Taming the Greeks: Option Portfolios with Inductive Biases

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Highly relevant for quantitative traders and risk managers dealing with derivatives. It addresses a critical gap in end-to-end ML trading models: the lack of explicit risk control. The proposed method offers a practical way to enforce delta neutrality without sacrificing performance, directly applicable to institutional trading desks.

Requires implementing custom loss functions that involve portfolio-level aggregation of Greeks and normalization terms. Needs access to high-quality options data with pre-calculated Greeks. The LSTM architecture is standard, but the joint optimization of performance and risk constraints adds complexity to the training loop.

The paper provides detailed hyperparameter search ranges, dataset descriptions (OptionMetrics Ivy DB), and specific mathematical formulations for the loss functions. However, the code is not explicitly linked in the provided text, and the dataset is proprietary/commercial.