Beyond Accuracy: A Validation Framework for Machine Learning in Cryptocurrency Trading

Abstract

Machine learning strategies for cryptocurrency trading routinely report exceptional backtest results, yet practitioners consistently fail to replicate them. We identify three systematic failure modes—directional prediction bias, statistical-economic disconnect, and transaction cost omission—through 340 strategy variants across four timeframes and three cryptocurrency assets. To address these failures, we propose VALID (Validation Architecture for Learning-based Investment Decisions), a 12-item reporting and validation framework for financial ML research—the first domain-specific checklist for this field. We demonstrate that gradient-boosted models predict long 90–97% of the time without class balancing; that standard validation tools (PBO = 0, permutation p = 0) cannot distinguish exploitable signals from noise; and that transaction costs consume 55–91% of gross alpha. Monte Carlo analysis (200 iterations) shows 27% false positive rates for AUC-based validation alone, reduced to 0% by CPCV with PBO. We provide the first empirical confirmation of Witzany's (2021) PBO critique through null-distribution analysis. A systematic audit of 80 papers reveals that the median study satisfies only 2.5 of 12 VALID items. We release an open-source implementation for reproducibility.