Technical analysis strategy optimization using a machine learning approach in stock market indices

Within the area of stock market prediction, forecasting price values or movements is one of the most challenging issue. Because of this, the use of machine learning techniques in combination with technical analysis indicators is receiving more and more attention. In order to tackle this problem, in this paper we propose a hybrid approach to generate trading signals. To do so, our proposal consists of applying a technical indicator combined with a machine learning approach in order to produce a trading decision. The novelty of this approach lies in the simplicity and effectiveness of the hybrid rules as well as its possible extension to other technical indicators. In order to select the most suitable machine learning technique, we tested the performances of Linear Model (LM), Artificial Neural Network (ANN), Random Forests (RF) and Support Vector Regression (SVR). As technical strategies for trading, the Triple Exponential Moving Average (TEMA) and Moving Average Convergence/Divergence (MACD) were considered. We tested the resulting technique on daily trading data from three major indices: Ibex35 (IBEX), DAX and Dow Jones Industrial (DJI). Results achieved show that the addition of machine learning techniques to technical analysis strategies improves the trading signals and the competitiveness of the proposed trading rules. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study proposes a hybrid approach combining technical indicators with machine learning methods to generate trading signals, which have been tested on daily trading data from three major indices. The results show that adding machine learning techniques to technical analysis strategies improves the quality of trading signals and the competitiveness of the proposed trading rules.