Scripts: Neural Loss-Pattern Auditor
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Neural Loss-Pattern Auditor:
Neural Loss-Pattern Auditor trains a small feed-forward neural network, written from scratch in native MQL5, on closed-deal history to test whether behavioral and market-context features predict which trades are more likely to lose. It reports an accuracy uplift over a naive baseline, a probability-calibration table, a permutation feature-importance ranking, and a configurable A-F composite grade with recommendations. On first run it uses a built-in synthetic demo, so the output is visible immediately with no setup; switch one input to InpUseDemoData=false to analyze real account history instead. Pure MQL5: no external libraries, no Python, and no AI service of any kind.
Author: Cristian David Castillo Arrieta