Backtesting betting strategies with AI means validating a model's predictions against historical data it was never trained on — out-of-time (OOT) testing — to produce a calibrated confidence score rather than an inflated, overfit accuracy figure. Backtesting on data the model has already seen during training produces artificially high accuracy that collapses in live production. This guide covers the technical requirements for a valid backtest: split-sample design, look-ahead bias prevention, and how to interpret a calibration curve.
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