Predicting different stock prices using Long Short-Term Memory Recurrent Neural Network in Python using TensorFlow 2 and Keras. Highly customizable for different stock tickers. Current ticker: AMZN (Amazon).
View deployment here:
GitHub Pages
- Install the required libraries by running pip install -r requirements.txt.
- Run train.py to train our model. (This will take some time approx. 4 hours)
- After training ends, run tensorboard --logdir="logs" to view the Huber loss as specified in the LOSS parameter, the curve is the validation loss. You can also increase the number of epochs to get much better results.
- Run test.py to test the model and to output the result
Note: the project is currently running on GitHub Actions, you can take a look at the example output down below. GitHub Actions allows the code to be ran offsite hence freeing up your development computer.
[
{
"Ticker": "AMZN",
"Future price after": "1 day",
"Predicted price for 2025-01-13": "218.35$",
"Mean absolute error": 0.776583827945525,
"Accuracy score": 0.5115774240231549,
"Total buy profit": 41.22167347371583,
"Total sell profit": 35.2675374597311,
"Total profit": 76.48921093344694,
"Profit per trade": 0.055346751760815445,
"Generated": "2025-01-12 20:15:08.860120+08:00"
}
]| Ticker | Future price after | Predicted price for 2025-01-13 | Mean absolute error | Accuracy score | Total buy profit | Total sell profit | Total profit | Profit per trade | Generated |
|---|---|---|---|---|---|---|---|---|---|
| AMZN | 1 day | 218.35$ | 0.776583827945525 | 0.5115774240231549 | 41.22167347371583 | 35.2675374597311 | 76.48921093344694 | 0.055346751760815445 | 2025-01-12 20:15:08.860120+08:00 |
Disclaimer: This is not finanical advice. Please don't bet your life savings on this.