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

VII

https://doi.org/10.22214/ijraset.2022.45961

July 2022

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

Published on Aug 15, 2022

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.

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