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

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.

• The paper introduces a novel deep learning framework that learns optimal trading signals directly from market data without relying on traditional pricing models.
• The paper evaluates multiple neural architectures—such as LSTM and linear models—demonstrating superior risk-adjusted returns measured by the Sharpe ratio.
• The paper assesses real-world applicability by testing model robustness against transaction costs and market volatility, including performance during the COVID-19 selloff.

An End-to-End Machine Learning Approach for Options Trading

The paper "Deep Learning for Options Trading: An End-To-End Approach" by Wee Ling Tan, Stephen Roberts, and Stefan Zohren introduces a machine learning algorithm tailored for options trading strategies. Unlike traditional methods that rely on underlying market dynamics or specific option pricing models, the proposed deep learning models circumvent these requirements by directly learning complex mappings from market data to optimal trading signals.

Core Contributions

The primary focus of this research lies in developing an end-to-end framework that uses neural networks to generate optimal trading signals for options. This marks a significant departure from conventional methods which often require predefined market assumptions and models. The neural networks proposed in this framework are trained to maximize a risk-adjusted performance metric—the Sharpe ratio—thereby integrating both trend prediction and optimal position sizing within a single function.

Data and Methodology

The dataset used in this study comprises more than ten years of daily data for options on equities listed in the S&P 100, sourced from the OptionMetrics database. The analysis includes filtering steps to ensure data quality, such as excluding options with pricing inconsistencies. The study centers its attention on static delta-neutral straddles, which balance the directional risks posed by the underlying assets.

Various neural network architectures were explored, including:

• Linear Model: A single fully-connected layer.
• Multilayer Perceptron (MLP): A model with one hidden layer.
• Convolutional Neural Networks (CNN): Adapted for time series data with autoregressive features.
• Long Short-Term Memory Networks (LSTM): Recurrent neural networks suited for sequential data.

These models were trained using a tailored loss function based on the Sharpe ratio, optimized through stochastic gradient descent with Adam. Early stopping and hyperparameter optimization were employed to refine model performance.

Comparative Analysis of Models

The performance of the proposed models was evaluated against several benchmark strategies, including:

• Long Only: A straightforward strategy of taking long positions on straddles.
• TSMOM: Time-series momentum strategy as per Moskowitz et al. (2012).
• MACD: Moving Average Convergence Divergence strategy.
• TSHestonMOM/TSHestonMR: Heston-based momentum and mean-reversion strategies.
• CSHestonMOM/CSHestonMR: Cross-sectional Heston-based strategies.

Results

The results demonstrate that mean-reversion strategies (e.g., TSMR, MACDMR, TSHestonMR, CSHestonMR) outperformed momentum-based counterparts, especially over the backtest period considered. Among the proposed deep learning models, the LSTM and Linear models exhibited the highest risk-adjusted returns, significantly outperforming all benchmark strategies. When scaling returns to target an annualized portfolio volatility of 15%, these models achieved superior Sharpe ratios, indicating robust performance under varying market conditions—including the volatility spike during the COVID-19 market selloff.

Impact of Transaction Costs

The research further scrutinizes the impact of transaction costs, revealing that the LSTM model maintains superior performance up to transaction costs of 20 basis points. Incorporating turnover regularization during model training effectively mitigates performance degradation at higher transaction costs, evidencing the model's practical applicability in real-world trading scenarios where transaction costs are non-negligible.

Implications and Future Directions

This paper underscores the potential of deep learning models to redefine optimal trading strategies by eliminating dependencies on explicit market dynamics or pricing models. The end-to-end framework can be extended beyond equity options to other derivative instruments, broadening its applicability. Furthermore, the use of machine learning models calibrated to Sharpe ratios represents a pivotal shift towards more adaptive and data-driven trading strategies.

Future research could explore the integration of additional market data, such as macroeconomic indicators or alternative data sources, to enhance model robustness. Additionally, extending the framework to incorporate more sophisticated risk management techniques or exploring the use of reinforcement learning could further advance the state-of-the-art in quantitative finance.

In conclusion, this work presents a comprehensive and data-centric machine learning approach for options trading, paving the way for more efficient and adaptive trading strategies in financial markets.