A stock analysis program written in Python that uses Keras's LSTM neural network framework to predict the direction of trends within the stock market.

Train.py: Run to create a model within the weights folder

Test.py: Run to test the model with the testing data, create graphs to see what the prediction looks like, as well as text output that shows the price prediction as well as MSE and MAE errors.

Param.py: Constants used by the other Python files.

Predict.py: Helper methods for both training and testing purposes.

The model has already been trained and the weight can be found in the weights folder. Re-train by running train.py if necessary. Run run.py to regenerate all the graphs and predictions about our function. The generated graphs with the predictions can be found in the graphs folder.