PyTorch-based framework for Deep Hedging

Implementation of the vanilla Deep Hedging engine

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

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A curated list of resources dedicated to Deep Hedging

This notebook presents an example of implementation of my paper Carbonneau, A. (2020). Deep Hedging of Long-Term Financial Derivatives.

Minimal implementation and experiments of "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging".

A Deep Learning Framework for Neural Derivative Hedging

Hedging unsing Deep Reinforcement Learning and Deep Learning

Trellis is a Python framework for research into deep hedging

Samson's MIT Master's Degree Thesis: "Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging".

Deep Neural Network Framework Based on Backward Stochastic Differential Equations for Pricing and Hedging American Options in High Dimensions: Source Code (CPU version)

Option hedging strategies are investigated using two reinforcement learning algorithms: deep Q network and deep deterministic policy gradient.

Research repo for reinforcement learning–based deep hedging of SPX & SPY options

SigFormer: Signature Transformer for Deep Hedging (ICAIF 2023)

Pytorch implementation of Deep Hedging, Utility Maximization and Portfolio Optimization

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This notebook presents an example of the equal risk pricing framework with deep hedging from my paper Carbonneau, A. and Godin, F. (2020). Equal Risk Pricing of Derivatives with Deep Hedging.

Robust deep hedging and Non-linear generalized affine processes

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Implementation of Deep-Hedging in PyTorch

Reinforcement Learning in Options Hedging: A Deep-Deterministic-Policy-Gradient-based Dynamic Hedging Strategy

JPMC & QCWare Quantum Deep Hedging

implementing deep learning techniques to hedge vanilla and rainbow options

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We then move to asset management, in which the use of large unstructured datasets and new techniques is generating significant innovation. We begin with a review of standard techniques used for portfolio construction in time-series and cross-section, and then consider new techniques used to predict variation in asset prices in the time series and cross-section. These techniques are useful, for example, in quant hedge fund portfolio construction. We then recognize that asset managers need to consider their liability structure as well as their asset structure. This implies a deeper understanding of ultimate end-investors’ decision making models and learning techniques. Insights from this portion of the course are increasingly proving to be a critical input into roboadvising strategies.

Simple implementation of deep hedging paper (Buehler et al., 2019)