In Jupyter notebook QuantGAN.ipynb Quant GAN from Wiese et al., Quant GANs: Deep Generation of Financial Time Series, 2019 is implemented. The task was performed for the Deep Generative Models course provided by HSE.
The model was trained on Google Colab. It is recommended to train and evaluate the model on Colab too. Actual environment requirements could be found in requirements.txt. This file was generated by executing command in Colab:
pip freeze > requirements.txtAll the required packages (not provided by Colab) are installed directrly in the notebook. If you try to execute the code on your local machine, you might experience inconvinience. Better execute the code using training platform - Colab.
Model is provided in Python, using PyTorch.
Core classes are TemporalBlock, TCN, Generator and Discriminator.
Data used for training is provided via SP500Dataset class.
To perform training go to Prepare and train GAN section on the notebook.
To generate the data go to Probe generation section on the notebook. Also the subsection Visualize results will be helpful to see the results of data generation.