YuMan-Tam/deep-hedging
Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.
This project helps financial traders and risk managers improve how they price and hedge financial derivatives like options. It takes information about underlying asset distributions and your trading preferences to generate an optimal hedging strategy. This strategy automatically accounts for real-world market imperfections, providing a more accurate and cost-effective approach than traditional methods.
162 stars. No commits in the last 6 months.
Use this if you are a quantitative trader, risk manager, or financial analyst looking to implement machine learning to develop more robust and efficient hedging strategies for derivative portfolios, especially when market frictions are significant.
Not ideal if you primarily work with simplified perfect market models or if your organization lacks the technical infrastructure for machine learning applications.
Stars
162
Forks
56
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Jan 17, 2021
Commits (30d)
0
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