Adding intermediate guide losses to an end-to-end portfolio model improves backtested Sharpe and Calmar ratios versus stage-wise and unguided end-to-end baselines.
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Guided Learning: Lubricating End-to-End Modeling for Multi-stage Decision-making
Adding intermediate guide losses to an end-to-end portfolio model improves backtested Sharpe and Calmar ratios versus stage-wise and unguided end-to-end baselines.