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

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

📚 https://arxiv.org/pdf/2407.21791

Original Problem 🔍:

Options trading strategies often require specifying market dynamics or option pricing models. This paper aims to develop a data-driven machine learning approach that doesn't rely on these prerequisites.

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Solution in this Paper 💡:

• Introduces end-to-end neural networks to directly learn mappings from options data to optimal trading positions

• Models trained to optimize Sharpe ratio, balancing risk and reward

• Tested various architectures: Linear, MLP, CNN, LSTM

• Applied to portfolios of delta-neutral equity options

• Incorporated turnover regularization to enhance performance with high transaction costs

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Key Insights from this Paper 💡:

• Deep learning models significantly outperformed benchmark strategies on risk-adjusted metrics

• Linear and LSTM models showed best performance, doubling Sharpe ratios of benchmarks

• Mean-reversion strategies generally outperformed momentum strategies for options

• Turnover regularization improved model performance under high transaction costs

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Results 📊:

• Best deep learning model (LSTM) achieved Sharpe ratio of 1.329 vs 0.762 for best benchmark

• LSTM maintained superior performance up to transaction costs of 20 bps

• With turnover regularization, LSTM outperformed at prohibitively high transaction costs (50 bps)

• Models recovered quickly after brief drawdowns during COVID-19 market selloff