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

1. Install the required libraries by running pip install -r requirements.txt.
2. Run train.py to train our model. (This will take some time approx. 4 hours)
3. 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.
4. 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 2025-02-02": "238.09$",
        "Mean absolute error": 0.7639587805310004,
        "Accuracy score": 0.4981949458483754,
        "Total buy profit": 20.030359268188484,
        "Total sell profit": 1.7735197693109692,
        "Total profit": 21.803879037499453,
        "Profit per trade": 0.015742872951263143,
        "Generated": "2025-02-01 20:14:14.019119+08:00"
    }
]

Generated report

Ticker	Future price after	Predicted price for 2025-02-02	Mean absolute error	Accuracy score	Total buy profit	Total sell profit	Total profit	Profit per trade	Generated
AMZN	1 day	238.09$	0.7639587805310004	0.4981949458483754	20.030359268188484	1.7735197693109692	21.803879037499453	0.015742872951263143	2025-02-01 20:14:14.019119+08:00

Graphs

Disclaimer: This is not finanical advice. Please don’t bet your life savings on this.