LTSM Stock Predictor
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
Basic Usage
- 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.
Raw data
[
{
"Ticker": "AMZN",
"Future price after": "1 day",
"Predicted price for 2025-02-19": "231.25$",
"Mean absolute error": 0.8094260204465358,
"Accuracy score": 0.5003604902667628,
"Total buy profit": 90.47045288980009,
"Total sell profit": -6.348515033721949,
"Total profit": 84.12193785607813,
"Profit per trade": 0.060650279636682146,
"Generated": "2025-02-18 20:16:45.293409+08:00"
}
]
Generated report
Ticker | Future price after | Predicted price for 2025-02-19 | Mean absolute error | Accuracy score | Total buy profit | Total sell profit | Total profit | Profit per trade | Generated |
---|---|---|---|---|---|---|---|---|---|
AMZN | 1 day | 231.25$ | 0.8094260204465358 | 0.5003604902667628 | 90.47045288980009 | -6.348515033721949 | 84.12193785607813 | 0.060650279636682146 | 2025-02-18 20:16:45.293409+08:00 |
Graphs
Disclaimer: This is not finanical advice. Please don’t bet your life savings on this.