Improving Stock Trend Prediction using LSTM Neural Network Trained on a Complex Trading Strategy

Abstract

Abstract: Technical analysis in stock trading addresses the crucial matter of making optimal trading decisions promptly. Predicting directional movement in the target market using technical indicators is quite common. Besides its many other applications, machine learning helps to solve the algorithmic trading problem of determining optimal trading positions, and some types of deep neural networks have been proven as up-and-coming methods for forecasting the returns of the stock market. The current work presents the idea of training a neural network on a new trading strategy, named, Unified Trading Strategy (UTS) that integrates technical indicators from three well-known categories referred to as leading, lagging, and volatility. The trained network serves as an excellent alternative to the classical technical analysis model by simplifying the process of finding potential events of effective trade with better performance and reusability.