DeepOptimalOptionStopping

This is a repository showcasing deep learning approaches for pricing American options. It implements two optimal stopping methods :

• Deep Longstaff-Schwartz: Uses a neural network in a Monte Carlo simulation to estimate continuation values.
• Deep Optimal Stopping: Applies reinforcement learning to learn the optimal exercise strategy.

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Bibliography

• Longstaff, F. A., & Schwartz, E. S. (2001). Valuing American options by simulation: a simple least-squares approach. The Review of Financial Studies, 14(1), 113-147.
• Becker, S., Cheridito, P., & Jentzen, A. (2019). Deep optimal stopping. Mathematical Finance, 29(3), 606-620.

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Setup

1. Clone the repository:

   git clone https://github.com/Shiaroku/DeepOptimalOptionPricing.git
cd DeepOptimalOptionPricing

2. Install dependencies:

   pip install -r requirements.txt

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Usage

• Notebooks: Explore the implementations and experiments in the notebooks/ directory.
• Source Code: Find the core implementations in the src/ directory.
• Tests: Run unit tests with pytest tests/ to verify functionality.

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License

This project is licensed under the MIT License.