Stock markets today have become fast moving dealing with a very high daily trade volume. It makes it difficult to be able to make reliable investments as prices are very volatile and can change rapidly. Inspired by recent advances in deep learning and the growing interest in RNN networks, we aim to use various deep learning techniques to implements models able to make reliable and accurate stock market predictions. We designed an LSTM based RNN network which predicts pre-selected stock prices for a given time in the future. We then proceed to use these predictions to design an investment model that attempts maximize profit while assessing the model’s integrity.
