Model implementation of Quant-GAN for Deep Generative Models course at HSE.
Full demonstration available via Jupyter notebook: quant.ipynb
.
Based on: https://arxiv.org/pdf/1907.06673.pdf
- torch==1.9.0+cu102
- numpy==1.19.5
- pandas==0.24.2
- scipy==1.6.1
- scikit_learn==0.24.2
Run with train.py FILENAME.csv
. To tune the hyperparameters, one can specify via argument options, which can been seen via the -h flag.
Resulting generator and discriminator models saved to QuantGenerator.pt
and QuantDiscriminator.pt
Dataset provided sp_data.csv
is S&P 500 stock prices from may 2009 till dec 2019.