Analysing Trends in Trading Patterns in Financial Markets Using Deep Learning Algorithms

Abstract

For many years, academics from various fields have been fascinated by the topic of financial market forecasting. Using machine learning techniques like support vector machines (SVM) and reinforcement learning, several studies have been conducted to anticipate movements in the stock market. The whole body of research on Trading Patterns prediction or trading that used reinforcement learning as their primary machine learning method was thoroughly reviewed. Unreasonable assumptions were made in every article that was evaluated. Therefore, employing a weighted sum unit, the research aims to give a combined Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM). The models' performance is maximised on a testing dataset by obtaining and combining predictions from both models with precisely chosen weights. The proposed method aims to capitalize on the complementary strengths of both models, mitigating their individual limitations. The Evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and direction accuracy validate the efficacy