Deep Learning with LSTM using Keras to Predict Returns with Technical Indicators and Lagged Returns.

Financial market prediction is a challenging yet crucial task in the domain of quantitative finance. In this tutorial, we will delve into utilizing deep learning techniques, specifically LSTM (Long Short-Term Memory) neural networks, coupled with Keras to predict returns in windows of 60 dates based on a set of features including technical indicators, lagged OHLCV (Open, High, Low, Close, Volume) values and correlated assets. The project aims to demonstrate how advanced machine learning models can be leveraged for accurate financial forecasting.

Photo by m. on Unsplash

Table of Contents

• Introduction: Presenting the project’s scope and objectives.
• Setting up the Environment: Installing necessary libraries and creating the project structure.
• Data Collection and Preprocessing: Gathering data and preparing it for model training.
• Feature Engineering: Calculating lagged features, technical indicators and correlated assets.
• Model Development: Building an LSTM model using Keras to predict returns based on the provided features.
• Visualizing the technical indicators alongside the price data.
• Training and Evaluating the…