Deep Learning for Options Trading: An End-To-End Approach
This addresses the problem of inefficient options trading strategies for financial practitioners, offering a data-driven alternative to traditional methods.
The paper tackled options trading by developing an end-to-end deep learning approach that learns directly from market data, eliminating the need for traditional assumptions. Backtesting on over a decade of S&P 100 equity options showed significant improvements in risk-adjusted performance over rules-based strategies.
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.