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

arXiv: https://arxiv.org/abs/2407.21791 · HTML: https://mlsanity.com/paper/2407.21791/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Deep Hedging](https://mlsanity.com/paper/1802.03042/) (2018)
- [Autoregressive Convolutional Neural Networks for Asynchronous Time Series](https://mlsanity.com/paper/1703.04122/) (2017)
- [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://mlsanity.com/paper/1904.04912/) (2019)
- [Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture](https://mlsanity.com/paper/2112.08534/) (2021)
- [Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection](https://mlsanity.com/paper/2105.13727/) (2021)
- [Building Cross-Sectional Systematic Strategies By Learning to Rank](https://mlsanity.com/paper/2012.07149/) (2020)
- [Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies](https://mlsanity.com/paper/2302.10175/) (2023)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)

## Built on by (most cited first, 2 of 2)

- [DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization](https://mlsanity.com/paper/2509.12753/) (2025)
- [Taming the Greeks: Option Portfolios with Inductive Biases](https://mlsanity.com/paper/2609.33767/) (2026)

## Abstract (arXiv)

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.