This repository try to use Metropolis-Hastings Algorithm and WGAN to do the stock prediction.
- Clone the repositroy and enter the folder
$ git clone [email protected]:ChickenBenny/Metropolis-Hasting-WGAN-on-stock-prediction.git
$ cd Metropolis-Hasting-WGAN-on-stock-prediction
- Set up the virtual environment
$ python -m venv venv
- Windows
$ venv\Scripts\activate
- Mac / Linux
$ source venv/bin/activate
- Install package
$ pip install -r requirements.txt
- Run the demo notebook. However, GAN is an unstable model, so you may need to run it for a longer period of time or adjust the hyperparameters to obtain better results.
- Metropolis-Hastings GAN : https://arxiv.org/abs/1811.11357
- Wasserstein GAN : https://arxiv.org/abs/1701.07875
- Repo from borisbanushev : https://github.com/borisbanushev/stockpredictionai
- Stock price prediction using Generative Adversarial Networks : https://thescipub.com/abstract/jcssp.2021.188.196
Incorporate the ideas from Boris Banushev's repository, as it focuses on implementing GAN-based models for stock prediction. These models leverage the power of GANs' ability to generate realistic and diverse samples, making them suitable for handling stock market dynamics, especially during high-volatility situations.
- Try using GAN to predict stock prices and simulate the stock distribution.
- Try enhancing the sampling process using the Metropolis-Hastings algorithm to achieve better convergence and explore a broader range of parameter values.
- Attempt to improve the prediction accuracy by employing a VAE to extract latent variables and enhance the prediction effect.
-
RMSE and MAE from testing dataset
RMSE MAE 2.077 1.673
- Pros
- Improve the stability of WGAN model.
- Improve the adaptability of the model.
- Might capture comple or highly volatile distributions.
- Cons
- Computational complexity and sesitivity to hyperparameters.
- Data limitations and highly dependency on data quailty.
The model exhibits instability and high volatility. Therefore, if you are interested in making stock predictions using GAN-based models, it is recommended to fine-tune the hyperparameters, explore various data preprocessing techniques, or consider alternative models.
MIT © ChickenBenny