Harnessing LSTM Networks for Predicting Stock Movements

Machine learning has proven to be a crucial tool for predicting stock market movements in the evolving landscape of financial technology.

Due to their ability to remember patterns over long sequences, Long Short-Term Memory (LSTM) networks have emerged as the most effective machine learning model. This article delves into the ‘Stock-Prediction-using-LSTM’ project, an innovative approach that utilizes LSTM networks to predict the stocks of 32 companies with an astonishing error rate of less than 1%. It uses a comprehensive approach to stock prediction developed by onepagecode. 

The process involves downloading stock data from Yahoo Download API, processing it, selecting features, and running it through an LSTM network. LSTM models are trained using various layers of Recurrent Neural Networks (RNNs), including a bidirectional LSTM layer, a fully connected layer, a dropout layer, and a fully connected TANH output layer. The system also optimizes for the best combination of learning rate, epochs, and dropout rate. In this article, we describe how LSTM networks can be used for stock prediction. The data acquisition and processing steps, the development of the model, and the results obtained from this sophisticated approach 1 will be discussed.

Download the source code using the link in comment box.