Stock price forecasting system to predict the trend of stocks from the S&P 500 index.
In case you would like to install and configure PySpark on your local machine, please follow the instructions described here. Otherwise, you can clone the notebook and import it into Databricks as described here.
For a simple and ready to use test, simply run the test/evaluate.py script that refers to the distributed system with pre-trained weights for the LSTM model. Otherwise, you can re-train the system using a model of your choice, and use the new weights to perform the evaluation.
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├── data/ # Stock prices and fundamental data
├── report/
│ ├── main.pdf # Project report for the dlai-2021 course
│ ├── main.tex
│ └── ...
├── test/
│ ├── data/ # Model weights and test data
│ ├── evaluate.py # Evaluation script
│ └── ...
├── dist_forecasting.ipynb # PySpark distributed stock prediction system
├── forecasting.ipynb # Stock prediction system
├── environment.yml # Training environment
└── ...