深度学习用于期权交易：一种端到端方法
Deep Learning for Options Trading: An End-To-End Approach
AI总结：
本文提出一种端到端深度学习方法，直接从市场数据学习期权交易信号，无需预设市场动态或定价模型，并在标普100成分股十余年数据上验证其风险调整后表现优于规则策略，且换手率正则化可进一步改善高交易成本下的表现。
摘要：
我们引入一种新颖的期权交易策略方法，使用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标的资产市场动态或对期权定价模型作出假设不同，我们的模型从根本上摆脱了对这些先决条件的需求，直接从市场数据中学习到最优交易信号的非平凡映射。我们在标准普尔100指数成分股超过十年的期权合约上进行了回测，结果表明，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表现上显著优于现有的基于规则的交易策略。我们发现，将换手率正则化纳入模型后，即使在极高的交易成本水平下，也能进一步提升表现。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
