safety factors matter more than a black-box peak score.
Machine learning is powerful, but markets are adversarial and non-stationary. A model that fits last quarter can fail the moment the regime shifts.
Civil and water-resources engineering taught me to design for failure modes, not just average-case performance. Safety factors, redundancy, and inspectable load paths matter.
Classic patterns are imperfect, but interpretable. When a signal fires, I can answer which gate opened, which closed, and what would falsify the trade.
That is why QuantRadar uses a top-down stack — Market → Sector → Stock — before pattern recognition runs.
ML still has a place: feature screening, regime clustering, post-trade review. It is a tool inside constraints, not a replacement for judgment.