hsinlichu/stock-price-prediction

Stock Price Prediction

Reproduce

Install Environment

$ python3 -m venv fund
$ . ./fund/bin/activate
$ pip -r requirements.txt

Run Tensorboard Visualization

Run below command at the project root, then server will open at http://localhost:1357

$ tensorboard --logdir saved/log/ --port 1357

Training

$ python train.py -c config.json -d 0

Testing

$ python test.py -c config.json -d 0 --resume saved/models/StockNet/1010_013058/model_best.pth

Note

• If I does not normalize the data, model will always predict the same value like 54.

• My train curve (loss and difference): http://meow2.csie.ntu.edu.tw:8787/#scalars

• prediction curve (training + testing phase)

  

• prediction curve (only testing phase)

  

• Performance is better when only use 1 layer GRU and don't use batch normalization.

• Use technical index (47 dim) result in better performance.