Quant GAN: Deep Generation of Financial Time Series (Wiese et al., Quant GANs: Deep Generation of Financial Time Series, 2019)
This repository contains the PyTorch implementation of Quant GAN, a Generative Adversarial Network designed for the deep generation of financial time series.
Quant GAN leverages Generative Adversarial Networks to generate realistic financial time series data. The model is specifically tailored for applications in quantitative finance, where generating realistic and diverse financial data is crucial for various tasks such as risk assessment, algorithmic trading, and market simulation.