  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.
