Quant GANs replication

Codes for my thesis project: replicating and modifying Quant GANs.

paper

Python files for neural network creation/training, preprocessing and metrics can be found in the backend package. Models ought to be trained in Colab or using a PC with a CUDA capable GPU.

Notebook files

Replication/testing:

• S&P 500 Quant GAN training
• Stylized facts of S&P 500 generated paths
• Mode collapse of Quant GANs on S&P 500 generated paths
• Training and testing for mode collapse of Quant GANS on the stocks compromising the Dow Jones index
• Generating multiple returns (MSFT/AAPL) with plausible dependencies

∆CoVaR related:

• Training and testing conditionally on JP Morgan*
• Training and testing conditionally on us publicly listed bank stocks

Experiments found in the appendix:

• Quant GANs using WGAN
• TCN with series invariant filters comparison
• Researching TCN learning bias

* Please note that this notebook makes use of an older CGAN implementation. I didn't have the time to use the new CGAN from the backend package here. In order to keep congruent with my thesis I will keep it like this.