We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an option pricing model, our models depart fundamentally from the need for these prerequisites, directly learning non-trivial mappings from market data to optimal trading signals. Backtesting on more than a decade of option contracts for equities listed on the S&P 100, we demonstrate that deep learning models trained according to our end-to-end approach exhibit significant improvements in risk-adjusted performance over existing rules-based trading strategies. We find that incorporating turnover regularization into the models leads to further performance enhancements at prohibitively high levels of transaction costs.

翻译：本文提出了一种新颖的期权交易策略方法，该方法采用一种高度可扩展且数据驱动的机器学习算法。与传统方法通常需要指定底层市场动态或对期权定价模型做出假设不同，我们的模型从根本上摆脱了对这些先决条件的依赖，直接从市场数据中学习到最优交易信号的非平凡映射。通过对标准普尔100指数成分股超过十年的期权合约进行回测，我们证明，按照我们的端到端方法训练的深度学习模型，在风险调整后的表现上，相较于现有的基于规则的交易策略有显著提升。我们发现，在模型中引入换手率正则化，可在交易成本极高的情况下带来进一步的性能增强。