Deep Learning for Options Trading: An End-To-End Approach

TL;DR

This paper presents a scalable deep learning approach for options trading that directly learns trading signals from market data, outperforming traditional methods in backtests over a decade of S&P 100 options.

Contribution

It introduces an end-to-end deep learning framework for options trading that eliminates the need for market model assumptions, demonstrating improved performance over existing strategies.

Findings

Deep learning models outperform traditional rules-based strategies.

Turnover regularization enhances performance under high transaction costs.

Models trained on a decade of data show significant risk-adjusted improvements.

Abstract

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.